title: Automated sleep apnea quantification based on respiratory movement.
abstract: Obstructive sleep apnea (OSA) is a prevalent and treatable disorder of neurological and medical importance that is traditionally diagnosed through multi-channel laboratory polysomnography(PSG). However, OSA testing is increasingly performed with portable home devices using limited physiological channels. We tested the hypothesis that single channel respiratory effort alone could support automated quantification of apnea and hypopnea events. We developed a respiratory event detection algorithm applied to thoracic strain-belt data from patients with variable degrees of sleep apnea. We optimized parameters on a training set (n=57) and then tested performance on a validation set (n=59). The optimized algorithm correlated significantly with manual scoring in the validation set (R2=0.73 for training set, R2=0.55 for validation set; p<0.05). For dichotomous classification, the AUC was >0.92 and >0.85 using apnea-hypopnea index cutoff values of 5 and 15, respectively. Our findings demonstrate that manually scored AHI values can be approximated from thoracic movements alone. This finding has potential applications for automating laboratory PSG analysis as well as improving the performance of limited channel home monitors.
authors.affiliation: 1. Neurology Department, Sleep Division, Massachusetts General Hospital, Boston MA, USA ; 2. Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
authors.forename: M T
authors.initials: MT
authors.lastname: Bianchi
authors.affiliation: 3. Rest Devices, Boston, MA, USA.
authors.forename: T
authors.initials: T
authors.lastname: Lipoma
authors.affiliation: 3. Rest Devices, Boston, MA, USA.
authors.forename: C
authors.initials: C
authors.lastname: Darling
authors.affiliation: 1. Neurology Department, Sleep Division, Massachusetts General Hospital, Boston MA, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Alameddine
authors.affiliation: 1. Neurology Department, Sleep Division, Massachusetts General Hospital, Boston MA, USA.
authors.forename: M B
authors.initials: MB
authors.lastname: Westover
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000295:instrumentation* / Q000379:methods; D012119:Respiration; D012891:Sleep Apnea Syndromes / Q000175:diagnosis / Q000473:pathology; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*
keywords: algorithm; prediction; respiration, classification; sleep apnea