Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms
dc.authorid | 0000-0002-9941-4347 | en_US |
dc.authorid | 0000-0002-9913-5946 | en_US |
dc.contributor.author | Bayram, Muhittin | |
dc.contributor.author | Arserim, Muhammet Ali | |
dc.date.accessioned | 2022-05-11T12:24:57Z | |
dc.date.available | 2022-05-11T12:24:57Z | |
dc.date.issued | 2021 | en_US |
dc.department | Dicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü | en_US |
dc.description | WOS:000730450200001 | |
dc.description.abstract | In 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.citation | Bayram, M. ve Arserim, M.A. (2021). Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms. IEEE Access, 9, 162520-162529 | en_US |
dc.identifier.doi | 10.1109/ACCESS.2021.3132128 | |
dc.identifier.endpage | 162529 | en_US |
dc.identifier.issn | 2169-3536 | |
dc.identifier.scopus | 2-s2.0-85120556130 | |
dc.identifier.scopusquality | Q1 | |
dc.identifier.startpage | 162520 | en_US |
dc.identifier.uri | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9632561 | |
dc.identifier.uri | https://hdl.handle.net/11468/9830 | |
dc.identifier.volume | 9 | en_US |
dc.identifier.wos | WOS:000730450200001 | |
dc.identifier.wosquality | Q2 | |
dc.indekslendigikaynak | Web of Science | |
dc.indekslendigikaynak | Scopus | |
dc.institutionauthor | Bayram, Muhittin | |
dc.institutionauthor | Arserim, Muhammet Ali | |
dc.language.iso | en | en_US |
dc.publisher | IEEE-Institute of Electrical Electronics Engineers INC. | en_US |
dc.relation.ispartof | IEEE Access | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Electroencephalography | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | Brain modeling | en_US |
dc.subject | Entropy | en_US |
dc.subject | Epilepsy | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Data modelsIntracranial electroencephalogram (iEEG) | en_US |
dc.subject | Epilepsy | en_US |
dc.subject | Entropy | en_US |
dc.subject | Convolutional neural network (CNN) | en_US |
dc.subject | Delta subband | en_US |
dc.title | Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms | en_US |
dc.title | Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms | |
dc.type | Article | en_US |
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