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ECG-FM: An Open Electrocardiogram Foundation Model
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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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Cited by 7 Pith papers
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DeepHHF, trained on day-long single-lead Holter ECGs from 40,174 patients, predicted incident heart failure within five years with AUROC 0.80 and external AUROC 0.81.
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Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms
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Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography
PhysioCLR adds physiology-based positive/negative pair selection, heartbeat shuffling, and peak-aware reconstruction to ECG contrastive learning, improving downstream arrhythmia AUROC on Chapman, Georgia, and private ...
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Self-DANA: A Resource-Efficient Channel-Adaptive Self-Supervised Approach for ECG Foundation Models
Self-DANA combines dimension-adaptive pooling with random lead selection to fine-tune ECG foundation models on reduced-lead inputs, cutting memory and time while maintaining diagnostic accuracy.
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From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining
MELP pretrains ECG and text encoders with token-, beat-, and rhythm-level cross-modal supervision and beats prior baselines on several ECG classification benchmarks.
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QualityFM, a multimodal ECG/PPG foundation model using self-distillation from clean to noisy signals, outperforms task-specific baselines on three ICU monitoring tasks.
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