CLASSIFICATION BASED ON FEATURE EXTRACTION FROM CLUSTER OF WAVELET COEFFICIENTS

Neural network is popularly used for pattern recognition. The training time of neural network increases rapidly with increasing large number of samples. It leads to a deterioration in performance of neural network. A classification approach based on fast wavelet transform for feature extraction is presented. The method divides the matrix of computed wavelet coefficients into clusters in every rows. The rows that represent important frequency range have a larger numbers of clusters than rows that represent less important frequency ranges. The input numbers of neural network are decreased, while retaining much information about classified signal. After feature extraction, energy values of wavelet coefficients are chosen as signal features. A radial basic function neural network is developed for classification of different lung sound signals. The effectiveness of this new method has been verified on experiments about recognizing lung sounds.