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

Abstract

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. 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). 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. ECG-AuxNet effectively integrates spatial-temporal features through auxiliary learning, achieving robust generalizability in cardiac disease diagnosis with interpretability aligned with clinical expertise.