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A wavelet neural network for the detection of heart valve diseases

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An expert system is presented for interpretation of the Doppler signals of heart valve diseases based on pattern recognition. We deal in particular with the combination of feature extraction and classification from measured Doppler signal waveforms at the heart valve using Doppler ultrasound. A wavelet neural network model developed by us is used. The model consists of two layers: a wavelet layer and a multilayer perceptron. The wavelet layer used for adaptive feature extraction in the time–frequency domain is composed of wavelet decomposition and wavelet entropy. The multilayer perceptron used for classification is a feedforward neural network. The performance of the developed system has been evaluated in 215 samples. The test results show that this system is effective to detect Doppler heart sounds. The classification rate averaged 91% correct for 123 test subjects.
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Keywords: Doppler heart sounds; decision support system; feature extraction; pattern recognition; wavelet decomposition; wavelet entropy; wavelet neural networks

Document Type: Research Article

Affiliations: 1: Firat University, Elazig, Turkey 2: Selcuk University, Konya, Turkey

Publication date: February 1, 2003

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