A Real-Time Deep Learning Approach for Electrocardiogram-Based Cardiovascular Disease Prediction with Adaptive Drift Detection and Generative Feature Replay

Cardiovascular diseases (CVDs) continue to present a leading cause of mortality worldwide, emphasizing the importance of early and accurate prediction. Electrocardiogram (ECG) signals, central to cardiac monitoring, have increasingly been integrated with Deep Learning (DL) for real-time prediction of CVDs. However, DL models are prone to performance degradation due to concept drift and to catastrophic forgetting. To address this issue, we propose a real-time CVDs prediction approach, referred to as ADWIN-GFR that combines Convolutional Neural Network (CNN) layers, for spatial feature extraction, with Gated Recurrent Units (GRU), for temporal modeling, alongside adaptive drift detection and mitigation mechanisms. The proposed approach integrates Adaptive Windowing (ADWIN) for real-time concept drift detection, a fine-tuning strategy based on Generative Features Replay (GFR) to preserve previously acquired knowledge, and a dynamic replay buffer ensuring variance, diversity, and data distribution coverage. Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques, achieving an average post-drift accuracy of 95.4%, a macro F1-score of 93.9%, and a remarkably low forgetting score of 0.9%. It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%. These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems, including wearable ECG devices and hospital-based patient monitoring platforms.