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

## TL;DR

This paper introduces a 34-layer deep residual ConvNet that processes raw single-channel EEG data for automatic sleep staging, achieving superior accuracy over existing methods across multiple datasets.

## Contribution

The paper presents a novel end-to-end deep residual ConvNet architecture for sleep staging directly from raw EEG signals, improving accuracy over prior approaches.

## Key findings

- Achieved 6.8% and 6.3% higher accuracy than state-of-the-art methods.
- Demonstrated robustness across hospital and household data sources.
- Provided publicly available code for reproducibility.

## 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. Codes are made publicly available on Github.