Title :
Wavelet packet based respiratory sound classification
Author :
Pesu, L. ; Ademovic, E. ; Pesquet, J.C. ; Helistö, P.
Author_Institution :
Lab. of Biomed. Eng., Helsinki Univ. of Technol., Espoo, Finland
Abstract :
Wavelet packet based methods are used for detection of abnormal respiratory sounds. The associated signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The classification is performed using learning vector quantization
Keywords :
acoustic signal processing; bioacoustics; feature extraction; medical signal processing; pattern classification; pneumodynamics; vector quantisation; wavelet transforms; abnormal respiratory sound detection; classification; feature vector; learning vector quantization; respiratory sound classification; segments; wavelet packet based methods; wavelet packet decomposition; Biomedical engineering; Discrete wavelet transforms; Diseases; Frequency; Laboratories; Timing; Vector quantization; Wavelet domain; Wavelet packets; Wideband;
Conference_Titel :
Time-Frequency and Time-Scale Analysis, 1996., Proceedings of the IEEE-SP International Symposium on
Conference_Location :
Paris
Print_ISBN :
0-7803-3512-0
DOI :
10.1109/TFSA.1996.550071
