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.
