Detailed Analysis of Clinical Electromyography Signals

Transcription

EMG Decomposition, Findings and Firing Pattern Analysis
in Controls and Patients with Myopathy and Amytrophic Lateral Sclerosis
EMGPAD
EMGPAD
Decomposition
CNS
EMG
amplifier
Filtering
Segmentation
Resolution
Clustering
A/D
Motor
nerves
Needle
Muscle
MUAPs
Firing patterns
A Ph.D. Dissertation
Submitted to the University of Copenhagen,
the Faculty of Health Science
by
M.Sc. Miki Nikolic
August, 2001
Advisors:
Professor, Christian Krarup, M.D. DMSc
The Department of Clinical Neurophysiology
The Rigshospital
Associate Professor, John Aasted Sørensen, Ph.D.
IT University of Copenhagen
To Mia,
our lovely daughters Nikoline and Laura
and the rest of my family.
Preface
This thesis presents an automatic system called EMGPAD for decomposing the clinical electromyogram (EMG) signal
into its constituent motor unit action potentials (MUAPs) and their corresponding firing patterns (FPs). The EMG
signals in this work were recorded under normal clinical conditions for quantitative MUAP analysis i.e. at low, constant and isometric contraction. The EMG signal recorded under these conditions contains an enormous amount of
information about the function/disfunction of the recorded motor units (MUs) but today only the MUAP parameters:
duration, amplitude and relative amount of polyphasic MUAPs are used in routine clinical evaluation of neuromuscular
disorders. This work is aiming at extracting even more information from the EMG signal by doing a decomposition
from which the FPs can be constructed and analyzed. While the number of works on both MUAP and FP analysis is
huge, no studies have been made on FPs recorded under normal clinical conditions for quantitative MUAP analysis.
For this reason a relatively detailed approach is used with focus on describing difficulties in analyzing these signals,
documented by a large selection of examples of special and important types of signals. Properties of the FPs are
described and parameters quantifying the FPs are investigated and compared both generally and in patients.
EMGPAD has been developed since 1990 in a cooperation between:
The Technical University of Denmark
Electronics Institute and Department of
Mathematical Modeling
2800 Lyngby
Denmark
The Department of Clinical Neurophysiology
The Neuroscience Center
The Rigshospital
2100 Copenhagen
Denmark
Several graduate students from the Technical University of Denmark have contributed to the development of EMGPAD
through their final graduation project. Their work was carried out at our Department of Clinical Neurophysiology, the
Rigshospital in Copenhagen. All the students have done an excellent work, but I dare to emphasis the work of Henrik
Winkel, Peter Bundgård Jensen and Kjeld Svendsen for their fundamental work in the development of EMGPAD.
I have been working with EMGPAD since 1994; first with improvement on the speed and stability and then on adaption to clinical usage. I have re-implemented EMGPAD on a PC platform which have resulted in further improvements
in speed and stability allowing a large number of EMG signals to be analyzed. Findings from these decompositions
are presented in this work.
I would like to thank my advisors, professor Christian Krarup, M.D. and associate Professor John Aasted Sørensen,
Ph.D. for their excellent guidance.
Twice did I have the pleasure of visiting Professor Erik Stålberg at his laboratory in Uppsala. I am thankful for his
generosity and interesting talks about jitter and jiggle.
I would like to thank Professor Werner Trojaborg for sharing his enormous experience in neurophysiology and for
proofreading parts of the manuscript.
Doctor Stefano Simoneti helped me with the recordings from normal subjects and suggested improvements to the
presentation of the decomposition results. I am grateful for that and for the discussions about EMGPAD.
Engineer Kristian Dahl has been very helpful in sharing his many years of biomedical experience with me. He
has been very influential in the development of EMGPAD and I enjoyed very much the frequent discussions about
EMGPAD. I am grateful for his support and encouragement.
I want to thank M.Sc.E.E. Victor Mazin and KengWah Niels Pang for being supporting and helpful in many practical
issues.
- But most of all, I thank Mia for her support and sacrifices during my Ph.D. project. I owe her everything for
taking great care of our daughters and me.
This work was supported by the Danish Research Council.
i
ii
Parts of this work was presented at:
”Investigation of MUAP shape variability by means of firing patterns” (poster presentation)
14th International Congress of EEG and Clinical Neurophysiology
Florence, Italy
August 24-30 , 1997
”An EMG decomposition system aimed at detailed analysis of motor unit activity” (poster presentation)
14th International Congress of EEG and Clinical Neurophysiology
Florence, Italy
August 24-30 , 1997
”Detailed Analysis of Motor Unit Activity” (oral presentation)
Nineteenth Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Chicago, USA
30/10 - 2/11, 1997
”Detaljeret MUAP analyse ved hjælp af fyringsmønstrer” (oral presentation in Danish)
Danish Society of Clinical Neurophysiology
Annual meeting 1997
”Validering af EMGPAD i forhold til konventionel kvantitativ EMG” (oral presentation in Danish)
Danish Society of Clinical Neurophysiology
Annual meeting 1998
”Firing pattern analysis in the brachial biceps muscle” (poster presentation)
VII Quantitative EMG Conference
Uppsalla, Sweden
June 13-15, 2001
A brief summary of the contents of this thesis is given in the following:
Chapter 1 gives an introduction to the subjects covered in this work and describes the purpose of the work.
Chapter 2 describes the difficulties associated with decomposition of EMG signals, summarizes and compares previous reported decomposition systems. Our decomposition system, EMGPAD, is described and an evaluation of
the MUAP findings is presented.
Chapter 3 presents examples of characteristic MUAPs found in clinical EMG recording, important types of EMG
signals and firing patterns which includes: Noise, amplitude variation, false potential classes, special firing
patterns, special EMG signals, needle movement, similar looking MUAPs, and MUAP shape variability.
Chapter 4 starts with a description of the difficulties associated with firing pattern analysis. Physiological and statistical properties are investigated and presented. Firing pattern parameters are listed and their inter-correlation
is investigated. Robust estimators of the mean and standard deviation of the Inter Potential Intervals (IPIs) are
compared and the best is selected. Correlation between MUAP parameters, FP parameters, age, gender and
strength is investigated. Mean(IPI) and SD(IPI) are compared in a control group and patients with myopathy
and ALS.
Chapter 5 discusses the three main parts of this work: 1)decomposition, 2)library of special and illustrative EMG
signal, MUAP and FP examples and 3)firing patterns analysis.
Chapter 6 In the conclusion chapter, the results are summarized and suggestions for further work are given.
Chapter 7 Summary
Chapter 8 Resumé på dansk
iii
Miki Nikolic
Frederiksberg, August, 2001.
Contents
1 Introduction
1.1 The Motor Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.1 Classification of Motor Units . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.2 Graduation of Muscle Force . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.3 Number of Motor Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.4 Recording and Analysis of Motor Unit Activity . . . . . . . . . . . . . . . . .
1.1.4.1 The Single Fiber Needle Electrode (SFNE) . . . . . . . . . . . . . .
1.1.4.2 Macro Needle Electrode (MaNE) . . . . . . . . . . . . . . . . . . . .
1.1.4.3 Multilead Electrodes . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.4.4 Scanning Electromyography . . . . . . . . . . . . . . . . . . . . . . .
1.1.4.5 Bipolar Needle Electrodes . . . . . . . . . . . . . . . . . . . . . . . .
1.1.4.6 Surface Electrodes . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1.4.7 Turns-Amplitude Analysis - Analysis at Higher Level of Contraction
1.2 The Motor Unit Action Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1 Recording of the Motor Unit Action Potential . . . . . . . . . . . . . . . . . .
1.2.2 Motor Unit Action Potential Parameters . . . . . . . . . . . . . . . . . . . . .
1.2.2.1 Duration of the Motor Unit Action Potential . . . . . . . . . . . . .
1.2.2.2 Amplitude of the Motor Unit Action Potential . . . . . . . . . . . .
1.2.2.3 Shape of the Motor Unit Action Potential . . . . . . . . . . . . . . .
1.2.3 Biologic and Physical Factors that Influence the Motor Unit Action Potential
1.2.3.1 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.3.2 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.4 Pathophysiological Changes of the Motor Unit Action Potential . . . . . . . .
1.3 The Motor Unit Firing Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Motor Unit Firing Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.2 Previous studies on computerized EMG . . . . . . . . . . . . . . . . . . . . .
1.3.3 Motor Unit Firing rates and Variability in Neuromuscular Disorders . . . . .
1.4 The purpose of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 EMG Signal Decomposition
2.1 Difficulties associated with decomposition of EMG signals . . . . . . . .
2.2 EMGPAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 System overview of EMGPAD . . . . . . . . . . . . . . . . . . .
2.2.2 Signal acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 Signal filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.4 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.5 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.5.1 Introduction to MUAP classification . . . . . . . . . . .
2.2.5.2 Difficulties in MUAP classification . . . . . . . . . . . .
2.2.5.3 Short introduction to MUAP classification . . . . . . .
2.2.5.4 MUAP Clustering in EMGPAD . . . . . . . . . . . . .
2.2.5.5 Discussion of the algorithm used in EMGPAD . . . . .
2.2.6 Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.6.1 Short introduction to resolution of compound segments
2.2.6.2 Difficulties in resolution of compound segments . . . . .
2.2.6.3 Resolution of false PCLs . . . . . . . . . . . . . . . . .
2.2.6.4 Resolution algorithm . . . . . . . . . . . . . . . . . . .
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CONTENTS
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4 EMG Firing Patterns
4.1 Introduction to firing patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Difficulties in FP analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Some properties and observations of the firing patterns . . . . . . . . . . . . . . . . . . . . .
4.2.1 Double discharges and prolonged intervals . . . . . . . . . . . . . . . . . . . . . . . .
4.2.2 Slow IPI changes and common drive . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.3 Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.4 IPI distribution and a simple IPI model . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Firing pattern parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Estimation of the mean and standard deviation for IPIs . . . . . . . . . . . . . . . . . . . .
4.4.1 Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.2 Simulation of estimator performances . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Patient material and recording conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6 Parameter correlation in Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7 Evaluation of FP parameters in patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7.1 Mean IPI in controls, myopathy and ALS recorded in biceps brachii . . . . . . . . .
4.7.2 Standard deviation of IPI in controls, myopathy and ALS recorded in biceps brachii
4.7.3 Mean IPI in myopathy and ALS recorded in medial vastus . . . . . . . . . . . . . . .
4.7.4 Standard deviation of IPI in myopathy and ALS recorded in medial vastus . . . . .
4.7.5 Mean IPI in myopathy recorded in biceps brachii and medial vastus . . . . . . . . .
4.7.6 Mean IPI in ALS recorded in biceps brachii and medial vastus . . . . . . . . . . . .
4.8 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5 Discussion
5.1 Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.1 Level of decomposition . . . . . . . . . . . . . . . . . .
5.1.2 Decomposition time . . . . . . . . . . . . . . . . . . .
5.1.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . .
5.1.4 Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.5 Recording conditions . . . . . . . . . . . . . . . . . . .
5.1.6 User interface . . . . . . . . . . . . . . . . . . . . . . .
5.2 Library of EMG signals and firing patterns . . . . . . . . . .
5.3 Firing pattern analysis . . . . . . . . . . . . . . . . . . . . . .
5.3.1 Firing rate and variability in different studies . . . . .
5.3.1.1 Recording conditions in EMGPAD and other
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96
2.3
2.4
2.2.6.5 Favorable Combinations . . . . . . . . . .
2.2.6.6 Optimization of the time shifts . . . . . .
2.2.6.7 Selection of the best combination . . . .
2.2.6.8 Discussion and relation to other methods
2.2.7 Decomposition output and visualization . . . . . .
2.2.8 System implementation . . . . . . . . . . . . . . .
Decomposition evaluation . . . . . . . . . . . . . . . . . .
2.3.1 Evaluation of the MUAP parameters . . . . . . . .
2.3.2 Performance evaluation . . . . . . . . . . . . . . .
Chapter summary . . . . . . . . . . . . . . . . . . . . . .
3 Library of EMG Recordings
3.1 MUAP Examples . . . . . . . .
3.2 EMG signal examples . . . . .
3.2.1 Noise . . . . . . . . . .
3.2.2 Amplitude variation . .
3.2.3 False potential classes .
3.2.4 Special firing patterns .
3.2.5 Special EMG signals . .
3.2.6 Needle movement . . . .
3.2.7 Similar looking MUAPs
3.2.8 MUAP shape variability
3.3 Chapter summary . . . . . . .
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vi
CONTENTS
5.3.2
5.3.3
5.3.4
5.3.5
5.3.1.2 MU firing rate and variability findings in seven studies . . . . . . . . . . . . . . . . .
Difficulties in FP analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Some FP properties and observations found with EMGPAD . . . . . . . . . . . . . . . . . . . .
Parameter correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Firing rate and variability findings in patients . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.5.1 Firing rate and variability findings in biceps brachii . . . . . . . . . . . . . . . . . . .
5.3.5.2 Firing rate and variability findings in medial vastus . . . . . . . . . . . . . . . . . . .
5.3.5.3 Comparing firing rate findings in biceps brachii and medial vastus for the myopathy
and ALS patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
97
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98
98
98
99
99
99
6 Conclusion and further Perspectives
100
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.2 Perspectives and further work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
Summary
114
Resumé
115
A Patient material
116
B All firing patterns used for analysis
118
C Selected error-free firing patterns
122
D Synchronicity and crosscorrelaton for simultaneous recorded MU
126
E Mean estimator results
131
F Standard deviation estimator results
136
List of Figures
1.1
1.2
1.3
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
2.13
2.14
2.15
2.16
2.17
2.18
Illustration of the MUAP parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
MUAPs from a normal subject, a patient with neuropathy and a patient with myopathy. Middle part:
A schematic interpretation of the distribution of muscle fibers. Black bars represent muscle fibers from
the active MU; dark grey bars are muscle fibers from another inactive MU and light grey bars are from
degenerated muscle fibers belonging to the active MU; light gray bars indicate degenetated muscle fibers
belonging to the active MU. Lower part: Histograms showing the distribution of durations of MUAPs
in the three cases. Durations for simple potentials are drawn with white bars and the polyphasic
potentials with black bars. For the patient with neuropathy the mean duration was increased 51%
compared to normal values matched for age and muscle, and the incidents of polyphasic potentials was
13%. The corresponding values for the patient with myopathy was 29% shortened mean duration and
31% polyphasic potentials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
An illustration of the relation between the EMG signal and the firing pattern when only one MUAP
is present. It also shows how the inter potential interval (IPI) is defined as the time between two
consecutive firings of the same MU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Two signals recorded at the same needle position, but at different level of contraction. The first signal
was recorded at 1 % of maximal voluntary contraction (MVC) and the second at 5 % of MVC. . . . .
Three EMG signal to illustrate the big amplitude variation. From top down, first a characteristic signal
from a patient with myopathy with very low amplitude, then a normal and then a signal from a patient
with neuropathy with big amplitudes. The amplitude scale is kept the same to better appreciate the
amplitude difference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
From top and down, the first signal contains a significant amount of high frequency instrumental noise.
The second signal is an example of baseline movements and the last signal contains distant MUAPs. .
An overview of EMGPAD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
An illustration of the three decomposition stages; segmentation, clustering and resolution. . . . . . . .
Signal acquisition overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Instrumental noise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(A) A raw EMG signal with instrumental and baseline noise. (B) After de-noising and (C) after denoising and high-pass filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illustration of the segmentation goals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The amplitude density function of the normalized variance signal. thrd is estimated as the first local
minimum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Illustration of how the segmentation algorithm works. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Four segmentation examples (A-D). (A) contains 4 PCLs and is correctly segmented. (B) contains 6
PCLs and is also correctly segmented. (C) has some errors because of some volume-conducted activity.
(D) is simply to complex, with a lot of volume conducted activity, to be segmented. . . . . . . . . . .
A small example of a distance matrix and its MST . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Number of potential classes as a function of the tree cutting threshold dt and the corresponding dendrogram below. The values of dt where there is an increase in number of PCLs are marked with arrows.
These values are stored in M AXdt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Resolution divided into three stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Three combinations of two templates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The upperpart shows a compound segment (partially superimposed). In the middle; the first of the
seven found templates is placed at time shift where the crosscorrelation coefficient is higher than 0.5 as
shown bellow. The last one to the right is the correct and also has the highest crosscorrelation coefficient.
An example of a resolution. Note that the compound segment was resolved with five templates, will
most other resolution algorithms reported, maximally can handle three templates. This is the third
segment in Fig. 2.19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
vii
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12
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23
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24
25
28
29
32
32
34
34
LIST OF FIGURES
2.19 A comparison of the resolution algorithms used in Christodoulos and Pattichis [1] (A) and in EMGPAD
(B). Many compound segments are not resolved correctly in (A) while only two errors are made in (B)
2.20 An example of the graphical output of an decomposition in EMGPAD. . . . . . . . . . . . . . . . . . .
2.21 The same as Fig. 2.20, but in raster mode and with the EMG signal shown. . . . . . . . . . . . . . . .
2.22 An example of the MUAP parameters display. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.23 Implementation of EMGPAD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.24 Decomposition time as a function of number of motor units. . . . . . . . . . . . . . . . . . . . . . . . .
2.25 Comparison of the MUAP duration found with the manual method and EMGPAD. Three groups of
patients are used: Myopathic, controls and neuropathic. The results are shown in pairs of two; the first
bar is from the manual method and the second is from EMGPAD. . . . . . . . . . . . . . . . . . . . .
2.26 A four channel recording. The templates and firing patterns from the same MU but in four different
EMG signal are shown. They are identical except from an error in channel 2. . . . . . . . . . . . . . .
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
3.11
3.12
3.13
3.14
4.1
4.2
4.3
Examples of MUAPs seen in CNE recordings: (A+B) Simple (≤4 phases), (C) polyphasic (≥5 phases),
(D) irregular, (E) satellite potential, (F) double discharges, (G) jiggle, (H) longe rise time and (I)
recording from the cannula. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Two examples of each of the three noise type; (A) and (B), baseline movements, (C) and (D), distant
MUAPs contributing to increased background noise, and (E) and (F), high frequency noise components.
See text in Section 3.2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Different MUAP amplitude examples. (A) and (B) shows examples of low amplitude MUAPs, (C) and
(D) high amplitude MUAPs and (E) and (F) mixture of low and high amplitude MUAPS. See text in
Section 3.2.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Three examples of false PCLs. To the left is the decomposition result shown without trying to detect
and resolve false PCLs and to the right after deletion of false PCLs. See text in Section 3.2.3 . . . . .
Examples of special FPs. (A)+(B) Irregular FPs, (B)+(C)+(D) different mean firing frequencies, (E)
synchronized FPs and (F) special firing sequence. See text in Section 3.2.4. . . . . . . . . . . . . . . .
Examples of special EMG signals. (A)+(B) and (C)+(D) Spontaneous MUAPs (marked with arrows)
and (E)+(F) double discharges (marked with arrows). See text in Section 3.2.5. . . . . . . . . . . . . .
Examples of special EMG signals. (A)+(B) and (C)+(D) Double discharges (marked with arrows) and
(E)+(F) blocking (marked with arrows). See text in Section 3.2.5. . . . . . . . . . . . . . . . . . . . .
Six examples of needle movements. (A)+(B) Increasing amplitude is seen from moving the CNE slightly
closer to the closest muscle fibers. (C)+(D) Decreasing amplitude. (E) Two MUAPs showing decreasing
amplitude. (F) Slight manipulation of the CNE resulting in periodically increase/decrease in amplitude.
See text in Section 3.2.6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Six examples of similar looking MUAPs. See text in Section 3.2.7. . . . . . . . . . . . . . . . . . . . .
Three examples of jiggle as a result of failure of transmission in the endplate (impulse blocking). See
text in Section 3.2.8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Three examples of jiggle as a result impulse blocking and jitter. See text in Section 3.2.8. . . . . . . .
Three examples of jiggle. (A)+(B) as a result of impulse blocking and jitter. (C)+(D) and (E)+(F)
primarily as a result of jitter. See text in Section 3.2.8. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Three examples of jiggle, primarily as a result of jitter. See text in Section 3.2.8. . . . . . . . . . . . .
Three examples of special jiggle; (A)+(B) Very big variation in shape as a result of both jitter and
blocking. (C)+(D) Three MUAPs was found; One with jiggle and two without. It seems like a AP
is gradually moving to the right, see (D). (E)+(F) Two MUAPs was correctly found; One with a low
risetime but without jiggle (indicating that it is not needle movement) and one with jiggle as a result
of blocking. See text in Section 3.2.8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Examples of: (A) a complete and regular FP, (B) false firings, (C) missed firings, (D) combination of
(B)+(C), (E) prolonged intervals and (F) slow varying IPIs. . . . . . . . . . . . . . . . . . . . . . . . .
Examples of slow IPI changes and common drive. Left side: The IPIs are decreasing throughout
the recording probably because of increasing contraction force so an additional MU is recruited just
before half-way in the recording. Right side: All five FPs show slow IPI changes. (A)+(D) show the
FPs. (B)+(E) show the mean IPI by using a moving average filter (see (4.6)). (C)+(F) show the
crosscorrelation coefficient for time shift between -0.5-0.5 seconds. . . . . . . . . . . . . . . . . . . . . .
Four examples of IPI plots. From top to bottom; The first FP and IPI plot shows no particular slow
IPI changes. The second shows decreasing IPIs. The third shows increasing IPIs. The fourth shows
periodically slow IPI changes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
viii
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38
38
39
39
41
43
45
53
54
55
56
57
58
59
60
61
62
63
64
65
68
69
70
LIST OF FIGURES
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
4.12
4.13
4.14
4.15
4.16
4.17
4.18
Scatter plots of mean IPI against standard deviation. The regression line and its equation, the correlation coefficient (r) and number of IPIs (N) is shown. From left to right: (1) IPI’s from 10 healthy
controls person recorded in brachial biceps(BB), (2) IPI’s from 7 patients with myopathy recorded in
brachial biceps(BB), (3) IPI’s from 7 patients with myopathy recorded in medial vastus(MV), (4) IPI’s
from 9 patients with ALS recorded in brachial biceps(BB), (5) IPI’s from 9 patients with ALS recorded
in medial vastus(MV). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The M EANI , SDI , M CDI , V ARI , RHOI and SDT RI parameters in controls, patients with myopathy
and ALS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
An example of the result of filtering the histogram with a triangular filter ([1,2,3,4,3,2,1]/16). . . . . .
Histograms of the MEAN IPI and the coefficient of variation (C=SD/MEAN) for the error-free FPs in
Appendix C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The standard deviation as a function of the mean for the error-free FPs in Appendix C. The dotted
lines (A), (C) and (D) shows the relation between SD and MEAN used for the simulations. Line (B) is
the regression line through the origin. (A) represents the ”difficult” FPs with high IPI variability, (C)
represents the ”typical” FPs and (D) represents the ”easy” FPs with very regular firings. . . . . . . .
Performance evaluation of the eight MEAN estimators. The upper row shows the normalized mean
estimate using (4.14) and expresses the accuracy of the estimators. A good estimator should be close to
one for all values of the detection error. The lower row shows the standard deviation of the normalized
mean using (4.15) and expresses the precision of the estimators. A good estimator should be close to
zero for all values the detection error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Performance evaluation of the six SD estimators. The upper row shows the estimated normalized
standard deviation using (4.16) and expresses the accuracy of the estimators. A good estimator should
be close to one for all values of the detection error. The lower row shows the standard deviation of
the normalized standard deviation using (4.17) and expresses the precision of the estimators. A good
estimator should be close to zero for all values the detection error. . . . . . . . . . . . . . . . . . . . .
Scatter plot of mean MUAP duration versus mean M EANI for the controls recorded from biceps brachii
(see Table 4.4). The full line is the regression line for all ten controls, while the dotted line is without C1.
Scatter plot of MUAP duration versus M EANI for all ten control persons. For each plot is the correlation coefficient and its P-value shown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) The M EANI distribution is shown for each individual by means of box plots for all three patient
groups; Controls, myopathy and ALS, recorded in biceps brachii. (2) Box plot of the mean M EANI
marked with * in (1). (3) The Kruskal-Wallis ANOVA table. . . . . . . . . . . . . . . . . . . . . . . .
(1) The SDI distribution is shown for each individual by means of box plots for all three patient groups;
Controls, myopathy and ALS, recorded in biceps brachii. (2) Box plot of the mean SDI marked with
* in (1). (3) The Kruskal-Wallis ANOVA table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) The M EANI distribution is shown for each individual by means of box plots for myopathy and
ALS, recorded in medial vastus. (2) Box plot of the mean M EANI marked with * in (1). (3) The
Kruskal-Wallis ANOVA table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) The SDI distribution is shown for each individual by means of box plots for myopathy and ALS,
recorded in medial vastus. (2) Box plot of the mean SDI marked with * in (1). (3) The Kruskal-Wallis
ANOVA table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) The M EANI distribution is shown for each individual by means of box plots for the myopathy
patients, recorded in brachial biceps and medial vastus. (2) Box plot of the mean M EANI marked with
* in (1). (3) The Kruskal-Wallis ANOVA table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) The M EANI distribution is shown for each individual by means of box plots for the ALS patients,
recorded in brachial biceps and medial vastus. (2) Box plot of the mean M EANI marked with * in (1).
(3) The Kruskal-Wallis ANOVA table. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ix
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74
75
77
77
78
79
81
82
83
84
85
86
87
88
A.1 Clinical and electrophysiological findings in healthy controls and patients with myopathy and ALS. . . 117
B.1 All the firing patterns from the 10 healthy control individuals. . . . . . . . . . . . . . . . . . . . . . . . 119
B.2 All the firing patterns from the 7 patients with myopathy. . . . . . . . . . . . . . . . . . . . . . . . . . 120
B.3 All the firing patterns from the 8 patients with ALS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
C.1 Selected error-free firing patterns from the healthy control individuals. . . . . . . . . . . . . . . . . . . 123
C.2 Selected error-free firing patterns from the patients with myopathy. . . . . . . . . . . . . . . . . . . . . 124
C.3 Selected error-free firing patterns from the patients with ALS. . . . . . . . . . . . . . . . . . . . . . . . 125
D.1 Examples of MU syncronicity and crosscorrelaton for simultaneous recorded MU. . . . . . . . . . . . . 127
LIST OF FIGURES
x
E.1 The results of using the average of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for deatils.131
E.2 The results of using the median of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for deatils.132
E.3 The results of using the trimmean of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for
deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
E.4 The results of using the mode of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for deatils. 133
E.5 The results of using the mode2 algorithm on the IPIs as an estimator for the mean IPI. See Section 4.4.1
for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
E.6 The results of using the RemOutliers algorithm on the IPIs as an estimator for the mean IPI. See
Section 4.4.1 for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
E.7 The results of using the EFE algorithm on the IPIs as an estimator for the mean IPI. See Section 4.4.1
for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
E.8 The results of using the McGill algorithm on the IPIs as an estimator for the mean IPI. See Section 4.4.1
for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
F.1 The results of using the standard deviation. See Section 4.4.1 for deatils. . . . . . . . . . . . . . . . . .
F.2 The results of using 0.74131IQR on the IPIs as an estimator for the standard deviation. See Section 4.4.1
for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F.3 The results of using 0.885MCD on the IPIs as an estimator for the standard deviation. See Section 4.4.1
for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F.4 The results of using the RemOutliers algorithm on the IPIs as an estimator for the standard deviation.
See Section 4.4.1 for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F.5 The results of using the EFE algorithm on the IPIs as an estimator for the standard deviation. See
Section 4.4.1 for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
F.6 The results of using the McGill algorithm on the IPIs as an estimator for the standard deviation. See
Section 4.4.1 for deatils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
136
137
137
138
138
139
List of Tables
1
List of abbreviations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
ii
1.1
1.2
2
1.3
1.4
1.5
1.6
Classification of motor units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Number of motor units and average number of muscle fibers per motor unit in human muscles.
Buchthal and Schmalbruch[2]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
EMG limits in normal muscles (95% confidence limits) . . . . . . . . . . . . . . . . . . . . . . .
EMG criteria of neuromuscular diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Comparing five automatic EMG analysis systems by five features. . . . . . . . . . . . . . . . . .
Overview of six different works on firing rate and variability in neuromuscular disorders. . . . .
. . . .
(From
. . . .
. . . .
. . . .
. . . .
. . . .
3
7
9
13
15
2.1
2.2
2.3
Pseudo-code for the recursive algorithm that finds favorable combinations. . . . . . . . . . . . . . . . .
Comparison of the recording and analysis in the manual method and EMGPAD. . . . . . . . . . . . .
Data for the three groups of patients and their signals. . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
40
41
4.1
4.2
Three test for normality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The correlation coefficient between the FP parameters. 176 FPs was used. The numbers with bold
fonts are statistically significant (H0 :r=0 versus HA :r6=0, p < 0.01). . . . . . . . . . . . . . . . . . . . .
Number of FPs. for each muscle and patient group. The numbers in parenthesis are the error-free FPs.
Age, sex, MUAP parameters (duration and amplitude), maximal contraction force and FP parameters
(mean and standard deviation) are shown for the ten normal subjects. . . . . . . . . . . . . . . . . . .
71
4.3
4.4
i
73
80
81
Abbreviations
ADEMG
ALS
AP
ARTMUP
BB
CNE
CNS
DAU
EFE
EMG
EMGPAD
FP
IPI
MaNE
MNE
MST
MU
MUAP
MUAPT
MV
MVC
PCL
RMS
SFAP
SFEMG
SFNE
Automatic Decomposition Electromyography a decomposition system developed By
McGill and Dorfman [3]
Amytrophic Lateral Sclerosis
Action Potentiall
Automatic Recognition and Tracking of Motor Unit Potentials a decomposition system
developed by Hass and Meyer [4]
The Biceps Brachii muscle
Concentric Needle Electrode
Central Nervous System
Data Acquisition Unit
An Error-Filtered Estimation algorithm developed by Stashuk and Qu [5]
ElectroMyoGraphy
EMG Precision Decomposition a decomposition system described in this thesis
Firing Pattern
Inter Potential Interval
Macro Needle Electrode
Monopolar Needle Electrode
Minimal Spanning Tree
Motor Unit
Motor Unit Action Potential
Motor Unit Action Potential Train
The Medial Vastus muscle
Maximal Voluntary Contraction
Potential CLass
Root Mean Square
Single Fiber Action Potential
Single Fiber EMG
Single Fiber Needle Electrode
Table 1: List of abbreviations.
ii
Chapter 1
Introduction
The motor unit (MU) is the smallest functional unit of a skeletal muscle, and its electrophysiologic characteristics
are central in the diagnosis of neuromuscular disorders associated with partial denervation and myopathy. Since the
introduction of electromyography (EMG), it has been apparent that the interpretation of the electrical activity of
muscle is influenced by both technical and biologic factors and that the variability of the individual muscle requires
quantitation for proper evaluation. With the introduction of more powerful, affordable and manageable computers
in the eighties many semi and fully automatic systems where developed for recording and analyzing the electrical
response of the MU. This electrical response is called the Motor Unit Action Potential (MUAP). The recording of
MUAPs has evolved from time consuming film recording, introduction of the trigger and delay units so the MUAPs
could be frozen on the oscilloscope screen and/or printed on paper to digital and automatic recording and selection
of the MUAPs. MUAP analysis has evolved from measuring the basic parameters of the MUAP (duration, amplitude
and number of phases) reflecting its morphology to measuring the stability of the MUAP at consecutive discharges
(Jiggle) and determination of its firing pattern (FP).
These developments on the recording and analysis of MUAPs has been driven by the desire to overcome some
limitations in previous methods like:
• Recording and analysis should be fast and easy in a busy clinical environment.
• As many MUAPs as possible should be identified in the EMG signal.
• The identification of MUAPs should be accurate and without bias in selection due to limitation in the operators
skills or possible individual preferences in selection.
• Extraction of more information than provided from the classical MUAP parameters. Of these parameters the
most important is the MUAP duration which is related to the number of activated muscle fibers and to estimate
possible loss or increase of muscle fibers per MU. The firing pattern depicts each activation of the α-motor
neuron which again is controlled by the central nervous system and provides new information compared to
MUAP parameters and at the level of the CNS. The instability of the MUAP shape at consecutive discharges
caused by disturbances at the neuromuscular junction, so-called jitter and blocking provides information at the
level of the neuromuscular junction.
1.1
The Motor Unit
The concept of the motor unit was developed by Liddell and Sherrington[6] and Sherrington[7] more than 70 years
ago; it is defined as the motor neuron, its axon, and the muscle fibers that it innervates. The basic organization of
motor units is similar in most mammals[8]. Multilead electrode studies have revealed that the territory of the motor
unit is circular or elliptical, with a diameter of 15 mm in the brachial biceps muscle with space for 15 to 30 motor
units[9, 10]. In animals, glycogen-depletion experiments have shown that the relative territory of the motor unit varies
considerably[11, 12] from 8% to 76% of the whole cross-section of the muscle with large areas in the soleus (41% to
76%) and smaller ones in anterior tibial muscle (8% to 22%)[13]. The muscle fibers of one motor unit are intermingled
with fibers of others; rarely, two to four muscle fibers from the same motor unit are adjacent to one another[11, 14, 15].
The likelihood in human muscle that a fiber is placed within 300 µm from another fiber of the same motor unit is
about 50%[16]. With single-fiber recording, about 1.5 fibers belong to the same motor unit[17], indicating that the
pickup area comprises about six motor units. There is a larger concentration of fibers at the center of the motor
unit than at its periphery[10, 12]. Muscle fibers have different physiological, biochemical, and mechanical properties,
and the motor unit composition, size, and number vary greatly in individual muscles[18, 19, 20, 21, 2]. This is of
1
2
CHAPTER 1. INTRODUCTION
Motor unit type
Slow fatigue resistant
Fast fatigue resistant
Fast fatigue
Mitrochondrial enzyme
Type C
(high mitrochondrial staining)
Type B
(intermediate mitrochondrial staining)
Type A
(low mitrochondrial staining)
Muscle fiber type
ATPase activity
(Ph 9.4)
Type1 muscle fibers
(low activity)
Type2a muscle fibers
(high activity)
Type2b muscle fibers
(high activity)
Physiologic classification +
oxidative metabolism
Slow oxidative
Fast-twitch oxidative glycolytic
Fast-fatigued glycolytic
Table 1.1: Classification of motor units
particular importance because involvement of motor units in a given disease may be determined by their properties.
The recruitment of motor units during the development of force is non-random; with weak efforts it is possible to
examine the characteristics of individual units.
1.1.1
Classification of Motor Units
All muscle fibers in the same motor unit are of a similar histochemical type[12]. Different criteria have been used to
classify motor units[2, 22, 23, 24, 25]. Three groups of motor units emerge when physiological, mechanical, biochemical,
and histochemical criteria are used[26, 27] (Table 1.1), although in fact they probably fall into a continuum[2]. In
experimental animals, fast-twitch motor unit fibers are characteristically larger, the twitch tension is increased, the
twitch:tetanus ratio is higher, the conduction velocity along the motor axon is faster, and the motoneuron diameter
is larger; however, the input resistance of the motoneuron is lower. The number of muscle fibers in each motor unit
or the innervation ratio, defined as the number of muscle fibers innervated by a single α-motor neuron, varies in
individual muscles from 5 to 20 fibers in extraocular eye muscles[28, 29] to more than 2,000 in large extremity muscles
(gastrocnemius)[30]. The innervation ratio is the main determinant of the force each motor unit can produce[31]; the
force per unit area in most studies is similar among different fiber types[32, 33]. The innervation area is subject to
influence by both biologic factors and pathologic lesions; increasing age is associated with a reduction in motor neuron
number and an increase in the innervation ratio due to collateral sprouting[34, 35, 36, 37, 38, 39]. The innervation
ratio is subject to change in pathologic processes that cause Wallerian, axonal, or neuronal degeneration. Collateral
sprouting in these conditions has a marked influence on the physiologic parameters of the motor unit and is of major
importance as a compensatory mechanism to delay or reduce the degree of weakness[40, 41, 42, 43, 44].
1.1.2
Graduation of Muscle Force
A muscle may increase its force output by recruitment of additional motor units and by modulation of the discharge
frequency of different motor units. Additional mechanisms may influence the force output[45] such as potentiation,
fatigue, and change in stiffness, related to prior activity. At low levels of force there is a recruitment of additional
motor units before changes in firing rate. In most muscles, the increase in force amounts to about 80% of the
maximum, whereas 20% derive from an increase in discharge frequency[46, 47, 48]. In some muscles, motor units may
be recruited at a force level of 50%, and the remaining force level is determined by rate coding that ensures more precise
gradation[46, 49]. In reflex movement and during gradual and rapidly increasing force production, the sequence of
recruitment of motor units follows the size principle[50, 51, 52]: Small, low force, slow-twitch, fatigue-resistant motor
units are recruited at low levels of force, whereas the largest fatigable motor units are recruited at maximal levels. The
input-output relationship of the motoneuron is, however, under additional suprasegmental control such that the order
of recruitment may be changed according to the specific task[53, 54, 55, 56]. The order of recruitment may also be
changed by altering afferent input[47, 57, 58] disease states and injury[59, 60]. The rise times of MUAPs in fast muscles
is faster than in slowly contracting muscle, without appreciable differences in the shape, amplitude, or duration of the
MUAPs[2]. It is believed that with steady isometric contraction, motor units active at the beginning of the contraction
remain active[47, 61, 62] with a time-dependent replacement of active motor units by different motor units believed to
delay fatigue[53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64], although this has not been confirmed in other studies[65, 66].
In human muscles during slow ramp contractions, motor units fire at 2 to 3 Hz and with slight effort reach a stable
regular firing rate of 5 to 7 Hz[2, 67, 68, 69, 70, 71]. At maximum effort, discharge frequencies vary somewhat in
different muscles but rarely increase above 30 to 40 Hz[46, 48, 72, 73, 74]; however, at brisk brief contractions, discharge
rates of 150 Hz have been recorded[75]. The firing rates of motor units with different recruitment thresholds suggest
that at moderate force, the low-threshold motor units have higher maximal firing rates than high-threshold motor
units[46, 72, 73]; however, the high-threshold motor units seem to have higher and phasic discharge rates at high
force[72, 76].
3
1.1.3
Number of Motor Units
The number of functioning motor units in a muscle is of considerable interest both to distinguish neurogenic from
myopathic processes as causes of muscle weakness and to follow the loss of motor units in neurogenic disorders.
Considerable efforts have been made to develop methods of motor unit number estimation (MUNE) by recording
single MUAPs evoked by electrical stimulation and during voluntary contractions. These methods include manual
incremental stimulation[77, 78, 79, 80, 81], computer-assisted incremental stimulation[82], multiple-point threshold
stimulation[83, 84], analysis of F waves[85, 86, 87], and spike-triggered averaging of MUAPs[88, 89, 90].
The basic principle of all these methods is the determination of the size of the physiological response of a representative motor unit that is then divided into the size of the maximal compound muscle response considered to be a sum
of individual motor unit responses. A number of assumptions are made to determine the representative quantal motor
unit response whether obtained as the force of the motor unit or the MUAP. These assumptions pertain to activation
of individual α-motor axons at low levels of stimulation and may be influenced by fluctuating activation of several
axons with similar thresholds (alternation).
The number of motor units differs greatly among muscles. Table 1.2 shows the number of MU in different muscles
and the number of muscle fibers and average number of muscle fibers per MU.
Number of muscle fibers
Number of motor unit
Muscle
(
a
-motor axons)
Average per
Per muscle
motor unit
Biceps brachii
774
580.000
750
Brachioradialis
330
130.000
390
Interosseus dorsalis 1
119
40.500
340
98
10.300
110
Lumbricalis 1
Opponens pollicis
133
79.000
595
Masseter
1020
1000.000
980
Temporalis
1150
1500.000
1300
Gastrocnemius medius
580
1000.000
1720
Tibialis anterior
445
270.000
610
Table 1.2: Number of motor units and average number of muscle fibers per motor unit in human muscles. (From
Buchthal and Schmalbruch[2])
1.1.4
Recording and Analysis of Motor Unit Activity
Many different technics have been developed over the years for recording and analyzing motor unit activity. Different
parts of the MU can be investigated and at different levels of contraction. These technics are often combined to
complement each other in an investigation.
Different electrodes exist for recording of action potentials from muscle fibers; either from a few muscle fibers as
with the single fiber needle electrode (SFNE), or from all the muscle fibers belonging to the same motor unit with a
concentric, monopolar, or a macro needle electrode (CNE, MNE and MaNE). The MaNE is less selective than the two
others because it has a larger uptake area. The CNE is used in this work and is described further in Section 1.2.2.
The MNE is in many respects similar to the CNE and it is used for the same investigations and analysis and it will
therefore not be further discussed, but the SFNE and the MaNE plus some more special needle electrodes will be
described in the following.
An EMG examination is usually performed at three levels of contraction: recording at rest to ascertain the presence
of diverse forms of spontaneous activity such as fibrillation potentials or positive sharp waves, fasciculations, myotonic
bursts, complex repetitive discharges, miniature end-plate potentials, and end-plate spikes; recording during low
levels of voluntary effort to obtain individual MUAPs without interference from other MUAPs; and recording during
maximal voluntary effort to evaluate the full recruitment pattern. The EMG signal is called an interference patterns
when the contraction force is so high that the individual MUAPs no longer can be distinguished. When using a CNE
an interference pattern is recorded already from a few percent of maximal voluntary contraction (MVC) because of
its relatively large uptake area so that many MUAPs are recorded resulting in many summations and cancelations of
MUAPs.
4
Central to most technics (SFNE, CNE, MNE and MaNE) is an evaluation of possible increase or decrease in the
mean number of muscle fibers per MU compared with healthy subjects because many neuromuscular disorders are
characterized by such changes. While the fiber density as evaluated with SFEMG, is a measure of local concentration
of muscle fibers belonging to the same MU, parameters like duration and area from motor unit action potentials
(MUAPs) recorded with either a CNE, BNE or MNE are measures of the total number of muscle fibers in a MU.
1.1.4.1
The Single Fiber Needle Electrode (SFNE)
The SFNE is used to measure fiber density (FD) and jitter. The small uptake area of the SFNE record on average
from 1.5 muscle fibers belonging to the same MU in normal adults with small variation in different muscles and an
increase with age. A FD analysis is performed by counting the number of single fiber action potentials from the same
MU in several (≥ 20) recording sites. An increase in the average FD is seen in reinnervation[16].
When a nerve fiber is electrically or voluntarily activated repetitively and the responses are recorded from a single
muscle fiber there is a latency variability. This latency variability is called jitter. The main source of the jitter is
the motor end-plate[16]. In voluntarily activated muscles, the jitter is measured by placing the SFNE such that a
pair of SFAPs from the same MU is recorded. There are many mathematical expressions of jitter, but all of them
fundamentally express the variability in the latency from the first SFAP to the second SFAP in consecutive firings.
1.1.4.2
Macro Needle Electrode (MaNE)
The MUAP recorded with a concentric needle is a relatively poor indicator of the total activity contained in the
motor unit. Recording with an electrode with a surface area of 15 mm in length improves the evaluation of the size
of the motor unit by sampling a larger portion of the motor unit[91, 92]. The motor unit activity is averaged to
reduce the contribution of activity from other motor units and recorded by means of a trigger potential picked up
by a SFEMG lead incorporated in the same electrode. The MUAP is the compound of a large number of muscle
fibers in the cylindrical recording area surrounding the exposed tip with a radius of about 2 mm and a length of 15
mm[93, 94, 95]. The area and amplitude of the MUAP are correlated with the number and size of muscle fibers in
the entire motor unit[91], whereas the duration is difficult to measure accurately. A variant of the method has been
developed by Jabre[96], where the macroelectrode has been combined with a concentric needle. The amplitude of
the macro MUAP increases with age due to reinnervation after motor neuron loss. Such changes are also found in
progressive neuropathy. In patients with ALS and the postpolio syndrome, the size of the MUAP decreases as the
disease progresses, suggesting loss of large motor units. In myopathy, the mean amplitude of the MUAPs has been
found to be normal or slightly decreased[97] probably due to increased density of muscle fibers in the motor unit, as
indicated by SFEMG.
