Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators.

Ricardo Espinosa,Jesica Talero,Alejandro Weinstein

doi:10.3390/e22111298

Ricardo Espinosa, Jesica Talero + Show 1 more

Open Access

https://doi.org/10.3390/e22111298

Copy DOI

Abstract

Electrocardiography (ECG) and electroencephalography (EEG) signals provide clinical information relevant to determine a patient’s health status. The nonlinear analysis of ECG and EEG signals allows for discovering characteristics that could not be found with traditional methods based on amplitude and frequency. Approximate entropy (ApEn) and sampling entropy (SampEn) are nonlinear data analysis algorithms that measure the data’s regularity, and these are used to classify different electrophysiological signals as normal or pathological. Entropy calculation requires setting the parameters r (tolerance threshold), m (immersion dimension), and τ (time delay), with the last one being related to how the time series is downsampled. In this study, we showed the dependence of ApEn and SampEn on different values of τ, for ECG and EEG signals with different sampling frequencies (Fs), extracted from a digital repository. We considered four values of Fs (128, 256, 384, and 512 Hz for the ECG signals, and 160, 320, 480, and 640 Hz for the EEG signals) and five values of τ (from 1 to 5). We performed parametric and nonparametric statistical tests to confirm that the groups of normal and pathological ECG and EEG signals were significantly different (p < 0.05) for each F and τ value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with Fs and τ. For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of Fs, and τ larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of Fs and τ larger than 1. Therefore, adjusting τ may be convenient for signals that were acquired with different Fs to ensure a reliable clinical classification. Furthermore, it is useful to set τ to values larger than 1 to reduce the computational cost.

Highlights

The health status of a patient can be evaluated through the signals generated by the body.These signals are captured by different biomedical technologies and provide relevant information that is interpreted by a clinician
This study focuses on Approximate entropy (ApEn) and sampling entropy (SampEn) because numerous studies that use these estimators have emerged in recent years, both at the research-theoretical level and at the level of technological development [10]
Results obtained with ApEn and SampEn were compared between normal and pathological signals (ECG, EEG) using parametric and nonparametric statistical tests

Summary

Introduction

The health status of a patient can be evaluated through the signals generated by the body These signals are captured by different biomedical technologies and provide relevant information that is interpreted by a clinician. One of the most popular techniques to capture biological signals is the placement of electrodes on the skin’s surface. These electrodes allow for the recording of the sum of action potentials generated by excitable cells (muscle and neuronal) in the body’s specific area. These signals are amplified, filtered, processed, and visualized and recorded in a digital medium or printed. The most common of these electrophysiological signals are electrocardiographic (ECG) and electroencephalographic (EEG)

Methods

Results

Discussion

Conclusion