2023/09/25 by Suvadeep Maiti, Maiti, Suvadeep, Shivam Kumar Sharma +3 · 1 citation
Engineering · Medicine · Psychology · #Artificial intelligence #Biology #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Health care #Machine Learning (cs.LG) #Medicine #Non-Invasive Vital Sign Monitoring #Political science #Psychology #Signal Processing (eess.SP) #Sleep (system call) #Stage (stratigraphy) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.03757
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.