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}
}
Runs of introvoyz041 ECGFounder on huggingface.co
13
Total runs
-1
24-hour runs
-1
3-day runs
4
7-day runs
9
30-day runs
More Information About ECGFounder huggingface.co Model
ECGFounder huggingface.co is an AI model on huggingface.co that provides ECGFounder's model effect (), which can be used instantly with this introvoyz041 ECGFounder model. huggingface.co supports a free trial of the ECGFounder model, and also provides paid use of the ECGFounder. Support call ECGFounder model through api, including Node.js, Python, http.
ECGFounder huggingface.co is an online trial and call api platform, which integrates ECGFounder's modeling effects, including api services, and provides a free online trial of ECGFounder, you can try ECGFounder online for free by clicking the link below.
introvoyz041 ECGFounder online free url in huggingface.co:
ECGFounder is an open source model from GitHub that offers a free installation service, and any user can find ECGFounder on GitHub to install. At the same time, huggingface.co provides the effect of ECGFounder install, users can directly use ECGFounder installed effect in huggingface.co for debugging and trial. It also supports api for free installation.