0494 Sleep In The Real World: A Computer Vision System To Passively Monitor Sleep And Sleep Disorders On A Continuous Basis

Abstract Introduction Sleep is a (generally) unobserved state, making it difficult to characterize normal sleeping patterns, as well as sleep disorders and their relationship to daytime function. The ubiquity of the internet of things provides a unique opportunity to gather a more realistic picture of sleep through continuous monitoring. However, most commercial devices lack sufficient validation of at-home deployment, and often require some degree of physical contact to gather signals. We sought to assess a contactless, computer-vision sleep system’s ability to detect sleep-disordered breathing events. Methods This first phase of model development derived breathing activity from reference points tracked by the computer vision system. Simultaneous comparison was made to a NoxT3 level 3 home sleep apnea testing device. Signals from the camera algorithm were correlated with the respiratory excursion signals from the respiratory inductance plethysmogram. Expert scorers were blinded to subject identity on randomly and independently presented algorithm-derived breathing signals as well as Nox studies. Breathing disturbances defined on the algorithm-derived signal and AASM-defined apneas and hypopneas from the NoxT3 study were compared. Results Comparison between the computer-vision breathing signal and NoxT3 abdominal RIP band demonstrated high fidelity, rho=0.921 (p