Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6136
Title: A recurrent neural network classifier for Doppler ultrasound blood flow signals
Authors: Güler, İnan
Übeyli, Elif Derya
Keywords: recurrent neural networks
Levenberg-Marquardt algorithm
signal classification
automatic diagnosis
discrete wavelet transform
doppler signal
ophthalmic artery
internal carotid artery
Issue Date: 2006
Publisher: Elsevier
Abstract: The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) trained with Levenberg-Marquardt algorithm on 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 and statistical features were calculated to depict their distribution. The RNNs were implemented for diagnosis of OA and ICA diseases using the statistical features as inputs. We explored the ability of designed and trained Elman RNNs, combined with wavelet preprocessing, to discriminate the Doppler signals recorded from different healthy subjects and subjects suffering from OA and ICA diseases. The classification results demonstrated that the proposed combined wavelet/RNN approach can be useful in analyzing long-term Doppler signals for early recognition of arterial diseases. (c) 2006 Elsevier B.V. All rights reserved.
URI: https://doi.org/10.1016/j.patrec.2006.03.001
https://hdl.handle.net/20.500.11851/6136
ISSN: 0167-8655
1872-7344
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

Show full item record

CORE Recommender

SCOPUSTM   
Citations

32
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

31
checked on Sep 24, 2022

Page view(s)

6
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.