Classification of normal and abnormal lung sounds using wavelet coefficients

Sinem Uysal,Husamettin Uysal,Bülent Bolat,Tulay Yildirim

Signal Processing and Communications Applications Conference（2014）

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摘要

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 subbands 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.

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关键词

medical signal processing,neural nets,signal classification,support vector machines,wavelet transforms,abnormal lung sounds,artificial neural network,frequency analysis,frequency subbands,lung diseases,lung sound classification,respiratory sound analysis,statistical features,support vector machine,wavelet coefficients,wavelet transform,artificial neural network,respiratory sounds,support vector machine,wavelet coefficient

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