ECG-FM: an open electrocardiogram foundation model

To develop ECG-FM, an open-weight foundation model for electrocardiogram (ECG) analysis, rigorously evaluate its performance on clinically salient tasks, and openly release it alongside a public benchmark.

In a study using 1.5 million 12-lead ECGs, we present ECG-FM, a transformer-based foundation model pretrained with hybrid self-supervision that combines masked reconstruction and contrastive learning with ECG-specific augmentation. Downstream, we evaluate multi-label ECG interpretation and prediction of reduced left ventricular ejection fraction (LVEF), introducing an openly available benchmark on the MIMIC-IV-ECG dataset. We assess ECG-FM's capabilities through data scaling experiments, latent-space structure analysis, and attention-based saliency.

Finetuned ECG-FM models outperform task-specific baselines in the small-to-medium-scale data regime, exhibit strong label efficiency and cross-dataset generalizability, and achieve high AUROC on salient labels, including atrial fibrillation (0.996) and LVEF $\le 40\%$ (0.929). The pretrained encoder showcases competitive linear probing performance, with functionally discriminative embeddings.

Findings indicate that ECG-FM is generalizable, label-efficient, and discriminative for screening, risk stratification, and monitoring. Its representations capture low-level morphology and high-order cardiac semantics, and the pretrained encoder serves as a robust feature-set generator. This work mitigates reliance on large labeled datasets, reduces compute and data requirements, and lowers barriers to reproducibility and cross-study comparison.

ECG-FM is an open, rigorously validated ECG foundation model intended to accelerate transparent, comparable research in the ECG analysis subfield. It is designed for rapid integration and evaluation, especially for delivering practical gains in low-label settings. We release our code, model weights, tutorials, and benchmark at https://github.com/bowang-lab/ECG-FM/.