Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks

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dc.contributor.authorAcir N.
dc.contributor.authorÖztura I.
dc.contributor.authorKuntalp M.
dc.contributor.authorBaklan B.
dc.contributor.authorGüzeliş C.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2005
dc.departmentNiğde ÖHÜ
dc.description.abstractThis paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance off the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.
dc.description.sponsorshipManuscript received December 18, 2002; revised May 13, 2004. The work of N. Acır was supported in part by the Turkish Scientific and Technical Research Council (TÜBİTAK) through the Münir Birsel Fund. Asterisk indicates corresponding author. *N. Acır is with Neuro-Sensory Engineering Laboratory, University of Miami, Miami, FL 33124 USA, on leave from the Dokuz Eylül University, Electrical and Electronics Engineering Department, 35160, Buca, 'zmir, Turkey. (e-mail: nurettin.acir@eee.deu.edu.tr). '. Öztura and B. Baklan are with Dokuz Eylül University, Medical Faculty, Neurology Department, 'zmir, Turkey.
dc.identifier.doi10.1109/TBME.2004.839630
dc.identifier.endpage40
dc.identifier.issn0018-9294
dc.identifier.issue1
dc.identifier.pmid15651562
dc.identifier.scopus2-s2.0-11244317025
dc.identifier.scopusqualityQ1
dc.identifier.startpage30
dc.identifier.urihttps://dx.doi.org/10.1109/TBME.2004.839630
dc.identifier.urihttps://hdl.handle.net/11480/1297
dc.identifier.volume52
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.relation.ispartofIEEE Transactions on Biomedical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAutomatic spike detection
dc.subjectEEG
dc.subjectNeural networks
dc.subjectRadial basis function networks
dc.subjectSupport vector machines
dc.titleAutomatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks
dc.typeArticle

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