A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function.

ABSTRACT:

Background

The wide availability of labeled electrocardiogram (ECG) data has driven major advances in artificial intelligence (AI)-based detection of structural and functional cardiac abnormalities and thus ECG-based diagnosis. However, many critical, high value clinical diagnostic applications, such as assessing myocardial ischemia and coronary microvascular dysfunction, remain underserved due to the limited availability of labeled datasets. We developed a self-supervised ECG foundation model and demonstrate how this approach can overcome this limitation.

Methods

A modified vision transformer model was pretrained using a large database of unlabeled ECG waveforms (MIMIC-IV-ECG, N=800,035). The model was then fine-tuned using smaller databases that included high-quality label

SUBMITTER: Moody JB 

PROVIDER: S-EPMC12724683 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature