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https://github.com/thucdx/TSleep
Automated Sleep Stage Scoring using Deep Learing
https://github.com/thucdx/TSleep
bi-lstm cnn deep-learning healthcare rnn sleep-eeg sleep-stage-classification sleep-stage-scoring
Last synced: 9 days ago
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Automated Sleep Stage Scoring using Deep Learing
- Host: GitHub
- URL: https://github.com/thucdx/TSleep
- Owner: thucdx
- Created: 2021-05-07T19:31:24.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-05-08T06:15:20.000Z (over 3 years ago)
- Last Synced: 2024-08-01T13:30:43.589Z (3 months ago)
- Topics: bi-lstm, cnn, deep-learning, healthcare, rnn, sleep-eeg, sleep-stage-classification, sleep-stage-scoring
- Language: Python
- Homepage:
- Size: 438 KB
- Stars: 6
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## TSleep - Automated Sleep Stage Scoring using Deep Learning
-------
+ Final project of "**CS598. Deep Learning for Healthcare**" course at [University of Illinois at Urbana-Champaign](https://illinois.edu/)
+ Team: `[email protected]` (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](report/thucd2-sleep-project-final.pdf)
### 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](https://github.com/akaraspt/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
```