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ECGBeat4AFSinus

A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm. 📃Read the paper

A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
arXiv preprint arXiv:2403.11405
Jun Lei, Yuxi Zhou, Xue Tian, Qinghao Zhao, Qi Zhang, Shijia Geng, Qingbo Wu, Shenda Hong

Last update on 21 May 2024

Dataset

You could get dataset at https://www.physionet.org/content/cpsc2021/1.0.0/

Run Project

  1. To modify the dataset path in 'My_util.py'.
  2. python train_net1d.py

Main dependencies

python==3.8.17
pytorch==1.13.0
numpy==1.24.3
scikit-learn==1.3.0
scipy==1.10.1
pandas==1.5.3
tqdm==4.65.0

Create an environment

Use the following command to create an environment based on the 'flowers_env.yml' file

conda env create -f flowers_env.yml

conda activate flowers_env

Reference

We appreciate your citations if you find our paper related and useful to your research!

@article{lei2024deep,
  title={A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm},
  author={Lei, Jun and Zhou, Yuxi and Tian, Xue and Zhao, Qinghao and Zhang, Qi and Geng, Shijia and Wu, Qingbo and Hong, Shenda},
  journal={arXiv preprint arXiv:2403.11405},
  year={2024}
}