MDT-HMTL: A Multidimensional Temporal Hierarchical Multi-Task Learning Framework for Myocardial Infarction Detection and State Classification Using PPG Signals.

Continuous assessment of myocardial infarction (MI) using photoplethysmography (PPG) signals has potential for out-of-hospital cardiovascular monitoring, yet conventional single-channel and short-window approaches have limitations in capturing complex hemodynamic variations and dynamic disease-state transitions. To address these limitations, we propose MDT-HMTL, a Multidimensional Temporal Hierarchical Multi-Task Learning framework tailored for simultaneous MI detection and disease-state classification. By integrating multi-channel physiological representations with hierarchical temporal modeling, MDT-HMTL captures local morphological characteristics and longer-range temporal dependencies within a unified multi-task architecture. Evaluation on the MIMIC-III clinical database achieved an average area under the ROC curve (AUC) of 0.908 and an overall accuracy of 86.4%. Furthermore, ablation studies demonstrated the contributions of multi-dimensional representation learning and hierarchical temporal modeling to the overall performance. The proposed framework demonstrates the feasibility of PPG-based MI detection and longitudinal disease-state assessment, highlighting its potential for future non-invasive and wearable-compatible cardiovascular monitoring.