Efficient and Interpretable ECG Abnormality Detection via a Lightweight DSCR-BiGRU-Attention Network with Demographic Fusion

Deep learning has advanced automated electrocardiogram (ECG) interpretation, yet many models are computationally expensive, opaque, and overlook demographic factors. We propose DBA-ASFNet, a lightweight network that combines depthwise-separable convolutional residual blocks with a BiGRU and an attention mechanism to extract rich spatiotemporal features from 12-lead ECGs while maintaining low computational requirements. The Age-and-Sex Fusion (ASF) module integrates demographic information without enlarging the model, enabling personalized predictions. On the PTB-XL and CPSC2018 datasets, DBA-ASFNet achieves competitive multi-label performance with only ~0.03 million parameters and ~6.43 MFLOPs per inference. Real-time testing on a Raspberry Pi 5 achieved an average inference latency of ~2 ms, supporting deployment on resource-limited devices. Shapley additive explanations (SHAP) analysis shows that the model focuses on clinically meaningful ECG patterns and appropriately incorporates demographic factors, enhancing transparency. These results suggest that DBA-ASFNet is suited for accurate, efficient, and interpretable ECG analysis.