Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis

TL;DR

This paper introduces a refined multiscale fuzzy entropy method based on standard deviation, enhancing the analysis of biomedical signals by capturing complexity across multiple time scales with improved differentiation of physiological states.

Contribution

It proposes the RCMFEσ method, which uses standard deviation in coarse-graining, providing a novel approach that improves upon traditional multiscale entropy techniques for biomedical signal analysis.

Findings

RCMFEσ offers complementary information to classical methods.

It demonstrates superior performance in distinguishing physiological activities.

The method effectively analyzes synthetic and real biomedical signals.

Abstract

Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of fluctuations in the local mean value of biomedical time series. Recent developments in the field have tried to improve the MSE by reducing its variability in large scale factors. On the other hand, there has been recent interest in using other statistical moments than the mean, i.e. variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite multiscale fuzzy entropy based on the standard deviation (RCMFE{\sigma}) to quantify the dynamical properties of spread over multiple time scales. We demonstrate the dependency of the RCMFE{\sigma}, in comparison with other multiscale approaches, on several straightforward signal processing concepts using a set of synthetic signals. We also investigate the complementarity of using the standard deviation instead of the mean in the coarse-graining process using magnetoencephalograms in Alzheimer disease and publicly available electroencephalograms recorded from focal and non-focal areas in epilepsy. Our results indicate that RCMFE{\sigma} offers complementary information to that revealed by classical coarse-graining approaches and that it has superior performance to distinguish different types of physiological activity.