title: Comparison of AR-based algorithms for respiratory sounds classification.
abstract: Respiratory sounds of pathological and healthy subjects were analyzed via autoregressive (AR) models with a view to construct a diagnostic aid based on auscultation. Using the AR vectors, two reference libraries, pathological and healthy, were built. Two classifiers, k-nearest neighbour (k-NN) classifier and a quadratic classifier, were designed and compared. Performances of the classifiers were tested for different model orders. The best classification results were obtained for model order 6.
authors.affiliation: Department of Electrical Engineering, Boğaziçi University, Bebek, Istanbul, Turkey.
authors.forename: B
authors.initials: B
authors.lastname: Sankur
authors.forename: Y P
authors.initials: YP
authors.lastname: Kahya
authors.forename: E C
authors.initials: EC
authors.lastname: Güler
authors.forename: T
authors.initials: T
authors.lastname: Engin
mesh_terms: D000465:Algorithms; D001314:Auscultation; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis / Q000503:physiopathology; D008433:Mathematics; D008954:Models, Biological; D012123:Pulmonary Ventilation / Q000502:physiology; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000145:classification* / Q000175:diagnosis / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted