Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram.

Electrocardiograms (ECGs) are widely used to assess cardiac health, but traditional clinical interpretation relies on a limited set of human-defined parameters. While advanced data-driven methods can outperform analyses of conventional ECG features for some tasks, they often lack interpretability. Variational autoencoders (VAEs), a form of unsupervised machine learning, can address this limitation by extracting ECG features that are both comprehensive and interpretable, known as latent factors. These latent factors provide a low-dimensional representation optimised to capture the full informational content of the ECG. The aim of this study was to develop a deep learning model to learn these latent ECG features, and to use this optimised feature set in genetic analyses to identify fundamental determinants of cardiac electrical function. This approach has the potential to expand our understanding of cardiac electrophysiology by uncovering novel phenotypic and genetic relationships.

31,118 European ancestry individuals