Hierarchical Multiattention Temporal Fusion Network for Dual-Task Atrial Fibrillation Subtyping and Early Risk Prediction

Atrial fibrillation (AF) is a common arrhythmia associated with major adverse cardiovascular events. Early detection and short-horizon risk prediction are therefore clinically critical. Prior attention-based electrocardiogram (ECG) models typically treated subtype classification and short-horizon onset risk prediction as separate tasks and optimized attention in only one representational dimension rather than in a coordinated hierarchy. We propose a hierarchical multiattention temporal fusion network (HMA-TFN). The proposed framework jointly integrates lead-level, morphology-level, and rhythm-level attention, enabling the model to simultaneously highlight diagnostically informative leads, capture waveform abnormalities, and characterize long-range temporal dependencies. Moreover, the model is trained for dual tasks—AF subtype classification and 30-min onset prediction. Experiments were conducted on three open-source databases and the Fuzhou University–Fujian Provincial Hospital (FZU-FPH) clinical database, comprising thousands of dual-lead ECG recordings from a diverse subject population. Experimental results show that HMA-TFN achieves 95.77% accuracy in classifying paroxysmal AF (PAAF) and persistent AF (PEAF), and 96.36% accuracy in predicting PAAF occurrence 30 min in advance. Ablations show monotonic gains as each attention level is added, delivering 14.0% accuracy over the baseline for subtyping and 5.2% for prediction. Grad-CAM visualization highlights clinically relevant features such as absent P-waves, confirming model interpretability. On the FZU-FPH clinical database, it achieves a generalization performance of 94.31%, demonstrating its strong potential for clinical application.