Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6395
Title: Combined neural network model employing wavelet coefficients for EEG signals classification
Authors: Übeyli, Elif Derya
Keywords: Combined neural network model
EEG signals classification
Diagnostic accuracy
Discrete wavelet transform
Issue Date: 2009
Publisher: Academic Press Inc Elsevier Science
Abstract: This paper illustrates the use of combined neural network model to guide model selection for classification of electroencephalogram (EEG) signals. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the EEG signals classification using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 94.83% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model. (C) 2008 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2008.07.004
https://hdl.handle.net/20.500.11851/6395
ISSN: 1051-2004
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

224
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

169
checked on Sep 24, 2022

Page view(s)

4
checked on Dec 26, 2022

Google ScholarTM

Check

Altmetric


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