A wearable apnea diagnostic system using PPG and machine learning achieved a diagnostic accuracy of over 85% compared to standard polysomnography.
Does a machine learning-assisted wearable PPG device accurately diagnose sleep apnea syndrome compared to standard polysomnography?
A low-cost, wearable PPG-based diagnostic system using machine learning provides a feasible and accurate alternative for screening and diagnosing sleep apnea syndrome.
Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.
Wang et al. (Mon,) conducted a other in Sleep apnea syndrome (SAS). Wearable apnea diagnostic system using PPG and machine learning vs. Compumedics Grael PSG sleep monitoring system was evaluated on Diagnostic accuracy. A wearable apnea diagnostic system using PPG and machine learning achieved a diagnostic accuracy of over 85% compared to standard polysomnography.