Leveraging the wearable 1-lead ECG signal. From cardiac rhythm to cardiac function assessment

Abstract

Traditional 12-lead ECGs offer comprehensive insights into the electrical activity of the heart, but require clinical settings and expert interpretation, which limits their accessibility. Smartwatch 1-lead ECGs can be recorded at home, allowing more frequent and rapid monitoring, opening opportunities for early adverse event detection and enhanced patient autonomy. This study investigates whether 1-lead ECGs can provide clinically meaningful information beyond heart rhythm assessment. Using explainable deep learning models, we predict left ventricular function (LVF) from both 1-lead and 12-lead ECGs in a post-myocardial infarction population, and compare their respective performances. Our findings demonstrate that LVF can be accurately predicted from 1-lead ECGs alone (AUC = 0.883), nearly matching the predictive performance of 12-lead ECGs (AUC = 0.897). Explainability analyses further reveal that the models leverage physiologically plausible ECG features, supporting the validity of this approach. These results suggest that 1-lead ECGs, when combined with explainable AI, have the potential to support broader clinical applications and empower patients, particularly in resource-limited or remote settings where access to traditional cardiac diagnostics remains constrained.