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

Liang-Hung Wang, Jia-Wen Wang, Chao-Xin Xie (), Zne-Jung Lee (), Bing-Jie Cai, Tsung-Yi Chen, Shih-Lun Chen, Chiung-An Chen, Patricia Angela R. Abu and Tao Yang ()
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Liang-Hung Wang: School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China
Jia-Wen Wang: School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China
Chao-Xin Xie: The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
Zne-Jung Lee: School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China
Bing-Jie Cai: The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
Tsung-Yi Chen: Department of Electronic Engineering, Feng Chia University, Taichung 40724, Taiwan
Shih-Lun Chen: The Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City 320317, Taiwan
Chiung-An Chen: Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
Patricia Angela R. Abu: The Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines
Tao Yang: The Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

Mathematics, 2025, vol. 13, issue 17, 1-18

Abstract: 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.

Keywords: atrial fibrillation; ECG signals; attention mechanism; neural network; early detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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