Accurate detection of ECG arrhythmias plays a critical role in enabling timely diagnosis and treatment of cardiovascular diseases, which remain the leading cause of mortality worldwide. However, achieving high classification performance remains challenging due to class imbalance, signal variability, and resource constraints in real-time deployments. This study aims to enhance ECG arrhythmia detection accuracy through an optimized ensemble approach combining multiple CNN models with a novel stage-wise sieving strategy.
Methodology: Three lightweight CNN models (ShuffleNet, MobileNet-v2, ResNet-18) were integrated into a multi-stage binary classification framework. Each stage systematically eliminated accurately classified arrhythmia classes. The novelty of the proposed approach lies in introducing a stage-wise sieving strategy that incrementally removes well-classified classes, combined with an optimized ensemble fusion of multiple CNN models guided by metaheuristic optimization techniques to boost performance. Optimization techniques, including Particle Swarm Optimization, Whale Optimization Algorithm, Grey Wolf Optimizer, Ant Colony Optimization, and Firefly Algorithm, were applied to improve model fusion. The approach was validated using combined public datasets (PTB-XL, MIT-BIH, and Shaoxing ECG databases). Results: The proposed stage-wise sieving ensemble significantly improved overall classification accuracy by 17.95%, reaching 96.29% accuracy using the Grey Wolf Optimizer. Classes previously misclassified, such as Conduction Disturbance and Hypertrophy, exhibited accuracy improvements of up to 32.44% and 25.19%, respectively.
Conclusion: The proposed optimized ensemble approach significantly enhances ECG arrhythmia detection performance and demonstrates feasibility for real-time deployment on resource-constrained platforms such as Raspberry Pi.