SleepEEGNet：使用序列到序列深度学习方法的自动睡眠阶段评分

# 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](https://arxiv.org/pdf/1903.02108). * The Model architecture  * The CNN architecture  ## Requirements * 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](https://physionet.org/physiobank/database/sleep-edfx/) published in 2013 and 2018. We have used the source code provided by [github:akaraspt](https://github.com/akaraspt/deepsleepnet) 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 [github:akaraspt](https://github.com/akaraspt/deepsleepnet) [deepschool.io](https://github.com/sachinruk/deepschool.io/blob/master/DL-Keras_Tensorflow) ## Licence For academtic and non-commercial usage