Two models:
- A convolutional neural network (CNN) which takes as input 30-second single-channel EEG signals and produces as output a sleep apnea classification.
- A CNN trained on the same data which classifies sleep stage. Possible classifications are N1, N2, N3, REM, and W (awake).
Both models include implementation, validation, and an example trained model.
- Python 3.5-3.8 (you can use pyenv to manage different python versions).
- Training data from Physionet.
- This project doesn't run easily on the M1 Macbook due to TensorFlow incompatibilities
This project is best set up in a virtual environment to isolate package and language versions.
In the terminal, navigate into the project directory and create a new virtual environment using a compatible (3.5-3.8) version of Python:
python3.8 -m venv env
Activate virtual environment:
source env/bin/activate
Install required packages:
python -m pip install -r requirements.txt
You also need wget if you want to automate the downloading of Physionet data:
brew install wget
You're all set! Run python3 -m src.sleepapnea.getdata to retrieve and preprocess physionet data, and python3 -m src.sleepapnea.modelarchitecture to train your first model.