LeadMix-MST: Robust Myocardial Infarction Detection from Minimal-Lead ECG Signals

The 12-lead electrocardiogram (ECG) is the standard for diagnosing myocardial infarction (MI), but its use in pre-hospital and resource-limited settings is limited by hardware complexity and electrode placement requirements. This study addresses a biomedical and health informatics challenge by investigating MI screening from minimal-lead ECG suitable for mobile and telemedicine applications. We propose LeadMix-MST, a lead-mixing multi scale spatio-temporal architecture for reduced-lead ECG analysis. Using three 12-lead ECG datasets, we conducted patient-level cross-dataset evaluations across devices and populations. The predefined lead set was an important design factor, with I-aVL-V5 and I-II-V3 performing most consistently. On these leads, the architecture remained competitive with larger baseline models at under 1Mparameters. A model trained on the Shandong Provincial Hospital dataset and evaluated on the Physikalisch-Technische Bun desanstalt dataset achieved an F1-score of 0.933 ± 0.003 and an AUROC of 0.937 ± 0.004 using a 3-lead configuration (I-aVL-V5), exceeding the full 12-lead setting on this dataset. The proposed framework maintains a favor able balance between diagnostic performance, parameter efficiency, and practical deployability, primarily by reducing electrode placement burden.