In Vivo Tachogram Analysis

Pierre R.Bertrand
INRIA Saclay and Clermont University
October 2010
We provide two solutions to overcome the current technological
obstacle for in vivo analysis of heart beat time series and its
regulation
120,000 heart beats
processed in 15 seconds*,
compressed by a factor of 1000
Shift from in vitro analysis to
in vivo analysis of
tachogram regulation
* Implemented in Matlab with a 2.8 GHz processor
2/8
Heart Frequency (b/mn)
Tachogram = the series of heart beats durations (R-R interval)
= speed of the heart engine
Time(hour)
3/8
First solution
‘Raw’ tachogram
Tachogram
cleaning**
‘Cleaned’
Tachogram
FDpV***:
change detection
technique
Frequency(b/mn) & log Wavelet Energy
Playing football
Task 1
Task 2
Picking
Free afternoon
Time(hour)
Sleeping
zoom
** A technique developped by Khalfa & Bertrand (2009)
*** Filtered Derivative with p-Value is a technique developped by Bertrand, Fhima & Guillin (2010)
4/8
With FDpV we are able to detect different periods of the training which is not obvious
by simply observing the tachogram
Warming -up
The play
Coach’s recommendations
Playing football
5/8
Frequency(b/mn) & log Wavelet Energy
Playing football
Task 1
Task 2
Picking
Free afternoon
Time(hour)
zoom
Sleeping
6/8
Frequency(b/mn) & log Wavelet Energy
Warming -up
The play
Coach’s recommendations
Orthosympathetic regulation
parasympathetic regulation
Playing football
Time(hour)
7/8
Our technology permits:
A speedy off-line segmentation (even for huge data sets)
To measure the variation of orthosympathetic and parasympathetic systems by using
only heartbeat times series
8/8