MousaviSajad / Sleepeegnet
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
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SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
In this study, we introduced a novel deep learning approach, called SleepEEGNet, for automated sleep stage scoring using a single-channel EEG.
Paper
Our paper can be downloaded from the arxiv website.
-
The Model architecture
-
The CNN architecture
Recruitments
- Python 2.7
- tensorflow/tensorflow-gpu
- numpy
- scipy
- matplotlib
- scikit-learn
- matplotlib
- imbalanced-learn(0.4.3)
- pandas
- mne
Dataset and Data Preparation
We evaluated our model using the Physionet Sleep-EDF datasets published in 2013 and 2018.
We have used the source code provided by github:akaraspt to prepare the dataset.
- To download SC subjects from the Sleep_EDF (2013) dataset, use the below script:
cd data_2013
chmod +x download_physionet.sh
./download_physionet.sh
- To download SC subjects from the Sleep_EDF (2018) dataset, use the below script:
cd data_2018
chmod +x download_physionet.sh
./download_physionet.sh
Use below scripts to extract sleep stages from the specific EEG channels of the Sleep_EDF (2013) dataset:
python prepare_physionet.py --data_dir data_2013 --output_dir data_2013/eeg_fpz_cz --select_ch 'EEG Fpz-Cz'
python prepare_physionet.py --data_dir data_2013 --output_dir data_2013/eeg_pz_oz --select_ch 'EEG Pz-Oz'
Train
-
Modify args settings in seq2seq_sleep_sleep-EDF.py for each dataset.
-
For example, run the below script to train SleepEEGNET model with the 20-fold cross-validation using Fpz-Cz channel of the Sleep_EDF (2013) dataset:
python seq2seq_sleep_sleep-EDF.py --data_dir data_2013/eeg_fpz_cz --output_dir output_2013 --num_folds 20
Results
- Run the below script to present the achieved results by SleepEEGNet model for Fpz-Cz channel.
python summary.py --data_dir output_2013/eeg_fpz_cz
Visualization
- Run the below script to visualize attention maps of a sequence input (EEG epochs) for Fpz-Cz channel.
python visualize.py --data_dir output_2013/eeg_fpz_cz
Citation
If you find it useful, please cite our paper as follows:
@article{mousavi2019sleepEEGnet,
title={SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach},
author={Sajad Mousavi, Fatemeh Afghah and U. Rajendra Acharya},
journal={arXiv preprint arXiv:1903.02108},
year={2019}
}
References
Licence
For academtic and non-commercial usage
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