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Neural Computing and Applications

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01.10.2012 | Original Article

Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy

verfasst von: Harun Uğuz

Erschienen in: Neural Computing and Applications | Ausgabe 7/2012

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Abstract

Listening via stethoscope is a preferential method, being used by physicians for distinguishing normal and abnormal cardiac systems. On the other hand, listening with stethoscope has a number of constraints. The interpretation of various heart sounds depends on physician’s ability of hearing, experience, and skill. Such limitations may be reduced by developing biomedical-based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary, and mitral stenosis heart valve diseases via stethoscope. Developed system comprises of three stages. In the first stage, for feature extraction, obtained heart sound signals were separated to its sub-bands using discrete wavelet transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of three types of heart sound signals were used as input patterns of the adaptive neuro-fuzzy inference system (ANFIS) classifiers. Developed method reached 98.33% classification accuracy, and it was showed that purposed method is effective for detection of heart valve diseases.

Vorheriger Artikel Global robust asymptotic stability analysis of uncertain switched Hopfield neural networks with time delay in the leakage term

Nächster Artikel Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions

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Titel: Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy
verfasst von: Harun Uğuz
Publikationsdatum: 01.10.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 7/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0610-x

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