ECG-FM

Base pretrained

University of Toronto / Vector Institute (Bo Wang Lab)

12-lead ECG

Filter catalog by Modality:
ECG

Atrial fibrillation

Filter catalog by Disease / Trait:
Arrhythmia

LV systolic dysfunction (LVSD)

Filter catalog by Disease / Trait:
Cardiac Function & Hemodynamics

General Purpose / Multi-task

Filter catalog by Disease / Trait:
General / Foundation

Binary classification

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Classification

Multi-label classification

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Classification

Transformer

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Transformer

PyTorch

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PyTorch

MIT

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Permissive

file_copy

View model files code View code

Open ECG foundation model with 90.9M parameters, built on a wav2vec 2.0-style Transformer and pretrained on 1.25-1.5 million ECGs using a hybrid contrastive-and-generative self-supervised objective. Base pretrained weights and MIMIC-IV-ECG-finetuned downstream checkpoints are both released. Developed on the fairseq_signals framework by the University of Toronto / Vector Institute's Wang lab.

memory Specifications

gavel License

MIT

check_small Open source check_small Commercial use OK check_small Modifications allowed Attribution required No share-alike requirement

License for model weights only. Associated code may be licensed seperately, check code source for specific terms.

description Publication

ECG-FM: An Open Electrocardiogram Foundation Model open_in_new

2024 · original paper

database Training & evaluation data

MIMIC-IV-ECG open_in_new

train

public 161,352 subjects · USA

800,035 12-lead ECG-report pairs from 161,352 subjects at Beth Israel Deaconess Medical Center.

science Capabilities & performance

Atrial fibrillation

Binary classification Atrial fibrillation

0.996 AUROC MIMIC-IV-ECG downstream benchmark · internal

LVEF <=40%

Binary classification LV systolic dysfunction (LVSD)

0.929 AUROC MIMIC-IV-ECG downstream benchmark · internal