Classification of Sleep Apnea via SpO2 in a Simulated Smartwatch Environment

Abstract

Sleep apnea is one of the most common sleep disorders. To diagnose sleep apnea, a patient must undertake a polysomnography where multiple physiological signals are recorded in a specialised sleep laboratory. Reducing the number of physiological signals necessary for a diagnosis and enabling data monitoring in a distributed fashion would assist in the detection of sleep apnea. Smartwatches are becoming more advanced, with the current generation capable of deriving blood oxygen saturation, which can indicate sleep apnea. This work evaluates the efficacy of sleep apnea classifiers in a simulated smartwatch environment. Results demonstrate that SpO2 is a performant signal for classifying sleep apnea. Naive Bayes trained with features extracted from a Long Short Term Memory Network is capable of classifying sleep apnea with an accuracy of 97.04%, outperforming state-of-the-art approaches. Classification within the simulated smartwatch environment demonstrates robustness up to a signal-to-noise ratio of 50 dB and maintains high levels of accuracy at sampling frequencies above 25 Hz. These encouraging results show substantial potential for smartwatches to provide timely, accessible sleep apnea screening and enable automated diagnostics reducing the reliance on specialist centres.