1.1.4.3
Multilead Electrodes
It has long been recognized that the shape and duration of the MUAP recorded with a concentric electrode depends
on its location within the motor unit. Buchthal et al.[10, 98] developed a multielectrode with up to 14 recording
areas placed 1 mm apart and each corresponding to that of a concentric needle to determine the spatial distribution
of individual spikes and hence delineate the shape and territory of the motor unit in normal and diseased muscle.
By placing two multielectrodes perpendicularly to each other, the motor unit in normal muscle was found to be
circular in shape with the largest concentration of muscle fibers at the center of the motor unit[10] in agreement with
histochemical findings[11, 99]. In myopathy[100], the size of the motor unit was found to be decreased, whereas it was
increased in neurogenic lesions[101]. However, an enlarged territory of the motor unit as indicated by the electrical
activity could in part be due to an increased fiber density that would cause the spike activity to be seen at a longer
distance, leading to an interpretation of a spatially larger motor unit.
Stålberg et al.[102] studied the extent of the motor unit by using a multilead single-fiber electrode. The SFEMG
recording area is 25 µm in length (0.005 mm2 ), and the uptake area is about 300 µm. Normal muscles spikes of at
least 200 µV are recorded from one to two muscle fibers from the same motor unit, and the average number of spikes
at 20 sites is used to calculate the fiber density (average 1.5 in normal muscle). Studies with multilead SFEMG showed
that the density of fibers within the motor unit increased in neurogenic lesions but that the territory of the motor unit
remains unchanged.
1.1.4.4
Scanning Electromyography
To evaluate the spatial distribution of the electrical activity within the motor unit, a concentric needle electrode was
pulled through the motor unit and the MUAP recorded at 50-µm-space intervals and averaged by means of a triggerpotential obtained from an SFEMG needle placed within the same motor unit[103]. The spatial profile showed that
in about one half of control patients, a single major negative peak was present, but some had up to four peaks. The
5
scan of patients with myopathy may show increased fractionation of the motor unit with a larger number of spikes,
whereas the number of silent areas with an activity of less than 50 µV was decreased in neurogenic lesions due to
reinnervation within the motor unit area. The relatively normal scan length supports the view of newly incorporated
muscle fibers originating within the original motor unit territory and that collateral branches do not cross fascicular
borders. In most cases, the technique does not distinguish between neurogenic lesions and myopathy[104], possibly
because of the contribution of regeneration in the latter.
1.1.4.5
Bipolar Needle Electrodes
In these electrodes, two concentric leads are placed together in the cannula and a differential recording is made from
them. This suppresses distant activity, but enhances activity close to one of the leads resulting in a more selective
recording and therefore enables recordings at a higher level of contraction. Bipolar needle electrodes was used by
Andreassen and Rosenfalck[105].
1.1.4.6
Surface Electrodes
Surface recordings by means of elaborate electrode arrays and spike triggering, may be useful to assess end-plate
distribution, muscle tendon transition, motor unit depth and firing rate, and MUAP propagation along muscle
fibers[106, 107, 108, 109].
1.1.4.7
Turns-Amplitude Analysis - Analysis at Higher Level of Contraction
In this analysis, the number of turns in the interference pattern and the mean amplitude difference between turns are
measured within an epoch of fixed duration[110, 111]. A turn was defined as a change in voltage of 100 µV and may
arise as a peak in a MUAP or interaction between MUAPs, and the number of turns reflects the number of active
motor units, the proportion of polyphasic MUAPs, and the firing rate. The mean amplitude increases with increased
force output and with the amplitudes of recruited MUAPs.
Fuglsang-Frederiksen et al.[112, 113, 114] counted the number of turns at a given relative force equal to 30% of
the maximal voluntary force. Measurements during a relative force output discriminated better between myopathy,
neurogenic lesions, and controls than during 2 or 5 kg fixed force output, respectively, in the brachial biceps and
medial vastus muscles[115]. The number of turns increases in patients with myopathy whereas the mean amplitude
between turns increases in patients with neurogenic lesions. An increased ratio of turns and mean amplitude was a
sensitive indicator of myopathy, and a reduced ratio was an indicator of neurogenic disorders[111, 112, 116, 117, 118].
In patients with myopathy, the number of small intervals between turns is increased, and decreased in neurogenic
disorders[111, 112, 119]. The use of turns-amplitude analysis of the interference pattern at 30% of maximal effort
had the same diagnostic yield as analysis of MUAPs at low effort but the two methods supplemented rather than
replaced each other[110, 111]. The diagnostic yield was not further improved by analysis at forces greater than 30%
of maximum[120, 121, 122].
In an effort to reduce the cooperation required by the patient, the turns-amplitude analysis has been modified
in various ways without force measurements. Thus, the number of turns as a function of the mean amplitude was
determined at three to five force levels from minimum to maximum. The scatter plot in control patients forms a cloud
wherein the borders include 90% of normal values. Studies in myopathy and neurogenic lesions deviate in opposite
directions[123]. The peak ratio method uses the ratio of turns:mean amplitude as a function of increasing force. It is
measured every 100 msec during gradually increasing force over a period of 10 seconds and at peak ratio; at maximum
force the intervals between turns are indicated[124, 125, 126]. Patients with myopathy have a higher peak ratio or an
increased number of intervals of less than 1.5 msec or both, whereas patients with neurogenic lesions deviate in the
opposite directions.
In addition to the turns-amplitude analysis, the interference pattern has been studied by power spectral analysis
in patients with neuromuscular disorders and in the study of muscle fatigue, which lead to a shift of the interference
pattern spectrum toward lower frequencies[127, 128].
1.2
The Motor Unit Action Potential
In most laboratories, the needle EMG examination is carried out by a concentric needle electrode referenced to the
cannula or a monopolar electrode referenced to a surface electrode at a frequency interval of 2 to 10k Hz. The
recording area of the concentric needle is 0.07 mm2 , whereas the monopolar needle is insulated with Teflon except
for a bared recording area of 0.17 mm2 . Because of the different properties of the electrodes, the MUAP parameters
are not directly comparable[129, 130]. The control values used in Copenhagen were obtained using concentric needle
electrodes[131].
6
The MUAP is a compound signal reflecting the summation and cancelation of phases of the action potentials from
individual muscle fibers in the motor unit. With intracellular recordings, the action potential is a monophasic waveform
of about 100 mV, whereas the extracellular potential is a volume-conducted derivative of the rate of membrane
depolarization. The MUAP represents the spatial and temporal summation of these bi(tri)phasic spikes, wherein the
negative spike in the normal muscle is obtained from two to three fibers within 0.5 to 1 mm of the electrode. The
contribution of activity from fibers at a slightly longer distance is negligible because of the steep spatial decay of the
high-frequency spike. In contrast, the slow initial and terminal positive phases represent activity from fibers at greater
distances because the decay of these slow components is much smaller[29]. The amplitude of the spike is determined
by the proximity of the closest active fibers as indicated by the fact that fibrillation potentials that originate from
single fibers may have as high an amplitude as the MUAP.
The shape and duration of the MUAP reflects the architecture of the motor unit. Recorded outside the end-plate
region, the MUAP typically has three phases: an initial positive phase, a negative spike, and a terminal positive
phase. A negative afterpotential is sometimes recorded and is enhanced when the lower limiting amplifier frequency
is set to 20 Hz[132]. At the end-plate region the MUAP is biphasic in shape with a sharp negative onset. In some
instances, the MUAP may be split up into four or more phases, reflecting a greater asynchrony of individual muscle
fiber discharges. In normal humans, the number of polyphasic MUAPs with five or more phases, comprises about 3%
of a large number of control subjects. When 20 to 25 MUAPs are recorded, less than 12% of the total number of
potentials are polyphasic. In the deltoid and facial muscles, less than 25% are polyphasic, and in the vastus lateralis
and anterior tibial muscles less than 20%[133, 134].
A MUAP may contain a spike component separate from the main spike, a so-called satellite potentials which
represent action potentials of a single or a few fibers temporally dispersed from the main bulk of the fibers in the
motor unit in disease states and contributes up to 3% of MUAPs of healthy muscle[135, 136, 137, 138, 139]. A
stationary far-field positive potential is sometimes recorded during the positive terminal phase of the MUAP with
monopolar needle electrodes; it represents extinction of the action potential at the myotendinous transition[140, 141].
1.2.1
Recording of the Motor Unit Action Potential
Sampling of a representative number of motor units is necessary because of the large variability in innervation ratios
and therefore the duration and amplitude of MUAPs of individual muscles. Reliance on a visual impression of MUAP
characteristics is inaccurate. We therefore require a mean MUAP duration or amplitude outside the 95% confidence
limits to establish abnormality. In instances of mild disease or heterogeneous affection of the muscle by myositis or
monoradicular disease, the mean duration and amplitude may not deviate from normal, and the determination of
outliers has been suggested as a supplementary criterion of abnormality[142].
In the recording of activity from single motor units, it is important to obtain signals that are not contaminated by
activity from adjacent motor units. At low effort, MUAPs are recorded by the use of a trigger circuit and a delay line
that allows the whole MUAP to be visible on the oscilloscope to measure its total duration and amplitude. To ensure
that the signal is derived from a single motor unit, undisturbed by activity from others, three to five similar signals
are recorded. This ”template” method has the inherent bias of recording large MUAPs, and it is therefore important
to ensure that all MUAPs at each needle position are included in the analysis. At low effort it is usually possible to
record two to three MUAPs at each site. The sampling should include recordings from about 10 recording sites. To
ensure that the sampling is well distributed over the entire muscle, we record from three sites at distances of more
than 0.5 cm apart at three to four positions. Even though modern integrated EMG machines allow the recording,
storing, and analysis of MUAPs to proceed faster and easier, the bias of the sampling may be greater because only
those MUAPs that are triggered are captured whereas smaller MUAPs are lost.
The reliability of MUAP analysis depends on the expertise of the examiner. For this reason we find it necessary to
keep hard copies or computer data files of all recordings to ensure that the mean and SD of amplitudes and durations
are obtained from different and undisturbed MUAPs. The values are calculated for simple and polyphasic MUAPs
separately if the incidence of polyphasic potentials is increased.
1.2.2
Motor Unit Action Potential Parameters
Three MUAP parameters are of clinical importance: the duration, peak-to-peak amplitude, and the number of phases
(Fig. 1.1). The mean duration and amplitude from about 25 MUAPs from the same muscle is compared with control
values[131] matched to age. The percentage of polyphasic (≥5) MUAPs is compared with the maximal limit for
controls (Table 1.3).
1.2.2.1
Duration of the Motor Unit Action Potential
The duration of the MUAP reflects the temporal dispersion of activity of fibers constituting the motor unit and is
primarily due to the spatial distribution of end-plates along muscle fibers measuring 20 to 30 mm, with a conduction
7
turn 2
5 ms
turn 4
200 µV
turn 3
Amplitude
p
h
a
s
e
2
p
h
a
s
e
p
h
a
s
e
1
turn 1
3
turn 5
Duration
Figure 1.1: Illustration of the MUAP parameters
Voluntary activity
Weak effort
MUAP duration
Spontaneous activity
Full effort
Number of sites
outside end-plate
±20%
a
Pattern
Full recruitment
a
MUAP amplitude
Incidence of
polyphasic MUAPs
fibrillation
+100%
-50%
b
12%
region with
Amplitude of
envelope curves
> 2 mV
potentials or
and
positive sharp
< 4 mV
waves
2
(upper limit)
_____________________________________________________
a
Mean of 25 or more MUAPs.
b
In the deltoid and facial muscles 25%, in the anterior tibial and lateral vastus muscles, 20 %.
Table 1.3: EMG limits in normal muscles (95% confidence limits)
velocity of 3 to 5 m/sec[9, 10, 143]. The duration is also related to the number of muscle fibers present in a semicircle
2.5 mm from the active recording surface[144, 145] and the diameter of muscle fibers in the motor unit[146, 147]. It
is measured from the first deflection of the MUAP from baseline to the return of the terminal positive phase (Fig. 1).
The duration is highly dependent on amplifier settings. Normal values from the Clinical Neurophysiology Laboratory
at Rigshospitalet were obtained at a gain of 100 µV/cm; the measured duration was shorter at lower gain settings and
longer at higher gains. In modern equipment with digitalized signals, the deviation and return to baseline is usually
determined by amplitude or slope criteria[148] and the normal values developed for the manual measurements may
therefore not be applicable.
In a given muscle, the normal duration of individual MUAPs can vary by a factor of 3 to 5 and in the brachial
biceps range from 5 to 15 msec. This variability is mainly due to the variability in fiber content of the motor units.
It is therefore necessary to record several MUAPs (20 to 30) at different sites in the muscle to obtain a representative
sample. The duration of simple units, defined by the presence of four or less phases, and polyphasic MUAPs are
averaged separately and together and the values compared with age-matched control subjects. The normal range is
defined as the normal mean ±20% Table 1.3. MUAP duration is the most important parameter in the separation of
myopathic disorders in which there is a loss of muscle fibers from chronic neurogenic disorders in which there is an
increased number of muscle fibers due to collateral sprouting[149, 150, 151].
1.2.2.2
8
Amplitude of the Motor Unit Action Potential
MUAP amplitude is measured peak-to-peak from the most positive to the sequentially most negative peak (Fig. 1.1).
Because of cancelation between phases, the MUAP amplitude is less than the sum of individual fiber potentials and
may even be smaller than the amplitude of single fiber potentials. It depends on the proximity of the closest 2 to 15
fibers of the motor unit within about a 0.5-mm diameter[144, 145, 152, 153] and is proportional to the number and
density of fibers in the motor unit. Because of the large effect of distance between the active fibers and the recording
surface, the amplitudes of the MUAPs are markedly variable. The amplitude also depends on the type of electrode
used. The amplitude is larger when using monopolar than concentric needle electrodes[154, 155]. It is important
to hear the crisp sound of discharging MUAPs indicating that the electrode is placed close to active motor units.
Manipulating the electrode should be avoided because this can yield larger amplitude MUAPs. The recordings that
form the basis for the control material at Rigshospitalet were obtained without manipulating the needle to obtain
larger amplitudes; however, the variability of the amplitude is such that the mean amplitude should be more than
100% above controlvalues to be considered abnormal.
Better standardization of recording MUAPs has been attempted by using the rise time of the MUAP, as measured
from the maximum positive to the maximum negative peak[156]. A general recommendation is to accept a rise time
of ≤0.5 msec[157]; however, this also depends on the number of fibers that summates to generate the MUAP. It tends
to increase in chronic partial denervation with reinnervation of muscle fibers, whereas it is shorter in myopathy due to
loss of muscle fibers. In practice, we exclude potentials with slow rise times and amplitudes of less than 50 µV. The
rise time is negatively correlated to the amplitude of the MUAP but, if less than 2 msec, has little influence on the
duration of the MUAP that is determined by its slow initial and terminal phases[158].
1.2.2.3
Shape of the Motor Unit Action Potential
MUAPs can be simple in shape or polyphasic. The latter is of long or short duration in myopathy depending on the
degree of muscle fiber regeneration. It is important to calculate amplitudes and durations of simple and polyphasic
potentials separately and to collect more MUAPs than necessary in case additional ones are needed[159]. Irregularities
of the MUAP that do not result in baseline crossings are termed ”turns” if they have an amplitude of more than 100
µV (Fig. 1.1). Studies of MUAPs have shown variability in the shape in successive discharges or jiggle[160] not due to
electrode displacement. This is more pronounced in myasthenia gravis and in early collateral sprouting with immature
axonal sprouts. A more detailed description of possible MUAP shapes and examples are given in Section 3.1.
1.2.3
Biologic and Physical Factors that Influence the Motor Unit Action Potential
Some MUAP parameters are dependant on the subjects age and the temperature during recording.
1.2.3.1
Age
The duration of the MUAP increases from persons age of 20 to 80 years, although the amount varies in different
muscles[161, 162, 163], and there is disagreement as to the effect of age on duration[164]. The increase may be due
to remodeling of the motor units with collateral sprouting[35, 36, 165] that presumably starts in the third decade due
to progressive loss of anterior horn cells[166]. The increase in MUAP duration at ages less than 20 years is probably
due to maturation. Single-fiber EMG (SFEMG) studies have shown an increase in fiber density with age above 60
years[167] partly due to a reduction in muscle mass and fiber diameter[168]. An increase in fiber density could cause
an increase in the amplitude of initial and terminal phases and consequently a longer duration of the MUAP. The
amplitude of the MUAP does not change significantly with age.
1.2.3.2
Temperature
A reduction in muscle fiber and axon conduction velocities occurs at low temperatures, and MUAP duration increases
with a reduction of the intramuscular temperature[161, 169]. The mean duration of MUAPs increases by 6%/◦ C
between 30 and 36◦ C and by 9%/◦ C between 22 and 30◦ C. At normal intramuscular temperatures, the MUAP does
not change enough to cause errors in diagnosis; however, at temperatures lower than 32◦ C, considerable errors may
occur. MUAP polyphasia increases from about 3% at 37◦ C to 25% at 29◦ C[134]. The amplitude of the MUAP may
increase or decrease at low temperature due to variable phase cancelations[161]. At ADEMG the MUAP duration is
prolonged at low temperature, although amplitudes and turns remain unchanged[170].
1.2.4
Pathophysiological Changes of the Motor Unit Action Potential
The primary aim of the EMG examination is to determine whether weakness is due to a myopathic or neurogenic
lesion. Proximal and distal weak muscles are generally studied. Spontaneous muscle fiber activity during rest may be
9
recorded in normal muscle and in myopathy and chronic partial denervation. In control and affected muscle, the endplate zone shows miniature end-plate potentials and spontaneous muscle fiber discharges that have a sharp negative
onset. Outside the end-plate zone, propagated action potentials are rarely recorded in normal muscle. In myopathy
fibrillation, activity is abundant in conditions with muscle fiber necrosis such as myositis and some types of muscular
dystrophy, whereas they are rare, for example, in mitochondrial myopathy or thyrotoxicosis. In neurogenic disorders,
denervation activity indicates failure of reinnervation but may be seen for several years after the insult has occurred, as
for example in poliomyelitis. An increased incidence of fibrillation potentials and positive sharp waves is a nonspecific
criterion of pathologic changes in the muscle, as are complex repetitive discharges that usually arise from groups of
muscle fibers and may be seen in both myopathy and neuropathy. Abundant waxing and waning complex repetitive
discharges are characteristically seen in radiation-induced peripheral nerve damage. Fasciculations are defined as
irregular discharges in groups of muscle fibers and may occur in normal muscle, in myositis and neuropathy. They
tend to have long discharge intervals of more than 3 seconds and most often arise from groups of muscle denervated
fibers.
Analysis of the MUAPs at weak effort has a central position in the EMG examination because the duration,
amplitude, and shape of the MUAPs recorded with a concentric or a monopolar needle electrode reflects the architecture
of the motor unit.
The basic pathophysiologic change in myopathy (Table 1.4) is the degeneration loss of muscle fibers in the individual
motor unit. Whether these abnormalities are reflected in the EMG depends on the stage of the disease and whether
compensatory regeneration mechanisms have already occurred. At early stages of the disorder, muscle contraction
may be affected primarily at the level of excitation-contraction coupling, in which case MUAP analysis may show
only mild or unspecific changes. The incidence of polyphasic potentials is increased in patients with myopathy for two
reasons. First, muscle fibers of the motor units are lost and hence summation of muscle fiber action potentials in the
MUAP is reduced. Because of the loss of muscle fibers, the duration of the MUAP is reduced (Fig. 1.2). Second, there
is regeneration of muscle fibers from satellite cells, and this is associated with polyphasic potentials due to increased
dispersion caused by a reduced conduction velocity along the regenerated muscle fibers or along collateral nerve sprouts.
The incidence of polyphasic potentials is related to the duration of the MUAPs and to the number of regenerating
basophilic fibers at muscle biopsy. At an early stage of myopathy, the incidence of long duration polyphasic potentials
is increased, whereas they decrease with advancing severity[150]. The presence of long polyphasic potentials may lead
to the erroneous conclusion of a neurogenic disorder. To avoid this error, we also measure the duration of simple
MUAPs separately (Fig. 1.2).
Specific criteria
Nonespecific criteria
Criteria of myopathy
Activity at rest
MUAP during weak effort
Recruitment pattern
Decrease in duration
Full recruitment pattern in weak and wasted muscles
Decreased amplitude of full recruitment pattern
Fibrillation activity and positive sharp waves
Increase in incidence of polyphasic potentials
Decreased amplitude
Reduced recruitment pattern
Criteria of peripheral nerve and root disease
Activity at rest
Recruitment pattern
Fibrillation activity and positive sharp waves (4-10 sites)
Increase in duration
Increase in amplitude
Discrete activity
Increased amplitude
Increase in incidence of polyphasic potentials
Reduced recruitment pattern
Criteria of anterior horn cell disease
Activity at rest
Recruitment pattern
Fasciculations (malignant, intervals of > 3 sec)
Increase in duration
Increase in amplitude (>500% increased)
Discrete activity
Increased amplitude (>6 mV)
Fasciculations (benign, intervals < 1 sec)
Increase in amplitude of individual MUAP (200%)
Table 1.4: EMG criteria of neuromuscular diseases
In chronic partial denervation, reinnervation due to collateral sprouting enlarges the motor unit territory (Table. 1.4).
In reinnervation due to axonal regeneration, nascent motor units may be polyphasic and of short duration. However,
even in such cases the mean MUAP duration of 20 to 30 MUAPs is usually prolonged, although amplitudes of
the severely polyphasic MUAPs may be small. In chronic neuropathies, the duration, amplitude, and incidence of
polyphasic MUAPs are increased (Fig. 1.2). Polyphasic potentials arise from dispersion of muscle fiber action potentials
in the motor unit due to low conduction velocity of reinnervated atrophic muscle fibers and axonal sprouts. In early
10
reinnervation, conduction along immature sprouts is insecure, and the MUAP may show pronounced variability (jiggle)
with intermittent dropout of segments of the MUAP. In long-standing neurogenic lesions, such as remote poliomyelitis,
polyphasic potentials may be near normal due to maturation of the collateral sprouts. It has been suggested that in the
post-polio syndrome, the extended sprout tree may show regressive changes with degeneration of sprouts; however, it is
also possible that the whole motor neuron degenerates, leaving motor units with a smaller innervation ratio and hence
a smaller metabolic load on the motoneuron. In patients with ALS, simple and polyphasic potentials are enlarged,
due to either long-standing reinnervation or coupled-discharges of more than one motor unit.
In cases where the findings of MUAP analysis are uncertain, it is often helpful to supplement the study with turnsamplitude analysis of the interference pattern, which allows examination at higher degrees of muscle force than the
slight effort required to study individual MUAPs. Evaluation of the interference pattern at maximal voluntary effort
is an integral part of the EMG examination and gives a semiquantitative measure of the presence of motor unit loss.
Normally, maximal voluntary contraction in cooperative subjects is associated with a full interference pattern where
individual MUAPs cannot be discerned. The amplitude of the envelope curve of the interference pattern is in most
muscles 2 to 4 mV[171]. In myopathy, the interference pattern is generally full and of lower amplitude than in normal
muscle. It starts abruptly even at low levels of effort than in controls. In severe myopathy with extensive muscle
fiber loss, the interference pattern may be reduced and may show less pronounced summation than in normal muscle.
In neurogenic lesions, the interference pattern is reduced in moderate cases with loss of motor units, whereas it is
discrete in severe cases wherein individual MUAPs stand out with a flat baseline between successive discharges. In
chronic motor neuron disorders with reinnervation due to collateral sprouting, the amplitude of the reduced or discrete
interference pattern is increased and may reach values of 8 to 10 mV (Table. 1.4).
The distribution, type, and degree of EMG abnormalities are important in the diagnosis of neuromuscular disorders.
Although the EMG examination should include clinically affected muscles, it may be crucial to study muscles not
overtly affected. For example, in patients with suspected motor neuron disease, the initial weakness and atrophy
may have a segmental distribution, raising suspicion of either radicular affection or mononeuropathy. However, EMG
examination of muscles with normal force and bulk often shows that the involvement is distributed more widely than
suggested by the clinical examination and thus indicate a generalized disorder.
11
Neuropathy
Myopathy
(Inflammatory polyneuropathy)
(Polymyositis)
54-year-old male
Muscle: Vastus medialis
5 ms
20 µV
5 ms
100 µV
5 ms
500 µV
Normal
65-year-old male
Muscle: Vastus medialis
41-year-old female
Muscle: Deltoideus
Number of potentials = 74
Number of polyphasic potentials = 3
Mean duration of all potentials = 11.2 ms
Mean duration of simple potentials = 11.1 ms
Mean amplitude of all potentials = 369 µV
14
14
12
12
10
10
8
8
18
16
14
%
10
%
%
12
6
6
4
4
2
2
0
0
0
0
8
6
4
10
20
Duration (ms)
30
2
10
20
Duration (ms)
30
0
0
10
20
30
Duration (ms)
Figure 1.2: MUAPs from a normal subject, a patient with neuropathy and a patient with myopathy. Middle part: A
schematic interpretation of the distribution of muscle fibers. Black bars represent muscle fibers from the active MU;
dark grey bars are muscle fibers from another inactive MU and light grey bars are from degenerated muscle fibers
belonging to the active MU; light gray bars indicate degenetated muscle fibers belonging to the active MU. Lower
part: Histograms showing the distribution of durations of MUAPs in the three cases. Durations for simple potentials
are drawn with white bars and the polyphasic potentials with black bars. For the patient with neuropathy the mean
duration was increased 51% compared to normal values matched for age and muscle, and the incidents of polyphasic
potentials was 13%. The corresponding values for the patient with myopathy was 29% shortened mean duration and
31% polyphasic potentials.
12
1.3
The Motor Unit Firing Pattern
The MU firing pattern (FP) is the temporal activation of a MU. It is graphically depicted, as a vertical line on a
time axes, for each firing of the MU. The FP can be determined by identifying each MUAP from a MU, in a EMG
recording. This is illustrated in Fig. 1.3. The FP provides further information compared to conventional EMG analysis
EMG signal
Firing pattern
IPI
Figure 1.3: An illustration of the relation between the EMG signal and the firing pattern when only one MUAP is
present. It also shows how the inter potential interval (IPI) is defined as the time between two consecutive firings of
the same MU.
(MUAP analysis) because the timing information in the EMG signal is extracted. The FPs provides information on
the function of the central nervous system and its control of the MUs. By analyzing the FPs further insight in how
the CNS orchestrates the complex task of recruiting/derecruiting and modulating the intensity of MU discharges
to produce a given muscle force. Quantifying the changes in firing rate and variability from the FPs can hopefully
reveal changes in the CNS and peripheral disorders compared to normals and even differentiate between different
CNS disorders. The possibility of indirectly monitoring the CNS behavior through a needle inserted in the muscle is
fascinating because it opens a window to the CNS without direct assault to the CNS.
1.3.1
Motor Unit Firing Pattern
Firing pattern analysis under different recording conditions have been described in the literature. Needle EMG
recordings are made at isometric contraction and either at:
constant or varying level of contraction. Varying contraction is used to investigate recruitment and de-recruitment
of MUs, but these investigations are not used in routine1 . Constant level of contraction is primary used to investigate the mean firing rate and variability, see Table 1.6.
low or high level of contraction. By using selective needle electrodes and special filters, FPs can be obtained
at higher level of contraction even at maximal contraction permitting studies of type 2 MUs. When using
conventional needle electrodes like the concentric and monopolar only recordings at low level can be obtained
permitting studies of type 1 MUs.
Only EMG signals recorded at constant isometric and low level of contraction are considered in this work.
While the FP shown in Fig. 1.3 is rather straight forward to extract from the raw EMG signal because only one
MUAP is present, this is rarely the case in real recordings. Even at very low levels of contraction several concurrently
active MUs will be recorded in the vicinity of the recording needle electrode. This means that inevitably two or more
MUAPs will be recorded simultaneously resulting in a superposition of them. The detection of every occurrence of
the MUAPs present in the recorded EMG signal even in case of superimposed MUAPs is called decomposition. The
decomposition process is described in details in next Chapter together with the difficulties involved when trying to
do a complete decomposition. The identification of the individual MUAPs in a superposition is the most difficult and
time consuming step in the decomposition process. The complexity of the decomposition process involves advanced
signal processing techniques that are computational intensive and thus time consuming. This has so far prevented FP
analysis to be used in a clinical setting and despite much work on the subject our understanding and utilization of the
FP information is still limited.
1.3.2
Previous studies on computerized EMG
Many automatic EMG analysis systems (AEAS) have been developed during the last twenty years[172, 173, 174, 3,
148, 175, 4, 176, 177, 178, 1]
The five most important features for comparison may be:
1 DeLuca and coworkers have done many studies with their decomposition system[172] based on selective multi chanel recordings. This
have permitted them to record both at varying contraction and up to 100% MVC, see Section 1.3.2
13
• On-line / off-line. For clinical use, the system should be on-line with just a few seconds response time.
• Full FP / partial FP / no FP. If parameters like firing frequency, firing variability or information about the
recruitment time is desired, then a full or partial FP must be produced. If the FP is only partial because the
superimposed MUAPs are not considered, then the parameters will only be an estimation of the true values.
The FP reflects the CNS motor unit control.
• MUAP shape parameters. Quantification of EMG signals by measuring of duration, amplitude, number of
phases and turns are widely used in diagnosis of neuromuscular disorders[134, 155, 161, 179]. Abnormalities in
these parameters reflect structural or physiological changes of the motor unit.
• Recording electrodes. Concentric or monopolar needle electrodes are standard in most AEAS. More and less
selective needles exist like the single fiber and macro electrodes and the quadrupolar multielectrode [172].
• Contraction force, low / medium / high. The muscle force is measured in relation to Maximal Voluntary
Contractile (MVC) force. A higher contraction results in recruitment of more MUs/MUAPs and the proportion
of superimposed MUAPs will increase, resulting in a more complex EMG signal which demands a more advanced
and time consuming AEAS. The properties of later-recruited (high force) MUAPs differ from those recruited
near threshold (low force)[180]. Normal values have traditionally been obtained at weak effort[179].
The following AEASs represent different solutions and Table 1.5 shows a comparison based on the five features.
Andreassen
Stålberg et al.
LeFever and DeLuca
McGill and Dorfman
Hass and Meyer
Our system
on-line/
off-line
on-line
on-line
off-line
on-line
off-linea
off-line
full FP/
partial FP/
no FP
no FP
partial FP
full FP
partial FP
full FP
full FP
MUAP
shape
parameters
yes
yes
no
yes
yes
yes
type
of
needle
conventional
conventional
special
conventional
conv. + spec.
conv. + spec.
contractile
force
low
low
low - high
low - medium
low - medium
lowb
Table 1.5: Comparing five automatic EMG analysis systems by five features.
a See
b The
the description of the system in the text.
force is not measured.
Andreassen, 1987[175]. This AEAS was designed to follow closely the manual method developed by Buchthal and
coworkers[134, 155, 161]. After amplification, filtering (2 Hz-10 kHz) and sampling (12-bit ADC at 11 kHz), MUAPs
are detected when they exceed an amplitude threshold of 50 µV, and a segment of 100 ms around this detection point
becomes the first template. If the next segment matches the first it is placed into the same buffer as the first one, else
it becomes a new template in a new buffer. A match is found if the power of the difference signal between segment
and template is less than 12% of the power of the template. This is repeated until a maximum of four templates are
found. To ensure that the template represents a MU, three more MUAPs closely matching the template are searched
for. If they are found, they are averaged to measure the MUAP shape parameters, else the buffer is cleared to give
room for a new template. Superimposed MUAPs are not resolved and no firing pattern is produced.
Stålberg et al. , 1995[181]. This AEAS is a fast and fully automatic clinical system based on template matching.
The EMG signal is continuously shown on the monitor and the gain is set so the amplifier is not overloaded. After
amplification, filtering (5 Hz-10 kHz) and sampling (12-bit ADC at 20 kHz), a 4.8 sec long signal is analyzed. MUAPs
are detected and used if the negative peak exceeds 30 µV2 , the slope at the steep positive negative going rising phase
at the triggering point exceeds 30 µV/0.1 ms and the time between successive MUAPs are greater than 2.5 ms. The
principles of the template matching are similar to Andreassens[175] except that the maximum number of templates is
six and a parametric method is used for the template matching; When the difference in amplitude, rising and falling
slope in the central part of the MUAP and template is less than a predefined value, then they match. If at least
five identical MUAPs are found then they form a class. MUAPs from the same class are averaged and the MUAP
parameters are measured. Only a partial firing pattern is produced because superimposed MUAPs are not resolved.
The results can be edited by the examiner e.g. classes can be merged or deleted and the duration can be manually
corrected.
LeFever and DeLuca, 1982[172] and Stashuk and DeLuca, 1989[182]. This decomposition system was
developed to separate an EMG signal with high accuracy into its constituent MUAPT (MUAP Train) at high contractile force using quadrupolar needle electrodes. The analysis occurs off-line and needs interaction from a skilled
2 corresponding
approximately to a peak-peak amplitude exceeding 50 µV
14
operator. A three-channel high pass filtered EMG recording is stored on FM tape, then later replayed, sampled, band
pass filtered and decomposed. The filtering removes slow rise time MUAPs distant from the electrode and shortens
the duration of all MUAPs to reduce the degree of superposition among MUAPs. When classifying MUAPs, both
the similarity in shape and probability of each MU firing at a given time are considered. The selection of templates
and firing statistics are updated with each MUAP classification, and when a MUAP has correctly been detected and
classified the template is subtracted. The signal can be rescanned to detect and classify superimposed MUAPs.
McGill and Dorfman (ADEMG), 1985[3]. ADEMG is specifically oriented toward clinical application and
it analyzes a 10 s EMG signal recorded at constant isometric contraction. The signal is band pass filtered (8 Hz-8
kHz), antializing filtered at 5 kHz, sampled at 10 kHz and stored on disk. The EMG signal is immediately loaded
for decomposition. First the signal is high pass filtered using either a first or second order differentiating filter. This
results in suppression of background activity and produces narrow spikes from MUAPs close to the needle with fast
rise time. These spikes are easier to work with because: (1) they can be easily selected by threshold detection, (2)
the superposition of MUAPs is reduced, (3) the peaks can be used for alignment and (4) the computational time is
reduced because the MUAPs are more narrow. The detection threshold is (typically) 3.5 times the standard deviation
of the base-line noise. After a spike has been detected, it is transformed using DFT to the frequency domain, where
they are rotated for peak alignment and classified using template matching (the squared error between the template
and the spike). The first spike is stored as the first template. If the second matches the first one, then the template is
updated by averaging, else it will be stored as a new template etc. ADEMG does not resolve superimposed MUAPs
and can therefore only produce a partial firing pattern, which is used to verify the classification. At the end, the
identified MUAPs are averaged and the MUAP parameters are measured and the firing rate and firing variability are
estimated.
Hass and Meyer (ARTMUP), 1989[4]. ARTMUP is an automatic decomposition system with a selectable
degree of decomposition. The sampled EMG signal (12 bit sampled at 10 kHz) is separated into segments with
MUAPs, by sliding a time window over the entire EMG signal and detecting when the mean slope within this window
exceeds a fixed threshold. This marks the beginning of the segment and the end is determined when the total variation
falls below another fixed threshold. All the segments containing MUAPs are compared against each other using a
nonparametric distance measure. This information is stored in a distance matrix from which the Minimum Spanning
Tree (MST) is constructed. By cutting nodes in the MST that represent distances above an estimated threshold,
the MST is divided into subtrees and the segments are clustered with similar segments each representing an active
MU in the EMG signal. At this stage the number of MUs are determined, the MUAP parameters are measured and
the partial firing patterns are determined. The firing patterns are only partial because segments with superimposed
MUAPs have not yet been resolved. The gaps in the firing pattern can be filled out in two stages. First by a preliminary
resolution of slightly overlapping MUAPs and then by a detailed resolution where up to three superimposed MUAPs
can be resolved. For low contraction force a conventional concentric needle is used and for varying and high force, two
platinum wires isolated with teflon is used.
1.3.3
Motor Unit Firing rates and Variability in Neuromuscular Disorders
While most works on MU firing rates3 and variability demonstrates changes in different disorders a few also reports
no diagnostic yield. In Table 1.6 is shown the results of six works on firing rates and variability in neuromuscular
disorders. It was concluded in the work of Fuglesang-Frederiksen et al.[183] that the average firing rate and variability
was the same in controls and patients with myopathy and neuropathy. The same result was found by Nandedkar
et al.[177] for the firing rate. Both investigated the brachial biceps muscle. In all the other studies myopathy was
associated with higher firing rate compared to normals and Halonen et al.[184] and Dorfman et al.[185] additionally
found the variability to be normal. In case of neuropathy Petajan et al.[68] and Halonen et al.[184] found higher firing
rates than normals and Halonen et al.[184] found higher variability. In contrast Dietz et al.[186] found lower firing
rates in polyneuropathy. Petajan[68]. Dorfman et al. found that in motor neuron disorders the firing rate increases as
well as the variability. In central disorders Dorfman et al.[185] found no difference in firing rate but higher variability.
Although some of these studies agreed on findings in myopathy, still different conclusions were reached regarding
neuropathy and in the usefulness of firing rate and variability analysis in neuromuscular disorders. These differences
can probably be explained by the differences methods used and different muscle were investigated (Table 1.6).
Instead of looking at the mean firing frequency and the variability Genreben and Schulte-Mattler[187] looked at
the maximal firing rate in recordings with 1, 2, 3 and 4 simultaneous activated motor units. The recordings were
made at slight voluntary contraction using a standard concentric needle electrode. They found no difference in the
maximal firing rate in case of 1, 2, 3 and 4 simultaneous active motor units. In 10 out of 15 patients with neuropathy
3 Or the mean IPI (inter potential interval), but the mean IPI and the mean firing rate are equivalent in that a simple relation exist
between them; they are each others inverse (fmean = IP I 1
. There will therefore in the following not be any general distinction
mean
between the firing rate (or frequency) and the IPI. The IPIs is what is actually determined in a decomposition system and it is therefore
the fundamental entity in a FP analysis.
15
Petajan
(1974)
Dietz et al.
(1975)
Halonen et al.
(1981)
?
Tungsten
microelectrodes
SFNE
Slight
(at suprathreshold)
Slight and steady
regular contraction
30 sec.
First dorsalinterosseus
+ extensor digitorum
Strain gauge
Anterior tibial
FuglsangFrederiksen et
al. (1987)
Nandedkar et
al.
(1995)
CNE
SFNE
CNE
Slight + 30 % MVC
10 % MVC
Slight
10 sec.
Brachial biceps +
Anterior tibial
Strain gauge
20 IPIs ≈ 1 – 4 sec.
6 sec.
Brachial biceps
Brachial biceps
Strain gauge
Visual
Dorfman et al.
(1989)
Recording
Needle electrode used
Recording length
At onset and
recruitment of a second
MU
?
Muscle
?
Feedback
Audio-visual
Contraction force
100 IPIs
Auditory
Method for determining the firing pattern (IPIs)
?
Using a level/window
peak detector triggering
device
?
Automatic
decomposition
Manual determination
of IPIs from paper
Automatic
decomposition
Control and patient groups
10 with myopathy
16 with neuropathy
8 with ALS
92 recordings from
healthy controls
44 recordings from
patients with myopathy
43 recordings from
patients with
polyneuropathies
20 healthy controls
11 with myopathy
12 with peripheral
neuropathy
? healthy controls
7 with myopathy
11 with MND (ALS
and spinal muscular
atrophy)
11 with central
disorders (multiple
sclerosis, MS)
14 healthy controls
13 with myopathy
8 with neurogenic
disorders
17 healthy controls
14 with myopathy
6 with neurogenic
disease (old polio and
neuropathy)
Results (patient groups compared to controls)
Myopathy: lower IPI
Neuropathy: lower IPI
ALS: lower IPI
and higher variability
Myopathy: lower IPI
Polyneuropathy: higher
IPI
Myopathy: lower IPI
and same variability
Neuropathy: lower IPI
Myopathy: lower IPI
and same variability
MND: lower IPI
MS: same IPI
No difference
No difference
Table 1.6: Overview of six different works on firing rate and variability in neuromuscular disorders.
a significant higher firing rate was found than those obtained in normal controls. In patients with myopathy firing
rates were always found to be normal. This work and the work of Conwit et al. showed that no significant difference
in firing rate for MU recorded at slight levels of contraction.
Howard et al.[163] reported that MU firing rate decreased with age (p = 0.01) when force was measured proportionately, but not when measured absolutely. Roos et al.[188] found no difference in mean MU firing rate at different
isometric force levels tested in the quadriceps muscle (10%, 25%, 50%, 75% and 100% MVC) between young and old
men.
Howard et al.[163] found that firing rates did not vary significantly with gender in brachial biceps and anterior tibial
muscles.
Despite some of the above described contradictory findings, some very promising results have been reported from
FP analysis in different disorders and especially its potential of detecting disorders in the central nervous system is
interesting[189, 190, 191, 192, 193].
1.4
The purpose of this work
The purpose of this study was to:
• describe EMGPAD, a decomposition system developed by the author with contributions from other students
from the Danish Technical University (DTU).
• present a library of important EMG signal and firing pattern examples and give a detailed description of them.
This serves three purposes:
– to illustrate the difficulties encountered in decomposition and FP analysis.
– to present important phenomena not previously described in detail and to document these phenomena with
examples.
– to illustrated the capabilities of EMGPAD.
• to investigate the possibility of using firing pattern analysis of EMG signals recorded under standard conditions
for MUAP analysis. This includes slight muscle contraction, sampling via a concentric needle electrode, approx-
16
imately 10 seconds recordings and with visual and audio feedback. This is done by comparing FP findings in
healthy controls and patient with myopathy and ALS.
• to investigate FP parameter with respect to inter-parameter correlation and to search for robust methods for
estimating the mean and standard deviation for the IPIs.
• to give a detailed description of the difficulties associated with EMG decomposition and FP analysis. These
difficulties have not been the focus of previous studies of FP analysis.
Chapter 2
EMG Signal Decomposition
Complete decomposition of an EMG signal involves detection and identification of every potential from all MUs within
the territory of the recording electrode. In case of superposition of two or more potentials each of them should be
identified so that the output from the decomposition is both the MUAPs and the firing patterns and measurement of
their respective parameters.
This chapter starts with a discussion of the problems associated with decomposition of EMG signals. A brief review
of different automatic EMG analysis systems (AEAS) is given followed by a description of our decomposition system
EMGPAD (EMG Precision Automatic Decomposition). The chapter ends with how EMGPAD is implemented and
an evaluation of its performance.
2.1
Difficulties associated with decomposition of EMG signals
The enormous variation amongst EMG signals makes it a very difficult task to perform a perfect decomposition. Some
of the sources for this variation include:
• Variation in complexity. With increasing level of contraction more MUs are activated resulting in a more
complex signal. Some neuromuscular disorders make it difficult for the patient to maintain a low and constant
level of contraction during the recording. This is also true for children because of the difficulties in cooperation
during the recording of signals. The electrode position also plays a role in the quality of the signal. Fig. 2.1
show an example of a simple and a complex signal.
