Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7029
Title: Measuring saliency of features extracted by model-based methods from internal carotid arterial Doppler signals using signal-to-noise ratios
Authors: Übeyli, Elif Derya
Keywords: feature saliency
signal-to-noise ratio
model-based methods
internal carotid arterial Doppler signals classification
Issue Date: 2008
Publisher: Academic Press Inc Elsevier Science
Abstract: Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. The noise in a classification model can be reduced by identifying a set of salient features and then more accurate classification can be obtained. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of internal carotid arterial Doppler signals (ICADS). In order to extract features representing the ICADS, model-based methods were used. The PNNs used in the ICADS classification were trained for the SNR screening method. The application results of the SNR screening method to the ICADS demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and nonsalient input features. (C) 2007 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2007.09.009
https://hdl.handle.net/20.500.11851/7029
ISSN: 1051-2004
1095-4333
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

2
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

1
checked on Sep 24, 2022

Page view(s)

2
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


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