TSleep - Automated Sleep Stage Scoring using Deep Learning

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• Final project of "CS598. Deep Learning for Healthcare" course at University of Illinois at Urbana-Champaign
• Team: thucd2@illinois.edu (1 member)

In this experiment, I construct two deep neural networks to score the sleep stage automatically. Both models are working on single-channel signals from Sleep Cassette study dataset in publicly available Sleep-EDF 2018 dataset. The first model comprises multiple convolutional neural networks (CNN) and bidirectional long short-term memory (Bi-LSTM) networks achieving overall accuracy of 75.4%, an average macro F1-score of 67.8% across all folds in 20-fold cross-validation and inter-rater reliability coefficient Cohen's kappa κ = 0.66. The second model is based on convolutional neural networks predicting with an average accuracy of 79% and average macro F1-score of 75% and κ =0.70 in the same cross-validation procedure. The result shows that my model is comparable to state-of-the-art methods with hand-engineered features.

Read the full report here

Download dataset

I evaluate the model with data from Sleep Cassette study of Sleep-EDF 2018 Dataset. Run the command below to download all data:

cd data
chmod +x download_sleep_edfx.sh
./download_sleep_edfx.sh

Extract channels from dataset

Then run the following script to extract specified EEG channels and their corresponding sleep stages. (this script was forked from DeepSleepNet)

python prepare_physionet.py --data_dir data/sleep-cassette --output_dir data/sc_eeg_fpz_cz --select_ch 'EEG Fpz-Cz'
python prepare_physionet.py --data_dir data/sleep-cassette --output_dir data/sc_eeg_pz_oz --select_ch 'EEG Pz-Oz'

Training & evaluate model

usage: main.py [-h] [--run RUN] [--data_dir DATA_DIR]
                [--output_dir OUTPUT_DIR] [--fold_ids FOLD_IDS]
                [--model MODEL] [--total_fold TOTAL_FOLD]

optional arguments:
  -h, --help            show this help message and exit
  --run RUN             running mode: train / summarize.
                        + train: Training. Need to specfiy data_dir, output_dir, fold_ids, model and total_fold
                        + summarize: View result of the last run in which the result is stored in `output_dir`
  --data_dir DATA_DIR   directory contain extracted channels
  --output_dir OUTPUT_DIR
                        Directory to store model, progress, result
  --fold_ids FOLD_IDS   fold/folds to train, each valid fold from [0 to total_fold-1].
                        + Can be a single fold, for example: 0 - the first fold, or 19 - the last fold in 20-fold cross validation
                        + Can contain multiple folds, separate by comma `,`, for example: 0,1,2.
                        + -1 to train on all folds
  --model MODEL         model to train and evaluate performance
                        + mod_sleep_eeg: Modified SleepEEG - using CNNS
                        + mod_deep_sleep: Modified DeepSleep - using CNNs + Bi-LSTMs
  --total_fold TOTAL_FOLD
                        Number of fold

Some quick usage examples

• Train & evaluate result on the first fold of Modified Sleep EEG Net

python main.py --model mod_sleep_eeg --fold_ids 0 --data_dir data/sc_eeg_fpz_cz --output_dir output/sc_eeg_fpz_cz

• Train & evaluate result on the first two folds of Modified SleepEEG Net

python main.py --model mod_sleep_eeg --fold_ids 0,1 --data_dir data/sc_eeg_fpz_cz --output_dir output/sc_eeg_fpz_cz

• Train & evaluate result on the last fold of Modified DeepSleep Net

python main.py --model mod_deep_sleep --fold_ids 19 --data_dir data/sc_eeg_fpz_cz --output_dir output/sc_eeg_fpz_cz

• Train and evaluate result on all folds of Modified SleepEEG Net

python main.py --model mod_sleep_eeg --fold_ids -1 --data_dir data/sc_eeg_fpz_cz --output_dir output/sc_eeg_fpz_cz

• View summary of performance of the last run of modified_sleep_eeg model (Can only view summary if you finished training)

python main.py --run summarize --output_dir output/sc_eeg_fpz_cz/modified_sleep_eeg

Environment

• My hardware:

  • AMD (R) Ryzen 7 3700x 8 cores 16 threads
  • 32 GB RAM
  • GPU GeForce GTX 1660 6GB
  • 1TB SSD.

• The software environment is as follows:

  • python 3.7.10
  • tensorflow/tensorflow-gpu 2.4.1
  • numpy 1.19.2
  • pandas 1.2.4
  • scikit-learn 0.24.1
  • mne 0.23.0

Notice: I have faced some runtime issues with tensorflow 2.1.x, and numpy 1.20, so it's advised to create a conda enviroment from environment.yml to make sure you have the same libraries as mine. You can use the script below:

conda env create -f environment.yml