Comparison of Entropy-Based Regularity Estimators: Application to the Fetal Heart Rate Signal for the Identification of Fetal Distress

Abstract

This paper considers the multiscale entropy (MSE) approach for estimating the regularity of time series at different scales. Sample entropy (SampEn) and approximate entropy (ApEn) are evaluated in MSE analysis on simulated data to enhance the main features of both estimators. We applied the approximate entropy and the sample entropy estimators to fetal heart rate signals on both single and multiple scales for an early identification of fetal sufferance antepartum. Our results show that the ApEn index significantly distinguishes suffering from normal fetuses between the 30th and the 35th week of gestation. Furthermore, our data shows that the MSE entropy values are reliable indicators of the fetal distress associated with the presence of a pathological condition at birth.

Entropy in Investigation of Vasovagal Syndrome in Passive Head Up Tilt Test

“…The in-depth description of the comparisons of both of these statistics can be found in the literature [20]. Despite elementary differences between ApEn and SampEn, both statistics are extensively used in cardiovascular physiology [20,22,23,[27][28][29][30][31][33][34][35][36][40][41][42]. In this paper we also show the possible applications of ApEn (m, r, N) and SampEn (m, r, N) for the analysis of physiological signals.…”

“…For large values of N and after some modifications [30][31][32][33][34][35][36], Formula (7) can finally be expressed as:…”

“…The presented algorithm was extensively used in many cardiovascular studies [33][34][35][36], but it is known to have some limitations. First of all, ApEn counts a self-match of vectors and hence generates a bias towards regularity [20,30].…”

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

“…For this purpose, we created a MIX process used in previous studies (Ferrario et al 2006, Pincus 1991. It is defined as:…”

“…, and y is a uniformly distributed variable on [ ] Ferrario et al 2006, Pincus 1991. The synthetic signal was based on a MIX process whose parameter p varied from 0.9 to 0.1 linearly.…”