Convolutional neural network which takes as input 30 second single-channel EEG signals and produces as output a sleep stage classification. Possible classifications are N1, N2, N3, REM, and W (awake). Includes implementation, validation, and example trained model.
- Python 3.5-3.8 installed (for tensorflow compatibility)
- Training data from Physionet. Store the data in the project folder data/mit-bih-polysomnographic-database-1.0.0.
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