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
You could get dataset at https://www.physionet.org/content/cpsc2021/1.0.0/
- To modify the dataset path in 'My_util.py'.
python train_net1d.py
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
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
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}
}