title: A novel expert classifier approach to pre-screening obstructive sleep apnea during wakefulness.
abstract: Obstructive sleep apnea (OSA) is a widespread disorder that is cumbersome to diagnose using the goldstandard, overnight polysomnography (PSG). This paper highlights further development of our Awake-OSA method for predicting whether someone has severe sleep apnea using breath sounds recorded during wakefulness. We propose the use of an expert classification approach that consists of individual majority-voting classifiers. Each classifier is trained to distinguish one class of subject from all other classes. The outcomes of these classifiers are, in turn, combined using a truth matrix to determine the final outcome. Using the breath sound features of 249 subjects, the classifiers attempted to classify 180 subjects as either non-OSA (AHI less than 5) or severe-OSA (AHI greater than 30). 79% and 75% of OSA and non-OSA subjects, respectively, could be classified. Of those classified, the resultant testing sensitivity and specificity were found to be 78% and 86%, respectively. The consistency of the testing to training accuracies indicates the robustness and generalizability of using multiple expert classifiers on the dataset. This technique has the potential to be used in a doctor's office to rapidly and cheaply pre-screen for OSA, so that physicians may be better able to determine which patients are in need of overnight PSG.
authors.forename: Cameron A
authors.initials: CA
authors.lastname: MacGregor
authors.forename: Zahra
authors.initials: Z
authors.lastname: Moussavi
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D017286:Polysomnography / Q000379:methods; D015203:Reproducibility of Results; D012119:Respiration; D012135:Respiratory Sounds / Q000503:physiopathology; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology*; D014851:Wakefulness / Q000502:physiology*