ECG-AuxNet: A Dual-Branch Spatial-Temporal Feature Fusion Framework With Auxiliary Learning for Enhanced Cardiac Disease Diagnosis

OBJECTIVE: Multiple limitations exist in current automated ECG analysis, including insufficient feature integration across leads, limited interpretability, poor generalization, and inadequate handling of class imbalance. To address these challenges, we develop a novel dual-branch framework that comprehensively captures spatial-temporal features for cardiac disease diagnosis. METHODS: ECG-AuxNet combines a Multi-scale Transformer Attention CNN for spatial feature extraction and a GRU network for temporal dependency modeling. A Dual-stage Cross-Attention Fusion module integrates features from both branches, while a Feature Space Reconstruction (FSR) auxiliary task is introduced as a manifold regularizer to enhance feature discrimination. The framework was evaluated on PTB-XL (15,709 ECGs) and validated in real-world clinical scenarios (SXMU-2k, 1,673 ECGs). RESULTS: For class-imbalanced disease recognition (NORM, CD, MI, STTC), ECG-AuxNet attained 78.34% F1-score on PTB-XL and 82.63% F1-score on SXMU-2k, outperforming 9 baseline models. FSR significantly improved feature discrimination by 11.7%, enhancing class boundary clarity and classification accuracy. Grad-CAM analysis revealed attention patterns that precisely match cardiologists' diagnostic focus areas. CONCLUSION: ECG-AuxNet effectively integrates spatial-temporal features through auxiliary learning, achieving robust generalizability in cardiac disease diagnosis with interpretability aligned with clinical expertise.