Automated detection of trace alternant during sleep in healthy full

Clinical Neurophysiology 112 (2001) 1893±1900
www.elsevier.com/locate/clinph
Automated detection of trace alternant during sleep in healthy full-term
neonates using discrete wavelet transform q
J.P. Turnbull, K.A. Loparo, M.W. Johnson, M.S. Scher*
Developmental Neurophysiology Laboratory, Department of Pediatric Neurology, Rainbow Babies and Children's Hospitals,
University Hospitals of Cleveland of the Case Western Reserve University Medical School, 11100 Euclid Avenue, Cleveland, OH 44106-6005, USA
Accepted 28 June 2001
Abstract
Objective: To develop an automated procedure for scoring neonatal sleep states using signal processing which are based on visual pattern
recognition techniques.
Methods: We are developing an automated computer system to study relationships among multiple non-cerebral physiologic measures
and brain activity in newborn infants, and are evaluating the usefulness of a number of different time±frequency domain transforms as
potential diagnostic tools.
Results: Wavelet transforms yield excellent results in the detection of all twenty trace alternant quiet sleep segments for 6 full-term
healthy infants.
Conclusions: We suggest that this method will be useful for the automated detection of neonatal sleep states, and may help delineate when
sleep cycle disturbances occur on either an environmental or disease basis. More accurate physiologic descriptions of neonatal state may
improve the clinician's ability to assess functional brain organization for a given post-conceptional age as well as document functional brain
maturation at progressively older corrected ages. q 2001 Published by Elsevier Science Ireland Ltd.
Keywords: Trace alternant; Electroencephalogram-sleep; Neonate; Computer; Wavelet transform
1. Introduction
Developmental neurophysiologists have traditionally
identi®ed by visual analyses 4 distinct electroencephalogram (EEG) patterns during sleep on the EEG of full-term
neonates (Stockard-Pope et al., 1992; Scher, 1999). These
states are also identi®ed by the occurrence of a cluster of
speci®c electrographic/polygraphic measures identi®ed by a
trained expert. Such patterns are conventionally expressed
on an analog EEG/polygraphic recording. These state
segments sequentially appear as: (1) mixed frequency active
sleep, (2) high voltage slow quiet sleep, (3) trace alternant
quiet sleep, and (4) low voltage irregular active sleep. Transitional or indeterminate sleep punctuate within and
between state segments. As a general rule, the neonate
expresses these 4 states in this ordinal sequence, and this
pattern repeats throughout the entire duration during which
the infant is asleep.
Visual identi®cation of these states is a laborious task for
q
Presented as part of the American Academy of Sleep Medicine Meeting,
Las Vegas, NV, June 2000.
* Corresponding author. Tel: 11-2168447791; fax: 11-2168448966.
E-mail address: [email protected] (M.S. Scher).
the neurophysiologist , especially when records extend for
several hours. It is, therefore, our goal to develop an automated procedure for scoring neonatal sleep states, using
signal processing methods which are based on visual pattern
recognition techniques. Previous work (Gath and Bar-on
1980; Principe and Smith, 1986; Principe et al., 1989;
Shimada and Shiina, 2000) accurately described automated
sleep state identi®cation in older patients, as de®ned by
Rechtschaffen and Kales (1968). The motivation for this
research effort will be to apply such methodology to the
identi®cation of neonatal sleep states and explore its applicability to detect pervasive neonatal brain disorders.
2. Methods
2.1. Description of neonatal EEG sleep stages
The following sequence characterizes the 4 major ordinal
EEG sleep state segments of the neonate (Figs. 1a±d): active
sleep is also called rapid eye movement (REM) sleep
(Stockard-Pope et al., 1992), and usually begins the sleep
cycle after falling asleep. Mixed frequency active sleep is
characterized by stationary and continuous signals with
1388-2457/01/$ - see front matter q 2001 Published by Elsevier Science Ireland Ltd.
