title: A lung sound classification system based on the rational dilation wavelet transform.
abstract: In this work, a wavelet based classification system that aims to discriminate crackle, normal and wheeze lung sounds is presented. While the previous works related with this problem use constant low Q-factor wavelets, which have limited frequency resolution and can not cope with oscillatory signals, in the proposed system, the Rational Dilation Wavelet Transform, whose Q-factors can be tuned, is employed. Proposed system yields an accuracy of 95 % for crackle, 97 % for wheeze, 93.50 % for normal and 95.17 % for total sound signal types using energy feature subset and proposed approach is superior to conventional low Q-factor wavelet analysis.
authors.forename: Sezer
authors.initials: S
authors.lastname: Ulukaya
authors.forename: Gorkem
authors.initials: G
authors.lastname: Serbes
authors.forename: Ipek
authors.initials: I
authors.lastname: Sen
authors.forename: Yasemin P
authors.initials: YP
authors.lastname: Kahya
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D012135:Respiratory Sounds / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis