Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

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dc.authoridTURK, Omer -- 0000-0002-0060-1880en_US
dc.contributor.authorTürk, Ömer
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned14.07.201910:50:10
dc.date.accessioned2019-07-16T20:43:49Z
dc.date.available14.07.201910:50:10
dc.date.available2019-07-16T20:43:49Z
dc.date.issued2019
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractThe studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.en_US
dc.identifier.citationTürk Ö, Özerdem MS. Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals. Brain Sci. 2019 May 17;9(5):115. doi: 10.3390/brainsci9050115. PMID: 31109020; PMCID: PMC6562774.
dc.identifier.doi10.3390/brainsci9050115en_US
dc.identifier.issn2076-3425
dc.identifier.issue5en_US
dc.identifier.pmid31109020en_US
dc.identifier.scopus2-s2.0-85068455653en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://dx.doi.org/10.3390/brainsci9050115
dc.identifier.urihttps://hdl.handle.net/20.500.12514/1216
dc.identifier.volume9en_US
dc.identifier.wosWOS:000472660100021en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofBRAIN SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEpilepsyen_US
dc.subjectEEGen_US
dc.subjectscalogramen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectContinuous Wavelet Transformen_US
dc.titleEpilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signalsen_US
dc.typeArticleen_US

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