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- W2034665005 abstract "In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. 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. Grow and Learn (GAL) neural network, Kohonen network and multi-layer perceptron (MLP) are used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network." @default.
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- W2034665005 date "2003-06-01" @default.
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- W2034665005 title "CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK" @default.
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- W2034665005 doi "https://doi.org/10.1142/s0218001403002526" @default.
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