Machine learning-enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support

This study presents an interpretable machine learning framework for classifying ECG arrhythmias, demonstrating that classical methods like Support Vector Machines can achieve high accuracy. By using specific feature extraction and selection techniques, the researchers offer a transparent alternative to complex black-box deep learning models.