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PyTorch implementation of sleep-stage classifier using photoplethysmography (PPG) data.

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Model Architecture

TinySleepNet Note: Fs is the sampling rate of the input EEG signals

Environment

  • CUDA 10.0
  • cuDNN 7
  • Tensorflow 1.13.1

Create a virtual environment with conda

conda create -n tinysleepnet python=3.6
conda activate tinysleepnet
pip install -r requirements.txt

How to run

  1. python download_sleepedf.py
  2. python prepare_sleepedf.py
  3. python trainer.py --db sleepedf --gpu 0 --from_fold 0 --to_fold 19
  4. python predict.py --config_file config/sleepedf.py --model_dir out_sleepedf/train --output_dir out_sleepedf/predict --log_file out_sleepedf/predict.log --use-best

Licence

  • For academic and non-commercial use only
  • Apache License 2.0

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