Automated system for detection of epileptiform patterns in EEG by using a modified RBFN classifier

dc.contributor.authorAcir, N
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2005
dc.departmentNiğde ÖHÜ
dc.description.abstractIn this paper, we present a two-stage system based on a modified radial basis function network (RBFN) classifier for an automated detection of epileptiforrn pattern (EP) in an electroencephalographic signal. In the first stage, a discrete perceptron fed by six features are used to classify the peaks into two subgroups: (i) definite non-EPs and (ii) definite EPs and EP-like non-EPs. In the second stage, the peaks falling into the second group are aimed to be separated from each other by a modified RBFN designed by a perturbation method that would function as a post-classifier. If there exist redundant data components in training data set, they can be discarded by analyzing the total disturbance of the RBFN output corresponding to the perturbed inputs. Thus, input dimension size is reduced and network becomes smaller. The classification performance of the system is comparatively evaluated for three different feature sets such as raw EEG data, discrete Fourier transform coefficients, and discrete wavelet transform coefficients. (C) 2005 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2005.04.040
dc.identifier.endpage462
dc.identifier.issn0957-4174
dc.identifier.issue2
dc.identifier.scopus2-s2.0-22144461419
dc.identifier.scopusqualityQ1
dc.identifier.startpage455
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2005.04.040
dc.identifier.urihttps://hdl.handle.net/11480/5583
dc.identifier.volume29
dc.identifier.wosWOS:000230947400023
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAcir, N
dc.language.isoen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEEG
dc.subjectautomatic spike detection
dc.subjectradial basis function networks
dc.subjectneural networks
dc.subjectpattern recognition
dc.titleAutomated system for detection of epileptiform patterns in EEG by using a modified RBFN classifier
dc.typeArticle

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