• Amplitude variations. Low amplitude MUAPs are characteristic for myogenic patients. Typical less than 100
µV and sometimes approaching the noise level resulting in a very pure signal to noise ratio (S/N). On the other
hand neurogenic patients are characterized with having very high amplitude MUAPs often more than 1000 µV
(see Fig. 2.2). Amplitude is also dependent on how close the tip of needle is to the muscle fibers. If it is very
close to one or a few muscle fibers, then the MUAP will have a high amplitude and low rise time.
• Noise. Everything else than the MUAPs in the EMG signal is considered to be noise. The noise can be
categorized into three groups; instrumental noise (from the needle, cables, amplifier, A/D-converter), baseline
movements and distant MUAPs that have low amplitudes and long rise times. Fig. 2.3 shows an example of
each. While instrumental noise and baseline movements can be dealt with efficiently using suitable filters, this
is not the case with distant volume conducted MUAPs.
The variation in MUAP shapes within an EMG signal is also big, and especially big difference in amplitude (ten fold
in some signals) poses problems. When small and big MUAPs are superimposed, the small ones can be very difficult
to extract.
All these factors attribute to the variation and a good decomposition system has to take them all into account. This
calls for an algorithm which is adaptive to the variations. A system that is based on too many assumptions about the
various properties of the EMG signal will fail to work across a broad selection of signals. How this is dealt with in
EMGPAD and comparisons to other system follow in the next sections.
When trying to identify MUAPs from the same MU, it is assumed that the similarity in shape is high for MUAPs
from the same MU and low for MUAPs from different MU. This is often the case but in some cases this assumption
is violated:
• MUAP shape variability (Jiggle).
• Needle movement.
17
18
CHAPTER 2. EMG SIGNAL DECOMPOSITION
150
100
50
µV
0
−50
−100
−150
−200
0
0.05
0.1
0.15
0.2
0.25
0.3
0.2
0.25
0.3
ms
250
200
150
100
µV
50
0
−50
−100
−150
−200
0
0.05
0.1
0.15
ms
Figure 2.1: Two signals recorded at the same needle position, but at different level of contraction. The first signal was
recorded at 1 % of maximal voluntary contraction (MVC) and the second at 5 % of MVC.
1500
1000
µV
500
0
−500
−1000
−1500
0
0.05
0.1
0.15
0.2
0.25
0.3
0.2
0.25
0.3
0.2
0.25
0.3
ms
1500
1000
µV
500
0
−500
−1000
−1500
0
0.05
0.1
0.15
ms
1500
1000
µV
500
0
−500
−1000
−1500
0
0.05
0.1
0.15
ms
Figure 2.2: Three EMG signal to illustrate the big amplitude variation. From top down, first a characteristic signal
from a patient with myopathy with very low amplitude, then a normal and then a signal from a patient with neuropathy
with big amplitudes. The amplitude scale is kept the same to better appreciate the amplitude difference.
300
µV
200
100
0
−100
−200
0
0.05
0.1
0.15
0.2
0.25
0.3
ms
400
300
µV
200
100
0
−100
−200
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ms
50
µV
0
−50
−100
−150
0
0.05
0.1
0.15
0.2
0.25
0.3
ms
Figure 2.3: From top and down, the first signal contains a significant amount of high frequency instrumental noise.
The second signal is an example of baseline movements and the last signal contains distant MUAPs.
19
• Similar looking MUAPs from different MU.
These issues are further discussed along with examples in Chapter 3.
2.2
EMGPAD
In this section the decomposition system EMGPAD will be described. To begin with conventional (manual) MUAP
recording and analysis is described along with their sources of errors and limitation followed by the goals and motivation
for EMGPAD. A system overview of EMGPAD is given and the four phases it consist of is described.
The purpose of electromyography (EMG) is to analyze electrical activity of skeletal muscle during rest, and during
weak and maximal voluntary contraction. During weak contraction a few motor units (MU) are activated allowing
analysis of undisturbed potentials from individual motor units, the motor unit action potential (MUAP).
In order to measure the MUAP amplitude and duration, MUAPs are recorded through a concentric needle electrode,
displayed on a oscilloscope screen using a trigger and delay unit, and evaluated to ascertain whether the same potential
is recorded several times, indicating that it is derived from a single MU. This procedure allows recording of usually
1-3 different MUAPs at a single insertion site using a time window of 50 ms. For diagnostic purposes in patients with
neuromuscular diseases, MUAPs are obtained at different sites to record the activity from at least 20 different MUs.
Triggering is adjusted during the contraction to obtain potentials from different MUs. Even though MUAPs recorded
around the triggered potential may have smaller amplitudes than the triggered potential, there is a tendency to bias
the recording towards the higher amplitude end of the MUAP spectrum. In integrated EMG machines, this tendency
may become more pronounced, since the potentials which do not trigger the oscilloscope beam are not included in the
analysis at all.
This method of recording MUAPs is associated with the following potential sources of error:
• Biasing towards higher amplitudes.
• The validity of the results depend on the expertise of the electromyographer.
• The analysis of the signal involves visual recognition of potentials from the same motor unit by similarity in
their shape.
This method does not allow evaluation of the firing pattern (FP) or detection of variability of the MUAP shape during
the contraction. The sources of errors and the limitations in analysis has led to the design of our new decomposition
system EMGPAD (EMG Precision Automatic Decomposition). The purpose of this system is to allow measurement
of the traditional parameters of the MUAP while also providing detailed information about the FP of each MU
contributing to the signal.
The main goals for EMGPAD are:
• To extract the information present in the 11.2 second time frame of the recorded EMG signal and to avoid bias
towards higher amplitude MUAPs.
• To obtain an analysis system which can determine both the MUAP and FP parameters with a response time
suited for clinical application.
• To be able to carry out detailed analysis of variations in the MUAP shape (MUAP shape variability) and the
firing patterns at low muscle contraction.
The motivations and considerations made in the design phase of EMGPAD was:
1. Clinical usage: EMGPAD was developed for clinical usage and not just for research. This implies using
standard concentric needle electrodes, standard filter settings for MUAP analysis, easy recording of signals and
an intuitive presentation of results.
2. Automatic operation: The decomposition should work fully automatic.
3. High precision: The firing patterns should be determined with high precision. This means detection of every
discharge of all MUAPs in the signal.
4. Only based on shape information not IPI: Our method for decomposing EMG signals only uses the shape
information for clustering and resolution compound segments. Stashuk et al. , McGill et al. and DeLuca et al.
also use the time information from the partial FPs obtained during clustering. We do not use it because: (1) It
is questionable to conclude any thing from the FPs if a prior assumption about their statistics have been used
to construct them. (2) We have seen many cases where the FPs are very irregular and they are often the most
interesting (the same was concluded by Haas and Meyer[4]).
20
5. Real signals: The algorithms where designed and tested by means of real EMG signal and not synthetic (see
Section 2.3). A broad selection of signals from both patient and normals and signals covering the variation
amongst EMG signals (see Section 2.1) are used.
2.2.1
System overview of EMGPAD
An overview of the system is shown in Fig. 2.4. On the left side the patient is represented by the central nervous
system (CNS), motor nerves and a muscle. A concentric needle is inserted in the muscle and the signal is amplified
and band-pass filtered. On the right side EMGPAD is shown. The decomposition is performed off-line so after the
EMG signal is digitized and saved in a file, it is later loaded for decomposition. The results are presented graphically
by the MUAPs and firing patterns along with measurements of relevant parameters.
EMGPAD
EMGPAD
Decomposition
CNS
EMG
amplifier
Filtering
Segmentation
Resolution
Clustering
A/D
Motor
nerves
Needle
Muscle
MUAPs
Firing patterns
Figure 2.4: An overview of EMGPAD.
The decomposition is carried out in the EMGPAD system which consists of (see Fig. 2.5):
1. Decomposition: The decomposition of the EMG signal is divided into four stages:
(a) Filtering: The EMG signal is filtered to suppress low frequency baseline movements and high frequency
instrumental noise.
(b) Segmentation: The EMG signal is searched for time intervals containing MUAPs. These time intervals are
called segments. A segment can either contain one MUAP or superimposed MUAPs (compound segments).
Time intervals without MUAPs are called baseline.
(c) Clustering: The segments are clustered into groups. If a group contains 5 or more segments, it is denoted
a potential class (PCL). From each PCL a template is chosen to represent the PCL. The chosen templates
are representatives of the MUAPs in the EMG signal.
(d) Resolution: The templates are used to determinate from which PCLs the MUAPs in a compound segment
originate and their time alignments.
2. Presentation of the results: MUAPs and firing patterns are displayed together with their parameters.
After the segmentation stage only the segments are considered in the following stages which is a great data reduction
compared to the whole EMG signal. The block-wise construction of the decomposition process simplifies the process
and permits optimization of one block without having to change the other blocks.
Parts of the decomposition algorithms have been developed earlier[194, 195, 196, 197, 198] and are used in EMGPAD.
In the following will be given a description of the algorithms as they appear at the completion of this dissertation.
21
Figure 2.5: An illustration of the three decomposition stages; segmentation, clustering and resolution.
2.2.2
Signal acquisition
The recording procedure is as follows: A concentric needle electrode is inserted in the muscle and a surface ground
electrode is placed on the limb. The cable connecting the needle to the amplifier is fixed to the muscle with a pice
of tape to avoid needle movement. The patient is asked to apply a slight and constant contraction. The signal is
monitored both visually and by sound. Two computer monitors are avaliable; one showing an oscilloscope picture
with a trigger and delay line system so that MUAPs that fulfill the trigger criteria are frozen on the screen. The other
monitor continuesly shows the last half second of the EMG signal. A speaker is also connected. A characteristic crispy
repetitive sound is heard when the tip of the needle is close to some muscle fibers. When the examiner finds that
the signal is of a usable quality (can be used for evaluating MUAP parameters; not to complex, not to noisy etc.) a
recording of a 11.2 second signal is started. The needle is then moved to another level of depth or to another insertion
place. Care is taken not to record from the same MU more than ones and to explore the whole muscle. In this way
approximately twenty signals are recorded.
The signal acquisition system used is shown in Fig. 2.6. A standard concentric needle electrode with a leading-off
area of 0.07 mm2 is used. From the needle the EMG signal is fed to a high impedance differential amplifier (DISA,
15C01) where it is amplified and analog band-pass filtered. All signals presented in this work are recorded at an
amplification of 500 µV/DIV (= 4000 times amplification) and with high- and low pas filters set at 2 Hz and 10 kHz.
After the amplifier the EMG signal is sampled and digitized at a sampling frequency of 23437.5 Hz in 16 bit resolution
(Motorola DSP56ADC16). The digital EMG signal is sent to a PC (Intel 486-33 MHz) where it is continuesly displayed
on a monitor. The operator can start a 11.2 second recording to the hard disc where the EMG signal is stored as a
file. Each signal is identified by three numbers; PatientNo/MuscleNo/RecordNo i.e. 34388/5/2 This is an example for
the second recording in Bicep Brichialis (5 is the number for this muscle) and patient number 34388.
With short circuited amplifier input the noise level was measured to 0.8 µV rms corresponding to approximately
22
4 µV (see Fig. 2.7). As can be seen from the lower part of Fig. 2.7 the instrumental noise can be approximated by
EMG amplifier
Bandpass
A/D
filter
Concentric
needle
electrode
Amplification: 4000
Filter: [2 Hz - 10 kHz]
F =23437.5 Hz
s
PC
16 bit
Figure 2.6: Signal acquisition overview.
Gaussian distributed noise.
Figure 2.7: Instrumental noise.
2.2.3
Signal filtering
For conventional quantitative EMG (MUAP analysis) undistorted recordings of MUAPs are important. Besides
amplifying the EMG signal it is also band-pass filtered. This is usually done by setting the 3 dB limit frequencies for a
low and high-pass filter individually. Buchthal and coworkers found that these limits should be set at 2 Hz and 10 kHz
for best tradeoff between noise reduction and signal distortion. Artefacts are introduced if the pass band is narrowed.
Increasing the high-pass filter from 2 Hz to 20 Hz can introduce false waves at the beginning or end of the MUAP[148].
lowering the low-pass filter will reduce the amplitude of low rise time MUAPs[148]. From a decomposition point of
view some further filtering can reduce some of the noise described previously (see Section 2.1). Baseline movements
can be reduced by increasing the high-pass filter and high frequency instrumental noise be reduced by lowering the
low-pass filter. For this reason many different filter settings are seen in other decomposition systems; Loudon et al.
[14 Hz, 2.5 kHz], Guiheneuc et al. [10 Hz, 10 kHz], Guiheneuc [5 Hz, 10 kHz], McGill et al. [8 Hz, 8 kHz], Gerber et
al. [32 Hz, 3 kHz], Stålberg et al. [5 Hz, 10 kHz].
In EMGPAD the EMG signal is first filtered to reduce the interference from high frequency instrumental noise (denoising). This noise can be modeled as Gaussian white noise (see Section 2.2.2 and the measured EMG signal, x(t),
can be expressed as x(t) = s(t) + e(t) where s(t) is the noise-free EMG signal and e(t) a Gaussian white noise process;
e(t)∈N (0, σ 2 ). A wavelet technique called soft-thresholding[199] achieves an optimal estimation of the unknown, noisefree EMG signal s(t), and is used for de-noising in EMGPAD. This technique was first applied on EMG signals by
Fang et al.[178] and does not reduce the amplitude of low rise time MUAPs as does classical low-pass FIR and IIR
filters.
Because of the relatively low setting of the 2 Hz high-pass filter in the EMG amplifier, different degrease of baseline
movements are seen in the recorded signals (see Fig. 2.3). This is a problem when MUAPs are compared against each
23
other in the clustering stage because they can look quite different when they are ”riding” on the changing baseline.
Therefor a digital high-pass filter (first order Butterworth, 50 Hz, 3 dB limit, with zero-phase distortion) is used to
suppress the low frequency baseline movements. As described above, this filter can introduce artefacts as extra phases
before and/or after the main potential and for this reason and possibly because of limitations in sampling frequency1
the high-pass filtered version of the signal is only used for the decomposition and not when the MUAP parameters
are measured.
The effect of the de-noising low-pass filter and high-pass baseline suppressing filters are seen in Fig. 2.8 on a EMG
signal.
200
A
µV
100
0
−100
−200
0
0.1
0.2
0.3
0.4
0.5
0.6
0.4
0.5
0.6
0.4
0.5
0.6
ms
200
B
µV
100
0
−100
−200
0
0.1
0.2
0.3
ms
200
C
µV
100
0
−100
−200
0
0.1
0.2
0.3
ms
Figure 2.8: (A) A raw EMG signal with instrumental and baseline noise. (B) After de-noising and (C) after de-noising
and high-pass filtering.
2.2.4
Segmentation
This section starts with the general goals for the segmentation algorithm, then the development of the segmentation
algorithm used in EMGPAD is described together with its limitations and comments to other methods.
After analog band-pass filtering, amplification, digitization and preprocessing (filtering) as described in the previous
section, the next task is to localize and estimate the beginning and end of all time interval segments containing MUAP
activity.
The following goals exist for a successful segmentation (see Fig. 2.9):
1. Detection of all MUAPs with out restriction on amplitude or rise time.
2. Good estimation of the beginning and end of segments (see Fig. 2.9, A).
3. Avoid MUAP splitting (see Fig. 2.9, B).
4. Close, but not overlapping MUAPs, should be separated (see Fig 2.9, C).
Goals 2) and 3) are conflicting with 4) because the two first will try to increase the extend of the MUAP/segment
while the last will narrow it. A compromise has to be found.
Segmentation, being the first stage in the decomposition process, has an obvious high influence on the following
stages and therefor on the overall performance. The first errors/limitations are introduced in the segmentation; A
sufficient amount of baseline around at least five MUAPs from each PCL is necessary for the PCL to be detected in
the clustering stage. This puts a limit on the complexity of the signal and therefor also on the level on contraction,
although highly complex signal can be seen even at low level in some cases. Undetected PCLs results in errors in
1 In older AEASs was where both a limit to the sampling frequency, but also to the computational powers available. Higher sampling
frequency results in more data which again calls for more computer power.
24
Figure 2.9: Illustration of the segmentation goals.
the resolution stage because the necessary templates are not avaliable. Undetected PCLs can also be a result of not
accepting MUAP/segments because of restriction on amplitude and or slope (goal 1).
The segmentation algorithm partitions the EMG signal (x(n)) into time interval segments containing MUAPs. A
segment can either contain a single MUAP or superimposed MUAPs (compound segment).
A variance signal v(n) is calculated from x(n) by passing along the EMG signal a window with length, N , and
calculating the variance inside it (see (2.1)). If v(n) exceeds a detection threshold thrd, a new segment is detected.
By searching backwards and forwards from this index until the variance drops below a delimiting threshold thrl, the
beginning and end of the segment is obtained .
m
m
X
X
1
1
2
v(k) =
x (k + i) − (
x(k + i))2
N − 1 i=−m
N − 1 i=−m
(2.1)
N=131 samples (5.59 ms), m=(N − 1)/2
Estimation of thrd is carried out from the amplitude density function of the normalized variance signal (Fig. 2.10). A
Figure 2.10: The amplitude density function
of the normalized variance signal. thrd is estimated as the first local minimum.
Figure 2.11: Illustration of how the segmentation algorithm works.
local maximum in the density function represents a set of MUAPs with the given variance. From this, thrd is defined
as the first local minimum searched, when starting from the origin where the smallest MUAPs are assumed to be
distinguished from the noise. The segment-delimiting threshold thrl is estimated from: thrl = 0.1(thrd−blmv)+blmv,
where blmv denotes the mean of the baseline variance, which is calculated by pre-segmenting the EMG signal with
thrl = thrd/3. The segmentation algorithm is illustrated in Fig. 2.11.
Four segmentation examples are shown in Fig. 2.12. The signals are of increasing complexity. The two first signals
are correctly segmented, the third has a lot of volume-conducted activity resulting in some errors and the last one is
to complex.
The first version of the algorithm[194] was based on two windows and fixed thresholds (Nd = 75, Nl = 133,
thrd = 160 and thrl = 50). The algorithm was designed and optimized based on a constructed signal by taking
segments from four different EMG signals. The detection window and threshold was chosen so that small endplate
potentials where not included but the smallest real MUAPs was. From the detection point a longer window and lower
25
100 µV
A
20 ms
3
1,2
4
3
1
2
4
1
3
2,4
1
2,3,4
1
2,4
100 µV
B
1,2,3,4,5
3
2
6
3
2
1
3
2,4,5,6
3
1,2,6
3
2
1,3,4,6 %
2
3,5
1,2,4,5,6
20 ms
100 µV
C
6
1,4,6,7
5,7,8
1
7,88
1,5,6,8
1,5,7,8
%
1,5,7
1
11
1,3,8
7
6 1,3,5,7,85,8
20 ms
1000 µV
D
20 ms
1
1
1
1
1 11
1
1
1
1
1
1
1
11
1 1
11
1
1
1 1
Figure 2.12: Four segmentation examples (A-D). (A) contains 4 PCLs and is correctly segmented. (B) contains 6
PCLs and is also correctly segmented. (C) has some errors because of some volume-conducted activity. (D) is simply
to complex, with a lot of volume conducted activity, to be segmented.
threshold is used to search for the beginning and end of the segment. The longer window and lower threshold is to
address goals 3) and 4) previously described. By using relatively long windows, many samples are used in calculating
the variance, this suppresses the noise and enhances the MUAPs (see Fig. 2.11). In a comparative study[194] this
algorithm was found superior to methods based on amplitude[175], slope + max amplitude[200] and activity[174, 4].
While the first version performed well on the few avaliable signals, the fixed thresholds did not work satisfactory on
a broader selection of signals that was avaliable later. Thats why it was decided for a method where the thresholds
are estimated for each signal. With the estimation of the threshold it was found sufficient to use only one window.
Our experience has shown that some kind of adaption to each signal is necessary because of the enormous variation
across signals (see Section 2.1). In EMGPAD and[1] the thresholds are estimated for each signal and in[176] the signal
is normalized to get around the problem. No adaption is used in[175, 148, 174, 4]. Two interesting adaptive solutions
are presented in[173], one based on a self-adapting Kalman filter and the other on two parallel reciding horizon filters.
The basic idea is to continuesly estimate, track and compare the spectra of the instrumental noise and the biological
activity.
The limiting factor in our algorithm is insufficient free baseline. This can happen if muscle fibers from many MUs are
active inside the recording territory of the needle resulting in a interference signal (see Fig. 2.12 (D)) rather than fairly
distinguishable MUAPs. It can also happen if muscle fibers from only a few MU are close to the needle but many are at
the boarder of the territory resulting in a lot volume conducted activity (see Fig. 2.12 (C)). Volume-conducted MUAPs
have lower amplitudes and longer rise times because the muscle tissue acts as a low-pass filter on them (Rosenfalck,
1964). This fact is utilized in the decomposition system ADEMG[3] where the EMG signal is preprocessed by using a
so-called ”low-pas differentiator filter” that suppresses volume-conducted activity and transforms MUAPs with highfrequency components into sharp spikes resulting in fewer compound segments. Unfortunately this also introduces a
bias towards MUAPs with high frequency components, see goal 1 on page 23.
2.2.5
26
Clustering
This section describes with MUAP classification in general and the clustering algorithm used in EMGPAD specifically
and consists of the following parts: (1) Introduction, (2) difficulties in MUAP classification, (3) short introduction
to MUAP classification, (4) description of our clustering algorithm and (5) experiences with- and improvements over
time to the algorithm and comments to other methods.
2.2.5.1
Introduction to MUAP classification
At this stage in the decomposition we have information about the localization of the segments containing MUAP
activity in the EMG signal. The next step is to identify which segments are similar in shape and therefor assumed to be
generated by the same MU and which segments are compound. This identification of similar looking segments is called
unsupervised classification because of the lack of a priori knowledge about shapes, number of classes, distribution within
classes and relation between classes. This calls for an exploitative method that is not based on strong assumptions
about class statistics or critical fixed thresholds. Many different methods have been applied to MUAP classification;
Different clustering methods including template matching, modified single linkage by means of minimal spanning trees
and k-means, matched filters, artificial neural networks and fuzzy logic. Regardless of the method, special adaption
of these methods has to be made because of the many difficulties (see next section) that exist for a successful MUAP
classification.
At this point an explanation of the terms; segment, potential class, template and MUAP will be given because there
seems to be some inconsistent use in the literature and to define these somewhat overlapping terms:
A segment is a segment of the EMG signal that by some criteria is distinguishable from the baseline and therefor is
presumed to consist of one or more MUAPs.
A PCL (Potential CLass) is a group (class) of similar looking segments based on some criteria and therefor presumed
to be generated by the same motor unit.
A template is a wavelet representing a PCL found by either averaging the members or choosing one of them.
A MUAP (Motor Unit Action Potential) is the recorded electrical response from an activated motor unit. Behind
the term MUAP lies some kind of confirmation that it is an undisturbed recording from a single MU for example
by observing several identical potentials or even better to have a complete firing pattern that supports it. With
out some kind of confirmation it is just called a potential, wavelet or spike.
2.2.5.2
Difficulties in MUAP classification
A perfect MUAP classification is generally very difficult to achieve except in some very simple cases. To get manageable
EMG signals only relatively simple signals recorded at low and constant level of contraction and of fairly good quality
(see Section 2.2.2) are used. Because of no a prior information on MUAP shapes, number of recorded MUs, their
statistical properties etc. it is difficult to put up some general criteria for MUAP classification. Next follows a
description of these difficulties encounter when working with real EMG signals.
A problem arises if the signal is too complex so that to few MUAPs are surrounded by free baseline see Fig. 2.1 and
2.12. This can result in one or more undetected PCLs because of to few members. Another problem of undetected
PCLs happens in some cases when a MU only fires a few times during the recording (spontaneous MUAPs) or fires
with a very small IPI (double discharges) Fig. 3.6 and 3.7. This is further discussed in Section 3.2.5
There is a big variety in MUAP shapes in the same signal and across signals see 3.3 and Fig. 3.1 . This is due
to the many factors that determine the MUAP shape: needle location in relation to the MU territory, number of
muscle fibers, distance of the closest muscle fibers to the recording surface of the needle etc. This is also reflected in
a wide range for the MUAP parameters. Here follows the range, and in parentheses the typical value, for the three
most important parameters; Duration: 4 − 50 (10) ms, amplitude: 30 − 5000 (300) µV and phases: 1 − 8 (3). This
illustrates the big variation in MUAP shapes. The biggest MUAPs are less vulnerable to noise and other artifacts and
are generally easier to detect and classify, but this is not always the case. Depending on the classification method,
some kind of normalization is necessary to avoid discrimination between big and small MUAPs.
Needle movement, MUAP shape variability (Jiggle) and background noise from undetected distant MUAPs results in
unstable MUAP shapes at consecutive discharges. In the classification phase this results in less compact classes/clusters
with bigger variance. This leeds to the dilemma of trying to cluster together MUAPs from the same MU despite possible
considerable variation in shape, while also avoiding to cluster together MUAPs frome different MUs but with very
similar shapes.
Another problem, in case of unstable MUAPs, is to find representative templates to be used in the following
resolution stage. If the shape is very constant2 and none of the three noise types; baseline movements, distant MUAPs
2 This
is most often the case.
27
and instrumental noise are significant, then the selection of a template from a PCL is not critical. Simple averaging
of the PCL members is the method used in most systems, but in case of unstable MUAPs important information
like turns can be averaged out. If the MUAP classification stage does not allow for splitting of unstable MUAPs into
several PCLs so that the MUAPs in the PCLs are more similar in shape than would they be if they where all put into
one big PCL, then the template could be a bad representative because it doesn’t look like any of the MUAPs.
2.2.5.3
Short introduction to MUAP classification
Among the many different MUAP classification methods reported, cluster analysis methods are the most used. The
general concept of these methods along with the five basic possible errors3 is described in the following.
Cluster analysis is a way to partition a set of objects into classes, or clusters, so that the objects in the same cluster,
by some criteria, are more similar than objects in different clusters. The number of classes and their properties are
usually not known a priori. This is also the case in MUAP classification.
Classes of segments that are similar in shape and therefore presumed to originate from the same MU are identified
by a measure of the distance or dissimilarity between segments. The distance measure must be small between two
segments containing MUAPs generated by the same MU. It must be large between segments containing MUAPs from
different MUs and between a simple and a compound segment. There are two different ways to produce a distance
measure:
1. Parametric distances are based on features describing the segments, for example the peak-to-peak amplitude,
duration, number of turns, maximal positive slope, area etc. Since it is insufficient to use one feature of the
segments, it raises questions as to which and how many features should be used, whether they are correlated
and what should be the weighting factor for each feature. In[181, 174, 176] parametric distances are used. The
advantage of these distance measures is that they are easy and fast to compute.
2. Nonparametric distances use all the information in the segments and not only selected features. Examples
could be the cross-correlation and the power of the difference signal between two segments. Often some kind
of alignment has to be performed before the distance can be calculated. The nonparametric distance measures
are more time consuming because all the sample points of the segments are used. The advantage on the other
hand, is a better segment discrimination which again results in a better clustering of similar segments[201].
Nonparametric distances are used in all the AEAS described in the Section 1.3.2 except [181].
Gerber et al. uses a feature vector consisting of both sample point of the segments and five features. For a more detailed
discussion of parametric and nonparametric distance measures for EMG signals see[201] and in general[4, 202].
The distance measures are used by cluster analysis methods to detect similar shapes. Two classes of cluster analysis
methods have been used in MUAP classification: serial and global methods. The first one is faster but has some
potential limits, while the other could be said to have the opposite properties.
1. Serial, also called template matching, clustering methods are well suited for clinical AEASs because they can
do the clustering on-line as the segments arrive after sampling and detection[175, 3]. This though presents
a problem because of the fixed clustering order, which means that a distance threshold dt has to be chosen
according to a worst case situation when the two first segments represent two extremities in a PCL. The result
therefor depends on the arrival order of the segments. In[3] the template is updated when a match has occured
so that slow changes in the shape can be compensated for. A good ”prototype” of a template matching method
is the one by Adreassen, see Section 1.3.2”.
2. Global clustering methods, usually a modified single-linkage (nearest-neighbor) hierarchical method[4, 176],
EMGPAD, are more time consuming because the distance between any two segments is calculated. On the basis
of all these distances a tree structure is constructed where the nodes represents the segments and the branches
represent the distance between connected nodes. In EMGPAD and ARTMUP the so called Minimum Spanning
Tree (MST) is constructed. The MST has the smallest sum of distances among all possible trees, spanning the
data set. A single-linkage clustering algorithm is easily and efficiently implemented by cutting the branches in
the MST so that it partitions into cluster of similar looking segments. All the branches in a cluster will have a
distance that is smaller than the value used to cut the tree. As in any hierarchical clustering method, the difficult
part is to determine a strategy of how to cut the tree, because no general rule exist for this. A dendrogram
(see Fig 2.14) is a convenient graphical representation of a hierarchical clustering. To the left there are as many
clusters as there are segments and as the tree cutting threshold dt (the value used to cut the tree) is increased
the dendrogram shows how the segments merge and form clusters. For very high values of dt they are all merged
together. For single-linkage method based on the MST, cutting the branches in the MST greater than a certain
value is equivalent to cutting the dendrogram at the same value by drawing a vertical line and noting what nodes
are merged to the left of this value.
3 These
five classes of errors are not only restricted to cluster analysis methods, but are general.
28
All combinations of parametric and nonparametric distance measures and serial and global clustering methods have
been described in the literature: (parametric,serial)[181], (parametric,global)[176], (nonparametric,serial)[175] and
(nonparametric,global)[4].
In actual EMG recordings, optimal clustering, where all MUAPs from the same MU are clustered together and
all the compound segments are clustered separately, is difficult to obtain. There are five categories of errors denoted
E1-E5. These, together with methods to detect them, are listed below.
• E1: MUAPs from different MU, but very similar in shape are erroneously clustered together. This may be
acceptable if only the MUAP parameters are of interest, but the FP is disturbed by superimposed individual
FPs. The resulting FP is dense and irregular. The number of active MU is under-estimated.
• E2: A compound segment is included in a PCL. This can happen when, by chance, two or more superimposed
potentials happens to look like a MUAP. The compound segment will not be resolved leading to holes in FPs
from other MUs.
• E3: MUAPs from the same MU are split into two or more PCLs. The resulting FPs will fit into each other.
The number of active MU will be overestimated and the FPs affected will be partial, but when summated they
will fit into each other forming a regular FP. This happens in case of needle movements or Jiggle.
• E4: Compound segments are placed in the same cluster. This can happen when, by chance, two or more
superimposed potentials happens to look like a MUAP, but this rarely happens. More common is the case when
the same combination of MUAPs and time alignments summates repeatedly into similar looking segments. This
is only a problem if it happens more frequently than the number of times necessary to create a new PCL. The
FP of such a PCL is usually sparse and where a pin is present there will be holes in those FPs where the MUAPs
belong. Such PCLs will be called false PCLs an their templates, false templates. This is further discussed in
Section 2.2.6.
• E5: A segment is erroneously excluded from a PCL. Such a segment will be detected in the resolution stage.
This error should be avoided because too many exclusions may result in a PCL (MU) not being detected.
The errors E1 and E2 are the most serious because they cannot be corrected in the later stages. In the case of
variability the potentials of one MU changes shape such that a population of subclasses generated (E3). This selection
allows analysis of variability of the MUAP shape and detection of needle movement and is therefor not actually an
error. Except if it happens with out any significant change in shape, because it then result in unnecessary prolonged
resolution time and increases the chance for errors in the resolution stage.
The errors E4 an E5 will be detected and solved at the resolution stage.
2.2.5.4
MUAP Clustering in EMGPAD
In a previous study[194] eight different nonparametric distance measures were compared to find which distance measure
lead to fewest errors when using a MST method. The resulting distance measure is shown in (2.2). It uses the variance
of the error normalized with the sum of the RMS values for the segments. s1 (n) and s2 (n) are two segments to be
compared and e(n) = s1 (n) − s2 (n).
V AR(e)
E(e2 (n)) − E 2 (e(n))
p
=
d(s1 , s2 ) = p
2
2
RM
S(s
1 ) + RM S(s2 )
E(s1 (n)) + E(s2 (n))
(2.2)
By comparing all the segments pairwise, using the distance measure (2.2), a distance matrix is build. To save
computation time only the distances between segments with an amplitude ratio less than two are calculated. The others
are set to infinity. From the information in the distance matrix the Minimum Spanning Tree (MST) is constructed
using Prims Method[203]. (Fig. 2.13).
Figure 2.13: A small example of a distance matrix and its MST
We have chosen to use a hierarchical clustering method that uses a Minimum Spanning Tree (MST), because it has
already proven useful[4] and the clustering process can be described by means of a dendrogram[204].
29
By cutting those edges in the MST, with values greater than a given threshold dt , the MST is divided into subtrees
(clusters). If a cluster contains at least five segments, it is defined as a PCL.
It is necessary to time align the segments before calculating the distance. If the length of the segments are unequal,
the shortest is padded with zeros to obtain the same length. The time alignment is carried out by first doing an initial
alignment according to the maximal positive slopes in a 23 samples wide window and then searching one sample at
the time (though maximally 23 samples away from the initial alignment) in the direction of lowest error energy. Other
methods have used peak alignment[173, 205, 4].
Figure 2.14 shows an example of a dendrogram and above the dendrogram is shown the number of PCLs as a
function of dt . When merged segments fulfill the criterion for a PCL (≥ 5) there will be an increase in the number of
PCLs and when two PCLs are merged it will result in a decrease in PCLs. Error E1 is often seen as an increase in the
number of PCLs followed rapidly by a decrease, because they are distinguishable for small dt , but indistinguishable
when dt is increased slightly. After the MST is constructed, dt for which there is an increase in PCLs are found and
6
No. of PCLs
5
4
3
2
1
0
↓
↓
↓
↓
↓
↓
↓
1
2
3
4
5
6
7
8
5
6
7
8
d
t
1
2
3
4
Figure 2.14: Number of potential classes as a function of the tree cutting threshold dt and the corresponding dendrogram below. The values of dt where there is an increase in number of PCLs are marked with arrows. These values are
stored in M AXdt .
stored in a vector M AXdt (see Fig. 2.14). The MST is cut with the first value in M AXdt and the corresponding
PCLs are found. Then the next value in M AXdt is used to cut the MST and the PCLs are found. This gives two sets
of PCLs (the first one P CLbasis and the second P CLnew ). It is tested to see if any PCL from P CLbasis are subsets
of any PCLs from P CLnew . If this is the case then that PCL in P CLnew is deleted. The remaining PCLs in P CLnew
are added to P CLbasis and a new P CLnew is found with the next value in M AXdt and so on. This is repeated for
all values in M AXdt . This procedure is necessary because the PCLs are constructed and merged at different levels
(see Fig. 2.14) and insures that only the most similar segments (MUAPs) constitutes a PCL so that the risk of E2 is
minimized.
Finally the PCLs are tested to see if they should be deleted or merged. If the amplitude of the MUAPs in a PCL
is less than 20 µV or the maximum distance between two MUAPs from the same PCL x is greater than 30, then the
PCL is deleted. A test is carried out to investigate if a PCL is erroneously split (E3) and therefore should be merged
using a merge-criterion. This is fulfilled if min(d(x, y)) < min(max(d(x, x)), max(y, y)) which means that if there can
be found two MUAPs, one from each PCL x and y, that have a smaller distance than the smallest maximum distance
within PCL x and y, then they will be merged.
A so called template is chosen from each of the PCLs. This is done by searching for the segment in a PCL with the
smallest sum of distances to the rest of the segments in the same PCL.
The templates together with the compound segments forms the input to the resolution process which is described
in the next section.
2.2.5.5
30
Discussion of the algorithm used in EMGPAD
In the first version of our algorithm[194], the MST was cut at a fixed value (dt = 0.5) that was found to work well on
a limited number of EMG signal avaliable at that time. Later it was observed that not all PCLs where found in this
way. The missing PCLs appeared at a higher value of dt because the MUAPs where less similar in shape. The reason
for this could be: (1) MUAP shape variability so that the MUAP shapes are not constant at consecutive discharges,
due to jitter and/or blocking. (2) Needle movement, resulting in gradually increasing or decreasing amplitudes, (3)
Complex EMG signals due to many active MU or a lot of background noise (see Fig. 2.1 and 2.3) so that the MUAPs
are often distorted by neighboring MUAPs or noise. It is not possible to just increase the value of dt because then the
risk of false PCLs also increases. A false PCL is a PCL with potentials from more than one MU or when two or more
MUAPs are close and time lock to each other more than four times. The later case will be further discussed in the
following section.
One of the criteria for constructing and choosing our distance measure was to have a normalizing effect so that high
and low amplitude MUAPs where clustered at approximately the same level so that a global fixed threshold could be
used. Actually the distance measure used in EMGPAD is not amplitude normalizing but amplitude weighting[195].
A true amplitude normalizing distance measure could be constructed by replacing the sum of RMS with the sum
of variance in the denominator of our distance measure, but it was found to work less satisfactory than the original
distance measure[195].
In ARTMUP[4], dt is chosen so that the variance of each cluster is as small as possible, while the distances between
the centroids of each cluster should be maximum. As a distance measure they used the integrated square error between
two segments normalized with the mean length of both segments. For alignment, peak alignment is used.
After the clustering partial firing patterns can be produced and in some systems they are used to support the
decision of deletion or merging of PCLs[3, 206]. This is not the case in EMGPAD for reasons outlined in Section 2.2.
2.2.6
31
Resolution
This section starts with a short introduction to the subject of resolution of compound segments followed by a discussion
of the difficulties encountered when trying to identify what individual MUAPs a compound segment is composed of.
The algorithm used in EMGPAD is described and at the end our method is compared to others.
2.2.6.1
Short introduction to resolution of compound segments
At this stage we have information on which segments are presumed to originate from the same MU and they define the
potential classes (PCLs). Those segments that did not fall into any PCL are considered to be compound4 . Also some
of the PCLs might be false (E4) because they consist of compound segments. In the resolution stage the compound
segments and the false PCLs are resolved by means of the templates from the PCLs. The solution is basically the same
for compound segments and false PCLs. The principal idea is to make a model of the compound segment by time
shifting a selection of the templates so that they imitate the compound segment. This results in many models and the
one that most resembles (with the smallest distance to) the compound segment is chosen. This method is called the
”modeling approach” and is one of two methods found useful to resolve compound segments. The other method is
called the ”peel off approach”. In short the peel off approach sequentially identifies and subtracts one MUAP at the
time. The modeling approach requires more computational effort, but can overcome some of the problems in the peel
off approach, such as being able to identify small MUAPs. The different approaches are discussed by Broman[207].
2.2.6.2
Difficulties in resolution of compound segments
The task in the resolution stage is to identify, in a compound segment, the individual MUAPs and their time alignments
on the basis of the templates found in the previous clustering stage. This task is difficult because, even for a small
number of templates, the number of possible combinations and time shifts is enormous plus of several difficulties that
can be encountered. These difficulties will be described her.
A compound segment is a result of two or more superimposed (algebraic summated) MUAPs. These compound
segments could be put into three categories[206]: partially, completely and destructive superposition. The first one is
the case when main parts, like the peaks, are still identifiable see for example the first and the last of the compound
segments in Fig. 2.16. The second is when they have similar shapes so that their peaks summates into bigger peaks
see Fig. 3.4 (A-D). And the last one is when the individual phases happens to cancel each other in the summation. In
case of two MUAPs this could happen if they have a similar shape but with opposite signs.
Being the last stage in the decomposition, the resolution stage depends on the results in the previous stages. If one
or more MUAPs are undetected or false PCLs are unresolved, then the resolution algorithm will attempt to resolve a
compound segment with missing or wrong template and the result will be wrong.
Sometimes it happens that a MU only fires a few times during a recording and therefor remains undetected see
Fig. 3.6. Such a MUAP will be called a spontaneous MUAP. Segments containing a spontaneous MUAP can not
be resolved on the basis of the existing MUAPs. Double discharges also called doublets and blocking5 can result in
similar problems see Fig. 3.7.
In superposition of very big and very small amplitude MUAPs6 , the small one can be difficult to find see Fig. 3.3
(E) and (F). This is specially a problem when the big MUAP is unstable due to shape variability or needle movement
because then the small MUAPs will fit in at many wrong places because the resolution algorithm will use them to
compensate for the shape variation see Fig. 3.3 (B) and Fig. 3.11 (A). As in segmentation and clustering, background
noise from distant volume conducted MUAPs also poses problems in the resolution stage see Fig. 2.3 and Fig. 3.2.
These distant MUAPs are superimposed with the identified MUAP and, specially with low amplitude MUAPs, makes
it difficult to resolve superposition.
From the above described difficulties an easy signal should contain MUAPs of similar amplitudes, the MUAPs
should be very different in shape, the superpositions should be partial and the correct templates should be found in
the clustering stage. In real EMG signal several of the described problems are seen and have to be addressed.
2.2.6.3
Resolution of false PCLs
When two or more MUs, inside the recording territory of the needle, fires at approximately the same time, then
the resulting segment found by the segmentation algorithm, will be compound. If this happens more than four times
during the recording with the same combination of MUAPs and the same time alignments a false PCL will be detected
during clustering (see error E4 in the previous Section 2.2.5.3). The compound segments are often longer and more
4 Also some segments containing only one MUAP can fall into this group, because noise could have prevented them from being included
in a PCL during clustering
5 doublets and blocking are explained in the section ”Library of EMG recordings”
6 The amplitude ratio can be more than 10:1
32
polyphasic and they do not form any PCL during clustering unless they form their own PCL as described above. In
this case the template is a summation of two or more of the other templates. Similarly the FP from the false PCL fits
into the FPs from the contributing PCLs. Three example of false PCLs are seen in Fig. 3.4. The FP for a false PCL
to fits into at least two FPs from other PCLs.
The algorithm for resolving false PCLs is basically the same as for resolving compound segments (see the next
section). A template is tried to be resolved by using the rest of the templates. A wrongly resolved template (false
positive) is a serious error because the basis for resolving the compound segments would be wrong then. Therefor a test
done to approve a template resolution: d(Ti , Tcomb ) < dmax
. Ti is the template from PCL i, Tcomb is the summation
i
of the template-combination found by the resolution algorithm, the distance measure d is defined in (2.2) and dmax
i
is the biggest distance between the template and the rest of the segments in PCL i. In other words if Ti and Tcomb
are more similar in shape than Ti and the most distant segment in the same PCL, then the template resolution is
accepted. All the segments from a false PCL are processed as being compound segments in the following stage.
False PCLs, as a result of the same MUs firing more than four times with approximately the same time alignments
each time, has been concluded by Hansen[208] to happen by chance. In[209, 182] synchronization between MUs is
shown, but in these studies the EMG recordings where much longer than the 11.2 sec. used in EMGPAD.
2.2.6.4
Resolution algorithm
When trying to resolve a compound segment (or template) one should ideally test all combinations of templates
and time shifts, but this would be too time consuming. The resolution is therefore divided into three stages as
shown in Fig. 2.15. For every compound segment, so called favorable combinations of templates and time shifts, are
Determine
favourable
combinations
of templates
and their
timeshifts
Optimize the
timeshifts for
the 20 best
combinations
Choose the
best
combination
Figure 2.15: Resolution divided into three stages
determined. These are then candidates for the correct combination, which is determined by optimizing the time shifts
of the favorable combinations, and then choosing the best combination among the optimized combinations.
