Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6871
Title: Implementing wavelet/probabilistic neural networks for Doppler ultrasound blood flow signals
Authors: Güler, İnan
Übeyli, Elif Derya
Keywords: probabilistic neural networks
discrete wavelet transform
Doppler signal
ophthalmic artery
internal carotid artery
Issue Date: 2007
Publisher: Pergamon-Elsevier Science Ltd
Abstract: In this paper, we present the probabilistic neural networks (PNNs) for the Doppler ultrasound blood flow signals. The ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and statistical features were calculated to depict their distribution. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients are the features which well represent the Doppler signals and the PNNs trained on these features achieved high classification accuracies. (c) 2006 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2006.04.006
https://hdl.handle.net/20.500.11851/6871
ISSN: 0957-4174
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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