A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG

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dc.contributor.authorAcir, N
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2006
dc.departmentNiğde ÖHÜ
dc.description.abstractIn this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a great importance in all pattern recognition and classification problems. A gradient descent algorithm, indeed a back propagation type, is adapted to the proposed artificial neural network. The performance of the proposed network is measured using a support vector machine classifier fed by features extracted using the proposed neural network. The results show that the proposed neural network model can effectively be used in pattern recognition tasks. In experiments, real EEG data are used.
dc.identifier.doi10.1007/s00521-005-0007-9
dc.identifier.endpage54
dc.identifier.issn0941-0643
dc.identifier.issue1
dc.identifier.scopus2-s2.0-28344444775
dc.identifier.scopusqualityQ1
dc.identifier.startpage49
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-005-0007-9
dc.identifier.urihttps://hdl.handle.net/11480/5535
dc.identifier.volume15
dc.identifier.wosWOS:000234344800007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAcir, N
dc.language.isoen
dc.publisherSPRINGER
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectgradient descent algorithm
dc.subjectfeature extraction
dc.subjectasymmetric basis functions
dc.subjectelectroencephalography
dc.titleA modified hybrid neural network for pattern recognition and its application to SSW complex in EEG
dc.typeArticle

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