2.2.6.5
Favorable Combinations
The compound segments consist of superimposed MUAPs from different MU/PCLs. The templates are representatives
from each PCL and therefore used to identify the MUAPs in the compound segment. Fig. 2.16 shows two templates
and three compound segments that was generated by the same two MU as the templates. It is easier to identify the
big template than the small. The templates are therefore sorted according to descending amplitude. The templates
Figure 2.16: Three combinations of two templates
are denoted temp1 , temp2 , tempNt where Nt is the number of templates which equals the number of PCLs. temp1
33
has the biggest amplitude. The compound segment to be resolved is denoted cseg. Favorable time shifts for temp1
relative to cseg are found. At the first time shift temp1 is subtracted from cseg, which gives the residual signal sr . sr
is resolved by using the rest of the templates. This is continued until there is no more templates and for all favorable
time shifts. The advantage of this process is that the small MUAPs are uncovered gradually. The recursive algorithm
is also shown in pseudo code in Table 2.1 called the first time with: Resolv(1,cseg).
Pseudo-code for Resolv
FUNCTION Resolve(q,cseg)
IF q> Nt
RETURN
ELSE
Resolve(q+1,cseg)
END
Abbreviations
• The crosscorrelation coefficient ρ(k) between
the segment seg(n) and template tempi (n) for
a given time shift k is defined as follows:
Csti
ρ(k) = p
Cs (0)Cti (0)
Calculate ρ(k) for tempq shifted over cseg
Build a vector Kmax with the time values (in
samples)
where ρ(k) has a maximum > cccmin (0.5)
For every k in Kmax
Shift tempq k places and calculate the residual
signal sr by subtracting tempq from cseg
IF σ 2 (sr ) < σ 2 (cseg)
Resolve(q+1,sr )
END
END
RETURN
(2.3)
where
Cs (k) = Rs (k) − (E(seg(n)))2
Rs (k) = E(seg(n)seg(n + k))
Cti (k) = Rti (k) − (E(tempi (n)))2
Rti (k) = E(tempi (n)tempi (n + k))
Csti (k) = Rsti (k) − E(seg(n))E(tempi (n))
Rsti (k) = E(seg(n)tempi (n + k))
• cccmin : Threshold value for ρ(k) to exceed for
k to be considered as a favorable time shift (experimentally found to be 0.5).
• q: A pointer to the current template.
Table 2.1: Pseudo-code for the recursive algorithm that finds favorable combinations.
Favorable time shifts are found by shifting the template over the segment and calculating the crosscorrelation
coefficient between them see Fig. 2.17. The crosscorrelation coefficient has the advantage of producing values in the
same interval (-1 to 1) regardless of the magnitude of the amplitudes and alignments between the segment and the
template and it can be implemented efficiently by filters. The RMS-value of the difference between segment and
template is used in ARTMUP[4] and the sum of absolute difference between segment and template is used by Gerber
et al.[174].
2.2.6.6
Optimization of the time shifts
After the favorable combinations of templates and time shifts are found, the twenty best combinations (With the lowest
error power, see later for an explanation) are chosen for a further optimization of the time shifts. This is necessary
because the favorable combinations are found by subtracting the templates one by one. This successive approach may
result in that the first subtracted templates are not always correctly time shifted.
The optimization is done to obtain minimal error power. The error power is defined as the power of the residual
signal found by subtracting the summation of templates at the given time shifts (the model) from the compound
segment. The error power is calculated according to:
Serr = cseg − model(templates, timeshif ts)
Perr =
N
1 X 2
S (n)
N n=1 err
(2.4)
(2.5)
The optimization is carried out for one template at a time. The template is time shifted one sample at a time
towards lower error power until a minimum is found. Then the same is done for the next template and so on. When
all the templates have been time shifted a new optimization cycles is started beginning with the first template. This
is repeated until there is no time shift during a whole cycles.
34
Conpound segment
Template no. 1
1
0.5
0
Crosscorrelation coefficient
−0.5
−1
Figure 2.17: The upperpart shows a compound segment (partially superimposed). In the middle; the first of the seven
found templates is placed at time shift where the crosscorrelation coefficient is higher than 0.5 as shown bellow. The
last one to the right is the correct and also has the highest crosscorrelation coefficient.
2.2.6.7
Selection of the best combination
After the twenty best combinations have been optimized, the best one is to be chosen amongst them. A simple selection
according to the lowest error power is not sufficient because there may be a trend in the error signal. The trend is
removed as the best straight line using a least squares method. The combination (model) with the lowest error power
after the trend has been removed, is chosen. Fig. 2.18 shows an example where five template where identified in a
compound segment.
Compound segment
+
Template no. 1
+
Template no. 3
+
Template no. 4
+
Template no. 5
Template no. 7
=
Template sumation
Error signal
Figure 2.18: An example of a resolution. Note that the compound segment was resolved with five templates, will most
other resolution algorithms reported, maximally can handle three templates. This is the third segment in Fig. 2.19
35
2.2.6.8
Discussion and relation to other methods
Some AEAS do not resolve compound segments and can therefore only produce a partial FP[3, 181], and others limit
the resolution to compound segments containing maximally three MUAPs to save computational time[210, 4]. In
EMGPAD system there is no such limitation.
From the partial FPs after clustering[206] and[176] estimates the probability for a MU firing at a given time shift
within the compound segment. This though requires fairly constant firing rates[176] and our experience shows that
this is often not the case neither in normals nor controls. Example of irregular FPs is shown in Fig. 3.5. For this
reason the firing statistics are not used in EMGPAD. The same is concluded in ARTMUP[4, 176],
The two most used methods for finding the time shift of a template in relation to the beginning of a compound
segment, is peak alignment[4, 176, 206] and crosscorelation coefficient alignment EMGPAD and[178, 1]. Peak alignment
is less time-consuming and easier to implement but noise and partial phase cancelation can distort or remove a peak.
The global maxima in the template is aligned with all the local maxima in the compound segment and the time shift
with the smallest residual energy when the template is subtracted from the compound segment is considered the best
candidate for the correct time shift. If the peak at the correct time shift is distorted then it will not be detected.
Due to time-quantitation, noise, two or more peaks with the same height, shape variability or badly defined maxima
the residual energy is not necessarily minimal at the peak alignment. Some of these problems are eliminated with
crosscorelation. While peak alignment depends on a single point, the maximum, crosscorrelation measures how close
the compound segment and the template increase and decrease synchronously within the interval of the template.
Christodoulos and Pattichis[1] also used maximum crosscorelation for determining the time alignments. Their
algorithm performed the following steps: (1) Use only the main spike of the templates. (2) crosscorrelate each reduced
template with the compound segment and find the time shift where the crosscorrelation coefficient takes its maximum
value. (3) Find the best matching template based on a test and if it is accepted as the correct goto (4) else end. (4)
Subtract the best matching template and take the residual segment and go to (2). This is repeated until no more
templates matches or max(cseg) < 30µV. This algorithm was implemented from the description in[1] and in Fig. 2.19
is the result seen together with the result from EMGPAD7 . It is seen that this algorithm is not resolving, a majority
(A)
100 µV
simple
9.6 ms
326 µV
96 ms
10 ms
simple
8.4 ms
295 µV
156 ms
4
(B)
5
simple
10.1 ms
269 µV
101 ms
6
simple
11.4 ms
141 µV
113 ms
simple
8.7 ms
114 µV
105 ms
7
simple
11.4 ms
84 µV
108 ms
simple
7.1 ms
75 µV
113 ms
1
2
simple
9.6 ms
326 µV
97 ms
10 ms
1
1
2
2
3
3
4
4
5
5
3
simple
8.4 ms
295 µV
160 ms
4
5
simple
10.1 ms
269 µV
100 ms
6
simple
11.4 ms
141 µV
109 ms
simple
8.7 ms
114 µV
105 ms
36223/51/1
3
100 µV
2
36223/51/1
1
7
simple
11.4 ms
84 µV
104 ms
simple
7.1 ms
75 µV
124 ms
6
6
7
3
4
5
6
7
8
%
4c
1c
4c
1,2
4
1,3
7c
2
1%
2
3
4
5
6
7
8
%
4c
1,3,5,6,7
4c
1,2,3,5,6,7
4
1,3,5,6
7c
7031
2
7031
1%
2
7
Figure 2.19: A comparison of the resolution algorithms used in Christodoulos and Pattichis [1] (A) and in EMGPAD
(B). Many compound segments are not resolved correctly in (A) while only two errors are made in (B)
of the compound segments, correctly. Fig. 2.18 shows how the third segment is correctly resolved in EMGPAD but
the this algorithm only managed to identify the first template of the five templates. This algorithm is chosen for this
comparison because like in EMGPAD it is based on crosscorrelation coefficient alignment and only on the templates and
not on statistics from partial FPs like in[206] and[176]. The following observations are made compared to EMGPAD:
1. It is fast at the expense of precision. Only maximally (7+6+5+4+3+2+1) 28 combinations of templates and
time shift are tried and tested for. This is when all seven templates are identified in the compound segment.
In EMGPAD, if for example in average three maxima higher than 0.5 are found for each template, this would
result in (73 ) 2187 combinations of which the twenty best are selected for further optimization of time shift.
7 The algorithm from Christodoulos and Pattichis was put into EMGPAD in replace of our own resolution algorithm, so the input is
identical; the same templates and compound segments. The templates in EMGPAD are sorted by descending amplitude while no sorting
is described in[1], and the EMG signal is sampled at 20 kHz in 12 bit while in EMGPAD this is 23437.5 kHz and 16 bit, but this should
not have any significant influence.
36
2. Only one possible time alignment is tried for each template namely the one where the crosscorrelation coefficient
takes its maximum value. In EMGPAD all maxima higher than 0.5 are tried, because the correct time shift can
have a smaller crosscorrelation value as a result of superimposition with other MUAPs. This can make another
part of the compound segments correlate better to the template.
3. By inspection it was found that the method in step (3) for selecting the best matching template and the following
test for it being the correct one, both often gave wrong results.
Of the many difficulties discussed Section 2.2.6.2 our many years of experience with EMGPAD has shown that
missing or false templates/PCLS and background noise from distant MUAPs are most often the reason for wrong
resolutions.
The resolution stage is the most time consuming part of the decomposition and is the reason why EMGPAD is not
fast enough for on-line use yet.
When comparing the results from different decomposition systems two things has to be considered; type of needle
electrode and filters. Only systems that uses the same needle electrode and similar filters can be directly compared.
The two perhaps most famous systems; ADEMG by McGill and Dorfman and the system by Lefever and De Luca
can not be compared with EMGPAD because the first one uses different filters and the other a special selective
multichannel electrode. Both systems where design for signals recorded at higher level of contraction, the so-called
interference pattern. The resolution algorithm by Etawil and stashuk[206] uses the same filter as in ADEMG, a
first-order differential filter see Section 1.3.2 and Section 2.2.4 which transform the EMG signal into its derivative.
EMGPAD together with[4, 176, 1] only uses conventional needle electrodes and filtering. The additional filters used
in EMGPAD only has a marginal effect on the MUAP shapes.
Any errors made in any of the decomposition stages are revealed in the final FPs. Undetected segments, Unidentified
MUAPs/PCLs, false PCLs, splitting and merging of PCLs, unresolved compound segments etc. are all seen in the
FPs. The next chapter contains several examples of this. For this reason the FPs reveals the overall performance of a
decomposition system. A decomposition system that can produce correct FPs (or with only a few errors) for signals
with more than five different MUAPs is considered a high precision system. With this in mind it is surprising to see
that in many reported decomposition systems no FPs with several MUAPs are shown. Fang et al. shows in[178] a very
impressive example with nine different MUAPs correctly decomposed at increasing contraction, but no description of
needle or filters is supplied. All segments has a duration of 5.33 ms which can hardly contain the durations of even
very small MUAPs seen in myopathy8 . when looking at the raw EMG signal it seems surprisingly nice and regular
compared to what is usually seen using a conventional concentric needle electrode. It looks like some kind of selective
needle electrode is used and/or increased high-pass filter settings compared to the conventional 2 Hz.
2.2.7
Decomposition output and visualization
After the decomposition all MUAPs are localized and identified9 so that they can be plotted and the firing patterns
constructed and shown. The actual output from the decomposition algorithm is a series of vectors and matrices of
which the most important is the Decomposition matrix (Dm). Dm’s size is: [rows,columns]=[number of potential
classes (N) + 4,number of segments]. the n’th column contains the decomposition information for segment number n:
ts T1
..
.
ts TN
T ype
dist
istart
iend
ts: time shift
Tx : Template
x
−1 ⇒ compound segment
T ype =
> 0 ⇒ simple segment belonging to PCL Type
dist: distance between segment and combination of templates
istart , iend : start and end position (index) for the segment
The first N rows tells what templates and their time shifts a segment consists of. Type tells if the segment is simple
(≥ 0) or compound (-1). If it is simple, type is the PCL number. dist is the distance described in Section 2.2.5.4
between the segment and the template(s). The last two rows contains the position of the segment in the EMG signal.
8 The
9 In
average duration for an adult person is about the double
Principe at least, because errors can occur as described in the previous section.
37
Bellow is shown an example how the first three columns (segments) in Dm could look if N (number of PCLs) is 4.
10000
−11
−13 · · ·
0 10000 10000 · · ·
10000 10000
5 ···
10000 10000
51 · · ·
2
1
−1 · · ·
0
0.55
1.29 · · ·
11005 120111 1157 · · ·
11594 120603 1801 · · ·
The first segment (column) is the template for PCL 2 because Type=2 and dist=0. The second segment is simple, but
not the template and belongs to PCL 1. The third segment is compound and was resolved by moving the beginning
of template 1, 13 samples to the left from the start of the segment. Template 3 is shifted 5 samples to the right and
template 4 is shifted 51 samples to right. 10000 for the time shift value means that the template is not present in the
segment.
To find all the templates one just search for the columns (segments) in Md where Type>0 and dist=0. All simple
segments belonging to PCL 3 are found by searching for the columns (segments) where Type=3. The firing pattern
for PCL 1 is constructed by searching for columns (segments) where the first row is not equal to 10000. For these
segments the exact position of the firing is found by adding the time shift value in row 1 and the value in row N+3
(the beginning of the segment).
1
100 µV/cm
2
3
simple
14.5 ms
580 µV100 µV/cm
simple
10.2 ms
348 µV100 µV/cm
4
simple
6.1 ms
258 µV100 µV/cm
simple
5.9 ms
176 µV
1
2
3
4
Figure 2.20: An example of the graphical output of an decomposition in EMGPAD.
From the information in Md the MUAPs and the FPs can be plotted. An example of how this looks in EMGPAD
is shown in Fig. 2.20. Different options exist for the graphical output, i.e. one can choose raster mode instead of
superimposition for the MUAPs and to see a portion of the EMG signal as in Fig. 2.21. Fig. 2.22 shows the result of
the measured MUAP parameters in a muscle with the parameters from each MUAP and their mean values across all
MUAPs.
2.2.8
System implementation
EMGPAD consists of a data acquisition unit (DAU) and two Intel based personal computers (PCs), see Fig. 2.23.
The DAU receives the analog EMG signal from an EMG amplifier and digitizes it at a sampling frequency of 23437.5
38
1
100 µV/cm
2
3
4
simple
14.5 ms
580 µV100 µV/cm
simple
10.2 ms
348 µV100 µV/cm
simple
6.1 ms
258 µV100 µV/cm
simple
5.9 ms
176 µV
1
2
3
12345/3/22
4
1
2
3
4
5
6
7
8
1c
2,3,4
1
2,3
4c
1c
3c
1,2,4
Figure 2.21: The same as Fig. 2.20, but in raster mode and with the EMG signal shown.
All potentials
MUAP parameters
Name:
EMG no.:
Born:
Muscle no.:
Age:
Muscle:
37133
6
Duration (ms) Amplitude (µV)
12.8
196
3.5
155
0.5
21
Mean:
SD:
ME:
Number:56
Normal:
12.6
193
3.5
157
0.5
22
Gender:
Mean:
SD:
ME:
Number of potentials
All potentials
8
←mean= 12.8 ms
6
Number:50
Normal:
Deviation:
Polyphasic potentials
4
2
0
Deviation:
Simple potentials
0
10
20
Duration (ms)
No.
Ins/pcls
Duration
(ms)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
1/1
1/2
1/3
2/1
2/2
2/4
2/6
3/1
3/2
3/3
4/1
4/2
4/3
4/4
4/5
4/6
5/1
5/3
5/4
5/5
5/6
6/1
6/2
6/3
6/4
6/5
7/1
7/2
7/3
8/1
16.7
7.5
8.8
7.6
18.2
14.8
12.9
15.5
15.5
11.8
16.1
12.4
10.0
14.6
13.9
11.3
18.6
15.1
11.6
10.7
11.3
17.7
11.6
16.5
9.3
9.8
18.1
12.6
15.5
13.5
14.3
223
3.2
149
1.3
61
Mean:
SD:
ME:
Number:6(11%)
Amplitude
(µV)
Phas/Turns
No.
291
104
64
308
60
40
28
358
267
111
506
242
248
59
40
39
527
373
163
152
113
280
238
238
87
89
426
272
92
603
5/1
1/1
0/0
2/1
0/1
0/0
0/0
2/1
3/1
1/1
3/1
2/1
2/1
0/1
0/0
5/0
4/1
3/1
2/1
2/1
1/1
2/1
1/1
5/1
1/0
1/0
2/1
2/1
1/0
3/1
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
Ins/pcls
Duration
(ms)
Amplitude
(µV)
Phas/Turns
8/2
8/3
9/1
9/2
9/3
9/4
9/5
9/6
10/1
10/2
10/4
10/5
12/1
12/2
12/3
12/4
12/5
12/6
12/7
12/8
12/9
13/1
13/2
13/3
14/1
14/2
9.4
7.5
13.5
18.4
16.0
9.3
14.7
7.2
13.3
11.1
8.2
9.5
18.3
15.5
11.8
16.5
9.6
14.5
7.9
7.8
8.7
11.0
9.3
13.1
16.3
19.3
113
64
578
456
192
140
100
66
406
223
50
34
440
306
88
74
55
53
52
48
40
242
174
171
264
129
1/0
0/0
2/1
5/1
2/1
2/1
1/0
0/0
2/1
5/0
0/0
0/0
2/0
1/0
5/0
1/0
0/0
0/0
0/0
0/0
0/0
2/1
2/1
2/1
3/1
1/0
No.
Ins/pcls
Duration
(ms)
Amplitude
(µV)
Phas/Turns
Normal:
No.
Ins/pcls
Deviation:
Duration
(ms)
Amplitude
(µV)
Phas/Turns
Figure 2.22: An example of the MUAP parameters display.
Hz and 16 bits resolution. The DAU is described in detail in[198]. The digital EMG signal is then serially send to a
39
Digital Signal Processor (DSP) board mounted in a ISA slot in the first PC (Intel 486, 33 MHz, 512 MB HD, 16 MB
RAM, Windows 3.11). A C-program in this PC controls the data flow so that the EMG signal is continuesly displayed
on the monitor and on the operators request a 11.2 second long signal is saved as a file on the hard disc. The second
computer (Intel Pentium II, 400 MHz, 10 GB HD, 128 MB RAM, Windows NT4.0) performs the decomposition and
displays the results. The main program is coded in Matlab 5.2 that provides powerful signal processing and data
display functions but the segmentation, clustering and resolution algorithms are coded in C to increase speed.
Digital
Analog
EMG
EMG
signal
signal
A/D
Ethernet
Decomposition
computer
Figure 2.23: Implementation of EMGPAD.
Decomposition of one signal takes in average one minute, but depends on the number MU in the signal and the
complexity of the signal and can thus take up to 30 minutes. To get an impression of the decomposition time a series
of synthetic EMG signals was constructed with from one to six MU and with added Gaussian noise to simulate the
instrumental noise (see Section 2.2.2). This was done by extracting MUAPs from a real EMG signal and putting them
in the synthetic signal at position following a Gaussian point process:
IP Ii = N (2900, 300)
for i = 1 · · ·number of MU
With a sampling frequency of 23437.5 Hz this corresponds to µ ≈124 ms (≈8 Hz) and σ ≈13 ms. These value are used
because they match the value estimated from FP’s from the original EMG signal where the MUAPs where taken from.
First six signals was constructed with only one MU by taking one template at the time. They where decomposed
and the average time was calculated. Then 15 signals was made by taking all combination of two templates and the
average decomposition time calculated etc. In this way the Decomposition time as a function of number of motor
units was estimated as shown in Fig. 2.24. The decomposition time is seen to increase exponentially as a function of
number of MU.
Figure 2.24: Decomposition time as a function of number of motor units.
2.3
Decomposition evaluation
EMGPAD is designed to find the MUAPs and firing patterns in a EMG signal and to measure their parameters. An
objective evaluation of a decomposition system is difficult. There is basically two approaches to this based on either
synthetic or real EMG signals, both having advantages and limitations. By constructing a synthetic EMG signal one
40
knows exactly what the outcome of the decomposition should be and one can evaluate the effect of an isolated feature
like noise or number of MU. Will synthetic signals might be valuable during a design phase, they can never adapt all
the variations seen in real EMG signals (see Section 2.1). The opposite is true for real EMG signals. The general
approach in this work will be to use real EMG signals, but some simulations with synthetic signals will also be used
to investigate a specific feature. The total number of available EMG signals for this work was 7436, recorded from
516 muscles and from 286 patients.
2.3.1
Evaluation of the MUAP parameters
All EMG signals recorded for this project where recorded in parallel on EMGPAD and our conventional manual setup.
This allows a comparison of the results found on EMGPAD against an older, well established and accepted method.
The two methods differs in both the recording and analysis phases and the influence of this on the final MUAP
parameters that are used for diagnosis is of interest. Table 2.2 outlines the differences:
Recording
Analysis
Manual method
A trigger and delay line is used to capture MUAPs on a oscilloscope screen. 50
ms epochs surrounding the trigger point is
plotted on a ink writer. This means that
only MUAPs that fulfill the trigger level or
are close to the trigger point (within the 50
ms epoch) will be plotted. Because high
amplitude MUAPs are easier to isolate by
triggering a bias toward higher amplitude
MUAPs is introduced. This method is
both time consuming and it takes some experience to master the trigger technic.
A ruler is used to measure the MUAP parameters are measured from the plotted
epochs on paper. A potential has to be
found at least three time to be accepted as
a MUAP. The MUAP parameters are put
into a table and the mean values are calculated and compared with normal values.
This can be very time consuming if several muscles are examined in a patient and
some limitations exist: The start and/or
end of a MUAP can be difficult to determine if there is insufficient free baseline before and after the MUAP, similar but different MUAPs can be difficult to distinguish and changing MUAP shape due to
needle movement or Jiggle can result in the
same MU being counted more than ones.
EMGPAD
The EMG signal is monitored on two computer screens. One showing the same oscilloscope picture as in the manual method
and the other shows the last half second
of the signal. In this way the examiner can
evaluate the quality of the signal and make
sure that the signal is usable for decomposition (sufficient baseline, not to complex
etc.). Because every thing within a 11.2
sec epoch is recorded there is no bias or
selection according to some possible preference of the examiner of MUAPs or a unconscious selection of MUAPs that support
a predetermined diagnosis.
When decomposition is performed in
EMGPAD the MUAP analysis is just the
first part of it, the FPs are also produced
and analyzed. The FPs along with different display possibility (raster or superimposition of the MUAPs etc.), enables a
more precise measurement of the MUAP
parameters. Similar looking MUAPs and
changing shape is efficiently detected in
the FPs. Approximately twice as many
MUAPs are detected compared to the
manual method.
Table 2.2: Comparison of the recording and analysis in the manual method and EMGPAD.
To compare the diagnostically most important of the MUAP parameters, the duration, three groups of patients
was selected: Myopathic, controls and neuropathic. The total number of MUAPs analyzed was 3834 (EMGPAD)
and 2032 (manual method); almost twice as many was found in EMGPAD. In the manual method, an expert (many
years of experience) electromyographer recorded and analyzed the signals. The result presented in EMGPAD could
be edited by the user by changing the cursor settings for duration or erasing MUAPs that was duplicates or of poor
quality. Because of the possibility of editing the results in EMGPAD it is not the algorithm for measuring the duration
in EMGPAD, but the difference in sampling of MUAPs, possible bias in selection of MUAPS during recording and
analysis and the enhanced precision in determining the start and end for MUAPs that influences the comparison of
the two systems. The mean values from both methods was compared with age-matched findings in normal subjects.
The findings in normal subjects was found using the manual method[179]. The normal range is defined as the normal
41
mean ± 20%. The MUAPs was shown in standard scale of 100 µv/cm and 10 ms/cm on the paper (manual method)
and on the computer screen (EMGPAD). Table 2.3 shows the data for the patients. Not all muscles in the myopathic
and neuropathic groups showed electrophysiological signs of disorders by falling outside the normal range but they are
included in the plot to show the total agreement between the two methods also in these cases. Fig. 2.25 summarizes
Number of patients
max
mean±1SD
Age:
min
Number of males/females
Number of examined muscles
Number of muscles showing signs of disorders
Myopathic
7
74
40.6±16.1
28
5/2
24
19
Controls
10
37
27.2±4.5
21
6/4
10
0
Neurogenic
10
86
58.9±14.1
35
7/3
30
22
Table 2.3: Data for the three groups of patients and their signals.
140
Myopathic
Controls
Neuropathic
120
100
% deviation from controls
80
60
40
20
0
-20
-40
-60
Figure 2.25: Comparison of the MUAP duration found with the manual method and EMGPAD. Three groups of
patients are used: Myopathic, controls and neuropathic. The results are shown in pairs of two; the first bar is from
the manual method and the second is from EMGPAD.
the result. The values can fall into one of three groups: Above (neurogenic), within (normal) or bellow (myopathic)
the ± 20% normal range. The result from the two methods agree if their duration fall in the same group. A 100%
agreement for the myogenic and control groups was found and of the 33 neurogenic muscles disagreement was found
in one (96.7% agreement). For all the signals a 98.4% agreement was found. This is considered to be an very good
agreement despite the previous explained fundamental differences between the two methods. A closer inspection of
the MUAPs found by EMGPAD compared to the manual method reveals that:
• All MUAPs found by the manual method was also found by EMGPAD, but the opposite was not always true.
• In average twice as many MUAPs where found with EMGPAD.
42
• More small MUAPs are found by EMGPAD.
• The duration is generally found slightly longer with EMGPAD.
2.3.2
Performance evaluation
The goal for a decomposition is to identify all occurrences of MUAPs in the EMG signal, so that the complete
firing patterns can be produced. While the evaluation of the MUAP parameters only shows the performance of the
decomposition up until the resolution of compound segments, the FPs shows the performance and possible errors for
all stages of the decomposition. The following methods can be used to evaluate the performance of the decomposition:
1. The reference could be found by doing a manual decomposition of the EMG signal. This method is used in
ARTMUP[4], but has the disadvantage of being very time-consuming. It is very difficult visually to perform a
decomposition and only possible at very low contraction force when the percentage of compound segments is
small and the number of contributing MUs is small.
2. A synthetic EMG signal could be produced by superimposing a number of MUAPTs (MUAP Trains). The
MUAPTs could be generated from a model that describes the MUAPT. This would provide the exact time for
each MUAP. We have not used this method since no comprehensive model of MUAP firing and control has been
designed [211]. This method can though be very useful when designing and testing the system, but can not
guarantee that the decomposition will be accurate when operating on a real EMG signal and all its variations.
In[212] several simulations with synthetic signals where performed to evaluate performance and accuracy of the
system.
3. A method, also suggested by C.J. De Luca[211], is to record MUAPs from the same MU at different locations
either by placing two (or more) needles lengthwise along the muscle or using a multichannel electrode. We have
used a multichannel electrode to record four channels of EMG signals. If the uptake area of all four channels
are within the MU-territory, all four EMG signals will contain MUAPs from the same MU, but with different
shapes due to different geometrical arrangements of the nearest muscle fibers. The probability of incorrectly
decomposing the four signals and yet having the same firing patterns for the same MU is extremely small. For
completeness, this has to be repeated under different conditions. This is probably the most objective evaluation
of the decomposition accuracy.
4. As described in the previous Sections, the variation across EMG signals is enormous. Decomposition of groups of
signals that covers both physiological and technical interesting/challenging signals would illustrate the robustness
and ability of the system.
Method 2 was used in previous section to evaluate the decomposition time as a function of number of MUs and the
influence of high frequency noise on the decomposition result.
Method 3 was tried using a old setup10 [155, 161] using a fourteen channel multielectrode connected to a fourteen
channel EMG amplifier. Four of the fourteen channels, located in the middle of the needle, was used for this purpose.
Only a few recordings where made with this setup because of several problems: The electrode is considerably bigger
in both diameter and length than a conventional concentric needle electrode resulting very painful recordings. The
recording characteristics is different than the one of a concentric needle electrode. The quality of the amplifier was
poorer than our standard one channel amplifier. Another data acquisition system that had four instead of only one
channels had to be used. This data acquisition system had poorer specification than the one described in Section 2.2.2
(20 kHz and 12-bit). Of these few recordings one was of relative good quality and the MUAPs from the same MU was
picked up in all four channels. The result after decomposing these four channels is shown in Fig. 2.26. Only one error
is seen to have been made in the middle of signal 2. A more useful setup would be to place two or more concentric
needle electrodes lengthwise along the muscle. For this, a multichannel data acquisition system is needed of a similar
quality as the one already used in EMGPAD. This was not available for this work but would be an interesting project
for the future.
Method 4 is the subject for the following chapter and contains decomposition examples of eight groups of signals
that covers both physiological and technical interesting/challenging signals. The eight signal groups are:
1. Noise: Baseline movement, distant volume conducted MUAPs and high frequency noise.
2. Amplitude variations: low and high amplitude MUAPs and mixture of them.
3. False PCLs.
10 This
setup was earlier used for volume conduction and motor unit territory studies
43
Figure 2.26: A four channel recording. The templates and firing patterns from the same MU but in four different
EMG signal are shown. They are identical except from an error in channel 2.
4. Special FPs: Varying firing frequencies, MUAPs with different firing frequencies and MUAP with common
varying firing frequencies.
5. Special EMG signals: Spontaneous MUAPs, double discharges, satellite potentials and blocking.
6. Needle movement.
7. Similar looking MUAPs.
8. MUAP shape variability (Jiggle).
This is qualitative assessment of the decomposition and not a quantitative, but it is based on a qualified selection of
relevant signals to cover a broad selection of EMG signals based on many years of experience with decomposition.
2.4
Chapter summary
In this chapter the decomposition system EMGPAD has been described. Focus is put on reflecting our many years of
experience with EMG signal decomposition by explaining the various difficulties encountered when trying to construct
a high precision decomposition system.
Chapter 3
Library of EMG Recordings
The first part of this Chapter presents examples of MUAP shapes seen when recording with a concentric needle
electrode (CNE) for conventional quantitative EMG analysis and their clinical interpretation is briefly discussed. In
the second part eight important types of EMG signals, MUAPs and FS in relation to decomposition are presented
and discussed in details. They illustrate the difficulties in decomposing EMG signals and the capability of EMGPAD.
3.1
MUAP Examples
The MUAP is the result of a summation of the individual action potentials (APs) from all the muscle fibers belonging
to the same MU. The APs from muscle fibers close to the recording surface of the CNE contributes relatively more to
the summation than distant. In this way usually the APs from the 2-12 closest muscle fibers to the needle determine
the spike whereas fibers at longer distances determines the slow initial and late components of the MUAP[147]. Many
factors influence on the MUAP shape. These factors can be separated into physiological- and recording condition
related factors. The physiological factors are for example: MU morphology, type and possible dysfunction of individual
parts (neuron, axon, neuromuscular junction and muscle fiber). This may result in: increased jitter, blocking, increased
temporal dispersion from reinnervation, loss of muscle fibers and/or neurons and damage to axons. These changes
are seen in pathological conditions and are to different degrees reflected in the MUAP shape and the FPs. The other
group of factors are related to the recording condition i.e. how the needle electrode sees the MUAP. These factors are
for example distance to the closest muscle fibers, position relative to the MU territory (well inside, at the border or
outside), insertion level, type of needle electrode (selective or not), filter settings etc. In this study a standard CNE
and standard filter settings (2Hz-10kHz) are used.
With so many factors influencing the MUAP shape, a wide range of shapes most be expected and this is also the
findings, but most pronounced in pathology. Below follows a short description of a general set of MUAPs which are
representative for the findings under standard clinical conditions. The MUAPs are shown in Fig. 3.1 and the letter
references are to the corresponding Figure. These by no means cover all possible shapes, but they represent the,
clinically most relevant type of shapes.
Simple MUAPs (A-B): MUAPs with fewer than five phases are called simple MUAPs. A phase is counted for each
portion of the MUAP having the same polarity and exciding an amplitude threshold. They comprise 80-90 %
of recorded MUAPs. The diphasic (A) and triphasic (B) shapes are the most common[143].
Polyphasic MUAPs (C): MUAPs with more than four phases are called polyphasic MUAPs. In most muscles an
average of 3 % of the recorded MUAPS are polyphasic and up to 12 % are still considered normal. In deltoid
and the facial muscles the average is 10 % and normal limit is about 25 %. An increase in number of polyphasic
MUAPs is seen in both myopathy and neuropathy[179]. Polyphasia results from increased temporal dispersion
of the single-fiber APs.
Irregular MUAP (D): MUAPs with more than four turns are called irregular or serrated. A turn is a change in
signal direction from upwards to downwards or vica verce, but it is only counted if the amplitude difference
between successive turns is above a threshold. Like in polyphasia irregular MUAPs reflects increased temporal
dispersion of single-fiber APs[148].
Satellite potentials (E): In average 3 % of the MUAPs in normal muscles, and many more in pathological conditions, have a satellite potential[138]. A satellite potential is defined as a clearly separated from and time-locked
component to the main potential. Satellite potentials can occur both before and after the main potential but is
most often seen after and is usually made up of just one or a few single-fiber APs. Satellite potentials can result
from muscle fibers with low propagation velocity or delay in longer axonal branches.
44
45
CHAPTER 3. LIBRARY OF EMG RECORDINGS
Double discharges (F): It is called a double discharge or doublet when the interval between consecutive discharges
from the same MU is less than about 20 msec. Double discharge can be seen at the onset and termination of
muscle contraction and at minimum rhythmic firing frequency[213].
Jiggle (G): Increased jitter and blocking of the individual APs results in increased MUAP shape variability also
called jiggle[160]. Increased jitter means increased variation in the time interval between single-fiber APs from
the same MU i.e. reduced synchronicity. Blocking (impulse blocking) means intermittent failure of transmission
in the motor endplate and results in loss of the single fiber APs in the the same MUAP. Variability is often seen
in case of reinnervation because of immature axon branches and neuromuscular junctions[160].
Low rise time MUAPs (H): The tissue acts as a low-pass filter on the APs so that distant MUAPs from the needle
electrode will be more filtered than close. This results in suppression of the high frequency components of the
MUAP i.e. the low rise time parts[10].
Recordings from the cannula (I): The cannula of a concentric needle electrode is connected to the negative input
of differential amplifier and the core to the positive. If a MUAP is outside the recording territory of the electrode
but close to the cannula a monophasic downwards (positive) going potential will be recorded.
300 µV
G
2 ms
200 µV
100 µV
2 ms
F
100 µV
2 ms
E
2 ms
H
2 ms
5 ms
I
100 µV
200 µV
2 ms
C
100 µV
2 ms
D
B
50 µV
100 µV
A
2 ms
Figure 3.1: Examples of MUAPs seen in CNE recordings: (A+B) Simple (≤4 phases), (C) polyphasic (≥5 phases),
(D) irregular, (E) satellite potential, (F) double discharges, (G) jiggle, (H) longe rise time and (I) recording from the
cannula.
3.2
46
EMG signal examples
The decomposition results from eight groups of EMG signals will be presented in this Section. These signals are
selected on the basis of many years of experience with EMGPAD for the purpose of illustrating and documenting
different signal types and special MUAP and FP phenomena. The signals show the difficulties encountered when
trying to decompose a wide range of EMG signals. The decomposition results from all the signals shows the capability
and accuracy of EMGPAD. The eight signal groups are:
1. Noise: Baseline movement, distant volume conducted MUAPs and high frequency noise.
2. Amplitude variations: Low and high amplitude MUAPs and mixture of them.
3. False potential classes: PCLs made up of superimposition of individual MUAPs.
4. Special FPs: Varying firing frequencies, MUAPs with different firing frequencies and MUAP with common varying firing frequencies.
5. Special EMG signals: Spontaneous MUAPs, double discharges, satellite potentials and blocking.
6. Needle movement.
7. Similar looking MUAPs from different MUs.
8. MUAP shape variability (Jiggle).
Many of the signal types presented here have only had little or no attention in the EMG litterateur. This Section
therefore serves as description and documentation for these important signal types both in respect to difficulties in
decomposition and MUAP and FP analysis. For example the important question of positively identifying needle
movement, similar looking MUAPs and Jiggle and differentiating between them can only be done by confirmation
from the FPs.
EMGPAD can generate many different visual outputs to illustrate different aspects of the decomposition, but the
main output screen presented to the user summarizes most of the decomposition information. Screen dumps of this
main output screen will be presented here. This output consists of two parts and an optional third part. The upper
part shows the identified MUAPs or more precisely the identified PCLs because a MUAP can be split because of
MUAP shape variation. In the upper part of Fig. 3.2 (A) four different MUAPs are shown. Below the MUAPs the
corresponding FPs are shown. As an option, part of the EMG signal can be shown in the bottom of the screen. The
numbers in the upper part of the EMG signal are the segment numbers and the numbers in the lower part are the
numbers of the PCLs that the segment is composed of. Looking at (A) in Fig. 3.2 the first segment has number 1
and it is compound because it consists of a summation of MUAPs from PCL 2 and 4. When the EMG signal is not
of primary interest then it is not shown to give more room for the MUAPs and the FPs. IPI histograms can also be
shown instead of the EMG signals.
The eight signal groups and their decomposition results are discussed in the following Sections.
3.2.1
Noise
In Fig. 3.2 examples are shown of the three types of noise seen in EMG recordings. These are described in Section 2.1.
Baseline movements are shown in (A) and (B). While the segmentation stage can be designed to be relatively
insensitive to baseline movement by using long analyze windows and calculating the variance, the clustering stage is
sensitive to it. A simple high-pass filter as described in Section 2.2.3 can effectively suppress the baseline movement,
see Fig. 2.3.
In (A) four MUAPs are identified. The two first are correctly identified but some errors are made in the last two.
The reason for this is, as described in Section 2.2.6.2, that the small amplitude MUAPs are more difficult to identify
in a compound segment than the relatively bigger MUAPs
In (B) five MUAPs are identified of which one MUAP is split (4 and 5) resulting in six PCLs. It can be seen from
the FPs that an error was made just above the 12th segment, where a firing is missing in FP 3. At closer inspection
it was found that in a compound segment the template from PCL 4 was wrongly identified instead of from PCL 3.
The reason for this is that the MUAPs from PCL 3 and 4 look similar. Besides this error some errors where made at
the end of the signal as seen from FP 3-6.
Activity from volume conducted distant MUAPs are seen in (C) and (D). The problem with these MUAPs is that
they overlap in frequency with close MUAPs so that a they cannot be efficiently filtered out without also significantly
distorting the close MUAPs.
47
In (C) only one MUAPs was identified and this was correct. In the FP four holes are seen, and at closer inspection of
the EMG signal it was confirmed that they are correct. It is seen from the EMG signal that many wrong segmentations
where made, but none of them where similar enough to result in a PCL.
In (D) three PCLs are identified but the last one is wrong, because it consists of many small similar looking distant
MUAPs. While FP 1 is correct some errors where made in FP 2 mainly because the MUAP is very small and similar
to the wrong PCL 3.
It was shown in Section 2.2.2 that high frequency instrumental noise was about 0.8 µV RMS corresponding to
approximately 4 µV. This was found by short circuiting the amplifier input, but in real EMG recordings the noise is
even higher because of noise from the CNE and the cable from the CNE to the amplifier. The total instrumental noise
was measured by inserting the CNE in a neutral 0.9 % saline solution and recording a signal. From this the noise
was found to be 2 µV rms corresponding to approximately 10 µV peak-peak[198]. It has though been observed that
the noise both can be significantly smaller and higher than this. The exact reason for this is not known. Considering
that the noise is constant for the EMG amplifier and the data acquisition system, the rest of the noise most come
from the needle electrode, the cable and/or the muscle. The needle electrodes comes in various lengths and when
investigated under a magnifying glass small variations in position and shape of the recording surface are seen. So the
small variation in the needles, perhaps the depth it is inserted into the muscle, small impurities on the recording surface
could attribute to the extra noise. The de-noising filter used in EMGPAD (see Section 2.2.3) efficiently suppresses
the instrumental noise. Even without the de-noising filter, the high frequency noise seldom had any influence on the
decomposition.
In (E) and (F) two examples are shown with significant high frequency noise. In (E) two MUAPs where correctly
identified in an EMG signal with a 3.5 µV RMS noise level. In (F) four MUAPs where correctly identified with 3.8
µV rms noise.
3.2.2
Amplitude variation
In Fig. 3.3 examples are shown of the enormous amplitude variation found within and across EMG signals. The
amplitude ratio between the smallest and the largest MUAP in (F) is about twenty and the ratio between the biggest
MUAP in (C) and the smallest in (B) is almost 200. Many factors determine the amplitude of a MUAP: the distance
between the recording surface of the CNE to the closest muscle fibers, the synchronicity of the AP from the closest
muscle fibers, the number of closest muscle fibers, the tissue penetration depth etc. Scanning EMG studies[103] have
shown how the amplitude varies within the MU territory. As illustrated in Fig. 1.2 the lose of muscle fibers in myopathy
is generally seen as a reduction in both duration and amplitude and in neuropathy an increase is seen due to increased
number of muscle fibers.
Very low amplitude MUAPs are seen in (A) and (B). The first one is from a person with no clinical sign of
neuromuscular disorders and the second is from a patient with myopathy.
In (A) two MUAPs were identified and the smallest with an amplitude of only 32 µV. No errors were made in the
decomposition. The first MUAP is only active in the beginning of the signal.
In (B) six MUAPs where identified of which three with an amplitude less than 50 µV and the smallest is only 22
µV. The two biggest MUAPs where correctly detected as seen by their complete FPs, but the smaller MUAPs have
increasing errors in their FPs with decreasing amplitude. This signal illustrates how the clustering algorithm can
detect even very small MUAPs, but the resolution algorithm has problems when the MUAPs are small compared to
background activity from distant MUAPs.
High amplitude MUAPs are seen in (C) and (D). Both are from patients with neuropathy. All MUAPs were correctly
identified. The signal to noise ratio is high for these high amplitude MUAPs and they are therfore not disturbed by
noise.
In (E) and (F) two examples are shown with more than a 1:10 ratio in amplitude between the largest and the
smallest MUAP. There are no errors in (E), but the signal is very easy to decompose because the smallest MUAP is
relatively big (170 µV). In (F) the ratio is 1:20 and as seen from the FP of MUAP 3, three errors where made. In
the lower part of (F) segment number 167 was wrongly resolved. MUAP 3 should not have been included in it. As
described in Section 2.2.6.2 when there is a mixture of small and big MUAPs, the small ones can be difficult to identify
especially if the smallest MUAP is less than 50 µV or when the big MUAP is unstable due to shape variability or
needle movement. In that case the small MUAPs may fit in at many wrong places because the resolution algorithm
will use them to compensate for the shape variation.
