Classification of respiratory sounds by using an artificial neural network

In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. A wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. A Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.