title: Neural classification of lung sounds using wavelet coefficients.
abstract: Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.
authors.affiliation: Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore-641 004, India.
authors.forename: A
authors.initials: A
authors.lastname: Kandaswamy
authors.forename: C S C Sathish
authors.initials: CS
authors.lastname: Kumar
authors.forename: Rm Pl
authors.initials: RP
authors.lastname: Ramanathan
authors.forename: S
authors.initials: S
authors.lastname: Jayaraman
authors.forename: N
authors.initials: N
authors.lastname: Malmurugan
mesh_terms: D000465:Algorithms; D001314:Auscultation / Q000706:statistics & numerical data*; D006801:Humans; D016571:Neural Networks, Computer; D012135:Respiratory Sounds / Q000145:classification* / Q000502:physiology