ECG-FM: An Open Electrocardiogram Foundation Model

• URL: http://arxiv.org/abs/2408.05178v2
• Date: Fri, 30 May 2025 15:29:06 GMT
• Title: ECG-FM: An Open Electrocardiogram Foundation Model
• Authors: Kaden McKeen, Sameer Masood, Augustin Toma, Barry Rubin, Bo Wang,
• Abstract summary: We present ECG-FM, an open foundation model for ECG analysis, and conduct a study using a dataset of 1.5 million ECGs.<n>ECG-FM is a transformer-based model pretrained using a hybrid contrastive and generative self-supervised learning approach.<n>We affirm that ECG-FM is robust, label-efficient, and functionally discriminative by showcasing data scaling experiments, performing a latent space analysis, and generating saliency maps.
• Score: 3.8270632390229777
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Conventional task-specific electrocardiogram (ECG) analysis models require large annotated datasets to train. Foundation models mitigate this burden by leveraging self-supervised pretraining; however, the scarcity of open-weight ECG foundation models hinders adoption and cross-study comparability. We present ECG-FM, an open foundation model for ECG analysis, and conduct a study using a dataset of 1.5 million ECGs. ECG-FM is a transformer-based model pretrained using a hybrid contrastive and generative self-supervised learning approach. Our downstream tasks include predicting reduced left ventricular ejection fraction (LVEF) and ECG interpretation labels, where we release a benchmark task on the MIMIC-IV-ECG dataset. We affirm that ECG-FM is robust, label-efficient, and functionally discriminative by showcasing data scaling experiments, performing a latent space analysis, and generating saliency maps. ECG-FM markedly outperforms task-specific models in the small-to-medium-scale data regime and demonstrates cross-dataset generalizability, achieving high AUROC on many clinically salient labels such as atrial fibrillation (0.996) and LVEF<=40% (0.929). We release our code, model weights, and benchmark task at https://github.com/bowang-lab/ECG-FM/.

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