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CombSleepNet

Hyeong-Jin Kim, Minji Lee, Dong-Ok Won, and Seong-Whan Lee, "CombSleepNet: Combination of Spectral–Temporal CNN and Bi-LSTM with Dense Connectivity for Automatic Sleep Stage Scoring," submit to IEEE Journal of Biomedical and Health Informatics.

How to run

  1. Download the Sleep-EDF database
    • Sleep-EDF database is available here.
      Note that, we used only 20 people of the Sleep Cassette data.
      i.e. from subject SC4001 to SC4192 in the sleep-cassette.
  1. Data pre-processing

    • Change directory to ./CombSleepNet/pre-processing
    • Unzip eeglab.zip
    • Run preprocessing.m
  2. Training and testing the CombSleepNet

    • Change directory to ./CombSleepNet
    • Refer to example.txt to train and test CombSleepNet.

Environment:

  • Matlab R2019b
  • Python3
  • Pytorch v1.3.1
  • numpy v1.17.4
  • scipy v1.3.3
  • scikit-learn v0.22

Result:

Hypnogram and posterior probability distribution with CombSleepNet for one subject of Sleep-EDF database

Contact:

Hyeong-Jin Kim

Department of Brain and Cognitive Engineering, Korea University
Email: kme0115@korea.ac.kr

License:

This project is released under the MIT License.

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