PII: S 1388-245 7(01)00641-1
CLINPH 2001050
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J.P. Turnbull et al. / Clinical Neurophysiology 112 (2001) 1893±1900
Fig. 1. Samples of an EEG recording of a healthy full-term infant, depicting the 4 segments of the neonatal sleep cycle: (a) mixed frequency active; (b) high
voltage slow; (c) trace alternant; and (d) low voltage irregular.
frequencies spread across the human EEG spectrum (with
most of the signal power between 0.5 and 10 Hz). The
energy is dominant in the theta band with intermittent
delta waveforms. High voltage slow quiet sleep appears
following mixed frequency active sleep, and is a brief
state, characterized by a shift in the frequency distribution
to the higher power in the lower (delta) frequencies. This
high voltage slow segment is the ®rst of two quiet sleep
segments, or non-rapid eye movement (NREM) sleep
segments (Stockard-Pope et al., 1992). Trace alternant
quiet sleep then follows high voltage sleep, characterized
by non-stationary signals of alternating broad band bursts of
activity with intermittent epochs of relative EEG quiescence, comprised over lower amplitude and less frequencies. Low voltage irregular is the last state segment
expressed during the neonatal sleep cycle, and is character-
Table 1
Percentages of neonatal sleep segments for 6 healthy full-term infants a
Patient
Mixed frequency
High voltage slow
Trace alternant
Low voltage irregular
Transitional indeterminate
61
63
72
75
85
88
Total
20.8
29.3
31.7
34.6
37.6
26.8
30.5
6.2
6.6
2.2
2.5
3.0
4.9
4.2
36.2
35.4
44.6
34.6
20.3
30.5
34.0
10.0
18.8
8.6
6.9
19.5
12.2
13.0
26.9
9.9
12.9
21.4
19.5
25.6
18.4
a
Based on 3 h of recording.
J.P. Turnbull et al. / Clinical Neurophysiology 112 (2001) 1893±1900
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Fig. 2. Method of performing DWTs using quadrature transforms.
ized by relatively lower amplitude signals at broadband
higher frequency distributions. Trace alternant is the most
distinctive EEG pattern of the 4 segments, given the discontinuity of EEG waveforms. Therefore, we chose this state
segment to compare selected signal processing algorithms
with respect to the accuracy of detection of state transitions
into and out of trace alternant.
2.2. Algorithm development for automated detection of
trace alternant
Six healthy neonates were selected to have an EEG
recording performed within 3 days after birth. Written
informed consent approved by our hospital institutional
review board, was obtained from all families before taking
part in the study protocol. All infants were full term in
gestational age (38±42 weeks), and age-appropriate in
birth weights. All infants were delivered via vaginal presentation and appeared healthy at birth without signs of fetal
distress or neonatal depression. Recordings were collected
at 64 samples per second with a 12 bit analog to digital
converter. A pediatric neurophysiologist visually scored
the ®rst 3 h of a 12 h recording assigning a sleep state
segment for each of the ®rst 180 min. Based on the computer
algorithms developed from one patient, similar analyses
were applied to the remaining 5 patients to assess the perfor-
Fig. 3. A discrete wavelet decomposition for a typical 20 s segment of one EEG channel.
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J.P. Turnbull et al. / Clinical Neurophysiology 112 (2001) 1893±1900
Fig. 4. Power estimation in each frequency band width for each 1 s epoch.
mance and accuracy of the selected algorithms to detect
trace alternant quiet sleep. The percentage of time each
patient spent in the various sleep stages was initially determined from the neurologist's visually derived scores in
Table 1.
Detection of trace alternant was chosen to compare signal
processing algorithms, given the higher percentage of
occurrence and the distinctive discontinuous electrographic
pattern.
As the algorithms used to classify state for a given patient
are de®ned in terms of the time evolution of the frequency
distribution of energy in the signal, we explored the use of
various time±frequency domain tools to extract salient
features of the electrographic portion of the recordings
that were useful in state classi®cation of trace alternant
quiet sleep.
Since the EEG signals were acquired with a sampling rate
of 64 samples per second, the Nyquist frequency is 32 Hz
and the frequency bands in our discrete wavelet transform
(DWT) through successive bisection of the frequency intervals 16±32, 8±16, 4±8, 2±4, 1±2, and ,1 Hz, respectively.
Since subsequent analyses were trained to energy distributions between speci®c frequency bands, we did not have the
¯exibility to use algorithms on EEG sampled at different
sampling rates. For example, some recordings were
acquired with a sampling rate of 80 samples per second.
