Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification

TL;DR

This paper presents a novel EOG-based method for sleep stage classification using a SE-Resnet-Transformer model, achieving high accuracy and improving patient comfort by reducing reliance on EEG data.

Contribution

It introduces an untapped EOG signal approach with a new deep learning model, enhancing sleep stage classification accuracy and interpretability.

Findings

Achieved macro-F1 scores of 74.72, 70.63, and 69.26 on three datasets.

Excels in identifying REM sleep stages.

Reduces need for EEG, increasing comfort and accessibility.

Abstract

We introduce an innovative approach to automated sleep stage classification using EOG signals, addressing the discomfort and impracticality associated with EEG data acquisition. In addition, it is important to note that this approach is untapped in the field, highlighting its potential for novel insights and contributions. Our proposed SE-Resnet-Transformer model provides an accurate classification of five distinct sleep stages from raw EOG signal. Extensive validation on publically available databases (SleepEDF-20, SleepEDF-78, and SHHS) reveals noteworthy performance, with macro-F1 scores of 74.72, 70.63, and 69.26, respectively. Our model excels in identifying REM sleep, a crucial aspect of sleep disorder investigations. We also provide insight into the internal mechanisms of our model using techniques such as 1D-GradCAM and t-SNE plots. Our method improves the accessibility of sleep stage classification while decreasing the need for EEG modalities. This development will have promising implications for healthcare and the incorporation of wearable technology into sleep studies, thereby advancing the field's potential for enhanced diagnostics and patient comfort.