3.2.3
False potential classes
If the same combination of two or more MUAPs are repeatedly recorded then similar looking compound segments will
be generated. If this happens five or more times and the compound segments are sufficiently similar in shape then
they will form a PCL in the clustering stage. These so called false PCL must be identified and removed because they
48
do not represent individual MUAPs. How this is done in EMGPAD is described in Section 2.2.6.3. In Fig. 3.4 three
examples of false PCLs are shown. To the left is the decomposition result shown without trying to delete the false
PCLs and to the right after they are correctly identified and deleted. The two first signals are from patients without
electrophysiological evidence of neuromuscular disorders, and the last one is from a healthy person used for normative
data. In the manual method described in Section 2.3.1 and in decomposition systems where no attempt is made to
detect these false PCLs, there is a risk of them being erroneously included for analysis.
In (A) and (B) a false PCL was detected in a signal with only two MUAPs. It is seen how the FP of the false PCL
fits into the FP of both FP 2 and FP 3 at the same time. After deletion of the false PCL the FPs of the two true
PCLs (MUAPs) becomes complete.
In (C) and (D) a false PCL was detected in a signal with three MUAPs. Usually a summation of the same MUAPs
forms a false PCL but in (C), from the FPs, it is seen that the compound segments in the false PCL are summation
of all combinations of the three MUAPs (2+3, 2+4 and 3+4). The reason for this is that the three MUAP are similar
in shape.
In (E) and (F) no less than six false PCLs were found from summation of only two MUAPs. All six was successfully
detected and deleted as seen in (F). When the MUs fire asynchronously then the risk of forming false PCLs increases
with increased firing frequency (F).
3.2.4
Special firing patterns
In Fig. 3.5 examples are shown of special FPs. (A) and (B) shows some irregular FPs. (B), (C) and (D) shows how
the firing frequency can differ between MUs in the same recording. (E) shows an examples of commonly varying
(synchronized) firing rates and (F) shows some very peculiar FPs. Many interesting and peculiar FPs have been
observed in signals that have been analyzed with EMGPAD. When looking at these FPs ones have to keep in mind
that the patient was instructed to apply a slight and constant contraction force. As described in Section 2.2.2 both
audio and visual feedbacks are available and the electromyographer will usually apply slight suitable resistance with
his hand, to control the force. These FPs also illustrate the difficulties in using the information from partial FPs for
MUAP classification and resolution[206, 176] because fairly constant firing rates cannot always be expected.
In (A) a single high amplitude MUAP was recorded in this signal from patient with ALS. In the first half of the FP,
the firing rate is very unstable, followed by a silent period probably due to relaxation of the muscle. Then the MU is
recruited again and with a higher firing rate than before but still very unstable.
In (B) three MUAPs was correctly identified in a signal from a referred patient but without electrophysiological
signs of disorders. The first FP shows fairly stable firing rate but the two others are more unstable with respectively
higher and lower mean firing frequency1 . This is also seen in the lower part of (B) where the histograms for the IPIs2
are shown. In the histograms the solid thin vertical line marks the mode and the doted line marks the mean.
In (C) three MUAPs was correctly identified in a signal from a referred patient with no electrophysiological signs of
neuromuscular disorders. The firing frequency for the third MUAP is more than twice as high as for the second and
it is more stable.
In (D) eight MUAPs was identified in a signal from a patient with myopathy (polymyositis). The first four FPs are
correctly identified, but some errors are seen in the rest. FP 3 is seen to have a very low firing frequency of 2.7 Hz.
which is less than one third of the next lowest.
In (E) four MUAPs was correctly identified in a signal from a patient with supraneuclar paresis. There is a
considerable amplitude variation, but what is interesting in these FPs is, that they vary commonly (synchronized).
This is seen in most of our recordings but not as pronounced as in this patient. This could perhaps be specific for this
disorder.
In (F) three MUAPs was identified in a signal from a patient with myopathy. The FPs for the first and third MUAP
are very peculiar; The first MU is firing in sequences of three and the other has a firing frequency of only 0.9 Hz. The
last potential is not a real MUAP but is an AP from a single muscle fiber. An error was made; the third discharge in
FP 3 should not have been there.
3.2.5
Special EMG signals
In Fig. 3.6 and Fig. 3.7 six examples of what could be called on-off phenomena in EMG signals are shown. These include
spontaneous potentials, double discharges and blocking. These signals can be difficult to handle for a decomposition
system, because they do not behave like ”normal”3 MUAPs. These phenomena seem to occur at random.
In Fig. 3.6 (A) and (B) the decomposition result and the raw EMG signal are shown, recorded from patients with
neuropathy. Two spontaneous potentials are marked with arrows. They only occur at these to places and look very
1 The mean firing frequency is estimated as the mode. The mode is the most frequent value, i.e. the highest value in the histogram. 10
msec class intervals are used.
2 IPI is the Inter Potential Interval.
3 In healthy persons, the MUAPs show a high degree of stability in both shape and IPI through out the 11.2 sec. long recordings.
49
similar in shape strongly indicating that they are from the same MU. Because they only occur twice they do not
form a PCL and the clustering algorithm therefore assumes they are compound segments. From the FPs and the
raw signal bellow it is seen how the resolution algorithm tried to resolve the two potentials by means of the identified
MUAP. This is wrong and false lines in the FPs are therefore seen. It is difficult to detect this kind of error because
the spontaneous potential could be superimposed with other correctly identified MUAPs. Fortunately this is a rare
phenomenon but it very well illustrates the problems that can be encountered when trying to decompose real EMG
signals. These spontaneous potentials are also called fasciculation potentials.
In (C) and(D) the same phenomenon as in (A) and (B) is seen, here there is only one spontaneous potential and it
is marked with an arrow. It is easy to see that it can not be a superimposition of a combination of the three correctly
identified MUAPs. All these examples show that the FPs always reveals any kind of errors made in the decomposition
process. This makes the FPs a very powerful tool for determining the fidelity of the decomposition result.
In Fig. 3.6 (E) and (F) and in Fig. 3.7 (A) and (B) two clear examples of the so-called double discharges (doublets)
are seen. It is called a double discharge when the interval between to consecutive discharges is significantly shorter
than the mean IPI. In[213] it is reported that the IPI for double discharges is usually less than 9-10 msec and very
rarely more than 22 msec. Double discharge can be seen at the onset and termination of muscle contraction and at
minimum rhythmic firing frequency[213, 214]. Characteristic for double discharges is the prolonged interval following
it (about 50 % longer than mean IPI). Conflicting results have been reported for findings of double discharges in
normal subjects and patients. In[105] it was found only in normal subjects but never in patient, while in[213] it was
observed in about 3 % of the 2000 MU they recorded in patients, but never in intact limb muscles. In our own material
(7436 EMG signals from 516 muscles from a total of 286 patients and normals) only six cases of double discharges
were found. Of these, only two showed prolonged IPI after a double discharge. The reason for this low observation
compared to others could be that our signals was recorded under normal clinical conditions without trying to get
double discharges.
In Fig. 3.6 (E) and (F) an example of a clear double discharge is seen. The same MUAP is shown in Fig. 3.1 (F).
This signal was recorded from a healthy control person. Three double discharges with prolonged IPI following them
is seen in this signal with MUAPs from only a single MU. The interval between the first and the second MUAP in the
double discharges are not constant which is more clearly seen in Fig. 3.1 (F). In this example the interval is decreasing
(11.7, 9.6 and 7.5 msec) which resulted in that the first double discharge with the longest IPI was segmented separately
but in the two others they where not. When they are not segmented separately, generally the first MUAP will be
detected because the second MUAP is often smaller.
In Fig. 3.7 (A) and (B) the second example of a clear double discharge is seen. This signal was recorded from a
patient with ALS. The second MUAP in the three double discharges is very close to the first with an IPI of only 4
msec so that the two MUAPs are almost fused. In this example the prolonged IPI (74 %, 96 % and 67 % more than
mean IPI) after the double discharges is even more evident than the previous example because the IPIs are fairly
constant.
In Fig. 3.7 (C) and (D) a possible third example of double discharge is seen. This signal was recorded from a patient
with neuropathy. This example differs from the two previous ones in that four and not just one MU are identified and
this makes it more ambiguous because of superpositions. The double discharge looks similar to the previous examples
because the IPI for the double discharges is small, 3.6 msec and the second MUAP is less than half amplitude of the
first. While the absence of prolonged IPI after the double discharges weakens the validation of double discharge, the
double satellite seen in the lower part, strengthens it. There is a small satellite potential after the first MUAP, so in
case of a double discharge one could expect a satellite for each one of them. This is seen for segment number 20 when
compared to segment 13 and 24. The interval between the two small satellite potentials is approximately the same as
between the two MUAPs in the double discharge in segment 20. The double discharges occur at random places and
there is five of them, but because three of them are superimposed with other MUAPs they don’t form a separate PCL.
The resolution algorithm assumes that all segments that did not fit in to any PCL during clustering are compound, so
it attempted to resolve them by means of the templates from the identified PCLs. Segment number 20 was therefor
erroneously found to be a summation of the MUAPs from PCL 1, 2 and 3, but this is obviously wrong as seen from
both the FPs for PCL 2 and 3 and from the lower part of (C).
In Fig. 3.7 (E) and (F) an example of blocking is seen. This signal was recorded from a patient with neuropathy.
Seven PCLs was found and PCL 2-5 are from the same MU as seen in the lower half of (E) where their respective
FPs are summated and forms a regular FP. In (F) the MUAPs belonging to PCL 2-5 are shown in a cascade plot
where the MUAPs that are missing the last spike are marked with an arrow. Besides blocking in six of the MUAPs
also considerable jitter and variability in shape of the last spike is seen. Hence this MUAP has been split into four
PCLs. Two types of blocking exist[16] neuromuscular and axonal blocking. The first one is a blocking in a single
neuromuscular junction (end-plate) so that the nerve AP do not reach the muscle fiber. The second is when a common
axonal branch supplying one or more muscle fibers is blocked. It is probably neuromuscular blocking in this example
because the last spike looks like an AP from a single muscle fiber.
3.2.6
50
Needle movement
In Fig. 3.8 six examples of needle movements are shown. This is seen as a continously increase or decrease in amplitude.
As described in Section 2.2.2 the cable connecting the CNE to the EMG amplifier is fixed to the muscle with a pice of
tape to avoid needle movement but still it happens some times. Besides seeing a change in amplitude in the raw EMG
signal, the decomposition result shows several PCLs for the same MUAP due to the changing shape (amplitude) and
the FPs are correspondingly split in a staircase like fashion. The main part of the MUAP is a summation of APs from
the few closest muscle fibers to the recording surface of the CNE. Because the amplitude for a single AP as a function
of distance to the recording electrode rapidly declines with distance, the MUAP amplitude is more sensitive to needle
movement if the closest muscle fibers are very close. As seen in all the examples the start and ending of the MUAPs
with changing amplitude, is practically constant, while the spike where the rise time is shortest is changing. As seen
in Fig. 3.9 needle movement can not always reliably be identified only from the MUAP shape but the FPs gives the
final prove because the partial FPs fit together, strongly indicating that they originate from the same MU. These
examples indicate why the MUAP amplitude is less used in quantitative MUAP analysis because of high dependence
on the distance to the closest muscle fibers, while the duration is seen to be practically constant. In these examples
the whole EMG signal is shown in the lower part of the figure so that the amplitude variation shown in the PCLs and
FPs can be compared to the raw signal.
In (A) and (B) two examples of increasing MUAP amplitude are shown. The CNE has moved slightly closer to the
closest muscle fiber(s). In (A) the same MUAP has been split into seven PCLs because of the increasing amplitude
but also because of additional shape variability because PCL 2,3 and 6 has an extra phase compared to the other
PCLs. This together with the fact that the amplitude is not just simply increasing but also slightly osculating result
in the many jumps between FPs. But like in (B) the steadily increase in amplitude is clear.
In (C) and (D) two examples of decreasing MUAP amplitude are shown. CNE has moved slightly away from the
closest muscle fiber(s). In (C) one MUAP was split into three PCL and an artifact is seen in the middle of the raw
signal but it didn’t influence the decomposition result. In (D) PCL 1-4 are showing decreasing amplitude and the
characteristic stair case FP is seen for them. In this signal two other MUAPs was also identified. PCL 6 is not showing
decrease in amplitude because the rise time is relatively long so it is not sensitive to slight needle movement because
it is not so close to the needle electrode.
In (E) a decrease in amplitude in two MUAPs is seen. Both MUAPs had muscle fibers very close to the needle
electrode tip so they both was influenced by the slight needle pull.
Needle movement and similar looking MUAPs, shown in the next Section, are important when discussing MUAP
shape variability (jiggle) because one has to distinguish between them. Two obvious question are:
1. How much must the amplitude change to create a new PCL ?
Answer: The similarity between two segments is not based on the amplitude relation but on the variance of the
residual signal when subtraction them after alignment (see (2.2) in Section 2.2.5.4). There is no simple relation
between the amplitude ratio of the two segments and our distance measure. Further more the clustering algorithm
is not based on fixed thresholds but is adaptive as described in Section 2.2.5.4. It is therefore not possible, in
case of for example needle movement (gradual increase or decrease in amplitude), to say when a new PCL will
be created.
2. Can the needle return to a previous position ?
Answer: To answer this question the following recording was made: A CNE was inserted in the biceps brachii muscle
of a healthy control subject and with the audio/visual feedback a position with clear MUAPs was found. Then
the needle was slightly and rhythmicaly pushed by applying slight pressure with the fingers. As seen in (F) it
is possible to return to the same shape and therefor also to the exact same position in muscle with the needle.
This is though an artificial situation because a rhythmic pressure to the needle like this would never happen in
a real recording. This example shows that there is a kind of elasticity because of the friction between needle and
muscle tissue so that if, the pull or pressure is only slight without exceeding this elasticity, then the needle will
return to its original position.
One could perhaps imagine that by accident the electromyographer or the patient slightly touches the cable or
needle so that for a very short time the needle is moved slightly and then returns to its original position. This would
only locally give rise to a change in shape. Alternatively could the shape remain different after the needle movement
resulting in two distinct shapes. Another situation could be that the patient because of tremor or other pathological
conditions can not keep a constant force but rather shakes. This would probably result in very big changes in the
EMG signal due to needle movement and re- and derecruitment of MUs.
3.2.7
51
Similar looking MUAPs
In Fig. 3.9 six examples of similar looking MUAPs are shown. Considering the many factors that determines the
MUAP shape it is not likely that the shape of two MUAPs in the same signal are identical. On the other hand about
80 % of all recorded MUAPs are di- and triphasic[143]. This fact increases the possibility of observing similar looking
simple potentials. This is also what is observed in our recorded signals; Similar looking MUAPs are very rare and
when it happens it only involves simple potentials (≤ 4 phases). All the examples of similar looking MUAPs in Fig. 3.9
are either di- and triphasic. Both manual MUAP analysis and EMG decomposition is based on the assumption that
potentials from the same MU are more similar in shape than potentials from different MUs, so when this assumption
is violated, errors in the analysis might be introduced. In the manual method, failure of identifying two similar
looking MUAPs as being different is not a big problem, because similar looking MUAPs provides the same MUAP
morphological information (duration, amplitude and phases). In a decomposition system this would result in merging
of PCLs and FPs, so that the FPs becomes unusable therefore similar looking has to be detected in a decomposition
system. Similar looking MUAPs are important when discussing MUAP shape variability (jiggle) because one has to
distinguish between them. The dilemma in a decomposition system is that MUAPs from different MUs can look more
similar in shape than MUAPs from the same MU because of needle movement and jiggle.
In (A) and (B) two examples are shown of similar looking MUAPs except for their amplitude. This could be
mistaken for needle movement in a manual method where only the MUAPs are avaliable and not the FPs. The FPs,
though reveals in both examples, that the MUAP are from different MUs because their respective FPs are regular and
complete. Note how even small details are similar in (B).
In (C) MUAP 2 and 3 and in (D) MUAP 1 and 2 are similar in shape, but the FPs are regular so they are separate
MUAPs. The signals was correctly decomposed and it shows that the clustering stage is able of managing so similar
MUAPs.
In (E) and (F) five MUAPs where found and three of them are similar in shape. From the FPs it is seen that the
clustering algorithm correctly found five PCLs but the resolution algorithm made some errors. This is seen because
the FPs for the main part are regular without errors but when holes or very close discharges are seen they match with
close discharges or holes in other FPs. When MUAPs are very similar in shape summation with noise or relatively
smaller MUAPs can make a MUAP from one MU more similar in shape to a template from another MU than MUAPs
from the same MU. This is more likely to happen if the MUAPs show variability in shape due to needle movement
or jiggle. Despite the errors seen in (E) and (F) the firing rate can still easily be estimated because the majority of
discharges was correctly identified.
3.2.8
MUAP shape variability (jiggle)
In Fig. 3.10 - 3.14 several examples of MUAP shape variability also called jiggle4 are shown. It is important to
distinguish between jiggle and other phenomena that could resemble it; needle movement, similar looking MUAPs and
superposition with smaller MUAPs and noise.
Jiggle results from increased jitter and/or impulse blocking beyond what is seen in normals. Jiggle is easily seen
visually when the MUAPs are plotted on top of each other (superimposition plots) or plotted beside each other (raster
plots). The Figures illustrating jiggle are arranged in pairs; the first one shows the usual decomposition result plots
with the PCLs on the upper half of the Figure and the FPs on the lower half. The second Figure is divided in three
plots; a raster plot of the PCL(s) showing jiggle, a superimposition plot and a summation of the FPs of the involved
PCLs.
Jiggle is influenced by the method of alignment. In[160] trigger and peak alignment was used. Both these methods
has some disadvantages; the first one is sensitive to noise at the trigger point and the other is sensitive to instable
or shifting maximal peak. their advantage is that they are easy and fast to determine. To overcome these problems
alignment according to maximal crosscorrelation coefficient was used in this study.
When MUAPs from more than one MU are recorded superpositions will inevitable happen, in the cascade and
shimmer plots only non superimposed MUAPs are shown and in the FP, these are marked with slightly longer lines.
Usually jiggle results in MUAPs from the same MU are split into several PCLs because of the changing shape. This is
specially true for blocking without significant jitter because then the MUAPs changes between two distinct shapes as
seen in Fig. 3.10 (C+D) and Fig. 3.10 (A+B). When looking at the jiggle examples three types of jiggle are observed:
Blocking. In Fig. 3.10 three examples of blocking is shown. The APs with blocking are localized at different places
in relation to the main potential; well Outside (B), at the end (D) and at the onset (F).
Blocking and jitter. In Fig. 3.11 (A+B) and (C+D) two examples where the blocking AP(s) also have considerable
jitter are shown.
4 This
name was given by Stålberg and Sonoo[160]
52
Jitter. In Fig. 3.12 (C-F) and Fig. 3.13 five examples of primarily jitter is shown.
All the examples with jiggle except the three first with blocking and (C+D) in Fig. 3.14 are from patients with
neuropathy.
Jiggle is naturally most easily seen and with greatest fidelity, when only one MU is recorded so that interference
with other MUAPs can be excluded. As mentioned earlier; errors in the resolution stage can happen when MUAP
with jiggle are recorded together with smaller MUAPs, see Section 2.2.6.2.
3.3
Chapter summary
Eight important groups of EMG signals/firing patterns have been presented and described. They represent signals that
illustrate difficulties in decomposition. Several ”good” examples of MUAP shape variability are given to document
this special phenomena. These examples also illustrates and documents the capability of EMGPAD to decompose a
variety of challenging EMG signals.
53
simple
18.4 ms
289 µV
10 ms
4
simple
16.4 ms
247 µV
simple
16.6 ms
59 µV
simple
15.0 ms
26 µV
1
2
10 ms
3
simple
10.2 ms
685 µV
4
simple
8.8 ms
473 µV
5
simple
11.7 ms
203 µV
34757/4/15
(B)
3
200 µV
200 µV
2
34482/110/18
(A)
1
6
simple
12.8 ms
195 µV
simple
12.3 ms
169 µV
simple
11.9 ms
119 µV
1
1
2
2
3
4
3
5
4
6
1
2
3
4
5
6
7
8
9
10
11
12
13
14 %15
16
17
18
1
2,4
3
1c
1,2,3
1
3
2,4
1,3
3
1,4
2c
1,3,4
2,4
1
1
2,4
1,4,5,62c 3
%3
2
3
4 5
6
7
8
9
10
11
12
13
14
15 16 17
18 19
20 21
22
5,61c
2c
3,4
1,6
2,3,4,6
1c
4c
2,3,6
1c
4
6
1c 4c
6
3,4 1c
2c
simple
13.5 ms
144 µV
10 ms
1
2
50 µV
50 µV
1
3
simple
11.2 ms
238 µV
10 ms
36786/5/15
(D)
36786/5/27
(C)
2c 3
23
simple
11.1 ms
44 µV
simple
11.4 ms
30 µV
1
1
2
3
%
%2
1c
3
%4
%
%
% 5% 6
%
7
1c
%
%
8
%
% 9
%
% 10
% 11
% 1c
1
2
3
4
5
6
1,2
3c
3c
2,3
1,2
3c
(F)
200 µV
2
simple
11.9 ms
747 µV
10 ms
simple
13.6 ms
265 µV
1
2
100 µV
34202/111/8
(E)
1
3
simple
14.6 ms
414 µV
10 ms
34202/111/5
1
4
simple
13.4 ms
264 µV
simple
12.9 ms
188 µV
simple
11.6 ms
128 µV
1
1
2
3
2
4
1
2
3
1
2
3
4
5
6
7
1,2
2c
1c
3c
2
1,4
3c
2c
4c
1c
Figure 3.2: Two examples of each of the three noise type; (A) and (B), baseline movements, (C) and (D), distant
MUAPs contributing to increased background noise, and (E) and (F), high frequency noise components. See text in
Section 3.2.1
54
(A)
1
2
50 µV
2
3
simple
12.7 ms
167 µV
10 ms
4
simple
7.8 ms
120 µV
5
simple
6.8 ms
57 µV
36954/3/12
(B)
36685/111/1
1
6
simple
9.8 ms
42 µV
simple
6.6 ms
28 µV
simple
5.6 ms
22 µV
1
50 µV
2
10 ms
simple
9.0 ms
137 µV
simple
11.5 ms
32 µV
3
4
5
6
1
1% 2
3
4
5
6
7
8
9
10
11
% 3,6
5
1,2,3,4,5,6
5c
1,4
2,3,6
5c
4c
1
2,3,5,6
2
2
600 µV
800 µV
simple
11.1 ms
4318 µV
10 ms
1
34209/111/3
(D)
37949/128/5
(C)
1
simple
29.0 ms
3987 µV
10 ms
simple
14.3 ms
1110 µV
1
1
2
2
400 µV
1
3
simple
9.4 ms
1923 µV
10 ms
32025/50/2
(F)
2
34486/110/5
(E)
1
simple
10.1 ms
450 µV
simple
7.8 ms
95 µV
400 µV
1
10 ms
simple
23.1 ms
1893 µV
simple
13.7 ms
170 µV
2
3
1
165
166
167
168
169
170
1c
3c
1,2,3
3c
1,2
3c
2
Figure 3.3: Different MUAP amplitude examples. (A) and (B) shows examples of low amplitude MUAPs, (C) and
(D) high amplitude MUAPs and (E) and (F) mixture of low and high amplitude MUAPS. See text in Section 3.2.2
55
simple
17.0 ms
340 µV
10 ms
36685/111/8
3
simple
15.8 ms
241 µV
simple
15.5 ms
136 µV
1
2
50 µV
100 µV
2
36685/111/8
(B)
(A)
1
simple
17.2 ms
238 µV
10 ms
simple
17.9 ms
138 µV
1
1
2
2
3
10 ms
simple
14.5 ms
659 µV
4
simple
13.1 ms
484 µV
simple
12.5 ms
454 µV
simple
12.3 ms
419 µV
1
1
2
10 ms
36032/111/8
(D)
3
100 µV
200 µV
2
36032/111/8
(C)
1
3
simple
17.3 ms
485 µV
6.5 Hz
simple
15.5 ms
460 µV
6.7 Hz
simple
16.0 ms
419 µV
8.6 Hz
1
2
2
3
3
4
5
simple
15.7 ms
349 µV
simple
12.1 ms
290 µV
6
simple
16.9 ms
278 µV
(F)
4
1
simple
12.8 ms
276 µV
7
simple
14.4 ms
272 µV
2
3
simple
18.7 ms
268 µV
8
simple
15.0 ms
259 µV
9
simple
5.8 ms
215 µV
simple
7.6 ms
40 µV
50 µV
10 ms
3
36810/5/7
200 µV
2
36810/5/7
(E)
1
10 ms
simple
14.5 ms
290 µV
simple
10.7 ms
215 µV
simple
11.2 ms
40 µV
1
2
1
3
4
5
2
6
7
8
3
9
Figure 3.4: Three examples of false PCLs. To the left is the decomposition result shown without trying to detect and
resolve false PCLs and to the right after deletion of false PCLs. See text in Section 3.2.3
56
(A)
2
200 µV
1
3
simple
11.5 ms
579 µV
11.6 Hz
10 ms
36400/50/10
32025/50/17
(B)
1
simple
7.9 ms
143 µV
15.1 Hz
simple
9.4 ms
85 µV
5.8 Hz
600 µV
1
simple
7.2 ms
2872 µV
10 ms
2
3
50
0
0
0.1
0.2
0.3
IPI (Seconds)
0.4
50
0
0
0.1
0.2
0.3
IPI (Seconds)
0
0
0.1
2
0.2
0.3
IPI (Seconds)
0.4
simple
12.7 ms
227 µV
9.0 Hz
simple
13.0 ms
137 µV
8.6 Hz
3
simple
18.3 ms
453 µV
9.7 Hz
4
50 µV
5
5
6
simple
16.2 ms
158 µV
8.7 Hz
simple
9.2 ms
118 µV
18.1 Hz
simple
13.2 ms
395 µV
10.0 Hz
simple
5.5 ms
235 µV
2.7 Hz
7
simple
6.4 ms
100 µV
14.9 Hz
36200/110/11
1
200 µV
3
10 ms
10 ms
0.4
10
(D)
2
38063/2/10
(C)
1
3
number of IPIs
2
number of IPIs
number of IPIs
1
1
8
simple
7.8 ms
48 µV
13.7 Hz
simple
9.3 ms
39 µV
11.9 Hz
simple
9.3 ms
35 µV
10.6 Hz
1
1
2
3
4
2
5
6
7
3
8
0.1
0.2
0.3
IPI (Seconds)
0.4
0
0.1
0.2
0.3
IPI (Seconds)
0.4
0
0.1
0.2
0.3
IPI (Seconds)
0 0.20.4
IPI (Seconds)
0.4
0
0 0.20.4
IPI (Seconds)
10 ms
1
simple
14.6 ms
973 µV
0
0 0.20.4
IPI (Seconds)
20
0
0 0.20.4
IPI (Seconds)
5
0
0 0.20.4
IPI (Seconds)
0
0 0.20.4
IPI (Seconds)
50
8
0
0 0.20.4
IPI (Seconds)
50
0
0 0.20.4
IPI (Seconds)
(F)
3
simple
11.7 ms
314 µV
4
simple
7.1 ms
173 µV
simple
6.7 ms
91 µV
1
2
50 µV
200 µV
2
35103/50/13
(E)
1
5
7
number of IPIs
20
6
50
number of IPIs
0
5
10
number of IPIs
20
4
number of IPIs
0
3
number of IPIs
0
2
40
10 ms
simple
8.4 ms
132 µV
3
simple
24.9 ms
66 µV
35874/4/16
0
10
40
number of IPIs
0
1
100
number of IPIs
20
20
number of IPIs
40
3
number of IPIs
2
number of IPIs
number of IPIs
1
simple
5.6 ms
59 µV
1
2
2
3
4
3
Figure 3.5: Examples of special FPs. (A)+(B) Irregular FPs, (B)+(C)+(D) different mean firing frequencies, (E)
synchronized FPs and (F) special firing sequence. See text in Section 3.2.4.
57
(A)
(B)
2
34209/111/1
1
3
600 µV
←
simple
22.3 ms
2497 µV
↓↓
10 ms
1
simple
17.3 ms
502 µV
←
simple
11.2 ms
287 µV
2
3
50
51
3c
52
1c
53
2c
54
←
55
56
57
58
59
60
2c
3c
←
1,3
1c
2c
3c
1c
1,2,3
(D)
2
34479/110/1
(C)
1
3
50 µV
↓
simple
14.8 ms
199 µV
↓
10 ms
simple
14.0 ms
195 µV
simple
10.3 ms
36 µV
1
2
3
58
59
60
61
62
63
64
65
2,3
1c
2,3
1c
1,3
2,3
1
2,3
↓
(F)
36744/5/19
(E)
100 µV
1
←
←
←
simple
19.8 ms
325 µV
10 ms
↓ ↓↓
1
6%
7
8
%
9
10
%
11
12
%
13
%
14
15
←
%
1c
1c
%
%
16
17
←
%
%
1c
%
1c
%
1c
%
←
%
1c
Figure 3.6: Examples of special EMG signals. (A)+(B) and (C)+(D) Spontaneous MUAPs (marked with arrows) and
(E)+(F) double discharges (marked with arrows). See text in Section 3.2.5.
58
(A)
37334/111/10
(B)
1
400 µV
←
poly
22.8 ms
2179 µV
10 ms
←
↓
1
←
69
70
71
1c
1c
1c
←
72
73
1c
1c
2
(D)
3
34209/111/8
(C)
1
4
←
600 µV
←
10 ms
1
simple
11.2 ms
3118 µV
↓
simple
11.6 ms
1276 µV
simple
12.4 ms
418 µV
simple
8.8 ms
295 µV
←
2
3
4
13
14
15
16
17
18
19
20
21
22
23
24
←
1
4c
2c
3c
4c
2c
3c
←
1,2,3
2,4
3c
2,4
1
2
3
4
(F)
5
6
7
37705/129/12
(E)
1
←
↓
200 µV
↓
10 ms
simple
11.0 ms
716 µV
7.4 Hz
simple
13.5 ms
485 µV
4.1 Hz
simple
12.6 ms
485 µV
8.2 Hz
simple
11.7 ms
477 µV
1.8 Hz
simple
13.3 ms
480 µV
8.1 Hz
simple
7.0 ms
76 µV
7.8 Hz
simple
3.8 ms
63 µV
7.5 Hz
2
3
4
5
2+3+4+5
↓
↓
↓
↓
Figure 3.7: Examples of special EMG signals. (A)+(B) and (C)+(D) Double discharges (marked with arrows) and
(E)+(F) blocking (marked with arrows). See text in Section 3.2.5.
59
10 ms
simple
8.0 ms
2164 µV
4.5 Hz
4
poly
7.6 ms
2107 µV
8.0 Hz
poly
7.9 ms
1932 µV
9.1 Hz
5
simple
7.9 ms
1611 µV
2.8 Hz
6
simple
7.4 ms
1516 µV
2.3 Hz
7
simple
7.3 ms
1513 µV
8.1 Hz
35695/4/10
3
simple
7.4 ms
1078 µV
8.8 Hz
1
1
2
400 µV
600 µV
2
3
poly
20.6 ms
2090 µV
6.5 Hz
10 ms
37501/111/16
(B)
(A)
1
simple
20.6 ms
1847 µV
6.9 Hz
poly
20.6 ms
1644 µV
7.0 Hz
1
2
3
2
4
5
3
6
1+2+3
7
1+2+3+4+5+6+7
poly
25.4 ms
2752 µV
7.9 Hz
10 ms
poly
25.5 ms
2139 µV
7.9 Hz
poly
25.3 ms
1456 µV
7.5 Hz
1
2
600 µV
600 µV
3
10 ms
3
simple
10.6 ms
2714 µV
8.7 Hz
4
simple
10.7 ms
1918 µV
8.6 Hz
5
simple
10.4 ms
1580 µV
8.5 Hz
6
simple
11.4 ms
1173 µV
8.6 Hz
simple
10.6 ms
565 µV
6.7 Hz
7
simple
10.4 ms
566 µV
6.6 Hz
36032/111/7
(D)
2
36871/110/7
(C)
1
simple
10.6 ms
544 µV
7.1 Hz
1
1
2
3
2
4
5
6
3
7
10 ms
simple
14.5 ms
354 µV
8.3 Hz
simple
14.3 ms
296 µV
8.6 Hz
(F)
4
simple
14.5 ms
256 µV
8.5 Hz
5
simple
10.8 ms
175 µV
6.1 Hz
6
simple
10.9 ms
151 µV
7.9 Hz
simple
11.2 ms
139 µV
7.9 Hz
1
2
10 ms
1
1
2
2
3
3
4
4
5
5
6
6
simple
7.0 ms
316 µV
9.8 Hz
3
simple
7.1 ms
299 µV
9.5 Hz
4
simple
7.0 ms
238 µV
1.6 Hz
5
simple
6.4 ms
166 µV
9.6 Hz
6
simple
6.2 ms
80 µV
9.7 Hz
36811/5/39
3
100 µV
100 µV
2
35489/110/13
(E)
1
simple
3.2 ms
22 µV
12.2 Hz
Figure 3.8: Six examples of needle movements. (A)+(B) Increasing amplitude is seen from moving the CNE slightly
closer to the closest muscle fibers. (C)+(D) Decreasing amplitude. (E) Two MUAPs showing decreasing amplitude.
(F) Slight manipulation of the CNE resulting in periodically increase/decrease in amplitude. See text in Section 3.2.6.
60
simple
21.2 ms
2196 µV
6.2 Hz
10 ms
4
simple
20.7 ms
1844 µV
5.4 Hz
simple
16.4 ms
1264 µV
6.9 Hz
34482/110/17
3
simple
10.0 ms
587 µV
5.1 Hz
1
2
50 µV
400 µV
2
35489/110/11
(B)
(A)
1
simple
18.7 ms
218 µV
6.9 Hz
10 ms
simple
18.7 ms
166 µV
6.2 Hz
1
1
2
3
2
4
simple
18.0 ms
875 µV
4.9 Hz
simple
13.8 ms
309 µV
7.4 Hz
simple
11.6 ms
313 µV
7.1 Hz
2
simple
17.3 ms
485 µV
6.5 Hz
10 ms
1
1
2
2
3
3
10 ms
simple
15.4 ms
616 µV
7.8 Hz
3
simple
15.4 ms
555 µV
7.7 Hz
simple
15.5 ms
460 µV
6.7 Hz
simple
16.0 ms
419 µV
8.6 Hz
(F)
4
simple
15.1 ms
548 µV
10.5 Hz
5
simple
15.0 ms
471 µV
10.4 Hz
simple
16.2 ms
167 µV
9.9 Hz
1
2
50 µV
100 µV
2
35630/111/6
(E)
1
3
10 ms
1
1
2
2
3
3
4
4
5
5
simple
15.1 ms
182 µV
10.4 Hz
3
simple
10.0 ms
117 µV
9.5 Hz
4
simple
9.4 ms
97 µV
11.6 Hz
Figure 3.9: Six examples of similar looking MUAPs. See text in Section 3.2.7.
5
simple
7.4 ms
93 µV
10.9 Hz
36809/5/23
10 ms
1
100 µV
200 µV
3
36032/111/8
(D)
2
35955/129/12
(C)
1
simple
6.7 ms
72 µV
10.1 Hz
61
(B)
200 µV
2
10 ms
simple
16.7 ms
1223 µV
7.0 Hz
3
simple
11.0 ms
854 µV
6.8 Hz
4
5
simple
4.6 ms
659 µV
4.9 Hz
simple
6.5 ms
425 µV
10.0 Hz
6
simple
11.7 ms
345 µV
8.6 Hz
7
simple
6.3 ms
111 µV
7.7 Hz
8
simple
9.3 ms
107 µV
6.6 Hz
35910/110/10
(A)
1
simple
8.0 ms
55 µV
6.4 Hz
1
2
3
4
5
6
7
8
50 µV
2
10 ms
simple
18.0 ms
342 µV
0.9 Hz
(D)
38063/110/9
(C)
1
simple
18.1 ms
252 µV
7.3 Hz
1
2
50 µV
2
10 ms
simple
13.7 ms
213 µV
11.5 Hz
(F)
36223/3/10
(E)
1
simple
14.3 ms
127 µV
11.8 Hz
1
2
Figure 3.10: Three examples of jiggle as a result of failure of transmission in the endplate (impulse blocking). See text
in Section 3.2.8.
62
(B)
(A)
100 µV
1
simple
38.0 ms
297 µV
10.7 Hz
2
37112/2/12
10 ms
3
simple
37.1 ms
287 µV
13.2 Hz
simple
7.8 ms
26 µV
10.5 Hz
1
2
3
200 µV
2
10 ms
poly
17.5 ms
1166 µV
8.0 Hz
(D)
3
simple
17.6 ms
1157 µV
9.3 Hz
4
poly
17.6 ms
1148 µV
8.8 Hz
36343/5/2
(C)
1
simple
4.1 ms
35 µV
12.0 Hz
1
2
3
4
(F)
50 µV
2
10 ms
poly
19.3 ms
333 µV
1.8 Hz
35609/128/13
(E)
1
simple
13.9 ms
307 µV
7.6 Hz
1
2
Figure 3.11: Three examples of jiggle as a result impulse blocking and jitter. See text in Section 3.2.8.
63
100 µV
2
10 ms
simple
14.9 ms
499 µV
15.6 Hz
(B)
3
4
simple
5.3 ms
352 µV
14.3 Hz
simple
14.0 ms
226 µV
14.3 Hz
5
simple
6.0 ms
71 µV
15.3 Hz
35645/50/12
(A)
1
simple
7.7 ms
36 µV
27.6 Hz
1
2
3
4
5
(D)
400 µV
2
simple
16.4 ms
1692 µV
16.7 Hz
10 ms
35655/5/11
(C)
1
simple
2.0 ms
252 µV
4.5 Hz
1
2
50 µV
2
10 ms
simple
11.4 ms
342 µV
7.3 Hz
(F)
35331/128/4
(E)
1
simple
11.1 ms
316 µV
6.9 Hz
1
2
Figure 3.12: Three examples of jiggle. (A)+(B) as a result of impulse blocking and jitter. (C)+(D) and (E)+(F)
primarily as a result of jitter. See text in Section 3.2.8.
64
(B)
400 µV
2
simple
18.8 ms
1481 µV
5.7 Hz
10 ms
3
simple
11.6 ms
125 µV
6.1 Hz
35787/128/21
(A)
1
4
simple
12.4 ms
129 µV
6.0 Hz
simple
11.9 ms
85 µV
6.1 Hz
1
2
3
4
100 µV
2
10 ms
simple
13.0 ms
629 µV
15.0 Hz
(D)
3
simple
12.2 ms
520 µV
6.5 Hz
4
simple
12.1 ms
495 µV
15.8 Hz
37013/231/11
(C)
1
simple
9.3 ms
150 µV
10.5 Hz
1
2
3
4
400 µV
2
10 ms
poly
11.7 ms
1251 µV
10.3 Hz
3
simple
10.3 ms
802 µV
8.5 Hz
(F)
4
simple
11.9 ms
116 µV
7.7 Hz
36192/128/9
(E)
1
simple
9.6 ms
117 µV
19.7 Hz
1
2
3
4
Figure 3.13: Three examples of jiggle, primarily as a result of jitter. See text in Section 3.2.8.
65
(B)
36192/128/4
(A)
2
400 µV
1
poly
39.2 ms
2496 µV
6.9 Hz
10 ms
poly
39.4 ms
1246 µV
7.4 Hz
1
2
(C)
10 ms
simple
11.9 ms
469 µV
10.4 Hz
5
6
simple
12.5 ms
427 µV
10.4 Hz
(D)
3
4
simple
12.1 ms
454 µV
11.6 Hz
7
simple
12.5 ms
448 µV
8.0 Hz
8
simple
12.4 ms
405 µV
10.5 Hz
simple
12.2 ms
455 µV
10.7 Hz
9
simple
12.6 ms
403 µV
10.7 Hz
simple
12.6 ms
416 µV
10.6 Hz
34388/2/5
2
200 µV
1
simple
8.6 ms
138 µV
9.1 Hz
1
2
3
4
5
6
7
8
9
600 µV
2
10 ms
simple
11.5 ms
3088 µV
5.4 Hz
3
simple
15.7 ms
1042 µV
8.7 Hz
(F)
4
simple
15.5 ms
989 µV
7.9 Hz
37642/110/11
(E)
1
simple
15.3 ms
998 µV
7.8 Hz
1
2
3
4
Figure 3.14: Three examples of special jiggle; (A)+(B) Very big variation in shape as a result of both jitter and
blocking. (C)+(D) Three MUAPs was found; One with jiggle and two without. It seems like a AP is gradually moving
to the right, see (D). (E)+(F) Two MUAPs was correctly found; One with a low risetime but without jiggle (indicating
that it is not needle movement) and one with jiggle as a result of blocking. See text in Section 3.2.8.
Chapter 4
EMG Firing Patterns
This chapter deals with firing patterns (FPs) produced by our decomposition system EMGPAD. An introduction to
FPs is presented and the difficulties in analyzing them are discussed. Some properties and observations of the FPs
are described. Parameters of the FPs are presented and compared. The mean and standard deviation of the Inter
Potential Interval (IPI) is compared between three groups: controls and patients with myopathy and ALS.
4.1
Introduction to firing patterns
If no errors are made during the EMG signal decomposition with EMGPAD, all MUAPs will be detected, their
occurences in time will be known and the firing pattern (FP) can be constructed. The FP illustrates where on a time
axe the MUAPs from a given MU are located, i.e. a vertical line is placed for each firing of the motor neuron. This
is illustrated in Fig. 1.3. The inter potential interval (IPI) is the time between two consecutive discharges from the
same MU. The firing frequency is the reciprocal of the mean IPI.
The firing pattern can be viewed as a window into the central nervous system (CNS) because there is a one to one
relation between the train of motor nerve impulses generated in the α-motor neuron controlled by the CNS and the
MUAP train. Decomposition of the EMG signal and construction of the FPs, reveals more information from the raw
EMG signal than conventional MUAP analysis. These new data can be extracted and used for research and aid in
establishing diagnoses. The drawback in FP analysis is the complexity and computational power needed for a complete
decomposition, which has so far, made daily clinical use impossible. Much research have been made in FP analysis
but little has been useful clinically because, the special recording techniques and procedures used, are not suitable in
daily routine work.
In some FP studies, selective needle electrodes have been used to avoid recording of background activity and thus
ease the manual determination of the FP[186, 184, 183] or to record at higher level of contraction[172, 105] and ease
the demand on the resolution stage in the decomposition, compared to signals recorded with conventional CNE1 .
In some studies[105, 3, 183] the FPs were determined from EMG signals recorded at a fixed level of muscle force
relative to maximal voluntary contraction (MVC2 ). By doing so the FP parameters can be compared as the mean
firing frequency (MFF) is positively correlated to force. This correlation has not been investigated in neuromuscular
disorders where a different relation between MFF and force may exist. Another problem with fixed level of contraction
is that only few muscle are suitable for measuring force output.
In the present work the FPs are produced from the same EMG signal used for MUAP analysis, so only one 11.2 sec
recording is needed to do both types of analysis. By doing so and thus avoiding special needle electrodes and force
measurements, FP analysis could be easier introduced in daily routine work.
