Ultra-Short entropy for mental stress detection

Abstract

Approximate Entropy (ApEn) and Sample Entropy (SampEn) are measures of signals’ complexity and are widely used in Heart Rate Variability (HRV) analysis. In particular, recent studies proved that almost all the features measuring complexity of RR series statistically decreased during the stress and therefore, thus showing ability to detect stress. However, the choice of the similarity threshold r and minimum data length N required for their computation are still controversial. In fact, most entropy measures are considered not reliable for recordings shorter than 5 min and different threshold values r have shown to affect the analysis thus leading to incorrect conclusions. Therefore, the aim of this study was to understand the impact of changing parameters r and N for the computation of ApEn and SampEn and to select the optimal parameters to detect stress in healthy subjects. To accomplish it, 84 RR series, extracted from electrocardiography signals acquired during real-life stress, were analyzed. ApEn and SampEn were estimated for two different values of r computed using previously published methods and for N = {100, 200, 300, 400, 500} data points. The statistical significance for the differences in mean ApEn and SampEn values was assessed by non-parametric tests. The two methods used to compute r produced entropy values significantly different over different N values. In contrast, ApEn and SampEn showed consistency in differentiating rest and stress conditions for different input parameters. More specifically, ApEnChon and SampEnChon showed to have a better discrimination power between stressed subjects and resting subjects on ultra-short recordings (N < 500).