Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization.
In this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound 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 segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.
Pesu
L
L
Laboratory of Biomedical Engineering, Helsinki University of Technology, Finland.
Helistö
P
P
Ademovic
E
E
Pesquet
J C
JC
Saarinen
A
A
Sovijärvi
A R
AR
D000465:Algorithms; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis* / Q000503:physiopathology*; D009716:Numerical Analysis, Computer-Assisted / Q000295:instrumentation; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000145:classification* / Q000502:physiology / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013018:Sound Spectrography / Q000295:instrumentation / Q000379:methods*