L2L-ESN: Electrocardiogram Lead Synthesis for Assisting Myocardial Infarction Diagnosis

Wearable electrocardiogram (ECG) monitoring devices, important terminals in the Internet of Medical Things (IoMT), can provide real-time and rapid myocardial infarction (MI) warning services for large populations. This enables early medical intervention and considerably improves the survival rate of MI patients, thereby alleviating the increasing prevalence of cardiovascular diseases. However, the number of leads in wearable ECG devices is insufficient to accurately diagnose complex cardiac conditions. To address this issue, this study proposes the Lead-to-Lead ECG Synthesis Network (L2L-ESN), which is based on a conditional Wasserstein generative adversarial network. It utilizes multihead generation to learn the relationships among leads, synthesize complete 12-lead ECGs from specified leads, and automatically identify the location of MI, thereby maximizing the utilization of available resources and improving diagnostic accuracy under limited-lead constraints, ultimately enabling accurate early warning and timely treatment of MI through wearable devices. The proposed method was evaluated in an IoMT-oriented reduced-lead setting on 2 real-world ECG datasets using waveform similarity metrics, downstream diagnostic tests, qualitative visualization, and deployment profiling. The results demonstrate that L2L-ESN generates more accurate electrocardiographic signals for MI diagnosis, achieving a higher diagnostic accuracy.