Automatic monitoring of obstructive sleep apnea based on multi-modal signals by phone and smartwatch

Obstructive Sleep Apnea (OSA) is the most common sleep-related breathing disorder, with an overall population prevalence ranging from 9% to 38%, and it is associated with many cardiovascular diseases. The diagnosis of OSA requires polysomnography (PSG) testing, which is unsuitable for large-scale preliminary screening due to its high cost and discomfort to wear. Therefore, a simple and inexpensive screening method would be of great value. This study presents a novel at-home OSA screening method using a smartwatch and a smartphone to obtain several physiological signals, snoring segments, and questionnaire information during a whole night's sleep. The proposed method can distinguish four OSA risk levels based on machine learning (ML) classifications; the system was validated by conducting an in-hospital study on 350 subjects with sleep disorders. The estimated OSA risk levels are in good agreement with the OSA severity diagnosed by PSG (correlation with apnea-hypopnea index (AHI) = 0.92), and an encouraging classification performance is achieved (accuracy = 88.1%, 84.5%, 85.1%, sensitivity = 89.1%, 84.2%, 85.6% for mild, moderate and severe OSA). These findings reveal that wearable devices have the potential for large-scale OSA screening.