Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7773
Title: Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals
Authors: Übeyli, Elif Derya
Keywords: Doppler ultrasound signals
eigenvector methods
probabilistic neural network
recurrent neural network
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: A new approach based on the implementation of the automated diagnostic systems for Doppler ultrasound signals classification with the features extracted by eigenvector methods is presented. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the Doppler ultrasound signals. Decision making was performed in two stages: feature extraction by the eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the Doppler ultrasound signals by the combination of eigenvector methods and the classifiers. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the Doppler ultrasound signals and the probabilistic neural networks (PNNs), recurrent neural networks (RNNs) trained on these features achieved high classification accuracies. (C) 2008 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.compbiomed.2008.02.003
https://hdl.handle.net/20.500.11851/7773
ISSN: 0010-4825
1879-0534
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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