xig007 / assc

End-to-end Sleep Staging with Raw Single Channel EEG

End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

Accepted in IEEE BHI 2019

Abstract: Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes raw single channel electroencephalogram (Fpz-Cz) signal as input and yields hypnogram annotations for each 30s segments as output. Experiments are carried out for two different scoring standards (5 and 6 stage classification) on the expanded PhysioNet Sleep-EDF dataset, which contains multi-source data from hospital and household polysomnography setups. The performance of the proposed network is compared with that of the state-of-the-art algorithms in patient independent validation tasks. The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6.8% and 6.3% on the household data and multi-source data, respectively.

For Sleep Stage benchmarking check out the benchmark folder containing filenames for the SC-task & RS-task, detailed in the paper.

Dataset Download:

Download the Physionet Sleep-EDF Expanded dataset by running the bash script on linux inside the ASSC root:

bash bulkdownload.sh

Usage:

Download and create .csv files containing EEG Data and hypnogram annotations, place them in the data sub-folder. The .csv files should have rows = number of 30s data epochs. Columns should have a size of 3003 and arranged as:

data points (columns 1-3000) | hypnogram annotation | epoch ID | recording ID

Recording IDs are renamed from 1-61.

Use the train.py file to train the proposed Resnet-34 Architecture for end-to-end sleep staging. Specify the .csv channel file to use from the data and the number of sleep stages to use (5 or 6).

python train.py FpzCz.csv 5 --epochs 200 --batch_size 64

Repo still under development. For issues contact [email protected]