Title :
Classification of normal and abnormal lung sounds using neural network and support vector machines
Author :
Abbasi, Shahbaz ; Derakhshanfar, Roya ; Abbasi, Ali ; Sarbaz, Yashar
Author_Institution :
Dept. of Biomed. Eng., Sahand Univ. of Technol., Tabriz, Iran
Abstract :
This work proposes feature extraction of lung sounds using wavelet coefficients and their classification by neural network and support vector machines. The lung sounds were classified into 6 classes. The results stated the advantages of a support vector machines for the classification of normal and abnormal lung sounds, and indicated that SVMs are a highly successful classifier with accuracy about 93.51-100 for classification of lung sounds.
Keywords :
acoustic signal processing; audio signal processing; bioacoustics; feature extraction; lung; medical signal processing; neural nets; signal classification; support vector machines; wavelet transforms; abnormal lung sound; feature extraction; lung sound classification; neural network; support vector machine; wavelet coefficient; Biological neural networks; Feature extraction; Lungs; Support vector machines; Wavelet transforms; Lung Sounds; Neural Network; Support Vector Machines; Wavelet Transform;
Conference_Titel :
Electrical Engineering (ICEE), 2013 21st Iranian Conference on
Conference_Location :
Mashhad
DOI :
10.1109/IranianCEE.2013.6599555