Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/7528
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Übeyli, Elif Derya | - |
dc.date.accessioned | 2021-09-11T15:57:37Z | - |
dc.date.available | 2021-09-11T15:57:37Z | - |
dc.date.issued | 2009 | en_US |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.issn | 1879-0534 | - |
dc.identifier.uri | https://doi.org/10.1016/j.compbiomed.2009.06.001 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7528 | - |
dc.description.abstract | This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. 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. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes. (C) 2009 Elsevier Ltd. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Pergamon-Elsevier Science Ltd | en_US |
dc.relation.ispartof | Computers In Biology And Medicine | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Electroencephalogram (EEG) signals | en_US |
dc.subject | Feature extraction/selection | en_US |
dc.subject | Lyapunov exponents | en_US |
dc.subject | Wavelet coefficients | en_US |
dc.subject | Eigenvector methods | en_US |
dc.title | Statistics Over Features: Eeg Signals Analysis | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Electrical and Electronics Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 39 | en_US |
dc.identifier.issue | 8 | en_US |
dc.identifier.startpage | 733 | en_US |
dc.identifier.endpage | 741 | en_US |
dc.identifier.wos | WOS:000268430700009 | en_US |
dc.identifier.scopus | 2-s2.0-67649601026 | en_US |
dc.institutionauthor | Übeyli, Elif Derya | - |
dc.identifier.pmid | 19555931 | en_US |
dc.identifier.doi | 10.1016/j.compbiomed.2009.06.001 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q2 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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