  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.
