Sleep stage classification using EEG data with a convolutional neural network
├── data
│ └── raw : Original data from physionet.org
│
├── graphs : Summary plots and results saved during model training
│
├── README.md
│
├── report
│ └── final : Final project report and presentation slides
│
├── requirements.yml : Python environment description for use with Conda
│
├── src
│ ├── code : All ETL, model building, and analysis code
│ ├── notebook : Exploratory analysis and data visualization
│ └── output : Exploratory analysis and data visualization
│ └── model : Saved object of the trained CNN model
For reproducibility, the raw data used for this project can be downloaded from physionet.org via
$ rsync -Cavz physionet.org::sleep-edfx data/raw
and a link for this project submission has also been made available on Google Drive.
To replicate the results of this project, first be sure to properly mirror the project's environment using Conda.
$ conda create --name sleep-study --file requirements.yml
$ source activate sleep-study
Once the raw data has been downloaded from Physionet as described above, train and evaluate the model.
(sleep-study)$ cd src/code/
(sleep-study)$ python3 train_sleep.py
The resulting plots and summary metrics will be found in graphs/ and the trained model objects can be found in src/output/model/. Load the trained PyTorch model object back into memory for later use with
torch.load('src/output/model/SleepCNNBest.pth')
Links to the final video presentation and slide deck can be found in the final report, saved in report/final/.