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We introduce an innovative approach to automated sleep stage classificationusing EOG signals, addressing the discomfort and impracticality associated withEEG data acquisition. In addition, it is important to note that this approachis untapped in the field, highlighting its potential for novel insights andcontributions. Our proposed SE-Resnet-Transformer model provides an accurateclassification of five distinct sleep stages from raw EOG signal. Extensivevalidation on publically available databases (SleepEDF-20, SleepEDF-78, andSHHS) reveals noteworthy performance, with macro-F1 scores of 74.72, 70.63, and69.26, respectively. Our model excels in identifying REM sleep, a crucialaspect of sleep disorder investigations. We also provide insight into theinternal mechanisms of our model using techniques such as 1D-GradCAM and t-SNEplots. Our method improves the accessibility of sleep stage classificationwhile decreasing the need for EEG modalities. This development will havepromising implications for healthcare and the incorporation of wearabletechnology into sleep studies, thereby advancing the field's potential forenhanced diagnostics and patient comfort.

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