Title :
Classification of respiratory sounds by using an artificial neural network
Author :
Sezgin, M.C. ; Dokur, Z. ; Olmez, T. ; Korurek, Mehmet
Author_Institution :
Dept. of Electron. & Commun. Eng., Istanbul Tech. Univ., Turkey
Abstract :
In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. A wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. A Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.
Keywords :
acoustic signal processing; bioacoustics; diseases; dynamic programming; medical signal processing; neural nets; pneumodynamics; vectors; wavelet transforms; artificial neural network; asthma; feature vector elements; grow & learn neural network; healthy subjects; respiratory sounds classification; wavelet coefficients; Acoustical engineering; Artificial neural networks; Discrete wavelet transforms; Dynamic programming; Feature extraction; Lungs; Neural networks; Signal analysis; Signal processing; Wavelet coefficients;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1019035