4.1.1
Difficulties in FP analysis
Irregularities in the FPs can make the analysis of difficult. In the following section these irregularities will be further
specified. Here they will be described as deviations from fairly constant IPIs throughout the 11.2 seconds of recording
(see Fig. 4.1).
The possible causes for FPs irregularities have to be considered when relevant FP parameters are estimated and
interpreted. They could be grouped into three main types on basis of their different origin: (A) errors introduced
during recording of the EMG signal, (B) errors introduced by the decomposition system and (C) special FP phenomena
1 A more selective needle electrode will pick up fewer single fiber action potentials and the MUAP will thus be narrower and higher
which means fewer superpositions of MUAPs and easier detection of them.
2 MVC is measured first.
66
CHAPTER 4. EMG FIRING PATTERNS
67
reflecting changes of the MU. (A) and (B) was discussed throughout Chapter 2 and (C) will bee discussed in the
following section. Here are the three factors described schematically:
Errors introduced during recording : All the recordings are made at low level of contraction, under usual clinical
conditions for conventional MUAP analysis. Both audio and visual feedback is used as described in Chapter 2.
In Appendix B all the FPs used in this work for FP analysis are shown. Many of them have periods without
firings due to brief relaxation of the muscle, recruitment of new MUs, or de-recruitment of active MUs as a
result of increased or decreased force during the recording. This is also seen as increasing or decreasing IPIs of
active MU. In a few cases the recording accidently was started before the patient reached a constant level of
contraction.
Errors introduced by the algorithm :
False firings: can occur if a MUAP is assigned to a wrong PCL or as result of a wrong resolution of a compound
segment.
Missed firings (MUAPs): can occur if a MUAP i left undetected either in the segmentation or the resolution
stage.
Description of these errors were given throughout Chapter 2 and examples were presented in Chapter 3.
Physiological effects :
Double discharges: are defined as two consecutive firings of a MU within less than 20 ms. (Fig. 3.1(F), 3.6
and 3.7). They are described in Section 3.2.5.
Prolonged intervals: are defined as two consecutive firings from a MU about twice or more the mean IPI3
(Fig. 3.3(C)). Fig. 3.6 (E+F)) and Fig. 3.7(A+B) shows how a double discharge is followed by a prolonged
interval.
Slow IPI changes: In most FPs different degrees of slow IPI changes was observed. These slow IPI changes
can often be seen directly from the FP as seen in Fig. 3.5(E) and Fig. 4.2. This is further discussed in
Section 4.2.2.
Fig. 4.1 shows examples of the factors causing irregularities in the FP. Next to each FP is the corresponding
histogram for the IPIs and a normal probability plot (NPP). The histogram is a discrete graphical approximation
to the probability density function (PDF). A straight line in the NPP expresses good approximation to a normal
distribution for the IPIs. All the FPs are taken from real EMG signals decomposed with EMGPAD. Row A shows
a FP of a regular firing MU. The histogram and the NPP confirms a good approximation to a normal distribution.
Row B is an example of false firings. The histogram and the NPP are skewed to the left. A false firing will divide a
IPI into two false IPIs which, as will be discussed in Section 4.2.4, will introduce a bias toward lower mean IPI and
bigger standard deviation. Row C is an example of missed firings. The IPIs are seen to group around multiples of
the true mean IPI. A mixture of false firings and missed firings is more common than just one of them, in complex
signals with many active MUs. Generally false firings occur more often than missed firings, because a MUAP is rarely
undetected in EMGPAD and split FPs due to shape variability or needle movement will either be automatically or
manually merged. False firings increase with increased number of MUAPs and in case of shape variability and similar
looking MUAPs. Row D shows a FP with both false and missed firings. Row E is an example of prolonged intervals.
This FP is the same as in Fig 3.7(A+B) where there is three double discharges followed by prolonged intervals. The
double discharges are not detected because the IPI is so small and almost fused4 . The last row (F) is an example of
slow IPI changes. The IPIs are repeatedly and slowly changing from small to bigger intervals. In a real EMG recording
any mixture of the above shown examples can occur making analysis very difficult. Another problem is that false
firing and double discharge can not always be distinguished by looking at the FPs and the same goes for prolonged
intervals and missed firings. This is unfortunate because important physiological phenomenon can not be identified
with certainty from artifacts.
4.2
Some properties and observations of the firing patterns
Some general properties and observations of the FPs produced by EMGPAD, are presented here. Many researchers
have found the IPIs to be nearly normally distributed[215, 216, 105, 192] when recorded under similar conditions.
To verify this the IPI distribution is investigated in this material. Factors other than errors from the decomposition,
3 Both double discharges and prolonged intervals could perhaps be more generally defined statistically as X standard deviations from
the mean IPI. What X should be has to be determined from a much larger material than is available in this work.
4 and are therefor in the same segment and EMGPAD is not expecting the same MUAP to appear twice in a segment.
68
200
100
ms
200
40
20
0
0
15
C
10
5
0
0
40
D
100
ms
200
100
ms
200
100
ms
200
100
ms
200
20
0
0
60
E
40
20
0
0
F
20
10
0
0.997
0.99
0.98
0.95
0.90
0.75
0.50
0.25
0.10
0.05
0.02
0.01
0.003
0.99
0.98
0.95
0.90
0.75
0.50
0.25
0.10
0.05
0.02
0.01
0.997
0.99
0.98
0.95
0.90
0.75
0.50
0.25
0.10
0.05
0.02
0.01
0.003
0.997
0.99
0.98
0.95
0.90
0.75
0.50
0.25
0.10
0.05
0.02
0.01
0.003
0
100
150
ms
20 40 60 80 100120
ms
200
0.997
0.99
0.98
0.95
0.90
0.75
0.50
0.25
0.10
0.05
0.02
0.01
0.003
60
400
ms
50
Probability
30
Probability
B
100
ms
Probability
0
60
Probability
0
Probability
20
0.997
0.99
0.98
0.95
0.90
0.75
0.50
0.25
0.10
0.05
0.02
0.01
0.003
Probability
40
A
100 150 200
ms
100120140160180
ms
80 100 120 140
ms
Figure 4.1: Examples of: (A) a complete and regular FP, (B) false firings, (C) missed firings, (D) combination of
(B)+(C), (E) prolonged intervals and (F) slow varying IPIs.
that can influence the IPI distribution, are described: double discharges, prolonged intervals, slow IPI changes and
stationarity. A general model for the IPIs is also presented.
As discussed in the previous section, errors are often introduced either by the algorithm or during the recording. A
FP with these errors is not suitable for detailed investigation of the underlying process producing the IPIs. The true
FP is in principle never available. Only a multichannel recording can determine this unambiguously. Nevertheless
as described in Chapter 3 the different errors are generally easily detected by visual inspection of the FPs and the
EMG signal through the many display option avaliable in EMGPAD. FPs without any obvious errors were selected
for further investigation. A total of 176 ”error-free” FPs were selected and are shown in Appendix C, and will be used
for various investigations throughout this Chapter.
4.2.1
Double discharges and prolonged intervals
A double discharge is seen as two firings from the same MU with a IPI much smaller than the mean IPI for that MU5 .
Discussion and examples of double discharge are presented in Section 3.2.5 and examples can be seen in Fig. 3.6 and
Fig. 3.7. Prolonged intervals are present when the IPI is double or more compared to the mean IPI. This can be seen
after a double discharge (Fig. 3.7(A)) as described by Partanen and Lang[213] and Dietz and Freund[74] who observed
IPIs of about double the mean IPI. They interpreted this as transient blocking of axonal conduction along the alphamotor neuron. These ”double intervals” is not the same as transient decrease in central drive of MUs and brief total
relaxation of the muscle, which also results in prolonged IPIs. As discussed in the introduction to this chapter, false
and missed firings are artefacts of the decomposition system and can sometimes be difficult, to distinguish from ”true”
phenomena of double discharges and prolonged intervals. It is important to have a strategy to detect abnormal IPIs
or determine their influence on the FP parameters. Both double discharges and prolonged intervals were rarely seen
in the present material. Double discharges were only identified in three recordings, probably because the recordings
were performed during constant level of contraction6 . They are generally not detected in EMGPAD because the IPI
is usually very small and the MUAPs tend to fuse. As seen in Fig. 3.1(F), the IPI in double discharges is not constant
5 defined
6 Double
as less than 22 ms by Partanen and Lang[213]
discharges are known to occur during varying contraction when new MU are recruited[213]
69
and the second MUAP is reduced compared to the first.
4.2.2
Slow IPI changes and common drive
The CNS is not controlling the firing rate of each MU individually but rather acts on the pool of homonymous MUs
in a uniform fashion[217]. This was investigated by DeLuca and his coworkers and termed common drive and can be
seen in the present recordings. In Appendix D, 36 recordings with two or more error-free FPs are shown, each figure
illustrates FPs from 9 recordings. All recordings shows the FPs, the slow IPI changes by means of a moving average
filter of length 19 (see (4.6) F M EAN ), and the crosscorelation coefficient. In 78% the crosscorelation coefficient was
higher than 0.5 at about zero lag. The common behavior is seen in almost all the 36 records from the moving average
plot.
(A)
(D)
Mean IPI, ms
150
100
50
160
34482/110/15
(B)
200
1
0
2
4
6
Time, seconds
8
(C)
0.5
0
−0.5
−0.5
−0.4
−0.3
−0.2
−0.1
0
Timeshift, seconds
0.1
0.2
0.3
0.4
0.5
36200/110/4
(E)
140
120
100
80
10
Mean IPI, ms
250
1
0
2
4
6
Time, seconds
8
10
(F)
0.5
0
−0.5
−0.5
−0.4
−0.3
−0.2
−0.1
0
Timeshift, seconds
0.1
0.2
0.3
0.4
0.5
Figure 4.2: Examples of slow IPI changes and common drive. Left side: The IPIs are decreasing throughout the
recording probably because of increasing contraction force so an additional MU is recruited just before half-way in the
recording. Right side: All five FPs show slow IPI changes. (A)+(D) show the FPs. (B)+(E) show the mean IPI by
using a moving average filter (see (4.6)). (C)+(F) show the crosscorrelation coefficient for time shift between -0.5-0.5
seconds.
While the common drive contains important information about the MU control it also has an impact on FP analysis
because it adds slow IPI changes to the IPIs. Slow IPI changes can also result from varying muscle force, as seen in
some FPs. The left side in Fig. 4.2 shows an example, where the IPIs are decreasing during the recording, probably
due to increasing force as an additional MU is recruited just before half way in the recording. The right side of the
figure is an example of common drive where all five MU, at about midway in the recording, show a decrease in IPIs.
Four examples of the so-called IPI plots, which are plots of IPI against IPI number is shown in Fig. 4.3. Together
with the examples in Appendix D, these four IPI plots illustrates the many different types of slow IPI changes found.
A gradual increase has been reported[105, 65] in long EMG recordings (>10 seconds). In our 11.2 second recordings
no general increase in IPI was observed. Of the 176 error-free FPs shown in Appendix C about 40% did not have a
significant slope (β) in the IPI plot (H0 : β = 0 vs. HA : β 6= 0, test for significant slope). The remaining FPs was
about equally divided between those having a significant negative and positive slop. Student’s t statistic was used for
the tests at a 0.05 significance level.
The slow IPI changes have rarely been mentioned in FP literature although they can have a big impact on FP
parameters like mean, standard deviation and serial correlation. Andreassen and Rosenfalck[105], used so-called
floating parameters like ”floating standard deviation” to avoid the influence of the trends on their parameters. These
and other parameters are presented in Section 4.3.
4.2.3
Stationarity
Stationarity means that the parameters describing the process are not time dependant, i.e. for a normal distributed
stochastic point process, the mean and standard deviation should not change with time. The findings in the previous
sections describing the slow IPI changes violate the stationarity assumption. Both the mean and standard deviation
are observed changed throughout the 11.2 seconds the recording last. There is no standard test for stationarity, but
one could for example divide the FP into smaller number of sub FPs and test if the statistics are equal. DeLuca
et al.[65] and Andreassen and Rosenfalck[105] reported increasing IPIs in longer recordings then ours. DeLuca and
coworkers divided the FPs into 10 second segments for which the FPs were considered stationary and Andreassen
and Rosenfalck[105] used ”floating” parameters to minimize effects of non stationarity. Our experience shows that
most FPs have some kind of slow IPI changes and are therefore not stationary. This is supported by the tests in
Section 4.2.2 which showed that more than half the FPs had a regression slope significantly different from zero, but
70
37492/111/9
170
IPI, ms
160
150
140
130
120
10
20
30
40
IPI number
37492/111/9
50
60
70
260
IPI, ms
240
220
200
180
160
5
10
15
20
25
30
IPI number
36200/110/2
35
40
45
50
55
240
220
IPI, ms
200
180
160
140
120
10
20
30
40
50
60
70
IPI number
38682/110/7
140
IPI, ms
130
120
110
100
90
10
20
30
40
50
IPI number
60
70
80
90
100
Figure 4.3: Four examples of IPI plots. From top to bottom; The first FP and IPI plot shows no particular slow IPI
changes. The second shows decreasing IPIs. The third shows increasing IPIs. The fourth shows periodically slow IPI
changes.
with approximately equally many positive and negative. DeLuca and Andreassen [65, 105] both recorded longer EMG
signals, at higher level of contraction, with force feedback by means of strain-gauges and with more selective needle
electrodes than ours. These differences are likely to account for the different findings. Englehart and Parker[218]
investigated the IPI PDF in the case of non-stationarity and found it to be broadened and positively skewed,, which
is in good agreement with our findings.
4.2.4
IPI distribution and a simple IPI model
As described in the previous sections, many investigators have reported the IPIs to be normally distributed, while others
have observed skewed distribution due to increasing IPIs in long recordings. To investigate this in our material, 176
error free FPs were selected from controls (43), myopathy (76) and ALS (57) patients (they are shown in Appendix C).
Normality was tested by means of three different tests: χ2 , Kolmogorov-Smirnov and D’Agostino-Pearson. The
proportion of accepted FPs at a level of α=0.05 are shown in Tab. 4.1. The result depends on the type of test;
The χ2 and Kolmogorov-Smirnov tests accepts more FPs than the D’Agostino-Pearson test, but they are also less
powerful than the D’Agostino-Pearson test[219]. The χ2 test was also used by Clamann[215], where 90% was accepted
at α=0.005 in controls. This is in good agreement with our findings of 88% for the controls with the χ2 test at the
same level. The setup used by Clamann was very different from ours: a concentric needle electrode versus a fine wire
electrode, digital sampling and stored on a file versus film strip photographs, automatic croscorrelation technic versus
manual measure of IPI, but perhaps the most important difference is the feedback method used. We use simple audio
and video feedback versus strain gauge readings used by Clamann[215]. Another important factor is that the FPs we
71
have manually selected, was done by visual inspection, so if no obvious error was seen, they would be accepted. In a
multi unit recording an error in one FP will usually reveal it self as missing or extra firings in other FPs, making the
FPs a powerful tool for detecting errors. Still, there is no guarantee that the FPs are 100% error free which can only,
in case of multi unit recordings, be established by using multi channel recordings. Clamann[215] reported no slow IPI
changes as we have observed in most FPs.
Type of test
Diagnosis
Muscle
2
N
(% accepted,
3
Controls
1
BB
1
Myopathy
BB
1
MV
1
ALS
BB
1
MV
All
1
BB+MV
a =0.05)
4
5
Chi
KS
DP
43
77
56
30
25
68
52
16
51
55
41
27
14
93
57
50
43
65
60
53
176
68
52
35
_____________________________
1
2
3
4
5
BB is Brachial biceps and MV is Medialis vastus.
Number of firing patterns tested for normality.
Chi-square test for normality.
Kolmogorov-Smirnov test for normality.
DAgostino-Pearson test for normality.
Table 4.1: Three test for normality.
Depending on which test is used roughly 50% of the FPs were accepted. The three tests are fundamentally different.
The χ2 and Kolmogorov-Smirnov are generally known tests, while the D’Agostino-Pearson is perhaps less known but
in a review study of many tests for normality it was found to be the overall best test[220]. It uses symmetry and
kurtosis measure to form a statistic that is used for the test and is therefore sensitive to the shape of the PDF and
thus to outliers.
By trying to remove possible slow IPI changes7 the total percentage accepted FPs increased from 68%, 52% and
35% (see Table 4.1) to 76%, 66% and 48%, respectively by using the χ2 test. Considering that a few of our ”error-free”
FPs may have errors from decomposition, a 76% acceptance at α=0.05 and 93% accepted at α=0.005 when the trends
filtered out, a normal distribution seems to be a reasonable approximation to the IPI distribution:
I ∈ N (µ, σ) = √
2
1
1
e− 2σ2 (I−µ)
2πσ
(4.1)
Considering the high proportion FPs with slow IPI changes observed in our recordings a better model would be:
Ii = IiN + Ti
(4.2)
where IiN ∈ N (µ, σ) and Ti is a unspecified trend function of the time i, representing the slow IPI changes.
Fig. 4.4 show scatter plots of mean vs. standard deviation of IPIs derived from recordings made in brachial biceps(BB)
and medial vastus(MV) in healthy control and patients with myopathy and ALS. There is a clear positive correlation
for the controls with a regression line σ = 0.186µ − 6.28. This is in good agreement with the findings by Clamann
of σ = 0.175µ − 3.54. This means that the mean and standard deviation are functionally related in these recordings.
The standard deviation can thus be replaced with 0.186µ − 6.28 and the IPI model is then only determined by the
mean. Unfortunately such a simple relation between the mean and standard deviation was not found in IPI’s from
ALS patients.
These empiric models are not intended to be accurate models describing the precise generation of IPIs, but rather
to illustrate the different observation we have made.
4.3
Firing pattern parameters
In this section the most often used FP parameters in the FP-litterateur will be presented and briefly described. M is
the number of firings in a firing pattern and N=M-1 is the number of IPIs. The firing times are denoted t1 , · · · tM ,
(ti < ti+1 ) and the associated IPIs I1 , · · · IN , where Ii = ti+1 − ti . The following FP parameters are divided into four
groups: Measure of location (M EANI , M ODEI ), measure of total variability (SDI , F SDI ), short term variability
(M CDI , M SSDI , V ARI , V ARII ) and serial correlation (RHOI , F RHOI ).
7 This
was done by subtracting the floating mean (F M EAN see (4.6) signal from the IPIs.
72
Controls (BB)
50
45
Myopathy (BB)
ALS (BB)
Myopathy (MV)
ALS (MV)
(1)
(2)
(3)
(4)
(5)
σ=0.18358µ−6.2765
r=0.70898
N=335
σ=0.1649µ−1.583
r=0.55419
N=210
σ=0.10182µ+4.7801
r=0.40635
N=186
σ=0.15461µ−2.8282
r=0.57328
N=217
σ=0.12668µ−0.7329
r=0.52903
N=185
40
SD(IPI), ms
35
30
25
20
15
10
5
0
0
50
100
150
MEAN(IPI), ms
200
250
0
50
100
150
MEAN(IPI), ms
200
250
0
50
100
150
MEAN(IPI), ms
200
250
0
50
100
150
MEAN(IPI), ms
200
250
0
50
100
150
MEAN(IPI), ms
200
250
Figure 4.4: Scatter plots of mean IPI against standard deviation. The regression line and its equation, the correlation
coefficient (r) and number of IPIs (N) is shown. From left to right: (1) IPI’s from 10 healthy controls person recorded
in brachial biceps(BB), (2) IPI’s from 7 patients with myopathy recorded in brachial biceps(BB), (3) IPI’s from 7
patients with myopathy recorded in medial vastus(MV), (4) IPI’s from 9 patients with ALS recorded in brachial
biceps(BB), (5) IPI’s from 9 patients with ALS recorded in medial vastus(MV).
Measure of location : These parameters expresses the location or the tendency of the IPIs.
Mean IPI:
N
1 X
M EANI =
Ii
N i=1
(4.3)
The mode of the IPIs: The mode is the most frequent IPI value, i.e the peak in the PDF (IPI histogram).
Mean firing rate:
1
f=
(4.4)
M EANI
Some authors prefer to express the FP parameters in frequency rather than interval lengths. The mean firing
rate is the inverse of the mean IPI.
Measures of IPI variability : These parameters expresses how unstable the α-motor neuron is firing.
Standard deviation of IPIs. It expresses the total variability; both long and short term variablity:
v
u
N
u 1 X
SDI = t
(Ii − M EANI )2
N − 1 i=1
(4.5)
The floating mean interval at interval number i[105] is:
F M EANi =
i+9
1 X
Ii
19 j=i−9
(4.6)
This is not a FP parameter, but is used in some of the following parameters and it is more commonly known as
a moving average filter which has the effect of low-pass filtering the signal.
Floating standard deviation[105]. It expresses the variability after removing the slow trends.:
v
u
N
u 1 X
t
F SDI =
(Ii − F M EANi )2
N − 1 i=1
(4.7)
Measures of short term IPI variability : These parameters expresses the variability between consecutive IPIs.
Mean consecutive deviation[221]:
N −1
1 X
|Ii − Ii+1 |
(4.8)
M CDI =
N − 1 i=1
73
Mean square successive difference[222]:
v
u N −1
u2 X
M SSDI = t
(Ii−1 − Ii )2
N i=1
(4.9)
Average proportional consecutive interval difference :
N −1
1 X |Ii − Ii+1 |
V ARI =
N − 1 i=1 (Ii + Ii+1 )/2
(4.10)
V ARII = V ARI M EANI
(4.11)
Measures of serial IPI correlation : These parameters expresses the tendency for consecutive IPIs to increase,
decrease or alternate.
Serial correlation coefficient[105]:
RHOI =
1
N −1
PN −1
i=1
(Ii − M EANI )(Ii+1 − M EANI )
(4.12)
(Ii − F M EANi )(Ii+1 − F M EANi )
(4.13)
SDI2
Floating serial correlation coefficient[105]:
F RHOI =
1
N −1
PN −1
i=1
SDI2
Table. 4.2 shows the correlation coefficients between the FP parameters. The last parameter in the table (SDT RI )
is the standard deviation of the trend found as SD(F M EAN ). The 176 ”error-free” FPs shown in Appendix C was
used to calculated the parameters.
M EANI is correlated with all the other parameters except V ARI and only little with RHOI . M EANI and
M ODE are highly correlated (r=0.99) and practically identical. SDI is highly correlated to the measures of short
term variability (F SDI , M CDI , M SSDI and V ARII ), the M EANI and SDT RI . The four measures of short term
variability, F SDI , M CDI , M SSDI and V ARII are highly correlated and have similar correlations to the other
parameters and thus contain the same information. RHOI and F RHOI have relatively low correlation to the other
parameters especially to the short term variablity parameters (M CDI , M SSDI and V ARII ). Significant negative
correlation was only found between V ARI and RHOI and V ARII and RHOI .
MEANI MODEI
MEANI
1 0,99
MODEI 0,99
1
SDI
0,72 0,66
FSDI
0,71 0,67
MCDI
0,65 0,60
MSSDI 0,63 0,58
VARII 0,65 0,60
VARI -0,09 -0,14
RHOI
0,28 0,26
FRHOI 0,57 0,57
SDTRI 0,48 0,41
SDI
0,72
0,66
1
0,92
0,90
0,92
0,90
0,45
0,22
0,39
0,74
FSDI
MCDI
MSSDI VARII
VARI
0,71 0,65 0,63 0,65 -0,09
0,67 0,60 0,58 0,60 -0,14
0,92 0,90 0,92 0,90 0,45
1 0,93 0,94 0,93 0,50
0,93
1 0,98 1,00 0,65
0,94 0,98
1 0,98 0,64
0,93 1,00 0,98
1 0,65
0,50 0,65 0,64 0,65
1
0,03 -0,13 -0,12 -0,13 -0,47
0,39 0,19 0,19 0,20 -0,31
0,57 0,50 0,52 0,48 0,16
RHOI
FRHOI SDTRI
0,28 0,57
0,26 0,57
0,22 0,39
0,03 0,39
-0,13 0,19
-0,12 0,19
-0,13 0,20
-0,47 -0,31
1 0,64
0,64
1
0,51 0,32
0,48
0,41
0,74
0,57
0,50
0,52
0,48
0,16
0,51
0,32
1
Table 4.2: The correlation coefficient between the FP parameters. 176 FPs was used. The numbers with bold fonts
are statistically significant (H0 :r=0 versus HA :r6=0, p < 0.01).
From this simple correlation analysis the number of parameters could be reduced to the following: M EANI , SDI ,
M CDI , V ARI , RHOI and SDT RI . M EANI and SDI are chosen because they are the simplest and because, as
shown in the next section, robust estimators exist for them. M CDI is favored to M SSDI and V ARII because it
74
MEANI
MCD
SDI
VAR
I
RHOI
I
28
220
200
24
24
22
0.6
0.25
10
0.5
22
20
0.4
0.2
20
0.3
16
0.15
16
120
12
12
80
10
10
60
8
C
M
A
0.1
M
A
4
0.1
0
−0.1
2
0.05
8
C
6
0.2
14
14
100
8
18
18
140
12
0.7
26
180
160
SDTRI
−0.2
C
M
A
C
M
A
C
M
A
C
M
A
Figure 4.5: The M EANI , SDI , M CDI , V ARI , RHOI and SDT RI parameters in controls, patients with myopathy
and ALS.
has the simplest expression. The rest of the parameters are chosen because of their low correlation to some of the
other parameters. A comparison of these parameters in controls and patients with myopathy and ALS, is illustrated
in Fig. 4.5. This figure only gives a rough picture of the parameters ability to differentiate between the three patient
groups because:
• The previously described ”error free” FPs are used and they are distributed as follows: 43 FPs from 10 controls
persons, 25 FPs from 7 myopathy patients and 14 FPs from 8 ALS patients (they are shown in Appendix C).
The patients are not equally represented and the number of FPs from each patient and in each patient group is
relatively small especially for the ALS group.
• Possible age, gender or maximal contraction force dependency i not considered.
Fig. 4.5 gives an indication of the parameters possible ability to differentiate between the three patient groups. The
general picture of M EANI being able to differentiate between the three groups and SDI as being higher in ALS
patient than in the two other groups, is confirmed in Fig. 4.13 and Fig. 4.14. Further investigation of the M EANI ,
SDI parameters and possible dependency on age, gender or maximal contraction force are given in Section ??.
4.4
Estimation of the mean and standard deviation for IPIs
The previous sections described how errors during recording, from the decomposition, double discharges, prolonged
intervals and slow IPI changes all complicates the analysis of the FPs. Nevertheless, when these difficulties to some
extent are eliminated, the IPIs can be reasonably approximated by a normal distribution. The obvious parameters to
describe the IPIs are therefor the mean and standard deviation (SD) and these are also the parameters most often
used in the FP literature. In this section different methods for estimating the mean and SD will be examined by
means of simulation. Estimators are used to produce estimates of parameters even in case of errors.
4.4.1
Estimators
Due to the different errors described in the previous sections, robust estimators for the mean and standard deviation
of the IPIs are desirable. In this section eight estimators for the mean and six for the standard deviation are presented.
Mean estimators: :
PN
Mean : M EANI = N1 i=1 Ii , calculates the sample average. This is the straight forward way to estimate the
mean. It is the best8 estimator of the mean if samples are taken from a normal distribution.
Median : The median is the 50th percentile of a sample. The median is a robust estimate of the center of a
sample of data, since a few outliers have little effect on it.
8 The
sample average is a Minimum Variance Unbiased Estimator
75
Trimmean : Calculates the mean of a sample excluding the highest and lowest 15% of the observations. The
trimmed mean is a robust estimate of the location of a sample. If there are outliers in the data, the trimmed
mean is a more representative estimate of the center of the body of the data. If all data are from the same
probability distribution, then the trimmed mean is less efficient than the sample average as an estimator of
the location of the data.
Mode : The mode of a sample is the most frequent value. A mode is a peak in a PDF. In a unimodal
distribution, the mode is the highest point in the PDF. For a normal distribution the mode is coincident
with the mean and median and is not affected by outliers as long as they do not form a mode of their own
that is bigger. IPIs are generally unimodally distributed9 but as seen in Fig. 4.1(F) in case of special trends
and as a result of many decompositions errors, the distribution can become multi-modal, although rarely
so. The straight forward way to get the mode is to look for the highest bin in a histogram. For higher
1
precision the following formula (Shcaum’s) is used for the mode: M ode = L1 + ( △1△+△
)c where L1 is the
2
lower bin boundary of the highest bin. △1 and △2 is the difference i height between the bin with the mode
and the two neighboring bins. c is the bin width. This is the same as fitting a parabola through center of
the highest bin and the two neighbor bins and finding the mode as the top point of the parabola.
Mode2 : In the algorithms by Stashuk et al. (EFE)[5] and McGill[212] the initial estimate of the mean is
performed by finding the peak in IPI histogram convolved with a triangular filter. This has the effect of
smoothing10 out the histogram and better defining the peak, as illustrated in Fig 4.6. Stashuk et al. used
the following triangular filter: [1,2,3,4,3,2,1] and a bin-width of 10 ms. The same bin-width is used in
Mode2 but the triangular filter can be [1,2,1], [1,2,3,2,1] or [1,2,3,4,3,2,1]. The shortest is used for FPs with
long IPIs and the longest for small IPIs. The mode is found as in Mode (see above).
Raw histogram
8
Filtered histogram
6
6
4
4
2
2
0
50
100 150
IPI, ms.
0
50
100 150
IPI, ms.
Figure 4.6: An example of the result of filtering the histogram with a triangular filter ([1,2,3,4,3,2,1]/16).
ExcludeOutliers : A false firing will divide a correct IPI into two false IPIs. IPIs smaller than 50% of the
mean IPI estimated with Mode2 (see above) are removed together with the neighbor IPI that is furthest
from the mean. In this way outliers are removed and the mean is calculated for the remaining IPIs.
EFE (Error-filtered estimation) : This algorithm was presented by Stashuk et al.[5]. It estimates the mean
and standard deviation for partial FPs after clustering. After clustering missed firings are more frequent
than false firings. This is different from a complete decomposition, where false firings are more frequent
and missed firing are rare. The mean is first estimated µ̂ (see Mode2 above) then the IPIs are sorted into
three regions R1 , R2 and R3 . IPIs less than 0.8µ̂ are placed in R1 and IPIs greater than 1.2µ̂ are put in R3
and the rest in R2 . The boundary between R1 and R2 and R2 and R3 are iteratively adjusted so small IPIs
due to false firings are contained in R1 and big IPIs due to missed firings are contained in R3 . in this way
only valid IPI are supposed to end up in R2 from which the mean and standard deviation are calculated.
For detail see Stashuk et all.[5]. This algorithm was presented as an improvement to a similar algorithm
by McGill[212].
McGill This algorithm is similar to EFE but only two regions are defined: R0 and R1 . IPIs in R0 are considered
valid and used to calculate the mean and standard deviation. For details description see[212].
Standard deviation estimators: :
q
P
2
SD : SDI = N 1−1 N
i=1 (IP Ii − M EANI ) This is the straight forward way to estimated the standard deviation. It is MVUE estimator of the standard deviation if samples are taken from a normal distribution.
9 all
10 A
reported IPI distributions: Normal, Poisson and Weibull are unimodal
triangular signal like [1,2,3,4,3,2,1] is a lowpass filter.
76
0.7413IQR : IQR is the difference between the 75th and the 25th percentiles of the sample. The IQR is a
robust estimate of the spread of the data, since changes in the upper and lower 25% of the data do not
affect it. If there are outliers in the data, then the IQR is more representative than the standard deviation
as an estimate of the spread of the body of the data. The IQR is less efficient than the standard deviation
as an estimate of the spread, when the data is all from the normal distribution.
0.885MCD : See (4.8). This measure of variability is used in single fiber recordings and is not influenced by
slow IPI changes.
ExcludeOutliers : See description for mean estimator above.
EFE : See description for mean estimator above.
McGill : See description for mean estimator above.
4.4.2
Simulation of estimator performances
The estimators for the IPI mean and standard deviation are compared by means of simulation. Synthetic FPs are
constructed by using a random number generator that generates normal distributed random numbers with mean, µ,
and standard deviation, σ. This will produce a full synthetic FP with the same duration as the real signal, i.e. 11.2
sec. To simulate the effect of errors from the decomposition a proportion of these firings are removed to simulated
missed firings and extra firings are added to simulate false firings. A good estimator should be robust to these errors.
These tests will be done along the lines described by Stashuk et al.[5]. They compared the performance of the simple
mean (4.3), the standard deviation (4.5) and the algorithms by McGill and Stashuk (EFE) by applying synthetic
FPs with known properties. The following important differences exist between Stashuk et al. and our evaluation of
performance:
• They analyzed partial FPs after clustering where the missed firings are in larger numbers than false firings. In
our FPs, after a decomposition, the opposite is the case; missed firings are few but the number of false firings
can be considerably higher in case of complex EMG signals primarily because of errors in the resolution stage.
Denoting the error types rf (false firings) and rm (missed firings) then the detection error Ed (total error)
is defined as Ed = rf + rm . Stashuk et all. used rm = 0.95 and rf = 0.05 for Ed > 10% and rm = 4rf
for Ed ≤ 10% and we used rm = 0.15 and rf = 0.85. They defined rf and rm in relation to the total
number of IPIs (N) and we defined it in relation to Ed . For example for c = 0.2, N = 100, Ed = 40%(= 40)
they would have Nf = rf ∗ N = 0.05 ∗ 100 = 5 and Nm = 40 − Nf = 35. In our case this would be
Nf = N ∗ Ed ∗ rf = 100 ∗ 0.4 ∗ 0.15 = 6 and Nf = N ∗ Ed ∗ rf = 100 ∗ 0.4 ∗ 0.85 = 34
• Different ranges of the parameters are use: they used µ = {30, 40, 60, 80, 100} and c = {0.1, 0.2, 0.3} and we used
µ = {50, 100, 150, 200, 250}, c = {0.06, 0.12, 0.2}, see Fig. 4.7 and Fig. 4.8.
• The duration of the synthetic FPs was 5 seconds for Stashuk et al. and 11.2 second for us.
• The number of synthetic FPs with the same parameters (µ, σ, Ed ) was 20 for Stashuk et al. and 100 for us.
The error-free FPs shown in Appendix C are used to estimate the range of the mean and standard deviation of the
IPIs from a MU recorded under our conditions. In Fig. 4.7, histograms are shown for the mean(IPI) and coefficient
of variation c (SD/mean). The mean IPI is seen to lie in the interval 60-250 ms and the coefficient of variation lies in
the interval 4-24%11 .
Fig. 4.8 shows a scatterplot of the same error-free FPs as in Fig. 4.7 and Appendix C. It is seen that there is a high
correlation between MEAN and SD (r=0.7237, p < 0.0001). For the evaluation of performance, µ and σ along line
A, C and D was used. c=0.12 (line C) is close to the regression line12 and thus represents most ”common” relation
between MEAN and SD. c=0.06 (line D) represents very stabile IPIs and c=0.2 (line A) represent high variability and
will therefore be a greater challenge for the estimators.
Next follows a description of how the simulation was performed for the MEAN estimators (the procedure was
the same for SD): The following parameters was used: c = {0.06, 0.12, 0.2}, µ = {50, 100, 150, 200, 250} (msec) and
Ed = {0, 10, 20, 30, 40, 50} (%).
1. Use the next value of c (=0.06 when starting).
2. Use the next value of µ (=50 first).
3. Use the next value of Ed (0 first).
11 Freund
12 This
et al. reported 10-25% for the coefficient of variation
is the regression line when doing regression through the origin.
77
30
30
(A)
25
Number of firing patterns
Number of firing patterns
25
20
15
10
20
15
10
5
0
0
(B)
5
50
100 150 200
MEAN(IPI), ms
250
0
0
5
10
15
20
C (=SD/MEAN), %
25
Figure 4.7: Histograms of the MEAN IPI and the coefficient of variation (C=SD/MEAN) for the error-free FPs in
Appendix C.
SD(IPI), ms
60
(A): σ=0.2µ
40 (B): σ=0.13381µ
(C): σ=0.12µ
20 (D): σ=0.06µ
0
0
50
(A)
(B)
(C)
(D)
100
150
MEAN(IPI), ms
200
250
Figure 4.8: The standard deviation as a function of the mean for the error-free FPs in Appendix C. The dotted lines
(A), (C) and (D) shows the relation between SD and MEAN used for the simulations. Line (B) is the regression line
through the origin. (A) represents the ”difficult” FPs with high IPI variability, (C) represents the ”typical” FPs and
(D) represents the ”easy” FPs with very regular firings.
4. Generate a series of simulated IPIs by using a random number generator that generates normally distributed
number with µ and σ = cµ (= 0.06*50=3 the first time) that when summated do not exceed 11.2 seconds. This
produces one synthetic FP with N IPIs.
5. Remove Nmissed = N Ed rm firings by using a uniformly distributed random number generator that generates
Nmissed numbers in the interval 1-N . Add Nf alse = N Ed rf firings by using a uniformly distributed random
number generator that generates Nf alse numbers in the interval 0-11200 ms. This produces a synthetic FP with
known parameters µ, c, Ed , (σ = cµ and (Nmissed , Nf alse ) = (N Ed rm , N Ed rf )).
6. Generate k=100 synthetic FPs with the same parameters.
7. For each of the 100 FPs calculate the MEAN (µ̂).
8. Goto step 3 unless all values in Ed are used.
9. Goto step 2 unless all values in µ are used.
10. Goto step 1 unless all values in c are used.
In this way 100 FPs are generated for all combinations of µ, c, Ed and used with the estimator. By using equations
(4.14) - (4.17) the performance of the estimators was evaluated and the results are shown in Fig. 4.9 and 4.10. µ and
σ are measures of the estimators accuracy13 and εµ and εσ are measures of their precision14 . While Fig. 4.9 and 4.10
shows the normalized values so the performance can be compared at different levels of detection error, Fig. E.1 - E.8
13 accuracy
14 precision
is the nearness of a measurement to the actual value of the variable being measured
refers to the closeness to each other of repeated measurements of the same quantity
78
and Fig. F.1 - F.6 shows the absolute values so the performance can be seen for any combination of parameters: µ, c
and Ed (σ, c and Ed ).


M
K
X
X
1 1
1
µ̂ij 
µ=
M i=1 µi K j=1


M
K
1 X 1 1 X 
σ=
σ̂ij
M i=1 σi K j=1
(4.14)
v
u
M
K
X
1 X 1u
t1
εµ =
(µ̂ij − µi )2
M i=1 µi K j=1
(4.15)
(4.16)
v
u
M
K
X
X
1u
1
t1
(σ̂ij − σi )2
εσ =
M i=1 σi K j=1
(4.17)
It is seen from Fig. 4.9 that for the mean estimators, the algorithm by McGill has a slightly better performance
than the ExcludeOutliers algorithm. For the standard deviation estimators Fig. 4.10 shows that McGill’s algorithm
is clearly the best. McGill’s algorithms will be used in the following.
Normalised mean estimate
c=0.06
c=0.12
1.02
1.02
1
1
1
0.98
0.98
0.98
0.96
0.96
0.96
0.94
0.94
0.94
0.92
0.92
0.92
0.9
0
20
40
Detection error, %
0.9
0
c=0.06
Standard dev. of norm. mean
c=0.2
1.02
20
40
Detection error, %
0.9
0
c=0.12
c=0.2
0.2
0.2
0.2
0.15
0.15
0.15
0.1
0.1
0.1
0.05
0.05
0.05
0
0
20
40
Detection error, %
0
0
20
40
Detection error, %
20
40
Detection error, %
0
0
Mean
Median
Trimmean
Mode
Mode2
RemOutliers
EFE
McGill
20
40
Detection error, %
Figure 4.9: Performance evaluation of the eight MEAN estimators. The upper row shows the normalized mean
estimate using (4.14) and expresses the accuracy of the estimators. A good estimator should be close to one for all
values of the detection error. The lower row shows the standard deviation of the normalized mean using (4.15) and
expresses the precision of the estimators. A good estimator should be close to zero for all values the detection error.
79
Normalised std estimate
c=0.06
c=0.12
4
4
3
3
3
2
2
2
1
1
1
0
0
20
40
Detection error, %
0
0
c=0.06
Standard dev. of norm. std
c=0.2
4
20
40
Detection error, %
0
0
c=0.12
c=0.2
1
1
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
0
20
40
Detection error, %
0
0
20
40
Detection error, %
20
40
Detection error, %
0
0
SD
0.7413IQR
0.885MCD
RemOutliers
EFE
McGill
20
40
Detection error, %
Figure 4.10: Performance evaluation of the six SD estimators. The upper row shows the estimated normalized standard
deviation using (4.16) and expresses the accuracy of the estimators. A good estimator should be close to one for all
values of the detection error. The lower row shows the standard deviation of the normalized standard deviation using
(4.17) and expresses the precision of the estimators. A good estimator should be close to zero for all values the
detection error.
80
4.5
Patient material and recording conditions
The material consisted of a normal control group, a group of patients with myopathy and a group of patients with
ALS. The control group consisted of 10 normal subjects aged 21-37 years, 4 females and 6 males. 6 out of 10 were
in very good physical shape, and the remaining except one were in general good shape. None in the control group
had signs or history of neuromuscular disorders. The group with myopathy consisted of 7 patients; 2 females and 5
males aged 19-63 years. All 7 had clinical and electrophysiological signs of myopathy15. The ALS group consisted of 8
patients; 4 females and 4 males aged 35-67 years. Besides clinical and electrophysiological signs compatible with ALS,
5 of the them died within a few years after onset of the disorder, supporting the diagnosis of ALS. Data and findings
for the individuals in this study are shown in Appendix A, Fig. A.1. The brachial biceps and medial vastus muscles
where used in this study because they were the most frequently investigated in the two patient groups.
In Table. 4.3 is shown the number of FPs used for the analysis in the following Sections. The numbers in parenthesis
are the error-free FPs used in previous Sections.
Biceps
brachii
Medial
vastus
Controls
Myopathy
ALS
335 (43)
210 (25)
186 (14)
217 (51)
185 (43)
Table 4.3: Number of FPs. for each muscle and patient group. The numbers in parenthesis are the error-free FPs.
All FPs were extracted from EMG signals recorded under usual conditions for MUAP analysis:
• The recordings were made at low (just above threshold) voluntary and constant level of contraction.
• Visual and audio feedback was used to monitor the signal quality.
• A standard concentric needle electrode was used.
• The EMG signals were recorded from five places in the muscle at three levels of insertion (deep, medium, low).
• The high and low pass filters of the EMG amplifier were set at 2 Hz and 10 kHz.
4.6
Parameter correlation in Controls
The correlation between different parameters in the control will be investigated. The group consists of ten relatively
young individuals (see Section 4.5) and the brachial biceps muscle was used. Table 4.4 shows different parameters for
the ten normal subjects (controls).
To investigate the possible difference in MUAP duration, MUAP amplitude, mean IPI, standard deviation of IPI and
maximal contraction force, between males and females, one-way ANOVA tests was performed. A significant difference
(p < 0.05) between men and women was only found for maximal contraction force.
No significant correlation (p > 0.05) existed between age and any of the other parameters in Table 4.4. The age
range was 21-37 years, six of whom were between 26-30 years i.e. a rather narrow age range.
Of the parameters in Table 4.4 only mean MUAP duration and mean M EANI were significantly correlated (r=0.68,
p < 0.01). It is seen from Fig. 4.11 that C1 is likely to be an outlier and the main contributer to the positive correlation.
When excluding C1 the correlation coefficient drops to r=0.18, p > 0.67. This suggest that the mean MUAP duration
and mean M EANI are uncorrelated.
