Exploring the role of chin electromyography in automatic sleep stage scoring

Accurate sleep stage scoring is vital for diagnosing a wide range of sleep disorders. However, traditional polysomnography (PSG) methods are time-consuming, expensive, and require complex setups, making automated sleep stage scoring an important advancement in sleep medicine. This study presents a framework for automating sleep stage scoring using surface electromyography (sEMG) features extracted from chin electromyography (chin EMG) muscle activity and traditional machine learning (ML) models. The analysis is based on data from the Sleep-EDFx database. Three sleep staging frameworks—4-stage, 5-stage, and 6-stage models—were evaluated, with the 6-stage model achieving the highest performance using the Random Forest (RF) model. A maximum testing accuracy of 82 % was obtained. F1-scores for the individual stages were 80.64 % for Awake, 84.87 % for REM, 95.94 % for N4, 92.5 % for N3, 62.9 % for N2, and 82.4 % for N1, showing notable performance even for the challenging N1 stage, which represents the sleep transition state. The physiological factors influencing model performance, particularly the difficulty in predicting the N2 stage, are explored in detail. Additionally, the proposed models were validated using the ISRUC-Sleep dataset, where a Support Vector Machine (SVM) model achieved a maximum accuracy of 70.5 % for control subjects aged 40–59 (middle adulthood) and 74.1 % for sleep-disordered subjects classified with normal weight based on Body Mass Index (BMI). These results demonstrate the framework's generalizability across datasets and varying subject demographics. Overall, this study marks a significant advancement in automated sleep scoring by leveraging chin EMG signals for accurate and efficient classification, offering the potential to streamline the diagnosis and management of sleep-related disorders in clinical settings.