Myocardial ischemia, the leading cause of heart attacks, remains a leading cause of death and disability worldwide. Delays in diagnosis are directly correlated with increased myocardial necrosis, increased complication rates, and increased mortality. Although conventional 12-lead ECG is the clinical gold standard for ischemia detection, its transient nature prevents it from capturing transient and unpredictable ischemic episodes during continuous monitoring in the outpatient setting. Although wearable ECG devices are excellent at detecting arrhythmias such as atrial fibrillation (>95% sensitivity), their usefulness in ischemia detection has long been limited by subtle multiscale temporal changes in the ECG waveform that indicate the progression of ischemia, from subtle changes in ST segments and T waves to beat-to-beat variability shifts that unfold over minutes to hours.
To address these long-standing clinical gaps, the research team developed a hierarchical time-fusion transformer architecture that simultaneously models ischemic dynamics over three physiologically important timescales. These include intrabeat morphological feature extraction to capture the earliest ischemic markers, beat-to-beat variability modeling to track the progression of cardiac stress, and long-term trend analysis with extended temporal convolutional networks. This framework uses dual-task learning to jointly predict impending ischemia, stratify injury risk after reperfusion, and improve performance through shared pathophysiological representations. The system pairs with an FDA-cleared chest-worn single-lead ECG patch with 14-day continuous monitoring capability and greater than 92% signal quality tolerance during daily activities.
Validated across four large datasets including a total of 108,778 patients (including 17,173 ischemia-positive cases), the framework achieved an area under the receiver operating characteristic curve (AUROC) of 0.947 for ischemia detection. This is a relative improvement of 4.8% to 9.5% compared to the state-of-the-art baseline model. Across all cohorts, sensitivity ranged from 84.1% to 87.3%, specificity reached 90%, and the concordance index (C-index) for risk stratification after reperfusion was 0.923. Importantly, the system maintained a positive predictive value (PPV) of 88.7% at 15 minutes and 84.1% at 20 minutes, ensuring reliable and actionable alerts with minimal false positives to avoid clinician alert fatigue. We achieved consistent performance across all age, gender, and comorbidity subgroups, with no evidence of demographic bias. In real-world deployment, the complete model performs inference on a 10-second ECG segment in just 47.3 ms. The lightweight pruned variant reduces inference time to 28.6 ms and maintains AUROC above 0.93, making it compatible with standard clinical hardware.
The 18.4-minute early warning window addresses the core clinical challenge of “time is of the essence” in the management of acute coronary syndromes, allowing bedside assessment, initiation of emergency protocols, and catheterization lab mobilization before irreversible myocardial damage occurs. The attention patterns of this system closely match the ischemic markers identified by cardiologists (Spearman correlation 0.78 to 0.84), ensuring strong clinical interpretability. Limitations include the primarily Chinese hospital-based study cohort, which requires further validation across diverse ethnic, socio-economic, and healthcare settings, in parallel with prospective clinical trials to confirm real-world patient outcomes. Future research will extend the framework to predict additional cardiovascular events, integrate electronic medical records for personalized risk assessment, and develop federated learning approaches to protect patient privacy while improving model performance.
Authors of this paper include Songtao An, Jiamin Yuan, Yang Pan, Miaoqing Ye, Zhenghan Chen, Minying Li, Panyue Yan, Jiali Yao, Yujie Guan, Yan Lin, Wenjuan Wang, Haliminai Dilimulati, Yuanyin Teng, Keyu Dai, Yuqi Bai, Junbo Ge, and Dong Deng.
This study was partially supported by the Youth Program of the National Natural Science Foundation of China (No. 82304983), the National Natural Science Grant of China (No. 81970312), the Soochow University Horizontal Project (code numbers: H230269 and H240140), and the Suzhou Multicenter Clinical Research Project for Major Diseases (grant number: DZXYJ202302). This research is supported by the Undergraduate Innovation Laboratory of the School of Pharmacy, Guangzhou University of Traditional Chinese Medicine.
sauce:
Beijing Institute of Technology Press
Reference magazines:
Ann, S. others. (2026). Bionic wearable ECG using MLLM: coherent temporal modeling for early ischemia warning. Cyborgs and bionic systems. DOI: 10.34133/cbsystems.0501. https://spj.science.org/doi/10.34133/cbsystems.0501
