Automatic Sleep Apnea/Hypopnea Detection based on Nasal Airflow Signal

Abstract

Sleep apnea is a common sleep-related disorder caused by the obstruction of the respiratory tract and the absence of respiratory flow. 18 million Americans are estimated to suffer from sleep apnea,out of which 80% are thought to go undiagnosed. Nowadays,apnea detection is done using a Polysomnography (PSG) test during sleeping hours,which requires that the patient spends a night in a specialized center wearing several sensors to monitor his/her state. After that,medical trained staff checks the recordings/ markings and manually corrects the scoring. This process is time-consuming,which sets the motivation for this study to provide and validate an automatic apnea detection algorithm using the raw PSG recordings of 50 patients. Apnea is detected primarily employing the nasal flow signal,combined with oxygen saturation (SpO2) for hypopnea classification. The proposed approach achieves 77.124% accuracy (SD was 7.7) with around 55.3% sensitivity and around 82.5% specificity. Such an automatic algorithm will help drastically reduce the necessary time to analyze patients’ condition.