Entropy Measures and Artificial Intelligence for Cardiac Arrhythmia Detection in ECG Signals

• André R. Coimbra UFG / IFG
• Ana Cristina S. Rebelo UFG
• Maria Ribeiro UFG / INESC TEC
• Antonio Oliveira-Jr UFG / Fraunhofer Portugal AICOS

DOI: https://doi.org/10.5753/sbcas.2026.20768

Abstract

Cardiac arrhythmia is a common cardiovascular condition characterized by irregular heart rhythms. This study evaluates entropy-based features extracted from short-duration Electrocardiogram (ECG) signals for the automatic detection of arrhythmias using Artificial Neural Networks (ANNs) in binary and multiclass settings. Five classical entropy measures were evaluated, individually and in pairs, in addition to Recurrence Microstate Entropy (RMEn). Our findings reveal that the combination of Sample Entropy (SampEn) and Singular Value Decomposition Entropy (SVDEn) achieved performance comparable to RMEn, outperforming it in 4 out of 6 binary classification scenarios and reaching an accuracy of 77.6% ± 2.1% in the multiclass classification task.

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