title: Breath-by-breath detection of apneic events for OSA severity estimation using non-contact audio recordings.
abstract: Obstructive sleep apnea (OSA) is a prevalent sleep disorder, characterized by recurrent episodes of upper airway obstructions during sleep. We hypothesize that breath-by-breath audio analysis of the respiratory cycle (i.e., inspiration and expiration phases) during sleep can reliably estimate the apnea hypopnea index (AHI), a measure of OSA severity. The AHI is calculated as the average number of apnea (A)/hypopnea (H) events per hour of sleep. Audio signals recordings of 186 adults referred to OSA diagnosis were acquired in-laboratory and at-home conditions during polysomnography and WatchPat study, respectively. A/H events were automatically segmented and classified using a binary random forest classifier. Total accuracy rate of 86.3% and an agreement of κ=42.98% were achieved in A/H event detection. Correlation of r=0.87 (r=0.74), diagnostic agreement of 76% (81.7%), and average absolute difference AHI error of 7.4 (7.8) (events/hour) were achieved in in-laboratory (at-home) conditions, respectively. Here we provide evidence that A/H events can be reliably detected at their exact time locations during sleep using non-contact audio approach. This study highlights the potential of this approach to reliably evaluate AHI in at home conditions.
authors.forename: T
authors.initials: T
authors.lastname: Rosenwein
authors.forename: E
authors.initials: E
authors.lastname: Dafna
authors.forename: A
authors.initials: A
authors.lastname: Tarasiuk
authors.forename: Y
authors.initials: Y
authors.lastname: Zigel
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography; D012119:Respiration; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis / Q000503:physiopathology*; D013637:Tape Recording / Q000379:methods*