Efficient ECG Beat Classification Using SMOTE-Enhanced SimCLR Representations and a Lightweight MLP

Cardiac arrhythmias are among the leading causes of morbidity and mortality worldwide, and accurate classification of electrocardiogram (ECG) beats is critical for early diagnosis and follow-up. Supervised deep learning is effective but requires abundant labels and substantial computation, limiting practicality. We propose a simple, efficient framework that learns self-supervised ECG representations with SimCLR and uses a lightweight Multi-Layer Perceptron (MLP) for classification. Beat-centered 300-sample segments from MIT-BIH Arrhythmia are used, and imbalance is mitigated via SMOTE. Framed from a symmetry/asymmetry perspective, we exploit a symmetric beat window (150 pre- and 150 post-samples) to encourage approximate translation invariance around the R-peak, while SimCLR jitter/scale augmentations further promote invariance in the learned space; conversely, arrhythmic beats are interpreted as symmetry-breaking departures that aid discrimination. The proposed approach achieves robust performance: 97.2% overall test accuracy, 97.2% macro-average F1-score, and AUC > 0.997 across five beat classes. Notably, the challenging atrial premature beat (A) attains 94.1% F1, indicating effective minority-class characterization with low computation. These results show that combining SMOTE with SimCLR-based representations yields discriminative features and strong generalization under symmetry-consistent perturbations, highlighting potential for real-time or embedded healthcare systems.