Classification of Normal and Abnormal Lung Sounds Using Wavelet Coefficients

UYSAL S., UYSAL H., BOLAT B., YILDIRIM T.

22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey, 23 - 25 April 2014, pp.2138-2141 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • City: Trabzon
  • Country: Turkey
  • Page Numbers: pp.2138-2141
  • Yıldız Technical University Affiliated: Yes

Abstract

Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency sub-bands using wavelet transform and a set of statistical features are inspected from the sub-bands. Then, lung sounds classified as normal and abnormal using these statistical features. Artificial neural network and support vector machine are used for classification process.