ECGFounder: An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains

This is the official implementation of our paper "An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains".

Authors: Jun Li, Aaron Aguirre, Junior Moura, Jiarui Jin, Che Liu, Lanhai Zhong, Chenxi Sun, Gari Clifford, Brandon Westover, Shenda Hong.

🚀 Getting Started

🚩 News (Mar 2025): The pre-training checkpoint is now available on 🤗 Hugging Face!

Installation

To clone this repository:

git clone https://github.com/PKUDigitalHealth/ECGFounder.git

Environment Set Up

Install required packages:

conda create -n ECGFounder python=3.10
conda activate ECGFounder
pip install -r requirements.txt

Fine-tune on Downstream Tasks

In our paper, downstream datasets we used are as follows:

MIMIC-ECG: Please download the MIMIC-ECG dataset from physionet.

Next, please download the model's checkpoint from the 🤗 Hugging Face. And place the model weights in path ./checkpoint

You can run the jupyter notebook to finetune the model by the example dataset.

References

If you found our work useful in your research, please consider citing our works at:

@article{li2024electrocardiogram,
title={An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains},
author={Li, Jun and Aguirre, Aaron and Moura, Junior and Liu, Che and Zhong, Lanhai and Sun, Chenxi and Clifford, Gari and Westover, Brandon and Hong, Shenda},
journal={arXiv preprint arXiv:2410.04133},
year={2024}
}