title: Two-stage classification of respiratory sound patterns.
abstract: The classification problem of respiratory sound signals has been addressed by taking into account their cyclic nature, and a novel hierarchical decision fusion scheme based on the cooperation of classifiers has been developed. Respiratory signals from three different classes are partitioned into segments, which are later joined to form six different phases of the respiration cycle. Multilayer perceptron classifiers classify the parameterized segments from each phase and decision vectors obtained from different phases are combined using a nonlinear decision combination function to form a final decision on each subject. Furthermore a new regularization scheme is applied to the data to stabilize training and consultation.
authors.affiliation: Biomedical Engineering Institute, Bogaziçi University, Bebek, 34342 Istanbul, Turkey.
authors.forename: Emin Cagatay
authors.initials: EC
authors.lastname: Güler
authors.forename: Bülent
authors.initials: B
authors.lastname: Sankur
authors.forename: Yasemin P
authors.initials: YP
authors.lastname: Kahya
authors.forename: Sarunas
authors.initials: S
authors.lastname: Raudys
mesh_terms: D000465:Algorithms; D001314:Auscultation / Q000145:classification*; D003661:Decision Support Techniques; D005103:Expert Systems; D006801:Humans; D008171:Lung Diseases / Q000503:physiopathology; D008954:Models, Biological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D029424:Pulmonary Disease, Chronic Obstructive / Q000503:physiopathology; D012119:Respiration; D012135:Respiratory Sounds / Q000145:classification*; D012815:Signal Processing, Computer-Assisted