Automated ECG Arrhythmia Classification Using Convolutional Neural Networks with Effective Class Imbalance Handling

Cardiac arrhythmias are a leading cause of cardiovascular mortality worldwide, necessitating accurate automated detection systems for continuous monitoring and clinical decision support. This study addresses the critical challenge of severe class imbalance in ECG beat classification, where normal beats comprise 82.8% of samples while life-threatening ventricular arrhythmias represent only 6.5%. We propose a lightweight one-dimensional convolutional neural network (1D-CNN) trained with a two-pronged class-balancing strategy: random oversampling of minority classes to 35% of the majority class size, combined with class-weighted cross-entropy loss. Recent work has achieved accuracies approaching 99–100% on the MIT-BIH database through increasingly complex architectures, including transfer learning, attention mechanisms, and multi-channel fusion. However, these approaches often require millions of parameters, limiting deployability on resource-constrained wearables. Despite the recent trend toward complexity, our simple four-block CNN with only 398,469 parameters achieves 99.18% overall test accuracy and a 96.38% macro-averaged F1-score on the MIT-BIH Arrhythmia Database—competitive with state-of-the-art methods while using 90–96% fewer parameters. Critically, the model attains 98.32% recall on ventricular beats, demonstrating high sensitivity for detecting life-threatening arrhythmias. Ablation studies confirm that both oversampling and weighted loss are essential: removing either component causes catastrophic performance degradation. Our results challenge the assumption that architectural complexity is necessary for ECG classification and demonstrate that proper class imbalance handling enables simple models to achieve state-of-the-art performances with superior computational efficiency suitable for deployment in wearable cardiac monitoring devices.