Abstract:
Respiratory sound contains information of lung condition which helps in the diagnosis of
lung diseases. Stethoscope is the traditional method used to obtain this information but it
depends on the physician experience and hearing. To avoid this limitation and to make
optimum benefit of the respiratory sound information a computer aided diagnosis system
was built. The respiratory sound signals were divided into segments each contains one
inspiratory and expiratory cycle, wavelet transform (WT) was used for analysis, features
were obtained from its coefficients and finally classifying using artificial neural network
(ANN) to normal sound and abnormal sound and classifying the abnormal sound to crackle
and wheeze. The accuracy of classification between normal and abnormal was 95.7% and
for classification between crackle and wheeze was 98.1%.
