ARRHYTHMIA DETECTION FROM IMBALANCED ECG DATA USING CNN, FOCAL LOSS, AND RR INTERVAL FEATURES WITH INTER-PATIENT EVALUATION

Abstract

Detection of arrhythmias from electrocardiogram (ECG) signals is crucial for early diagnosis of heart rhythm disorders. However, the imbalanced distribution of arrhythmia classes poses challenges for classification models. This study proposes a Convolutional Neural Network (CNN) model combined with Focal Loss and RR features to address class imbalance using an inter-patient evaluation approach. The dataset includes three classes: N (Normal), SVEB (Supraventricular Ectopic Beats), and VEB (Ventricular Ectopic Beats), divided into training (DS1), validation (DS1-Val), and testing (DS2) sets. The proposed CNN + Focal Loss + RR model achieved an accuracy of 95.75% and an F1-score of 95.19%. Additionally, Explainable AI (XAI) using LIME is applied to improve model interpretability by identifying important features influencing predictions. The results demonstrate that the proposed approach provides accurate and interpretable arrhythmia detection, supporting reliable medical decision-making. However, the model's performance on the minority SVEB class remains limited, with a recall of only 34.24%, indicating that SVEB beats are frequently misclassified as Normal due to morphological similarities and severe class imbalance. This limitation highlights the need for further improvements in minority-class detection for clinical applicability.