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
Biomedical Signal Processing and Control
Jun Lei, Yuxi Zhou, Xue Tian, Qinghao Zhao, Qi Zhang, Shijia Geng, Qingbo Wu, Shenda Hong

Last update on 26 December 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{lei2025deep,
  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={Biomedical Signal Processing and Control},
  volume={100},
  pages={107028},
  year={2025},
  publisher={Elsevier}
}