Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms

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dc.authorid0000-0002-9941-4347en_US
dc.authorid0000-0002-9913-5946en_US
dc.contributor.authorBayram, Muhittin
dc.contributor.authorArserim, Muhammet Ali
dc.date.accessioned2022-05-11T12:24:57Z
dc.date.available2022-05-11T12:24:57Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000730450200001
dc.description.abstractIn this paper, Convolutional Neural Networks (CNN) method was applied to low frequency scalograms in order to contribute to the development of diagnostic and early diagnosis systems of epileptic intracranial EEG (iEEG) signals of brain dynamics at preictal, ictal, and postictal states, and to achieve results that will be the basis for determining the pathological conditions of iEEG signals. As part of this study, iEEG data obtained from epileptic subjects were first decomposed into their subbands by discrete wavelet transformation, and then Shannon entropy was applied to these five subbands (delta, theta, alpha, beta, and gamma). The results obtained made us observe that the delta subband entropy value is lower than other subband entropy values. A low entropy value means that the data is less chaotic. A low degree of chaos means better predictability. Within this context, scalogram images of low-frequency delta subband were obtained at preictal, ictal, and postictal stages and treated with the CNN method, and consequently, a 93.33% accuracy rate was obtained.en_US
dc.identifier.citationBayram, M. ve Arserim, M.A. (2021). Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms. IEEE Access, 9, 162520-162529en_US
dc.identifier.doi10.1109/ACCESS.2021.3132128
dc.identifier.endpage162529en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85120556130
dc.identifier.scopusqualityQ1
dc.identifier.startpage162520en_US
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9632561
dc.identifier.urihttps://hdl.handle.net/11468/9830
dc.identifier.volume9en_US
dc.identifier.wosWOS:000730450200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBayram, Muhittin
dc.institutionauthorArserim, Muhammet Ali
dc.language.isoenen_US
dc.publisherIEEE-Institute of Electrical Electronics Engineers INC.en_US
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectElectroencephalographyen_US
dc.subjectConvolutional neural networksen_US
dc.subjectBrain modelingen_US
dc.subjectEntropyen_US
dc.subjectEpilepsyen_US
dc.subjectDeep learningen_US
dc.subjectData modelsIntracranial electroencephalogram (iEEG)en_US
dc.subjectEpilepsyen_US
dc.subjectEntropyen_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectDelta subbanden_US
dc.titleAnalysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalogramsen_US
dc.titleAnalysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms
dc.typeArticleen_US

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