It is important to asses whether there is a correlation between MUAP duration and M EANI for the MUs in a
muscle under the current recording condition (low level of contraction). A positive correlation would suggest that long
duration MUAPs generally had longer IPIs i.e. a lower firing rates than small MUAPs. In Fig. 4.12 the scatter plot
of MUAP duration versus M EANI is shown for all ten controls individually and non except C7 showed a significant
correlation (p < 0.01). The scatter plot for all ten controls combined were not correlated (r=0.12, p > 0.01). We
therefore conclude, from this data, that no linear relation exists between MUAP duration and M EANI . The same
was found for MUAP duration and SDI . MU firing rate and variability are therefor not related to the size of the MU,
expressed as the mean MUAP duration, under the used recording condition.
15 Only
patients with clear diagnosis were included in the two patient groups.
81
Patient
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
Age
(Yr)
29
26
37
27
23
21
27
23
29
30
Sex
M
F
M
F
M
F
M
F
M
M
Mean MUAP
Duration
(ms)
Mean MUAP
Amplitude
( V)
Max.
Contr.
Force
(Kg)
12,2
9,3
10,5
10,6
10,2
10,0
10,3
10,2
10,7
9,4
150
111
202
198
209
119
177
110
154
176
17
12
30
11
34
9
20
15
19
46
Mean
MEANI
(ms)
133,4
106,8
103,1
98,7
116,0
118,3
107,9
110,4
114,8
99,2
Mean
SDI
(ms)
16,5
12,6
13,0
12,3
12,6
14,4
11,5
15,2
17,9
14,5
No.
of
FPs
36
25
36
36
33
32
36
31
32
38
Table 4.4: Age, sex, MUAP parameters (duration and amplitude), maximal contraction force and FP parameters
(mean and standard deviation) are shown for the ten normal subjects.
135
C1
130
125
Mean MEANI (ms)
C1−C10
120
115
C2−C10
110
105
100
95
9
9.5
10
10.5
11
11.5
Mean MUAP duration (ms)
12
12.5
Figure 4.11: Scatter plot of mean MUAP duration versus mean M EANI for the controls recorded from biceps brachii
(see Table 4.4). The full line is the regression line for all ten controls, while the dotted line is without C1.
4.7
Evaluation of FP parameters in patients
In following Sections a comparison between the control, myoapthy and ALS groups is made for the mean M EANI
and mean SDI . M EANI expresses the mean MU firing rate and SDI expresses the firing variability. In Section 4.3
the two patient groups were compared with controls for M EANI and SDI based on a limited number of error-free
FPs. In the following comparisons all the FPs from each subject are used which means a much higher number of FPs
are included giving a better statistical basis for comparison (see Table. 4.3).
82
r=0.050799, p=0.78781
r=−0.1223, p=0.46598
C1
30
20
0
30
0
10
20
MUAP Duration (ms)
C5
C6
0
0
30
20
10
0
10
20
MUAP Duration (ms)
0
30
0
MEANI, (ms)
20
10
30
0
C1−C10
30
20
10
30
40
30
20
10
20
MUAP Duration (ms)
r=0.096979, p=0.058838
MEANI, (ms)
MEANI, (ms)
30
10
20
MUAP Duration (ms)
30
50
C10
40
0
0
30
C8
r=0.1237, p=0.40303
50
C9
40
0
10
20
MUAP Duration (ms)
10
20
MUAP Duration (ms)
40
10
r=−0.071083, p=0.67775
50
C7
20
10
30
0
r=−0.086863, p=0.61645
50
30
20
10
0
30
MEANI, (ms)
30
20
10
20
MUAP Duration (ms)
40
MEANI, (ms)
MEANI, (ms)
30
10
20
MUAP Duration (ms)
0
r=0.26596, p=0.093312
50
40
0
0
30
10
r=0.095513, p=0.53896
50
40
0
20
10
r=0.17988, p=0.33322
50
30
MEANI, (ms)
10
20
MUAP Duration (ms)
C4
40
20
10
0
C3
30
20
10
r=0.19536, p=0.22564
50
40
MEANI, (ms)
30
0
C2
40
MEANI, (ms)
40
r=0.21255, p=0.16891
50
MEANI, (ms)
50
MEANI, (ms)
50
10
0
10
20
MUAP Duration (ms)
30
0
0
10
20
MUAP Duration (ms)
30
Figure 4.12: Scatter plot of MUAP duration versus M EANI for all ten control persons. For each plot is the correlation
coefficient and its P-value shown
83
4.7.1
Mean IPI in controls, myopathy and ALS recorded in biceps brachii
Fig. 4.13 shows the results of the M EANI parameter used on FPs from biceps brachii. The upper part of Fig. 4.13
shows the distribution of the FPs from each individual by means of boxplots. To the left in the lower part of the figure
is shown a boxplot of the mean(M EANI ) for the three groups. The mean(M EANI ) for each individual is marked
with a star (*) in the upper part of the figure. The boxplot show clear separation of the three groups. The result from
a Kruskal-Wallis (nonparametric one-way ANOVA) test is shown to the right in the figure and confirms the difference
among at least two of the three groups (p < 0.0001). A multiple comparison analysis confirmed the difference between
all three groups at a 95% confidence level. These results shows that the M EANI parameter in this patient material
is able to differentiate between healthy controls, patients with myopathy and patients with ALS. The mean IPI is
lower for FPs from patients with myopathy and higher for patients with ALS compared to healthy controls. A further
discussion of these results and their relation to other studies will be given in Section 5.3.5.1.
(1)
Controls
250
Myopathy
ALS
MEANI, ms
200
150
100
50
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10 M1
M2
M3
M4
M5
M6
M7
A1
A2
A3
A4
A5
A6
A7
A8
(2)
180
Controls
Myopathy
ALS
160
(3)
MEANI, ms
140
120
100
80
60
C
M
A
Figure 4.13: (1) The M EANI distribution is shown for each individual by means of box plots for all three patient
groups; Controls, myopathy and ALS, recorded in biceps brachii. (2) Box plot of the mean M EANI marked with *
in (1). (3) The Kruskal-Wallis ANOVA table.
84
4.7.2
Standard deviation of IPI in controls, myopathy and ALS recorded in biceps
brachii
The SDI parameter is investigated in the same way as (M EANI ) in the previous section. The FPs were recorded
from biceps brachii. The second boxplot in Fig. 4.14 shows that the SDI is higher in ALS patients than controls
and patients with myopathy. The Kruskal-Wallis test confirms the difference between at least two of the three groups
(p < 0.001). A multiple comparison analysis at the 95% confidence level confirmed the difference between the ALS
patients and the two other groups, but not between the healthy controls and patients with myopathy. This means
that the FPs in ALS patients are generally more unstable whereas FPs in patients with myopathy are simular to
those in the control group. A further discussion of these results and their relation to other studies will be given in
Section 5.3.5.1.
(1)
Controls
Myopathy
ALS
50
30
I
SD , ms
40
20
10
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10 M1
M2
M3
M4
M5
M6
M7
A1
A2
A3
A4
A5
A6
A7
A8
(2)
26
Controls
Myopathy
ALS
24
22
(3)
SDI, ms
20
18
16
14
12
C
M
A
Figure 4.14: (1) The SDI distribution is shown for each individual by means of box plots for all three patient groups;
Controls, myopathy and ALS, recorded in biceps brachii. (2) Box plot of the mean SDI marked with * in (1). (3)
The Kruskal-Wallis ANOVA table.
85
4.7.3
Mean IPI in myopathy and ALS recorded in medial vastus
FPs from medial vastus were only recorded in patients. Fig. 4.15 shows that the M EANI parameter, like in biceps
brachii, is smaller in patients with myopathy than in patients with ALS. The Kruskal-Wallis test and the multiple
comparison analysis at 95% confirms this. A further discussion of these results and their relation to other studies will
be given in Section 5.3.5.2.
(1)
Myopathy
ALS
300
MEANI, ms
250
200
150
100
50
M1
M2
M3
M4
M5
M6
M7
A1
A2
A3
A4
A5
A6
A7
A8
(2)
Myopathy
ALS
200
180
MEANI, ms
(3)
160
140
120
100
M
A
Figure 4.15: (1) The M EANI distribution is shown for each individual by means of box plots for myopathy and ALS,
recorded in medial vastus. (2) Box plot of the mean M EANI marked with * in (1). (3) The Kruskal-Wallis ANOVA
table.
86
4.7.4
Standard deviation of IPI in myopathy and ALS recorded in medial vastus
FPs from medial vastus were only avaliable in patients. Fig. 4.16 shows that the SDI parameter, although not as clear
as in biceps brachii, is smaller in FP from patients with myopathy than in those with ALS. The Kruskal-Wallis test
and the multiple comparison analysis at 95% confirms this. A further discussion of these results and their relation to
other studies will be given in Section 5.3.5.2.
(1)
Myopathy
ALS
50
30
I
SD , ms
40
20
10
M1
M2
M3
M4
M5
M6
M7
A1
A2
A3
A4
A5
A6
A7
A8
(2)
28
Myopathy
ALS
26
24
(3)
SDI, ms
22
20
18
16
14
12
M
A
Figure 4.16: (1) The SDI distribution is shown for each individual by means of box plots for myopathy and ALS,
recorded in medial vastus. (2) Box plot of the mean SDI marked with * in (1). (3) The Kruskal-Wallis ANOVA table.
87
4.7.5
Mean IPI in myopathy recorded in biceps brachii and medial vastus
The M EANI parameter was investigated in biceps brachii and medial vastus in patients with myopathy. The two
muscles were examined in each patient and both showed reduced MUAP duration. Despite differences among the
patients (age, type of myopathy, duration of symptoms, grade of muscle force atrophy etc.) a clear difference between
the two muscles is illustrated by the boxplots and confirmed with the Kruskal-Wallis test (p < 0.002). This probably
reflects structural differences between the two muscles rather than pathology. A further discussion of these results and
their relation to other studies will be given in Section 5.3.5.3.
(1)
Brachial biceps
Medial vastus
MEANI, ms
200
150
100
50
BB1
BB2
BB3
BB4
BB5
BB6
BB7
MV1
MV2
MV3
MV4
MV5
MV6
MV7
(2)
140
Brachial biceps
Medial vastus
130
MEANI, ms
120
(3)
110
100
90
80
70
60
BB
MV
Figure 4.17: (1) The M EANI distribution is shown for each individual by means of box plots for the myopathy
patients, recorded in brachial biceps and medial vastus. (2) Box plot of the mean M EANI marked with * in (1). (3)
The Kruskal-Wallis ANOVA table.
88
4.7.6
Mean IPI in ALS recorded in biceps brachii and medial vastus
The M EANI parameter was investigated for biceps brachii and medial vastus in patients with ALS. The two muscles
were recorded in each patient and showed increased MUAP duration. Although the M EANI visually seems to be
higher in vastus this was not confirmed by the Kruskal-Wallis test (p > 0.07). A further discussion of these results
and their relation to other studies will be given in Section 5.3.5.3.
(1)
Brachial biceps
Medial vastus
300
MEANI, ms
250
200
150
100
BB1
BB2
BB3
BB4
BB5
BB6
BB7
BB8
MV1
MV2
MV3
MV4
MV5
MV6
MV7
MV8
(2)
Brachial biceps
Medial vastus
200
180
MEANI, ms
(3)
160
140
120
BB
MV
Figure 4.18: (1) The M EANI distribution is shown for each individual by means of box plots for the ALS patients,
recorded in brachial biceps and medial vastus. (2) Box plot of the mean M EANI marked with * in (1). (3) The
Kruskal-Wallis ANOVA table.
4.8
89
Chapter summary
The difficulties in FP analysis and general FP observations have been described. The IPIs are roughly normally
distributed and many FPs had some degree of slow IPI changes. Robust estimator for the mean IPI M EANI and the
standard deviation of the IPIs SDI was found. These estimators, developed by McGill, was evaluated in three groups:
Healthy controls, patients with myopathy and ALS in two muscles: biceps brachii and medial vastus. The M EANI
was different in the three groups and SDI was higher for ALS patients.
Chapter 5
Discussion
The chapter discuss the three main parts of this work, 1) decomposition, 2) Library of EMG signal, MUAP and FP
examples and 3) firing pattern analysis.
5.1
Decomposition
Detailed descriptions of EMGPAD and other decomposition systems were given in Chapter 2 and other techniques for
recording and analyzing EMG signals were presented in Chapter 1. In this Section the goal for a high performance and
clinical useful decomposition system is discussed in relation to EMGPAD and other decomposition systems. When
comparing and evaluating different decomposition systems is it valuable to view the decomposition system as a black
box. In a broad sense the input is the raw EMG signal and the recording condition. The output is the identified
MUAPs and the corresponding FPs together with their respective sets of parameters. Two decomposition systems
that have different inputs can not be directly compared i.e. if different needle electrodes have been used or if the
recordings were made at different muscle strength or with or with out force control. The black box model is useful for
an electromyographer without detailed knowledge of the technical or algorithmical aspects of the system. The user
knows how to obtain the recordings (the recording condition) and to interpret the output.
The main factors influencing on the decomposition time and errors are: Number of MUs, firing frequency and noise.
More MUs and higher firing frequencies generally results in fewer isolated MUAPs and more superimposed MUAPs.
Of the different noise types, background noise from distant MUAPs is the most critical while high frequency noise has
practically no influence on the result and low frequency baseline movement can efficiently be reduced by high pass
filtering.
The FPs obtained from a decomposition are in many respect a powerful tool because: besides the initial purpose of
studying CNS MU control, they reveal errors made in the decomposition1 . With respect to MUAP analysis they reveal
falsely detected MUAPs (false PCLs, see Fig. 3.4), needle movements (Fig. 3.8), similar looking MUAPs (Fig. 3.9) and
jiggle (Fig. 3.10 - 3.14). All of whom are seen as characteristic splitting of FPs that when merged forms a complete
FP. We have found that about twice as many MUAPs were detected using EMGPAD compared to our conventional
method. This fact can be used in two ways: a) reduce the number of recording sites and thus the chance of recording
the same MU twice, b) increase the number of MUAPs for better statistical estimates of the MUAP parameters and
c) increase the chance of detecting outliers. All of this enhances the quality of a MUAP analysis.
The following five questions are important when comparing decompositions systems, evaluating a specific system or
designing a new one:
Level of decomposition. Are only isolated MUAPs found or are all MUAPs attempted to be found even when
superposition of two or more MUAPs occur?
Decomposition time. Is the decomposition performed on- or off-line ?
Robustness. Is the system robust across the variation of real EMG signals ?
Bias. Is the bias during recording and decomposition minimized ?
Recording conditions. What is the setup and the equipment ? What type of needle electrode is used ?
User interface. Are recordings fast and easy to make ? Is the decomposition automatic ? Are the results presented
in an intuitive way ?
1 Errors due to wrong resolution of compound segments appears as holes in the FPs of the MUs whose MUAPs are undetected and as
extra firings in the FPs of the MUs whose MUAPs are wrongfully included in the compound segments by the resolution algorithm.
90
CHAPTER 5. DISCUSSION
91
These questions are discussed in the following Sections.
5.1.1
Level of decomposition
EMG systems that identifies groups of MUAPs generated by the same MU are called Motor Unit Analysis Systems
(MUAS). These systems can roughly be divided into three categories: (a) identification of isolated MUAPs, (b)
identification of isolated MUAPs plus identification of MUAPs in simple summations and (c) identification of all
MUAPs even with arbitrary summations of MUAPs. The three categories are characterized by how complete an
identification of the MUAPs from each MU is performed. With more complete identification a more complex system
is needed and the process more time consuming.
The first category is called MUAP analysis system and are used for conventional quantitative MUAP analysis.
Each identified MUAP waveform is represented by a set of parameters (duration, amplitude, phases and turns)
that describes the MUAP morphology. Changes in these parameters compared to normal values primarily helps
to differentiate between myopathic and neuropathic disorders. Conventional quantitative MUAP analysis is a well
established technique which has been used for half a century. The MUAP analysis systems has developed from only
supporting the acquisition and identification of MUAPs to highly clinically adapted automatic systems like MultiMUP[148].
The second category is called partial decomposition system. With less expensive and more powerful computers
in the eighties it became possible to investigate MU firing patterns. When the decomposition algorithm is limited
regarding the number MUAPs that can be identified in a compound segment, then it is called a partial decomposition,
because only partial FPs are produced2 . Examples of partial decomposition system are ADEMG[3] and ARTMUP[4].
ADEMG uses filters that enhances spike components with short rise time and suppresses activity from distant MUAPs.
This reduces the MUAPs to a single spike and thus reduces the number of superposition, so even though superimposed
MUAPs are not resolved, the fact that there are fewer of them enables ADEMG to produce a partial FP. ARTMUP
has a build in restriction in the resolution algorithm so only superpositions of up to three MUAPs can be resolved. If
used on EMG signals recorded at slight level of contraction, that usually results in 1-6 active MUs in the recording,
the probability of superpositions of more than 3 MUAPs is small. This of course places ARTMUP between partial and
full decomposition. In theory only decomposition systems without any limitation in the resolution phase can produce
complete FPs.
The third category is called full decomposition or just decomposition system. Systems with the potential of identifying all MUAPs belong to this category. Examples of decomposition systems are EMGPAD and the one designed
by Fang et al.[178]. The decomposition system by LeFever and DeLuca[172] can decompose EMG signals at full effort
using a special multi channel needle electrode and user interaction to achieve it but can not provide MUAP quantification. It is therefore not comparable to the other systems mentioned, but is probably the most accurate system for
producing FPs.
The decomposition systems reported the last twenty years have mainly dealt with partial decomposition. This type
of decomposition systems have the advantage of being able to provide both the MUAP parameters and estimates of
the FP parameters from partial FPs within an acceptable time for clinical usage. The main difference between partial
and full decomposition systems is the resolution of compound segments stage. In partial decomposition systems this
stage is either left out or limited to save time, whereas in EMGPAD, it is the most time consuming stage. In contrast
to the segmentation and clustering stages that are relative independent of the signal complexity with respect to time,
the decomposition stage can last from a few seconds to several minuts depending on the number of MUs, noise and
similarity in shape of the templates. For this reasons a decomposition system aimed for busy clinical usage must
restrict or leave out the resolution stage. The resolution task is to find the right combination of MUAPs and their
position within the compound segment among all possible combinations. For example a compound segment of length
L and six MUAP templates of length l1 , l2 ...l6 , an exhaustive test to find the right combination would take too long
time for any of todays general available computers3 . An exhaustive search for the right combination is therefor not
realistic. To do a search within an acceptable time the number of combinations must be reduced without compromising
the quality of the resolution. This selective search is the key to perform a precision decomposition within a reasonable
time.
Compared to partial decompositions systems, full decomposition systems tries to resolve every compound segment
completely, but this also increases the possibility of making wrongful resolutions and thus adding false firings to the
FPs. Generally, a partial decomposition system produces FPs with more missing firings but only few extra firings
2 Fig. 2.18 shows an example of a compound segment containing 5 MUAPs correctly resolved by the resolution algorithm in EMGPAD.
Some other decomposition systems[4, 176], can only resolve up til 3 MUAPs in a compound segment.
3 All combinations means all number of templates and all latencies for the templates inside the compound segment. For example for
three templates it is: (L − l1 )(L − l2 )(L − l3 ) + (L − l1 )(L − l2 ) + (L − l1 )(L − l3 ) + (L − l2 )(L − l3 ) + (L − l1 ) + (L − l2 ) + (L − l3 )
combinations. For L = 600 and l = l1 = l2 = l3 = 300 this reduces to: (L − l)3 + 3(L − l)2 + 3(L − l) = 27270900 combinations. A good
approximation is just to use the first term (L − l)3 = 27000000. For 6 templates, L = 700 and l = 300 the total number of combination is
approximately (700 − 300)6 = 4000000000000000.
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because of wrong resolutions compared to a full decomposition that will have few missing firings but also may have
wrong firings in case of a complex signals. A good decomposition system should minimize all errors and not just
try to detect all firings at the expense of increasing the number of false firings. The many difficulties in resolving
compound segments were described in Section 2.2.6.2 and the most important are the mixture of very large and very
small MUAPs in a compound segment, similar looking MUAPs and errors propergated from the segmentation and
clustering stages. The two first difficulties have been described and illustrated in Section 3.2.2 and Section 3.2.7, but
the propagation of errors will be described here because it is an important problem in any decomposition system.
Most decomposition systems are divided into three stages: (1) segmentation of the EMG signal into time interval of
MUAP activity, (2) identification of groups of similar looking MUAPs and (3) resolution of compound segments. This
modular devision and sequential execution of a decomposition system, has the advantage of data reduction and a clear
separation of the stages enables each stage to be optimized independently of the others. The sequential execution
and the dependency on the findings in the stages, means that an error made in a previous stage will influence the
performance in the following stages. For example if a segment is left undetected in the segmentation stage, because
two or more MUAPs cancels each other the firings from the involved MUs will be missing. If a MUAP was not found
in the clustering stage it will effect the resolution stage because it will be missing when trying to resolve compound
segments with it and other wrong combination may be found.
Complete FPs for all EMG signals is impossible to achieve. One impossible event occurs with a summation of two
or more MUAPs so the net sum is close to zero. This is called destructive summation with cancelation of MUAPs.
Most resolution algorithms are based on some kind of correlation technique which means that a correlation in time is
search for between the compound segment and the templates. One approach to overcome this problem is to use the
temporal information gained from an analysis of the partial FPs after clustering. This can in simple cases be used to
estimate the probability of a MUAP occuring in a given time interval and thus in a given compound segment. This
approach has its limitations especially when very unstable firings occur as in ALS patients. As seen in Fig. 3.5 in
Chapter 3 many special FPs can be found, so any decomposition depending on fairly regular FPs would probably fail.
Any decomposition system designed for recordings with a concentric needle electrode or other non-selective electrode
can be disturbed by destructive summations or interferens from distant MUAP activity. Only decomposition systems
using selective multi channel needle electrodes are able to decompose correctly any signal. Such a decomposition
system was designed by LeFever and DeLuca[172] but the lack of MUAP parameters, the need for user interaction
and the relatively time consuming acquisition means the system is primarily used for research.
Considering the high increase in the complexity and decomposition time needed for a full decomposition compared
to a partial, is it then reasonable to construct a full decomposition system? If detailed studies of MU firing behavior
or parameters that are sensitive to errors are in question, then a complete FP as possible is desired. For example
the mean IPI can be relatively precisely estimated even with high percentage of missing firings, as long as the correct
IPIs are in majority so that in a histogram representation, they represent the highest peak. In this way the mean is
estimated as the mode. This is not so with the standard deviation which is more sensitive to errors or if correlation
analysis between FPs is wanted.
Our decomposition system, EMGPAD, was designed for EMG signals recorded under the same conditions as for
MUAP analysis i.e during slight muscle contraction, concentric needle electrode recordings, and audio and visual
feedback to ensure signal quality and without force control. This usually results in recordings of MUAPs from 1-6
different MUs. It works fully automatic and produces both the MUAPs and the FPs and their respective parameters.
Chapter 2 described the many difficulties associated with EMG decomposition. EMGPAD was designed to be robust
to the real-world variation across signals and noise components so a high precision decomposition could be achieved
over a broad spectrum of signals. The key features of EMGPAD to achieve this are: 1) precise filtering to remove noise
with minimal distortion of the EMG signal. 2) fixed thresholds are not used instead they are estimated for each signal.
3) a recursive algorithm is used for resolving superimposed MUAPs without limitation in the number of MUAPs in a
compound segment.
5.1.2
Decomposition time
The decomposition time is an important factor when evaluating a decomposition system, as it for clinical use, needs
to be fast with a response time similar to or less than what it takes to record a signal (10 sec) or move the needle to
a new position.
The exact decomposition time as a function of the number of active MUs can not generally be given, because it is
different for each signal. A signal with many MUs but with a high signal to noise ratio4 can be faster to decompose
compared to a signal with fewer MUs but with more noise, especially with much activity from volume conducted
distant MUAPs. The decomposition time as a function of number MUs estimated by simulation in Section 2.2.8 (see
Fig. 2.24) was, found to increase exponentially when all other factors besides the number of MUs were fixed.
4 The
influence of the different noise components on the decomposition were discussed through Chapter 2 and illustrated in Section 3.2.1
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The most time consuming part in EMGPAD is the resolution of compound segments. While the two earlier phases,
segmentation and clustering, only varies little with the complexity of the signal, the resolution phase can last a few
seconds up to half an hour. A practical solution to the problem was given by Hass and Meyer in ARTMUP[4] by doing
an iterative decomposition. Segmentation and clustering is first performed within a few seconds, then the MUAPs
can be presented and gathered for usual MUAP analysis. While recording from the next position in the muscle,
partial FPs are constructed by skipping the difficult and time consuming superpositions. This means that at the end
of a muscle investigation both the MUAP parameters and preliminary FP parameters from the partial FPs can be
presented. After the examination, a final and complete decomposition can be performed by processing the rest of the
compound segments.
5.1.3
Robustness
The enormous variation across real world EMG signals posses a big challenge to a decomposition system. The many
EMG signal and FP examples in Chapter 3 illustrates the variation of both noise, number of MUs, difference in
MUAP amplitude etc. These difficulties was addressed in EMGPAD in two ways by (1) using real EMG signals for
development and testing and (2) not using fixed critical parameters but rather estimate them for each signal.
We have identified eleven important classes of EMG signals for testing and developing EMGPAD. These classes
represents the different types of signals that can challenge the decomposition. The eleven classes of EMG signals are:
• Baseline noise
• High frequency noise
• Background noise
• Small MUAPs
• Large MUAPs
• Mixture of small and large MUAPs
• MUAP shape Variability
• Needle movement
• Similar looking MUAPs but from different MUs
• False MUAPs
• Many MUs
Examples from each signal class were given in Chapter 3. Improvements to EMGPAD over the years were tested with
these signals. An improvement in the different algorithms in EMGPAD must result in fewer errors in the decomposition
for some of the signals and no increase in errors for any of the signals. The errors are primarily identified in the FPs.
The FPs easily reveal errors from the decomposition.
Another way of improving the robustness of the decomposition across different EMG signals is to avoid fixed critical
parameters. EMGPAD adapts to a given signal by estimating critical parameters or search for the right solution. In
the segmentation algorithm the two critical thresholds: detection and delimiting threshold are estimated as described
in Section 2.2.4. In the clustering algorithm an interval of cluster detection thresholds is investigated rather than
just using one, because the potential classes (PCLs) are created at different levels as seen in Fig. 2.14. Very stable
(similar looking) MUAPs are created at a low detection level compared to very unstable (for example due to jiggle)
MUAPs created at a higher detection level (see Section 2.2.5.4 for details). In the resolution algorithm the correct
combination of MUAPs and time shifts in a compound segment is found after a rather thorough search among possible
combinations. All combinations of MUAPs and all time shifts resulting in a cross correlation coefficient higher than 0.5
are investigated. This means that a compound segment consisting of a high number of MUAPs (≥3) can be resolved.
This is in contrast to the algorithms used in ARTMUP[4] and the algorithm by Loudon et al.[176] where only up
to three MUAPs can be resolved and the algorithm by Christodoulos and Pattichis[1] where only one time shift per
MUAP is tried (the one with the highest cross correlation coefficient).
5.1.4
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Bias
As described earlier, background activity from distant MUAPs is posing a great challenge to a decomposition system.
Compared to low frequency baseline movements and high frequency instrumental noise, the frequency content of distant
MUAPs is not as well separated from the close MUAPs. This means that distant MUAPs can not be efficiently filtered
out with classical filters because there is a significant spectral overlap. A solution to the problem of distant MUAPs
was presented by McGill[212] and implemented in ADEMG[3] and later also in other decomposition systems. Socalled ”high-pass differentiator filters”[212] are used to enhance high frequency components of the EMG signal and
reduce low frequency components. The muscle tissue acts as a low pass filter on volume conducted MUAPs so that
distant MUAPs lacks high frequency content compared to MUAPs that are close to the recording needle electrode.
The result of McGills filter is a efficient suppression of background activity and a transformation of MUAPs, close to
the recording surface of the needle electrode, into spike-like potentials with reduced duration. This enables systems
based on such filters to analyze more complex EMG signals recorded at higher level of contraction or signals recorded
at low contraction but with much background activity. The risk though is, that MUAPs with a long rise time are
excluded as described by McGill. Our experience with these filters is that too many acceptable MUAPs are filtered
out. A similar case is seen in the Multi-MUP system[148] that only accepts MUAPs with a rise time below a certain
threshold to ensure that only MUAPs close to needle electrode are included in the analysis. The Multi-MUP system
implemented in the KEYPOINT EMG machine by Medtronic, has been used in our department for six years and
has often been unable to extract big MUAPs with long rise time, from patients with neuropathy and very small
MUAPs from patients with myopathy. The electromyographer was able to extract these MUAPs by switching to a
conventional trigger system. Compared to the manual method described in Section 2.3 such exclusions of MUAPs
based on spectral content, rise time or amplitude is in principle unacceptable and not in line with the conventional
method, where acceptance of MUAPs is based on reproduceability5 . Systems that exclude certain MUAP wave forms
are called biased MUAP analysis systems. It is important not to mix normal values between biased and unbiased
systems.
One of the aims for EMGPAD was to minimize the possible bias both from the user and the decomposition system.
The user can bias the result by selecting MUAPs that supports an assumed diagnosis or if less experienced in using
a trigger system and thus fails to collect the small MUAPs. The decomposition can bias the result by excluding
MUAPs as described above. The ideal decomposition system should identify MUAPs based on reproducibility, and
postpone any decision making by the user, till after the decomposition. The user should be allowed to merge or exclude
MUAPs, for example because of multiple instances of the same MUAP due to needle movement, or wrongful merging
by the system of similar looking MUAPs. The best support for such decisions is found in the FPs as demonstrated
in Chapter 3 and which will be further discussed in the next Section. One of the results in Section 2.3 was that all
MUAPs found by the manual method was also found with EMGPAD, but many small and more difficult MUAPs for
example because of jiggle, were only found with EMGPAD.
5.1.5
Recording conditions
The recording conditions includes the recording procedure, type of needle electrode, amplifier and filter settings,
feedback method (audio, visual, force etc.), digitizing of the EMG signals (sampling frequency and resolution in bits),
duration of the recording and level of contraction force. All of these influence the recorded EMG signal. This means
that in principle the output of two decomposition systems can only be compared objectively if the recording conditions
are identical. Else the question, whether a possible deviation in the results comes from the recording conditions or
from the decomposition, may be difficult to determine. For example if different filters are used then different MUAPs
may be recorded as described in the previous Section 5.1.4. Different feedback methods will probably influence on the
MU firing statistics. Different types of needle electrodes will record fundamentally different EMG signals. Depending
on how selective the needle electrode is, different portions of the MU territory is recorded. Selective needle electrodes
like the single fiber electrode records only action potentials from a few muscle fibers, concentric and monopolar needle
electrodes records from the whole MU but the spike part is determined by the nearest few muscle fibers and Macro
needle electrodes record from the whole MU. Selective needle electrodes are less disturbed by background activity and
can thus record at higher level of contraction. With a concentric needle electrode an interference pattern is recorded
already at 5-10 % MVC, making it impossible to identify the individual MUAPs.
In some studies of FPs the EMG signals were recorded at a fixed percentage of maximal contraction (MVC). Using
a strain-gauge to measure the force. Such setups are generally adapted for a specific muscle that is fixated in a certain
position so the force output is isolated. This fixation can only be performed on few muscles which limits its usefulness.
While force controlled recordings guarantees common force levels for the recordings, it also involves more complicated
setups compared to what is used for conventional MUAP analysis. Force controlled recordings requires more time and
are more complicated to do because of the initial determination of the MVC and the continuous monitoring of the
5 at
least three potentials must be found with the same shape
95
strength. This has prevented its general use in busy routine clinical investigation.
The EMG signals in this project were recorded in parallel with the conventional manual setup (see Section 2.3.1).
EMGPAD was plugged in to the existing system and used under usual recording conditions. However the user had to
make sure the patient kept the contraction steady through the 11.2 seconds a EMGPAD recording lasted. Then the
MUAP findings from EMGPAD could be directly compared with the analysis. The FPs are derived from EMG signal
recorded under usual conditions for MUAP analysis i.e. without force feedback or special complicated setups. The
same criteria is used for recording a signal found by adjusting the trigger level while looking on a oscilloscope screen
and listening to the sound from a speaker.
Different solutions have been used to overcome some of the previously described difficulties associated with decomposition of EMG signals. The solution for extracting FPs at higher levels of contraction lies in the restriction of
influence from distant activity. Some systems use selective needle electrodes that only record from a few muscle fibers
compared to conventional electrodes and others use conventional needle electrodes but use special filters to eliminate
disturbances from distant MUAPs. In either cases the conventional MUAP analysis can not be performed together
with the FP analysis.
For MUAP analysis, the introduction of computers have eliminated the cumbersome part of collecting the MUAPs
from film or paper and manual measurement of the MUAP parameters with a ruler. Commercial avaliable MUAP
analysis systems have been designed for efficient and fast MUAP collection and analysis in busy clinical environments.
What previously toke hours now take minutes. But the fundamental recording conditions are still the same i.e. same
conventional needle electrode, amplifiers and filter settings6 are used and the recordings are made at a contraction
level just above the recruitmental threshold. The development in MUAP analysis is build on a solid foundation in a
well described and well known method developed by Buchthal and co-workers in the 50’ies taking technical as well as
the physiological facts into consideration. This is not so for FP analysis and will be further discussed in Section 5.3.
5.1.6
User interface
The user is communicating with the decomposition system through the user interface, which is one of the most
important factors for a useful system, in a busy clinical environment. EMGPAD is fully automatic. A recording
session starts by typing the EMG and muscle number. Each recording is started by pushing a button after which, the
progress of the a 11.2 second long recording can be followed on the screen. The decomposition is fully automatic and
starts after the user has specified which EMG signals to decompose. After the decomposition, the results can be seen
on a high resolution computer monitor, where the results can be edited if wished and printed on paper.
It must be emphasized that to complete a useful decomposition system, a large selection of graphical representations
must be avaliable. This includes plots where all the information from a decomposition is compiled and plots where
recordings can be explored in more details. This could for example be an illustration of how a compound segment was
resolved with the position of each of the contributing MUAP, merging of FPs, different plots to further investigate
jiggle, plots that summarize the MUAP or FP parameters etc. Throughout this work different graphical outputs from
EMGPAD have been shown.
In some decomposition systems a user interaction is necessary to assist in decision making[172, 178] but most
decomposition are fully automatic like EMGPAD.
5.2
Library of EMG signals and firing patterns
The purpose of including a ”library” of EMG, MUAP and FP examples is to:
• illustrate the capability of EMGPAD
• show good reference examples of different types of EMG signals, MUAPs and FPs
• show good reference examples of special EMG, MUAP and FP phenomena
• exemplify findings discussed throughout the text
Some of the shown examples are known to experienced electromyographers and developers of decomposition systems,
but they have rarely been mentioned in the literature despite their obvious importance. These include:
• how false PCLs7 can be difficult to detect (Fig. 3.4)
• how needle movement and similar looking MUAPs can resemble each other in some cases if only the MUAPs
are avaliable
6 The amplifiers are now integrated in the EMG machine and build with newer technics, but their basic function is the same and the
high pass filter has changes from the 2 Hz used originally by Buchtahl and co-workers to about 10 - 20 Hz to avoid baseline movements.
7 superimposed MUAPs that looks like individual MUAPs.
96
• different jiggle types (Fig. 3.10 - 3.14)
Much have been written about decomposition and FP analysis but surprisingly few FPs have actually been shown.
In some decomposition articles needle movements have been related to MUAP shape variability, but as described here,
jiggle can also be the responsible cause.
Only few articles has been written about jiggle (MUAP shape variability)[160, 223] but none of them shows FPs
to rule out other causes like noise, superpositions and similar looking MUAPs. The examples with jiggle therefore
contributes to the discussion of jiggle. It is shown how jiggle can be due to instability of all contributing muscle fibers
(Fig. 3.13) or from blocking of one or few (Fig. 3.10) or combination of these (Fig. 3.11).
5.3
Firing pattern analysis
Motor unit firing pattern analysis in neuromuscular disorders is not, like conventional quantitative MUAP analysis,
a well established technique. It is neither known or available to all electromyographers. No ”Golden method” is
avaliable as the one known from conventional MUAP analysis developed by Buchthal and coworkers in the 1950’s.
Thus, no general directions for recording, analyzing or interpretation are avaliable or generally accepted. Much of the
knowledge about firing rate and variability is experimental or related to newly developed decomposition system and
the findings are specific to the methods used. One of the reasons is the difficulties associated with obtaining the FPs
as described in Section 1.3.1. No comprehensive work has been presented that fully describes one particular method
for recording, analyzing and physiological interpretation of the findings. Dependency and sensitivity of the parameters
to age, gender, muscle type, level of contraction etc. has to be determined so normal data can be established. The
group of people that developed the decomposition system ADEMG has made the most comprehensive evaluation of
these factors in two muscles but the result are related to a particular decomposition that has some known limitation.
All this shows what a young discipline FP analysis is.
5.3.1
Firing rate and variability in different studies
As seen in Table 1.6 in Section 1.3.3 five studies including the present found changes in the FP parameters in patients
while two studies did not. The reasons for this are discussed in the following Sections.
5.3.1.1
Recording conditions in EMGPAD and other systems
As shown in Table 1.6 in Section 1.3.3 many different recording conditions have been used to record the EMG signal
for FP analysis. What varies is the type of needle electrode, level of contraction, recording time, method of feedback,
and type of muscle used. These variations are probably responsible for different findings. This is in contrast to the
conventional MUAP analysis where the method are defined so that the recording conditions can be reproduced.
The FPs used here were extracted by decomposing EMG signals recorded under usual conditions for conventional
MUAP analysis. The reasons for this are:
• EMGPAD was designed to extract both the MUAPs and FPs for each identified MU in the EMG signal. Thus,
the recordings were made as for conventional MUAP analysis with minimal distortion of the MUAP shape from
filtering.
• The recording conditions are well known and reproducible as described above. No special equipment is needed
besides a conventional concentric or monopolar needle electrode, a standard EMG amplifier with high and low
pass filters, an osciliscope and data acquisition system for digitizing the EMG signals and storing them as files
on a computer. This equipment is commercially available.
• These recording conditions represents a common framework for initial clinical studies with evaluations of FP
analysis at slight contraction.
• These recordings are easy to perform compared to recordings with force feedback (strain-gauge setup), use of
special needle electrodes and multi channel recordings.
• The study by Genreben and Schulte-Mattler[187] indicates that there is no difference in the firing rate of the first,
second, third and fourth recruited MU. This suggests that the exact level of contraction is not that important
as long as it is low and only few MU are recruited.
In the seven studies of MU firing rates and variability, two types of needle electrodes were used: selective and
none-selective. This could have an impact on the FP analysis. Selective needle electrodes record only action potentials
97
(APs) from few muscle fibers but when acquiring the FP8 the recordings could be affected by impulse blocking and
jitter. This means, increased firing rate variability could be due to neuromuscular disorders rather than from the CNS
and the α-motor neuron, when selective needles are used.
In six of the seven studies slight contraction force was applied9 . The firing rate and variability is generally dependant
on the contraction force.
The brachial biceps muscle was used in four of the studies while the rest used different muscles. The distribution
of type 1 and 2 MUs is different in different muscles. That is why some muscles primarily rely on recruitment of
additional MUs and others on increasing the firing rate, for increasing the contraction force. So the combination of
muscle and force is probably relevant. A muscle primarily relying on recruitment of additional MUs to increase the
force at low contraction levels will probably not be as sensitive to the force level.
The FP parameters are probably dependent on the type of feedback used. The strain-gauge feedback guarantees
common level of contraction and was used by Fuglesang-Frederiksen et al.[183]
The recording length influence also the FP parameters, because the IPI’s increase with duration of the recording[65,
105] resulting in higher mean IPI and higher standard deviation.
5.3.1.2
MU firing rate and variability findings in seven studies
Changes in MU firing rate and variability in patients was found in all studies except the studies by Fuglsang-Frederiksen
and Nandedkar et al.
In the five studies that found changes in patient the firing rate was increased in patients with myopathy. This
strongly indicate that myopathy is generally associated with increased firing rates at slight effort. In three of the
studies the firing rate variability revealed no changes in patients with myopathy compared to normals.
In the studies of patients with neuropathy two found higher and one found lower firing rates.
In three studies, including our own, ALS patient was investigated. All three showed higher firing rate variability,
but our findings showed clearly lower firing rates compared to controls while Dorfman et al.[185] and Petajan et al.[68]
found higher firing rates.
5.3.2
Difficulties in FP analysis
In Section 4.1.1 the difficulties associated with FP analysis was described. (a) Steady contraction can sometimes be
difficult to achieve, (b) errors in the FP can be introduces by the decomposition algorithm, (c) double discharges and
prolonged intervals are sometimes present which can be difficult to distinguish from errors made during decomposition
and (d) different degrees of slow IPI changes occur in most FPs.
Because different errors may be introduced in the FPs, robust estimators should be used to produce accurate FP
parameters. Decomposition errors can occur in all single channel concentric needle recordings. A good decomposition
system will minimize the number of errors but can not eliminate them all. For all decomposition systems the number
of errors will generally increase with increasing background noise from distant MUAPs, with the number of MUs,
the firing rate, similar looking MUAPs and mixture of small and big MUAPs. Therefore good estimators for the FP
parameters are needed, to extract useful parameters and minimize errors. A good estimator is both accurate (small
variance) and precise (small bias) even with a high amount of errors. Two types of FP error can occur: a firing can
be missed and a false can be produced. In partial decomposition systems missed firings are more common than false
ones because not all compound segments are resolved whereas with full decomposition systems the opposite is the
case. Some parameters are more sensitive to errors than others, for example the firing variability expressed by the
standard deviation of the inter potential interval SD(IPI) is more sensitive to errors than the MEAN(IPI) as shown in
Section 4.4.2. In partial decomposition systems robust estimators enables extract useful parameters from partial FPs.
In full decomposition systems the robust estimators produce even more accurate parameters.
A missed firing due to a undetected segment or a wrong resolution of a compound segment reduces the number of
correct IPIs with two and with fairly stable firings the missed firing will result in a false IPI about twice the length
of the correct IPIs. A false firing will divide a correct IPI into two wrong ones and reduce the number of correct IP
with one.
In Section 4.4.2 was it described that the mean and SD estimators developed by McGill[212] performed best compared
to the other estimators and were therefore used to determine mean and SD of the IPI in the three patient groups.
The focus of the present study has been on describing the difficulties associated with all levels of decomposition
and FP analysis for a better understanding of the problems involved. This has generally had little attention in the
8 a SFAP both reflect firing of the motor neuron and possible disturbances in the neuromuscular junction from blocking and jitter. The
MUAP recorded with none selective needle electrode like the CNE, on the other hand, is a summation of many SFAP and thus not so
sensitive to disturbances of the individual SFAPs
9 In the study by Dorfman et. al recordings were made at slight contraction, 10% of MVC and 30% of MVC, but the results from slight
contraction is referred to here.
98
litterateur. Many decomposition systems and FP analysis have been presented without addressing the fundamental
difficulties.
5.3.3
Some FP properties and observations found with EMGPAD
To investigate various properties of the FPs found with EMGPAD, 176 FPs with no observable errors were selected.
Most FPs showed slow IPI changes present also in all the FPs in multi unit recordings. This is called common drive
and have been investigated by DeLuca and coworkers with their decomposition system using selective multi channel
needle electrodes high level of contraction. Common drive have not previously been described in recordings at slight
effort.