Our proposed solution was to use interpolation methods to
transform via up-sampling for all recordings to the highest
sampling rate. As reported in early analyses study of aliasing with these data sets (Sun et al., 1993), we assumed that
energy above Nyquist was negligible. We then reconstructed signals at a higher sampling rate where no information was lost or created.
In this study, we initially compared various time±
frequency domain transforms to extract features used in
the determination of state. Methods applied to the data
Fig. 5. Sample of Fourier transform of theta power for a 60 s segment of
trace alternant quiet sleep.
J.P. Turnbull et al. / Clinical Neurophysiology 112 (2001) 1893±1900
sets included Wigner-Ville (Boashas, 1988; Reid and
Parsin, 1992; Strang and Ngugen, 1996), Gabor transforms,
continuous wavelet transforms (CWT), and DWT. We
present the results of trace alternant detection with DWT,
since this algorithm yielded the most accurate results.
3. Results
3.1. The discrete wavelet transform
The concept of wavelets operates within the constraints of
the Heisenberg uncertainty principle. Given limits to the
product of the time and frequency resolution, we were interested in high time resolution for high frequency components
and high frequency resolution for low frequency components. Rather than partitioning the time±frequency domain
into regular rectangular elements, we used a multiresolution
scheme that partitioned the domain in a way that was suitable to our requirements. A helpful introductory tutorial for
wavelets can be found in the October 1991 edition of the
IEEE Signal Processing Magazine (Rioul and Vetterli,
1991) The DWT has the following properties (Chui,
1992). We performed the DWT using quadrature mirror
®lters (Ruskai et al., 1992). This is a pair of complementary
high and low-pass ®lters, each with a cut-off frequency set to
one half of the Nyquist frequency. After applying the two
®lters to the signal, we down sampled every other data
sample. We then took the output of the lower half and
repeated the process. Fig. 2 illustrates this method. This
method is very ef®cient ± of order O…n log…n††.
We experimented with a variety of different wavelet
functions to perform this decomposition and have found
satisfactory results in both feature extraction and computational ef®ciency using 6th order Daubechies wavelets
(Strang and Nguyen, 1996). Fig. 3 illustrates a discrete
wavelet decomposition for a typical 20 s segment of one
EEG channel. We then estimated the power in each band for
each one second epoch (Fig. 4).
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We then used the same smoothing technique (Fig. 6) as
described in the previous section.
We then post-processed this signal with a low-pass ®lter.
This acted as a means of determining long-term trends in the
evolution of the observed processes. For this, we used a noncausal forward and backward 3 pole low-pass Butterworth
®lter with a cut-off of 1/10 of the Nyquist frequency. The
forward±backward ®ltering eliminates the phase shifting
effects of ®lters.
The plots shown in Fig. 7 demonstrate the robustness of
the trace alternant detection algorithm applied to all 6
patients. The clinical neurophysiologist independently by
visual analysis veri®ed the occurrence of trace alternant
quiet sleep segments for each 3 h EEG recording. Note
that all 20 visually identi®ed segments of trace alternant
were successfully detected by the automated state detector.
We investigated if the accuracy of state detection varied,
3.2. Detection of trace alternant quiet sleep using discrete
wavelet transform
We ®rst performed a DWT of the EEG for selected channels and then estimated the power of each frequency band for
each 1 s epoch. The Fourier transform of the power estimates
were computed for the 4±8 Hz (theta) frequency band. It was
then empirically observed that spectral peaks occurred
between 5 and 10 cycles per min during trace alternant.
Fig. 5 is a sample of this analysis for a 60 s segment processed
for an EEG during trace alternant quiet sleep.
We then used the following:
F… f † ˆ F{Pu …t†}
A ˆ sup
5#f #10
F… f †
uF… f †u
Fig. 6. (a) Initial smoothing technique for a 180-min EEG recording
(shaded areas indicate visually identi®ed minutes of trace alternant quiet
sleep). (b) Filtered data depicted in (a) used to eliminate phase shifting.
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depending on which combination of channels were used.
The same prediction of trace alternant occurred using anterior and posterior as well as left and right channel derivations over the scalp.
