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

arXiv: https://arxiv.org/abs/2310.03757 · HTML: https://mlsanity.com/paper/2310.03757/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy](https://mlsanity.com/paper/1710.02094/) (2017)
- [mulEEG: A Multi-View Representation Learning on EEG Signals](https://mlsanity.com/paper/2204.03272/) (2022)
- [Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis](https://mlsanity.com/paper/1811.04662/) (2018)
- [Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability](https://mlsanity.com/paper/2309.07156/) (2023)

## Built on by (most cited first, 3 of 3)

- [Sleep Stage Classification using Multimodal Embedding Fusion from EOG and PSM](https://mlsanity.com/paper/2506.06912/) (2025)
- [AI Generalisation Gap In Comorbid Sleep Disorder Staging](https://mlsanity.com/paper/2603.23582/) (2026)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)

## Abstract (arXiv)

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.