A gradual increase in IPI has been reported[105, 65] in long EMG recordings (>10 seconds), but was not confirmed
in our 11.2 second recordings. Both slow increasing, decreasing and fluctuating changes in IPI were seen. This means
that the sequence of IPIs in a FP is generally not stationary.
In Section 4.2.4 tests were performed to evaluate whether the IPIs are normally distributed. Using a χ2 test 88% of
the error-free FPs from controls were accepted at a level exceeding 99.5% (alpha=0.005). This is in good agreement
with the finding by Clamann[215], where 90% was accepted at the same level. It was also found that if the slow IPI
changes occuring in most FPs were minimized10 the percentage of accepted FPs increased. This led to the following
IPI model: Ii = IiN + Ti , where IiN ∈ N (µ, σ) and Ti is an unspecified trend function of the time i representing the
slow IPI changes.
A functional relation σ = 0.186µ−6.28 was found for FPs in brachial biceps of ten healthy controls in good agreement
with the findings by Clamann of σ = 0.175µ − 3.54 and expresses that the firing variability decreases with increasing
firing rate.
5.3.4
Parameter correlations
The FP is described by a set of parameters which allows establishment of normative data. These parameters should
be investigated for influence of age, gender, muscle, force etc. so the normal values can be matched for the factors they
are dependent on. The FP parameters described in the literature can be divided into four groups: measure of location
(M EANI , M ODEI ), total variability (SDI , F SDI ), short term variability (M CDI , M SSDI , V ARI , V ARII ) and
serial correlation (RHOI , F RHOI ) (see Section 4.3). From a simple correlation study the following parameters were
selected when also simplicity of the parameters and the existence of robust estimators are considered: M EANI , SDI ,
M CDI , V ARI , RHOI and SDT RI . A preliminary investigation of these parameters ability to differentiate between
controls and patients with myopathy and ALS. It was based on the 176 error free FPs and without consideration to
possible relation to age, gender and other factors. As shown in Fig. 4.5 this investigation showed the M EANI to be
valuable to separate the three patient groups compared to the other parameters. The most often used FP parameters
are the M EANI and SDI . They express the firing rate and variability. Most studies, including the present, have
found the IPIs to be approximately normally distributed, therefore the M EANI and SDI are the two most obvious
parameters to use, furthermore rubout estimators for them have been presented by Stashuk et al. (EFE)[5] and
McGill[212]. The FP analysis in this work is primarily based on these two parameters.
In the ten normal subjects studied, there was no correlation between the following parameters: age, gender, MUAP
duration, MUAP amplitude, mean IPI, standard deviation of IPI and maximal contraction force, except for gender
and maximal contraction force. The absence of age correlation could be due to the rather narrow range of age for the
controls. In a more comprehensive study on age effects by Howard et al.[163] MUAP duration, MUAP amplitude and
turns increased linearly with age whereas maximal contraction force decreased linearly with age. The firing rate in
the present study did not change significantly with age at slight muscle contraction. In the study by Roos et al.[188]
there were no differences in firing rate at different levels of contraction in young and old men in the quadriceps muscle.
The study by Howard et al. showed no variation in firing rate in relation to gender.
The mean MUAP duration and mean M EANI were not correlated, indicating no relation between MUAP size
(expressed by the duration) and the firing rate, in our recordings.
However the present material is to small to investigate correlation with age and gender, but the results are in
accordance with larger studies.
5.3.5
Firing rate and variability findings in patients
The M EANI and SDI parameters in healthy controls were compared to those from patients with myopathy and ALS.
The recordings were made in biceps brachii and in medial vastus, but for medial vastus only patients with myopathy
and ALS were compared.
10 This
was done by subtracting the floating mean (F M EAN see (4.6)) signal from the IPIs.
5.3.5.1
99
Firing rate and variability findings in biceps brachii
In the biceps brachii muscle, the mean M EANI differed significantly in all three groups (Fig. 4.13). The myopathy
group generally had smaller IPIs indicating a higher firing rate than in controls and ALS patients. On the other hand
in the ALS group IPIs were longer indicating a lower firing rate than in the control and patients with myopathy. The
mean M EANI level was strikingly different suggesting that the firing rate is changed in patients and therefore could
be used to differentiate different neuromuscular disorders. With the exception of the first control person C1, the mean
M EANI is more homogeneously distributed than in the patient groups.
Our findings for the ALS group are in contrast with findings of Dorfman et al. They found increased firing rates in
motor neuron diseases (MND). The reason for the different findings could be that they transformed the parameters into
standard (z) scores, representing standard deviation units of difference from the corresponding normal means stratified
according to muscle tested, age and level of contractile force. The FP parameters from the ADEMG decomposition
system used by Dorfman et al. are estimated from partial FPs whereas ours are from more complete FPs. They
stratified according to age, but the firing rate was found to be unrelated to age.
The findings for the myopathy group fits with all the studies in Table 1.6. All found increased firing rate.
The firing rate variability in biceps brachii was increased in the ALS group but the same in the control and myopathy
groups, see Fig. 4.14. Similar findings were made by Dorfman et al. and shows that the firings are more unstable in
ALS patients at slight effort.
5.3.5.2
Firing rate and variability findings in medial vastus
For the medial vastus muscle only FPs from the two patient groups (myopathy and ALS) were available, but not from
the control group. The M EANI was significantly higher in the ALS group compared to the myopathy group. The
myopathy group was rather homogenous compared to the ALS group. These findings was similar to those in biceps
brachii. None of the six studies described in Table 1.6 were made in the medial vastus muscle.
The firing rate variability was higher in the ALS group although not as clearly as for the biceps brachii.
5.3.5.3
Comparing firing rate findings in biceps brachii and medial vastus for the myopathy and ALS
patients
When comparing the M EANI for the biceps brachii and medial vastus in the myopathy group, the M EANI was
significantly higher for themedial vastus. The atrophy was about the same for the two muscles, so the clear difference
in M EANI is probably due to sctructural difference between the muscles rather than pathology. This suggest that
the M EANI is dependant of the muscle.
The M EANI is not significantly different between the two muscles for the ALS patients. Both groups are rather
inhomogeneous which probably reflect different progression of the disorder and except for patient MV7 the values
are correlated between the muscles so that a high values for one muscle is correspondingly hight for the other. This
indicates that disorder is equally affecting the two muscles which is contrast with the findings for myopathy.
Chapter 6
Conclusion and further Perspectives
In this Chapter a conclusion for this work is presented together with suggestions for further work.
6.1
Conclusion
We have developed a fully automatic decomposition system called EMGPAD (EMG Precision Decomposition), that can
extract both the MUAPs as well as the related firing patterns from a raw EMG signal recorded under usual conditions
for MUAP analysis,i.e. at slight muscle contraction with a standard concentric needle electrode and with audio and
visual feedback. Many difficulties exist in doing a decomposition of which, the big variation across EMG signals is
the greatest challenge. This has been addressed in EMGPAD by avoiding critical fixed thresholds, instead they are
estimated for each signal. The aim with our decomposition system is to do a complete decomposition which means
without restriction to the number of MUAPs in a compound segment. This was realized with an advanced recursive
resolution algorithm. EMGPAD was clinically evaluated by comparing the duration found with a conventional MUAP
analysis method with the duration found with EMGPAD and good agreement was found. This suggests that our
old normal values perhaps could be used with MUAP analysis done with EMGPAD. About twice as many MUAPs
were found with EMGPAD compared with the conventional method. Generally more small MUAPs were found with
EMGPAD. These small MUAPs are often difficult to find with a conventional method. No restriction is used in
EMGPAD for the MUAP rise time, no spike enhancing filters is used and the MUAPs are automatically found.
This means that both the possible bias introduced by the user because of individual preferences in selection MUAPs
or experience and the possible bias introduced by the system because only MUAPs with a certain configuration are
accepted (rise time, amplitude and spectral content), is minimized. Many graphical presentations of the decomposition
result are available and interactive tools have been developed to explore different properties of the EMG signal, the
MUAPs and firing patterns. The decomposition time depends on how complex the EMG signals is and it can take
from a few seconds to several minutes so the decomposition is performed of-line.
A library of EMG, MUAP and FP examples is presented. These examples shows both important signal types with
respect to the difficulties associated with decomposition as well as interesting MUAP and FP phenomena. MUAP
shape variability, also called jiggle, is seen as varying MUAP shape at consecutive discharges. Few studies exist on
this and non have provided convincing examples, although it sometimes can be observed on a simple oscilloscope with
a trigger and delay line. By providing the FPs, we have further documented jiggle with extra fidelity because artifacts
from superimposed MUAPs and similar looking MUAPs can be ruled out. These examples also serve to illustrate the
capabilities of EMGPAD; EMG signals of various complexities can be handled. In general up to 5-6 concurrent MUs
can be decomposed with high precision. It is the resolution algorithm that mostly sets the limit both in terms of time
and precision. It is the most time consuming stage of the decomposition and most errors made by EMGPAD can be
traced to the resolution algorithm by inspecting the FPs and the EMG signal.
Basic properties of the firing patterns have been observed and described. Double discharges and prolonged intervals
have been observed in a few FPs and are thus rare in recordings made under our conditions. Slow IPI changes was
observed in most FPs and these slow changes was common for the FPs in the same recording. This is the so-called
common drive described by DeLuca and coworkers. The IPIs can roughly be approximated by a normal distribution
plus a trend function representing the slow IPI changes we have observed. The median of the mean and standard
deviation for the IPIs in healthy controls recorded from brachial biceps was found to be σ = 106.4 ms and µ = 12.6
ms. The IPI mean and standard deviation are functionally related by σ = 0.186µ − 6.28 which is in good agreement
with the findings of Clamann[215].
Eleven FP parameters divided into four categories; location, total variability, short term variability and serial
correlation was compared. From a simple correlation analysis, that showed high correlation between many parameters,
six candidates was selected; M EANI , SDI , M CDI , V ARI , RHOI and SDT RI . From a preliminary analysis based
100
CHAPTER 6. CONCLUSION AND FURTHER PERSPECTIVES
101
on a subset of the FPs, consisting on only of error-free FPs, the M EANI , SDI and M CDI showed most promising
in differentiating between controls and patient with myopathy and ALS. The M EANI and SDI FP parameters was
further investigated. To be able to deal with FPs with possible errors, robust estimator for M EANI and SDI has to
be used. Several estimators have been investigated by means of simulation. Synthetic FPs with known and varying
amount of errors was constructed and applied to the estimators. From this, measures for accuracy and precision was
evaluated. This showed that the estimators developed by McGill[212] performed best. McGills estimators was used
for the following analysis.
To further evaluate the potentials of using the FP parameters as a diagnostic tool, the M EANI and SDI parameters
was compared in healthy controls and patients with myopathy and ALS. FPs from two muscles: biceps brachii and
medial vastus, was used although controls were available from biceps brachii. A simple correlation analysis was
performed in the control group to investigate if any correlation could be found between age, gender, mean MUAP
duration, mean MUAP amplitude, maximal contraction force, mean M EANI and mean SDI . Only a significant
correlation between gender and maximal contraction force was found, showing that the males were stronger than the
females. The age range in our control group was rather narrow; 21-37 years which means that in a wider age range
other findings my be found. A group wise comparison of mean M EANI in biceps brachii showed clear inter-group
differences between controls and patients with myopathy and ALS. The myopathy group had smaller mean M EANI
than the control group which again had smaller mean M EANI than the ALS group. This means that in our study,
compared to the control group, the mean firing rate was higher in patients with myopathy and lower in patients with
ALS. The mean IPI variability expressed in the mean SDI , was found to be higher for the ALS group compared to
the control and myopathy group, but no significant difference was found between the control and myopathy group.
The same results was found for the medial vastus muscle except the control group was not available; the firing rate
was higher in myopathy compared to ALS and the mean SDI was higher in ALS although not as clear as in biceps
brachii. When comparing the mean M EANI for biceps brachii and medial vastus, in case of myopathy, it was found
to be significantly higher in medial vastus. The same comparison showed no significant difference in case of ALS.
These analysis of FP parameters has shown the mean M EANI parameter to be useful in differentiating between the
controls and patients with myopathy and ALS. The mean SDI parameter showed significantly higher values for ALS
compared to controls and myopathy, but could not differentiate between controls and myopathy. This indicate that the
MU firing instability could be a marker for disorders in the alpha-motor neuron and the CNS. Other studies have shown
changes in the FPs for many different disorders. This shows promising for the prognoses of using EMGPAD for FP
analysis in other disorders than those investigated here, thus for including FP analysis in routine EMG investigations.
We have been using and continously improving EMGPAD for teen years now and much experience in working with
a decomposition system have been accumulated from this. The special features of EMGPAD is that it is a fully
automatic decomposition system constructed to extract both the MUAPs and their parameters (duration, amplitude
and phases) as well as the firing patterns and their parameters (mean(IPI) and SD(IPI)). The EMG signal is recorded
under the same conditions as for conventional MUAP analysis; slight and constant muscle contraction and using a
concentric needle electrode. For the electromyographer, the investigation is performed as for usual MUP analysis but
the information generated from the following decomposition, is much higher. This makes EMGPAD easier to use
compared to decomposition system where special needle electrodes have been used or the recordings have been made
at a fixed level of contraction by using a strain-gauges setup.
Viewing EMGPAD just as an advanced MUAP analysis system, several advantages exist compared to the conventional method, as well as compared to the commercially available systems:
• Bias in the selection of MUAPs, compared to the manual method is eliminated because it is made automatically
and with the same criteria for each EMG signal and thus not influenced by the preferences and skills of the
electromyographer.
• Bias in the selection of MUAPs compared to commercially available methods is eliminated because, the acceptance critiria is not based on the rise time.
• More MUAPs are found compared to the manual method.
• Especially EMGPAD was better at finding small MUAPs both compared to the manual method as well to
commercially available systems.
• The firing patterns combined with powerful graphical representations can aid in determining ambiguous cases
like needle movement, similar looking MUAPs and jiggle.
Viewing EMGPAD as a decomposition system for extracting highly accurate firing patterns the following advantages
exist compared to other reported decomposition systems:
• EMGPAD was design to be robust across a wide variety of EMG signals. This is achieved by avoiding fixed
critical thresholds but rather estimating them for each signal.
CHAPTER 6. CONCLUSION AND FURTHER PERSPECTIVES
102
• Precision is the aim. No other decomposition system reported performs the resolution phase with the same
degree of accuracy. Other systems either skips the resolution of compound segments or introduces limitations in
for example the number allowed MUAPs in the compound segment. This is not the case in EMGPAD.
• The EMG recordings are made under the same conditions as for MUAP analysis. This means the electromyographer will be doing the recording under familiar conditions and get both the MUAPs and the FPs and their
respective parameters.
The main limitation in EMGPAD, in its current version, is that the resolution of compound segments still takes
too long time for on-line analysis. With ever more powerful computers available at affordable prices and further
optimization of the algorithm and implementation, this limitation is likely to be overcome in the near future.
The overall aim of EMGPAD is to extract more information from the raw EMG signal than what is possible
from a MUAP analysis. The MUAP analysis only utilizes very limited information present in the EMG signal; the
morphological information extracted from the MUAP parameters, but disregards all together the temporal information
present in a firing pattern, the MUAP shape instability known as jiggle and possible inter FP correlation etc. EMGPAD
tries to include most of this in an automatic decomposition system and without additional requirement to the recording
conditions than those known for MUAP analysis. This will hopefully help the introduction of FP in routine clinical
neurophysiological investigations.
6.2
Perspectives and further work
As described in the introduction different techniques can be used to investigate changes in the MU. For example
MUAP analysis is used to see morphological changes seen through changes in the MUAP shape parameters of which
the duration is the most sensitive. Basicly the duration is used to investigate possible increase or decrease in number
of muscle fibers. A jitter analysis from single fiber recordings describes possible disturbances in the neuromuscular
junction. In MUAP recordings with a concentric needle electrode jiggle has been suggested to reflect the same
disturbances and blocking. Firing patterns shows the central nervous systems modulation of the MUs. In present and
in previous studies, changes have been demonstrated in the FPs in both peripheral and central disorders. These three
and possibly more different techniques targeting three different parts of the MU; muscle fiber, neuromuscular junction
and alpha-motor neuron discharges controlled by the CNS, results in three set of parameters. The obvious step in
the future would be to combine these different set of parameters, into one feature vector and investigate how different
disorders are mapped in the spanning multi dimensional feature space. Many different statistical tools like principle
component analysis and clustering techniques could be used to explore the groupings of the different disorders and
the relative importance of the parameters for a specific disorder. Artifical neural networks could be trained to suggest
diagnosis even i case of incomplete parameters.
To further improve EMGPAD, support the result found or further investigate related areas the following is suggested:
• A iterative decomposition like in ARTMUP by Hass and Meyer would also be suitable for EMGPAD because the
resolution stage is much more time consuming compared to the rest, for complex signals. In this way MUAPs
and partial FPs together with their parameters could be found in a few seconds and the complete decomposition
could be done later.
• The resolution stage is the most time consuming, therfore it is still needed to optimize the algorithm to reduce
the time.
• The MUAP shape variability also called jiggle should be further investigated by simultaneous recordings with
both concentric and single fiber needle electrodes. By doing this both in normal muscles and muscles with known
neuromuscular disturbances like myostania gravis or reinnervation1 the jitter jiggle relation could be investigated.
Is jitter always associated with jiggle ? and is increased jitter resulting in increased jiggle ?
• The jiggle should be followed in case of early reinnervation to see if the jiggle decreases with time.
• The patient material in this study is relatively small and should be increased so FP parameters dependency on
age and gender can be investigated. More muscles should be investigated to study inter muscle variation for the
FP parameters.
• Many more types of disorders should be investigated with FP analysis to determine the differentiation ability
between other disorders and search for specific configurations in the FP parameters for specific disorders.
• The influence of the recording conditions on the FP parameters should be further investigated.
1 where
the nerve endings and neuromuscular junctions are immature
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Summary
EMG Decomposition, Findings and Firing Pattern Analysis
in Controls and Patients with Myopathy and Amytrophic Lateral Sclerosis
Author: Miki Nikolic
The motor unit (MU) is the smallest functional unit of a skeletal muscle, and its electrophysiological characteristics
are central in the diagnosis of neuromuscular disorders associated with partial denervation and myopathy. The MU
comprises the alpha-motor neuron, its axon and the group of muscle fibers innervated by it. The electrical response of
a MU can be recorded and is called a Motor Unit Action Potential (MUAP). At low and constant level of contraction
a few MUs are activated by a train of discharges from the individual alpha-motor neurons resulting in repetitive
recordings of their MUAPs.
In conventional MUAP analysis the duration, amplitude and the percentage of polyphasic potentials are measured.
Mean values of the MUAP parameters are compared with normal values to asses abnormalities. The temporal information from the train of MU activations is called a Firing Pattern (FP) and is not utilized in clinical investigations
because it involves a rather complicated and time consuming process.
We have developed a decomposition system for extracting the MUAPs as well as their corresponding FPs from
recordings under usual conditions for MUAP analysis. It has the advantage of being fully automatic, robust across the
variation of real EMG signals, identifies about twice as many MUAPs compared to our conventional method, avoids
some of the bias problems reported with other systems both during sampling and analysis and does not require any
special recording conditions.
Our decomposition system, called EMGPAD (EMG Precision Automatic Decomposition), is described as well as
the difficulties associated with performing a decomposition. It consists of three stages: (1) identification of MUAP
activity in the EMG signal, (2) identification of individual MUAPs and (3) resolution of superimposed MUAPs.
Important and illustrative EMG signal, MUAP and FP examples are presented to show the capability of EMGPAD
and to illustrate important phenomena not previous documented in detail. One of these are MUAP shape variability.
The difficulties in FP analysis are outlined. The Inter Potential Intervals (IPIs) were found to be approximately
normally distributed and most FPs showed slow IPI changes known as common drive.
The correlation between different FP parameters were investigated. FPs often contain error primarily introduced
by the decomposition algorithm, so robust estimator for the mean(IPI) and SD(IPI) were compared and the best were
found by means of simulation.
The mean(IPI) and SD(IPI) were investigated in healthy controls, patients with myopathy and ALS determined in
the biceps brachii and medial vastus muscle. In biceps brachii the mean(IPI) was found to be significantly lower in the
myopathy group compared to controls which was significantly lower than the ALS group. The SD(IPI) were similar
for the control and myopathy group but significantly higher for the ALS group. Controls were not available for the
vastus medialis muscle but only the two patient groups. Mean(IPI) was significantly lower in the myopathy group
compared to the ALS group. In medial vastus the SD(IPI) however, was not significantly different between patients
with myopathy and ALS. A significantly higher mean(IPI) was found in the medial vastus muscle compared to the
biceps brachii muscle for the myopathy patients both for the individual patients and for the groups. The same test
for the ALS patients did not show significant differences.
Our patient material is rather small, so the findings should be confirmed in a larger material. FP analysis in other
disorders should be investigated especially in CNS disorders because the FPs reflect CNS control of the MUs. Further
improvements to reduce the decomposition time should be investigated.
114
Resumé af Ph.D.-afhandling
Detaljeret analyse af klinisk elektromyografiske signaler
EMG Dekomponering, Fund og Firingsmønsteranalyse
i Normale og Patienter med Myopati og ALS
Forfatter: Miki Nikolic
Den motoriske enhed (MU) er den mindste funktionelle del i skelet muskulaturen og dens elektrofysiologiske karakteristika er centrale i diagnosen af neuromuskulære lidelser associeret med partiel denervering og myopati. En MU
omfatter en forhornscelle med tilhørende axon og gruppen af muskelfibre der innerveres heraf. Det elektriske signal
fra den MU kan registreres og benævnes det Motoriske Enheds Potentiale (MUAP). Ved lav og konstant kontraktion
aktiveres nogle få MUer via et tog af impulser fra de individuelle alfa-motor neuroner.
Ved konventionel MUAP analyse måles de enkelte MUAPs mht. varighed, amplitude og form. Middelværdierne af
disse parametre sammenlignes med normalværdier. Den temporale information ved repetitiv aktivering af den MU
kaldes for et fyringsmønster (FP) og bruges ikke ved kliniske undersøgelser, da det involverer en temmelig kompliceret
og tidskrævende proces.
Vi har udviklet et dekomponeringssystem til udtrækning af MUAPer og deres tilhørende FPer fra målinger udført
under sædvanlige forhold for MUAP analyse. Systemet har følgende fordele: Det er fuldt automatisk, er robust mht.
variationer EMG-signaler imellem, identificerer ca. dobbelt så mange MUAPer sammenlignet med den konventionelle
metode, undgår nogle af de bias problemer, der er kendte fra andre systemer både under registreringen og analysen
uden brug af specielt registreringsudstyr.
Vores dekomponeringssystemt, EMGPAD (EMG Precision Automatic Decomposition), er beskrevet såvel som de
vanskeligheder der er forbundet med dekomponeringen. Systemet består af tre trin: (1) identifikation af MUAP
aktivitet i EMG-signalet, (2) identifikation af individuelle MUAPer og (3) opløsning af superponerede MUAPer.
Vigtige og illustrative EMG-signaler, MUAP og FP eksempler præsenteres for at vise EMGPADs formåen og for at
illustrere vigtige fænomener, der ikke tidligere er blevet dokumenteret i detaljer. Et af disse er MUAP variabilitet.
Vanskelighederne ved FP analyse er beskrevet. Inter Potentiale Intervallet (IPI) var approksimativt normal fordelt
og de fleste FPer udviste langsomme IPI ændringer kendt som ”common drive”.
Korrelationen mellem forskellige FP parametre blev undersøgt. FPerne indeholder ofte fejl primært introduceret fra
dekomponeringsalgoritmen, så robuste estimatorer for middelværdier(IPI) og standarddeviationer(IPI) blev undersøgt
og de bedste blev fundet vhja. simuleringer.
Mean(IPI) og SD(IPI) blev sammenlignet mellem raske kontrol personer, patienter med myopati og ALS i musklerne
biceps brahii og vastus medialis. I biceps brahii var mean(IPI) signifikant mindre for gruppen med myopati sammenlignet med kontrolgruppen, som igen var signifikant mindre end gruppen med ALS patienter. Mht. SD(IPI) var
resultaterne sammenlignelige for kontrolgruppen og gruppen med myopati patienter, men signifikant højere for gruppen med ALS patienter. For vastus medialis var der ikke ingen kontrolgruppe, men kun de to patientgrupper. For
mean(IPI) var gruppen med myopati patienter signifikant lavere end gruppen med ALS patienter. For SD(IPI) var
der ingen signifikant forskel. For myopati patienterne var mean(IPI) mindre i biceps brachii end i vatus medialis både
for den enkelte patient og for de to patientgrupper. Tilsvarende fund var der ikke for ALS patienterne.
Patientmaterialet var begrænset i dette studie, så fundne burde efterprøves i et større matriale. FP analyse i andre
lidelser burde undersøges, specielt har centrale affektioner interesse da FPerne reflekterer central nerve systemets
kontrol af de MUer. Yderligere forbedring mht. reducering af dekomponeringstiden bør undersøges.
115
Appendix A
Patient material
116
117
APPENDIX A. PATIENT MATERIAL
Healthy controls / Brachial biceps
Age
(Years)
/
Sex
(M/F)
29/M
26/F
37/M
27/F
23/M
21/F
27/M
23/F
29/M
30/M
Patient
C1 (36744)
C2 (36769)
C3 (36776)
C4 (36777)
C5 (36786)
C6 (36787)
C7 (36809)
C8 (36811)
C9 (36812)
C10 (36821)
Muscle strength
(1-5)
Kg
% of
strongest
5
5
5
5
5
5
5
5
5
5
17
12
30
11
34
9
20
15
19
46
40
26
65
24
74
20
43
33
41
100
MUAP parameters
Ampl.
Durat.
%
(ms)
(µV)
PolyMean
Mean
phasic
value
value
11.2
174
10
9.2
136
3
9.8
229
11
10.4
231
6
10.3
213
13
9.9
114
5
10.7
185
9
8.2
119
10
10.3
179
0
9.2
213
9
Myopathy
Patient
M1 (34860)
M2 (35910)
Age
(Years)
/
Sex
(M/F)
28 / M
44 / M
Brachial biceps
Duration of
symptoms
Diagnosis
Remarks
(Years)
12
26
Limb-girdle
dystrophy
Plexus
scapulohumoral
dystrophy
NN1
M7 (38682)
19 / M
0
Full
1,3 mV
Reduced
0,8 mV
Full
0,9 mV
Full
2,0 mV
Full
2,0 mV
6 DTF,
5 PSW3
8,3
306
No
7,1
123
0
02
4
2
NN1
Polymyositis
Myopathy4
unclassified
Myopathy4
unclassified
1 DTF,
1 PSW3
5
NN
Myopathy4
unclassified
7
Full
0,5 mV
4
Polymyositis
< 1 month
2
2
1
63 / F
No
+++
2
33 / M
Full
1,5 mV
3
41 / F
M6 (37416)
+++2
4-
1
26 / M
M5 (37025)
Spontanious
activity
Atrophy
NN
M4 (36954)
NN1
0
4+
5-
MUAP parameters
Recruitment
pattern at
MVC
Muscle
force
(1-5)
1
M3 (36200)
Medial vastus
0
Mean
durat.
(ms)
Mean
Ampl.
(µV)
% Polyphasic
6
167
28
6,8
4 DTF,
4 PSW3
4 DTF,
2 PSW3
6 DTF,
1 PSW3
284
54
Muscle
force
(1-5)
Atrophy
2
+++2
2
5-
++
49
5
0
2
11
5
02
2
8,6
103
74
4
0
6,8
169
23
3
+++2
9,0
155
43
5
+++2
Recruitment
pattern at
MVC
Spontanious
activity
Full
1,3 mV
Full
3,0 mV
Full
1,3 mV
Full
0,7 mV
Full
1,0 mV
Full
0,8 mV
Reduced
1,5 mV
MUAP parameters
Mean
durat.
(ms)
Mean
Ampl.
(µV)
% Polyphasic
No
7,9
251
33
No
7,5
441
10
7 DTF,
6 PSW3
8,7
323
28
No
6,7
130
2
No
7,4
121
28
No
7,8
263
30
No
9,8
339
62
_______________________________
1
Normal sensoric and motoric nerve findings.
0 normal, + mild, ++ moderate, +++ severe.
DTF Di/Tri-phased, PSW Positive Sharp Waves.
4
The diagnosis is bases on EMG changes in at least two muscles.
2
3
Amytrophic lateral sclerosis (ALS)
Patient
Age
(Years)
/
Sex
(M/F)
Brachial biceps
Duration of
symptoms
Diagnosis
Remarks
(Years)
Muscle
force
(1-5)
Atrophy
Recruitment
pattern at
MVC
Spontanious
activity
Medial vastus
MUAP parameters
Mean
durat.
(ms)
Mean
Ampl.
(µV)
% Polyphasic
Muscle
force
(1-5)
Atrophy
Recruitment
pattern at
MVC
Spontanious
activity
1 FA5
A1 (34467)
56 / M
½
ALS
5 muscles investg.1
† 2 yrs after symp.2
NN3
5
04
Full
2,5 mV
1 DTF,
1 PSW,
5 FA5
14,8
638
4
5-
+4
Discrite
2,5 mV
A2 (35713)
35 / M
5
ALS
6 muscles investg.1
5
+4
Reduced
2,5 mV
2 FA5
14,4
297
32
5
04
Discrete
12,0 mV
6 muscles investg.
Mainly bulbar ALS † 2 yrs. after symp.2
NN3
4+
+4
Many DTF,
Many PSW,
Many FA5
22,6
653
21
4+
+4
ALS
5 muscles investg.1
† 1 yr. after symp.2
5-
1 DTF,
2 FA5
14,1
431
31
5
+4
ALS
4 muscles investg.1
NN3
Discrete/
Submax.
1,5 mV
Reduced/
Max.
2,5 mV
Reduced
2,0 mV
15
603
22
5
1
A3 (36551)
61 / F
1½
A4 (37334)
67 / F
½
A5 (37492)
52 / F
1½
4+
+4
3 muscles investg.1
Mainly bulbar ALS
† 1½ yrs. after symp.2
Reduced
3,5 mV
A6 (37642)
56 / M
½
A7 (39029)
65 / M
1
ALS
3 muscles investg.1
† 1½ yrs. after symp.2
4-
+++4
A8 (39685)
60 / F
<½
ALS
4 muscles investg.1
NN3
5-
04
Discrete/
Max.
6,0 mV
Reduced/
Submax.
2,5 mV
1 DTF,
3 FA5
5 DTF,
5 PSW,
Many FA5
4 DTF,
7 PSW,
7 FA5
7 DTF,
1 PSW,
7 FA5
Discrete/
Submax.
0,9 mV
Reduced/
Max.
5,0 mV
Reduced
2,0 mV
Reduced
3,0 mV
16,8
400
43
16,6
861
33
4-
+++4
16,1
471
38
5
04
Discrete/
Max.
3,0 mV
Reduced/
Submax.
3,5 mV
MUAP parameters
Mean
durat.
(ms)
Mean
Ampl.
(µV)
% Polyphasic
18,2
897
8
18,9
1460
0
18,1
653
65
17,7
1199
18
7 FA5
16,2
881
11
1 FA5
20,5
958
20
Few FA5
21,5
967
25
3 DTF,
1 PSW,
6 FA5
15,2
398
36
1 DTF,
1 PSW,
4 FA5
Many DTF,
Many PSW,
Many FA5
3 DTF,
2 PSW,
4 FA5
___________________________
1
Total number of investigated muscles.
Number of years the patient died after the onset of symptoms.
3Normal sensoric and motoric nerve findings.
4
0 normal, + mild, ++ moderate, +++ severe.
5
DTF Di/Tri-phased, PSW Positive Sharp Waves and FA fasiculation activity.
4
The diagnosis is based on EMG changes in at least two muscles.
2
Figure A.1: Clinical and electrophysiological findings in healthy controls and patients with myopathy and ALS.
Appendix B
All firing patterns used for analysis
118
119
APPENDIX B. ALL FIRING PATTERNS USED FOR ANALYSIS
C1 (36744), N=36
C2 (36769), N=25
C3 (36776), N=36
C4 (36777), N=36
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C8 (36811), N=31
C9 (36812), N=32
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Figure B.1: All the firing patterns from the 10 healthy control individuals.
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B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
120
M1 (34860), N=37
M2 (35910), N=27
M3 (36200), N=66
M4 (36954), N=51
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
M5 (37025), N=41
M6 (37416), N=74
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M7 (38682), N=131
Figure B.2: All the firing patterns from the 7 patients with myopathy.
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
121
A1 (34467), N=37
A2 (35713), N=45
A3 (36551), N=59
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
A4 (37492), N=32
A5 (37642), N=51
A6 (37334), N=60
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
A7 (39029), N=28
A8 (39685), N=59
Figure B.3: All the firing patterns from the 8 patients with ALS.
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
Appendix C
Selected error-free firing patterns
122
APPENDIX C. SELECTED ERROR-FREE FIRING PATTERNS
123
Control
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
BB,10%
BB,10%
BB, 5%
BB,10%
BB,10%
BB, 1%
BB,10%
BB,10%
BB,10%
BB,10%
BB, 5%
BB,10%
BB, 1%
BB, 1%
BB, 1%
BB,10%
BB, 5%
BB,10%
BB,10%
BB, 5%
BB, 1%
BB,10%
BB, 5%
BB,10%
BB,10%
BB,−−−
BB,10%
BB,10%
BB,10%
BB,10%
BB, 1%
BB,10%
BB,−−−
BB, 5%
BB,−−−
BB,−−−
BB,10%
BB, 1%
BB,10%
BB,10%
BB,10%
BB,10%
BB, 1%
Figure C.1: Selected error-free firing patterns from the healthy control individuals.
124
Myopathy
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
BB,10%
BB,10%
BB,10%
BB,10%
BB,10%
BB,10%
BB, 1%
BB, 1%
BB,10%
BB,10%
BB,10%
BB,10%
BB,10%
BB,−−−
BB, 5%
BB,−−−
BB,10%
BB, 1%
BB,10%
BB, 5%
BB,10%
BB,−−−
BB,−−−
BB, 5%
BB, 1%
MV,10%
MV,−−−
MV,−−−
MV,10%
MV,−−−
MV,10%
MV,10%
MV,10%
MV,−−−
MV,−−−
MV,10%
MV, 1%
MV,10%
MV, 1%
MV,−−−
MV,10%
MV, 5%
MV, 1%
MV, 1%
MV,−−−
MV,−−−
MV,−−−
MV,10%
MV,−−−
MV,10%
MV,−−−
MV,10%
MV,10%
MV,10%
MV, 1%
MV,10%
MV,−−−
MV,−−−
MV,−−−
MV,−−−
MV,10%
MV,10%
MV,10%
MV,−−−
MV,10%
MV,10%
MV, 1%
MV,10%
MV,10%
MV,10%
MV,10%
MV,−−−
MV, 1%
MV,10%
MV,10%
MV,10%
Figure C.2: Selected error-free firing patterns from the patients with myopathy.
125
ALS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
BB, 5%
BB,10%
BB,10%
BB,10%
BB,10%
BB, 5%
BB,10%
BB,10%
BB,10%
BB,10%
BB,10%
BB,10%
BB, 1%
BB,−−−
MV,10%
MV,10%
MV,10%
MV,−−−
MV,−−−
MV,−−−
MV,10%
MV,−−−
MV,10%
MV,10%
MV, 1%
MV, 5%
MV,−−−
MV, 1%
MV,10%
MV,10%
MV, 1%
MV,10%
MV,10%
MV,10%
MV,10%
MV, 5%
MV,−−−
MV,10%
MV, 1%
MV,10%
MV,−−−
MV,10%
MV,10%
MV, 5%
MV,10%
MV,10%
MV,10%
MV,10%
MV,10%
MV,10%
MV,10%
MV, 1%
MV,−−−
MV,10%
MV,10%
MV,10%
MV, 1%
Figure C.3: Selected error-free firing patterns from the patients with ALS.
Appendix D
Synchronicity and crosscorrelaton for
simultaneous recorded MU
126
34482/110/18
300
35698/110/3
250
100
150
80
0
−0.5
−0.5
0
Timeshift, seconds
35698/110/11
0.5
150
0
IPI, ms
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
37492/111/9
50
0.5
4
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
35713/110/4
0.5
200
0
1
2
4
6
Time, seconds
0.5
0
0
Timeshift, seconds
10
0.5
4
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
37492/111/1
0.5
150
100
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
37492/111/11
0.5
140
120
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
37642/110/10
0.5
250
150
150
IPI, ms
I200
100
0
1
2
4
6
Time, seconds
0.5
0
−0.5
−0.5
0
Timeshift, seconds
8
10
0.5
100
0
1
2
4
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
0.5
127
−0.5
−0.5
8
2
180
100
0
1
F160
250
200
70
H200
IPI, ms
300
2
1
E
100
G
100
10
IPI, ms
8
6
Time, seconds
0.5
IPI, ms
4
1
D
IPI, ms
2
0
150
IPI, ms
200
IPI, ms
C 90
IPI, ms
B200
Figure D.1: Examples of MU syncronicity and crosscorrelaton for simultaneous recorded MU.
A250
APPENDIX D. SYNCHRONICITY AND CROSSCORRELATON FOR SIMULTANEOUS RECORDED MU
34482/110/16
140
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
38682/110/7
0.5
0
90
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
38682/110/16
0.5
200
50
0
1
2
4
6
Time, seconds
0.5
0
0
Timeshift, seconds
10
0.5
8
10
−0.5
−0.5
0
Timeshift, seconds
38682/110/13
0.5
120
100
80
0
80
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
34467/111/11
0.5
200
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
F
100
120
0
Timeshift, seconds
38682/110/14
0.5
140
120
100
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
34482/110/15
0.5
250
I200
150
100
0
1
2
4
6
Time, seconds
0.5
0
−0.5
−0.5
0
Timeshift, seconds
8
10
0.5
150
100
0
1
2
4
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
0.5
128
−0.5
−0.5
8
100
6
Time, seconds
0
H
IPI, ms
G150
4
0.5
IPI, ms
100
2
1
E
IPI, ms
80
1
6
Time, seconds
IPI, ms
4
2
IPI, ms
0
100
80
C
IPI, ms
IPI, ms
100
120
IPI, ms
38682/110/5
B120
120
D110
IPI, ms
A
38682/110/4
37416/110/4
38682/6/22
120
36551/5/1
120
200
80
180
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
36551/5/14
0.5
250
60
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
34860/110/14
0
60
IPI, ms
80
IPI, ms
C190
IPI, ms
B100
0.5
160
170
0
−0.5
−0.5
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
36200/110/2
0.5
IPI, ms
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
36200/110/4
0.5
200
100
0
1
2
4
6
Time, seconds
0.5
0
0
Timeshift, seconds
10
0.5
0.5
0
2
4
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
36200/110/13
0.5
200
100
IPI, ms
100
50
0
1
2
4
6
Time, seconds
0.5
0
−0.5
−0.5
0
Timeshift, seconds
8
10
0.5
50
0
1
2
4
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
0.5
129
−0.5
−0.5
8
0
Timeshift, seconds
35910/110/1
1
I150
150
10
75
H150
IPI, ms
200
100
8
6
Time, seconds
1
G
IPI, ms
IPI, ms
80
IPI, ms
120
4
8
90
150
2
6
Time, seconds
0
F 85
0
4
0.5
E140
100
2
1
D200
A100
38682/6/18
140
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
36786/5/9
0.5
110
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
36786/5/13
0.5
200
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
35910/5/3
IPI, ms
60
0
1
2
4
6
Time, seconds
0.5
0
0
Timeshift, seconds
10
0.5
4
6
Time, seconds
8
10
0
−0.5
−0.5
0
Timeshift, seconds
36786/5/17
0.5
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
38682/6/6
0.5
120
110
0
2
4
1
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
38682/6/14
0.5
120
I100
100
80
0
1
2
4
6
Time, seconds
0.5
0
−0.5
−0.5
0
Timeshift, seconds
8
10
0.5
80
60
0
1
2
4
6
Time, seconds
8
10
0.5
0
−0.5
−0.5
0
Timeshift, seconds
0.5
130
−0.5
−0.5
8
80
IPI, ms
50
H
2
0.5
120
0.5
120
0
1
100
0
80
140
IPI, ms
100
100
F130
120
120
E150
IPI, ms
140
120
2
C
IPI, ms
IPI, ms
0
100
G100
IPI, ms
36777/5/23
B130
IPI, ms
140
120
D
IPI, ms
A
36744/5/27
36744/5/23
Appendix E
Mean estimator results
c=0.06
c=0.12
c=0.2
300
Mean IPI estimate, ms
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.1: The results of using the average of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for deatils.
131
132
APPENDIX E. MEAN ESTIMATOR RESULTS
c=0.06
c=0.12
c=0.2
300
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.2: The results of using the median of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for deatils.
c=0.06
c=0.12
c=0.2
300
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.3: The results of using the trimmean of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for
deatils.
133
c=0.06
c=0.12
c=0.2
300
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.4: The results of using the mode of the IPIs as an estimator for the mean IPI. See Section 4.4.1 for deatils.
c=0.06
c=0.12
c=0.2
300
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.5: The results of using the mode2 algorithm on the IPIs as an estimator for the mean IPI. See Section 4.4.1
for deatils.
134
c=0.06
c=0.12
c=0.2
300
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.6: The results of using the RemOutliers algorithm on the IPIs as an estimator for the mean IPI. See
Section 4.4.1 for deatils.
c=0.06
c=0.12
c=0.2
300
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.7: The results of using the EFE algorithm on the IPIs as an estimator for the mean IPI. See Section 4.4.1
for deatils.
135
c=0.06
c=0.12
c=0.2
300
250
200
150
100
50
0
0
20
40
Detection error, %
0
20
40
Detection error, %
0
20
40
Detection error, %
Figure E.8: The results of using the McGill algorithm on the IPIs as an estimator for the mean IPI. See Section 4.4.1
for deatils.
Appendix F
Standard deviation estimator results
std IPI estimate, ms
c=0.06
c=0.12
c=0.2
15
30
50
12
24
40
9
18
30
6
12
20
3
6
10
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
Figure F.1: The results of using the standard deviation. See Section 4.4.1 for deatils.
136
50
137
APPENDIX F. STANDARD DEVIATION ESTIMATOR RESULTS
c=0.06
c=0.12
c=0.2
15
30
50
12
24
40
9
18
30
6
12
20
3
6
10
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
Figure F.2: The results of using 0.74131IQR on the IPIs as an estimator for the standard deviation. See Section 4.4.1
for deatils.
c=0.06
c=0.12
c=0.2
15
30
50
12
24
40
9
18
30
6
12
20
3
6
10
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
Figure F.3: The results of using 0.885MCD on the IPIs as an estimator for the standard deviation. See Section 4.4.1
for deatils.
138
APPENDIX F. STANDARD DEVIATION ESTIMATOR RESULTS
c=0.06
c=0.12
c=0.2
15
30
50
12
24
40
9
18
30
6
12
20
3
6
10
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
Figure F.4: The results of using the RemOutliers algorithm on the IPIs as an estimator for the standard deviation.
See Section 4.4.1 for deatils.
c=0.06
c=0.12
c=0.2
15
30
50
12
24
40
9
18
30
6
12
20
3
6
10
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
0
10 20 30 40
Detection error, %
50
Figure F.5: The results of using the EFE algorithm on the IPIs as an estimator for the standard deviation. See
Section 4.4.1 for deatils.

Detailed Analysis of Clinical Electromyography Signals

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