ISENet: a deep learning model for detecting ischemic ST changes in long-term ECG monitoring.

Abstract

Long-term ECG monitoring is crucial for detecting asymptomatic or intermittent myocardial ischemia, as it mitigates irreversible cardiac damage and prevents disease progression. Myocardial ischemia appears on ECG as transient ST-segment level and morphology alterations, known as ischemic ST change events (ISE). However, automatically identifying ISE based on ECG signals is challenging, as its recognition is highly susceptible to interference from non-ischemic ST change events, including heart rate-related ST change events (HRE), axis shift events (ASE), and conduction change events (CCE). To address this challenge, this study proposes ISENet, a lightweight deep learning-based neural network for ISE detection. The model was trained and evaluated using ECG signals and annotations from the PhysioNet long-term ST database, with tenfold cross-validation to ensure robustness and generalizability. Experimental results show that ISENet achieves an average ISE detection accuracy of 83.5%, surpassing benchmark models like VGG19 and ResNet50 while significantly reducing model complexity. This study is the first to apply a deep learning-based neural network for ISE detection using ECG signals from the long-term ST database. Compared to previous feature-engineering and feature-learning approaches, ISENet addresses key limitations in experimental design and methodology, representing a significant advancement in automated myocardial ischemia detection.