Analyse de la variabilité du rythme cardiaque pour l’évaluation du système nerveux autonome « Etude de cas physiologique »

Abstract

The heart rate variability (HRV) is defined as the fluctuation time-series in the beat-to-beat RR-intervals, calculated from the electrocardiogram (ECG), is a key indicator of an individual’s cardiovascular condition. Assessment of HRV has been shown to aid clinical diagnosis. The analysis of heart rate variability is a subject of active research due to the easy access of this type of measurement in the individual medical interpretations that can be made and the richness of the number of treatments that can be considered. From a physiological standpoint, the HRV is the consequence of the modulation of heart rate by the autonomic nervous system. The HRV spectral parameters are classically used for studying the autonomic nervous system, as they allow the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm. However, this evaluation is usually based on fixed frequency regions, which does not allow possible variation. Besides, the bounds defining the low and high frequency regions may dynamically vary and instantaneous bounds should be defined.It is therefore necessary to use a method that makes the boundaries adapt to the data as a function of time. A solution has been proposed with the individual time dependant spectral boundaries (ITSB) algorithm sensitive to noisy environments. In this context, In order to overcome these difficulties, we proposed the constrained Gaussian modeling (CGM) method that dynamically models the power spectrum as a two Gaussian shapes mixture. It appeared that this procedure was able to accurately follow the exact parameters in the case of simulated data, in comparison with a parameter estimation obtained with arigid frequency cutting approach or with the ITSB algorithm. Real data results obtained on a classical stand-test and on the Fantasia database are also presented and discussed. In the case of transitory events, a greater sensitivity was observed with this CGM method and in the case of a classification purpose, the same method also showed better results. In addition this method CGM showed the interaction between HRV and blood pressure better than the other two methods.