Entropy Analysis of Electrophysiological Signals

Dear Colleagues,

In recent decades, entropy-based analyses have emerged as powerful tool for quantifying the complexity, regularity, and information content of complex biological signals, such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG).

Signal processing methods based on information theory and statistical mechanics, such as Approximate Entropy, Sample Entropy, Multiscale Entropy, and others, have been increasingly employed for studying the complex nonlinear dynamics of electrophysiological signals under both healthy and pathological conditions, as well as the effects of their modulation by pharmacological, psychological, or non-invasive stimulation treatments.

Traditional linear methods, while valuable, are often limited in their ability to capture subtle or multi-scale dynamical changes. Entropy-based measures, in contrast, provide approaches that allow us to characterize these signals across different scales and conditions. By assessing entropy across different temporal and spatial scales, these techniques provide insights into neural dynamics, autonomic regulation, and disease progression, contributing to more accurate diagnostics and personalized interventions.

This Special Issue aims to bring together cutting-edge research and methodological developments focused on the entropy analysis of electrophysiological signals, including theoretical advances, algorithmic improvements, and applied studies utilizing entropy-based tools to better understand the structure and function of neural, muscular, or cardiac systems.

Topics of interest include, but are not limited to, the following:

• Novel entropy measures and complexity metrics for biomedical signal analysis;
• Multiscale and multivariate entropy approaches;
• Applications in cognitive neuroscience, cardiology, neurology, and sleep research;
• Entropy-based methods in brain–computer interfaces and wearable health monitoring;
• Challenges in signal preprocessing, parameter selection, and real-time implementation;
• Comparative studies using traditional or machine learning-based approaches.

Dr. Elzbieta Olejarczyk
Guest Editor

Manuscript Submission Information

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