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Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel

Yu Wei, Yongpeng Zhu, Yihan Zhou

IEEE Journal of Biomedical and Health Informatics

|November 13, 2023

Summary

This summary is machine-generated.

This study introduces Co-ScaleNet, a novel deep learning model for sleep staging using contextual scalograms from single-channel electroencephalography (EEG). The method enhances accuracy in sleep stage classification for both healthy individuals and patients.

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Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Single-channel electroencephalography (EEG) is increasingly utilized for sleep monitoring.
Accurate sleep staging is crucial for diagnosing sleep disorders and neurological conditions.

Purpose of the Study:

To develop an effective deep learning model for single-channel EEG-based sleep staging.
To introduce contextual scalograms as a novel input representation for sleep staging.

Main Methods:

A convolutional neural network with attention modules, named Co-ScaleNet, was developed.
Contextual scalograms were generated by combining RGB channels from consecutive EEG epochs.
A data augmentation strategy was implemented for enhanced performance.

Main Results:

Co-ScaleNet achieved high accuracy: 87.0% for healthy individuals and 84.7% for depressed patients.
Comparable performance was observed on public datasets (Sleep-EDFx, ISRUC, SHHS).
Attention modules improved recall for specific sleep stages (N1 and N3).

Conclusions:

Contextual scalograms offer a novel approach for feature extraction and data augmentation in sleep staging.
The Co-ScaleNet model demonstrates broad applicability across diverse patient populations, including those with depression and sleep apnea.