Abstract—Automated Electrocardiogram (ECG) heartbeat classification plays an important role in arrhythmia diagnosis and clinical decision-support systems. Although deep-learning approaches have recently achieved remarkable performance, many require substantial computational resources and lack interpretability. This study proposes an interpretable feature-based machinelearning framework for automated heartbeat classification using the MIT-BIH Arrhythmia Database. Twenty physiologically meaningful morphological and temporal features were extracted from each heartbeat. Random Forest, XGBoost, and CatBoost, were evaluated under a rigorous patient-independent inter-record validation protocol. Random Forest achieved the highest overall accuracy (89.34%), whereas CatBoost obtained the highest weighted precision (90.90%), weighted F1-score (89.28%), and macro F1-score (40.77%). ROC analysis demonstrated strong discrimination for ventricular ectopic beats (AUC = 0.986) and fusion beats (AUC = 0.937), whereas supraventricular ectopic beats remained challenging (AUC = 0.653) because of their morphological similarity to normal beats and severe class imbalance. Feature-importance analysis identified the previous RR interval and waveform statistical descriptors among the most influential predictors. These findings demonstrate that interpretable feature-based machine learning can provide computationally efficient ECG classification under patient-independent evaluation, although external validation and real-time implementation studies are required before clinical deployment.

Index Terms—Electrocardiogram (ECG), arrhythmia classification, machine learning, random forest, patient-independent validation, feature engineering, explainable artificial intelligence, biomedical signal processing

Cite: Leila Sahraoui, Abdelaziz Frih, and Hacene Frih, "Explainable Feature-based Machine Learning for Patient-independent ECG Arrhythmia Classification," International Journal of Electrical and Electronic Engineering & Telecommunications, vol. 15, no. 5, pp. 378-393, 2026. doi: 10.18178/ijeetc.15.5.378-393

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).