ECGT2T: Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads

Electrocardiograms (ECGs) are a non-invasive measurement used to observe the condition of the heart. Modern wearable devices can record ECGs, but typically provide fewer leads--signal outputs from electrodes--than the 12 in standard electrocardiograms. Consequently, data generated from these devices may be insufficient for accurately diagnosing more complex cardiac conditions. To bridge this gap, we propose {ECGT2T}, a deep generative model that synthesizes ten leads from an asynchronous Lead I and Lead II input to simulate a 12-lead ECG. Compared to the R-peaks of the original waveforms, the generated signals had timing and amplitude errors under 15 milliseconds and 10%, respectively. Experiments on two widely used ECG datasets show classifiers trained on synthesized 12-lead electrocardiograms generated with ECGT2T outperforms models trained on one- or two-lead ECGs in detecting myocardial infarction and arrhythmia.