基于深度学习pytorch框架的单通道EEG睡眠分期项目Python源码.zip

This repository reproduces TinySleepNet for sleep stage prediction based on the signal channel EEG using PyTorch and implements a new smaller and faster network called EmbedSleepNet. # Environment Setup 1. Create a virtual environment with Python 3.8: `virtualenv venv --python=python3.8` 2. Activate the environment: `source venv/bin/activate` or `venv\Scripts\activate.bat` (for Windows) 3. Install dependencies: `pip install -r requirements.txt` 4. Download and extract Sleep-EDF dataset from https://www.physionet.org/content/sleep-edfx/1.0.0/ 5. Preprocess the data by running `python preprocess.py --data_path PATH_TO_sleep-cassete_FOLDER` # Running To start the training process simply run: `python train.py --flavor=[embed, tiny]`, where tiny represents original TinySleepNet, and embed represents newly introduced EmbedSleepNet. You may additionally specify the number of epochs and model output name, for example: `python train.py --flavor embed --epochs 450 --model-name model`