# 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

  ![Alt text](/images/seq2seq_sleep.jpg)  

 * The CNN architecture  

  ![Alt text](/images/seq2seq_cnn.jpg)

## 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

```

![Alt text](/images/results.jpg)

## 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