4. Discussion
The neonatal sleep cycle is unique during the human lifespan with respect to both sleep architecture and continuity.
Fig. 7. Demonstration of trace alternant detection for each of the six full-term neonates (shaded areas indicate visually identi®ed minutes of trace alternant quiet
sleep).
J.P. Turnbull et al. / Clinical Neurophysiology 112 (2001) 1893±1900
Re¯ecting fetal brain organization, 4 distinctive sleep state
segments are expressed starting after 36 weeks post-conceptional age until approximately 4±6 weeks after birth for the
40 week gestational age full-term infant (Scher, 1999).
Trace alternant is the most recognizable of the 4 sleep
state segments, given its discontinuous electrographic
expression, depicting periods of electrographic activities
alternating with periods of relative quiescence of both
frequencies and amplitudes of EEG waveforms. Trace alternant is also the most abundant of the 4 state segments within
the ultradian sleep cycle of the newborn (Stockard-Pope,
1992).
Trace alternant is an important physiologic marker for
infant functional brain organization and maturation of the
neonate and is ®rst expressed in the pre-term neonate or
fetus after 36 weeks estimated gestational age in both
humans (Scher et al., 1992; Scher, 1999) and lower primates
(Myers, 1995). This discontinuous state segment represents
the expected physiologic evolution at near term gestational
ages of the discontinuous EEG pattern (i.e. trace discontinu)
noted on EEG-sleep recordings of pre-term neonates.
Automated detection of this distinctive EEG pattern may
be helpful for both research and clinical purposes. Given
that higher percentages of quiet sleep after fetal/neonatal
stress (Emde et al., 1971; Freudigman and Thoman, 1993;
Scher, 1995) are expressed under environmental or medical
conditions, accurate identi®cation of repetitive sleep cycles
might aid in both medical management and prognosis of
sick and convalescent neonates. Also, since the chronobiologic expression of the repetitive neonatal ultradian rhythm
predominates over diurnal or circadian rhythms suggests
that the study of individual sleep state segments within the
30±70 min ultradian sleep cycle may better elucidate functional brain organization and maturation. How an EEG
pattern such as trace alternant coalesces in time with other
physiologic behaviors (i.e. autonomic and somatic behaviors) will better characterize the multiple neural networks
responsible for neonatal sleep state transitions as a function
of post-conceptional age.
Various computer strategies may improve our techniques
to detect the other sleep state segments. One method will be
to develop algorithms that can detect and reject artifact; noise
artifact is high amplitude power at the high frequency ranges
of the DWT, while cerebral EEG exhibits lower power in
these frequency bands. One could then improve the overall
estimated power distributions by eliminating those epochs
with suspect artifact-laden frequency components. Secondly,
the use of neural-network algorithms may accurately detect
relations that better map input±output pairs from a given
training set. Finally, Markov modeling can be applied to
the physiologic data sets to give a context sensitivity evaluation. Since it is known that the 4 states of neonatal sleep
progress in an ordinal sequence, it will be possible to exploit
this property to assess for the prediction of state transitions.
We recognize that there are a number of limitations in this
preliminary study. Firstly, the possibility of a liasing error
1899
exists, based on our choice of sampling rate compared to the
low-pass ®lter setting. However, we believe this error is
small, especially for 95% of the spectral energies, which
pertain to the delta and theta frequency ranges (Sun et al.,
1993). Secondly, only 6 neonates were included in this
preliminary study. The adequacy of the computer algorithms will have to be tested on a larger sample size over
multiple sleep cycles. Thirdly, strict artifact rejection procedures need to be included, as mentioned above.
We have demonstrated that it is possible to discern with
good reliability one segment of the neonatal sleep cycle (i.e.
trace alternant) using DWT. We will apply similar signal
processing techniques to detect the other 3 EEG-sleep state
segments of the neonatal ultradian sleep cycle. Also, we
intend to explore the role of neural networks to provide
clues as to which physiologic features best detect sleep
stages, comparing detection methods for both cerebral and
non-cerebral measures.
Acknowledgements
This research is supported in part by an NIH Institutional
Training Grant HL/NS 07913, and NS34508 and NR01894
(to M. S. Scher, MD Principal Investigator).
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