Self-Supervised Learning for Sleep Stage Classification with Predictive and Discriminative Contrastive Coding

This repository contains the implementation of our proposed model SleepDPC of paper Self-Supervised Learning for Sleep Stage Classification with Predictive and Discriminative Contrastive Coding in ICASSP2021.

Basic Usage

A typical command to run the model on the SleepEDF dataset would be:

python main.py --data-name sleepedf --data-path <your-data-path> --pretrain-epochs 50 --seed 2020 --optimizer adam --fold 0 --kfold 10 --batch-size 32 --channels 2

To see more options, please type python main.py -h.

Main Results

Citation

If you find our paper is helpful for your research, please cite this paper:

@INPROCEEDINGS{9414752,
  author={Xiao, Qinfeng and Wang, Jing and Ye, Jianan and Zhang, Hongjun and Bu, Yuyan and Zhang, Yiqiong and Wu, Hao},
  booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Self-Supervised Learning for Sleep Stage Classification with Predictive and Discriminative Contrastive Coding}, 
  year={2021},
  volume={},
  number={},
  pages={1290-1294},
  doi={10.1109/ICASSP39728.2021.9414752}
}