Study of the ANS Sympathovagal Behavior Using the ReliefF and the KS-Segmentation Algorithms

Abstract

The Autonomous Nervous System (ANS) sympathovagal balance was studied using several features derived from Heart Rate Variability signals (HRV). The HRV signals are, however naturally, non-stationary since their statistical properties vary under time transition. A useful approach to quantifying them is, therefore, to consider them as consisting of some intervals that are themselves stationary. To obtain the latter, we have applied the so called the KS-segmentation algorithm which is an approach deduced from the Kolmogorov-Smirnov (KS) statistics. To determine, accurately, these features, we have used the ReliefF algorithm which is one of the most successful strategies in feature selection; this step allows us to select the most relevant features from thirty three features at the beginning. As result the ratio between LF and HF band powers of HRV signal, the Standard Deviation of RR intervals (SDNN), and Detrended Fluctuation Analysis with Short term slope (DFA α1), are more accurate for each stationary segment, and present the best results comparing with other features for the classification of the three stages of stress in real world driving tasks (Low, Medium and High stress).