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 tau (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 tau, for ECG and EEG signals with different sampling frequencies (F-s), extracted from a digital repository. We considered four values of F-s (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 tau (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 tau value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with F-s and tau. For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of F-s, and tau larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of F-s and tau larger than 1. Therefore, adjusting tau may be convenient for signals that were acquired with different F-s to ensure a reliable clinical classification. Furthermore, it is useful to set tau to values larger than 1 to reduce the computational cost.