Estimation of spectral parameters of nonstationary HRV signals using Gaussian fitting spectra

The heart rate variability (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 based on the definition of frequency bands that are found to be controversial because of possible changes in the frequency boundaries due to physiological factors or experimental conditions. We propose to overcome this difficulty by dynamically modelling the power spectrum as a two Gaussian shapes mixture. It appeared that this procedure was able to more accurately follow the exact parameters in the case of simulated data, in comparison with parameters estimation obtained with a rigid frequency cutting approach or with the ITSB algorithm. Real data results obtained on a classical stand-test are also presented and discussed.