Development of OSA Event Detection Using Threshold Based Automatic Classification

Obstructive Sleep Apnea (OSA) is a very serious sleeping disorder resulting in the temporary blockage of the airflow airway that can be deadly if left untreated. OSA is not a rare condition; in the US, from 18 to 50 million people, most of them remain undiagnosed due to cost, cumbersome and resource limitations of overnight polysomnography (PSG) at sleep labs. Instead, automated, at-home devices that patients can simply use while asleep seem to be very attractive and highly on-demand. This paper presents a method for OSA screening and user notification based on the respiratory recording and video monitoring as a secondary system during sleep in order to alert of the apnea event and help patient to recover