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Title: Detecting variability of internal carotid arterial Doppler signals by Lyapunov exponents
Authors: Güler, İnan
Übeyli, Derya Elif
Keywords: Doppler signals
internal carotid artery stenosis
internal carotid artery occlusion
chaotic signal
Lyapunov exponents
multilayer perceptron neural network (MLPNN)
Levenberg-Marquardt algorithm
Issue Date: 2004
Publisher: Elsevier Sci Ltd
Abstract: The new method presented in this study was directly based oil the consideration that internal carotid arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for detecting variabilities such as stenosis and occlusion in the physical state of internal carotid arterial Doppler signals. The computed Lyapunov exponents of the internal carotid arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The internal carotid arterial Doppler signals were classified with the accuracy varying from 94.87% to 97.44%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis and occlusion in internal carotid arteries. (C) 2004 IPEM. Published by Elsevier Ltd. All rights reserved.
ISSN: 1350-4533
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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