Epilepsy detection by using scalogram based convolutional neural network from eeg signals

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dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorTürk, Ömer
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2021-09-15T05:59:46Z
dc.date.available2021-09-15T05:59:46Z
dc.date.issued2019en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe studies implemented with Electroencephalogram (EEG) signals are progressing veryrapidly and brain computer interfaces (BCI) and disease determinations are carried out at certainsuccess 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, ingeneral, EEG studies are used in addition to conventional methods as well as deep learningnetworks that have recently achieved great success. The most important reason for this is that inconventional methods, increasing classification accuracy is based on too many human efforts asEEG is being processed, obtaining the features is the most important step. This stage is based onboth the time-consuming and the investigation of many feature methods. Therefore, there is a needfor 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 byapplying Continuous Wavelet Transform to EEG records containing five different classes.Convolutional Neural Network structure was used to learn the properties of these scalogram imagesand 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 Bonnwas used. The data set consists of five EEG records containing healthy and epilepsy disease whichare labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-Dand B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% intriple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternaryclassification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of93.60%.en_US
dc.identifier.citationTürk, Ö. ve Özerdem, M. S. (2019). Epilepsy detection by using scalogram based convolutional neural network from eeg signals. Brain Sciences, 9(5), 115.en_US
dc.identifier.doi10.3390/brainsci9050115
dc.identifier.issn2076-3425
dc.identifier.issue5en_US
dc.identifier.pmid31109020
dc.identifier.scopus2-s2.0-85068455653
dc.identifier.scopusqualityQ3
dc.identifier.startpage115en_US
dc.identifier.urihttps://www.mdpi.com/2076-3425/9/5/115
dc.identifier.urihttps://hdl.handle.net/11468/7586
dc.identifier.volume9en_US
dc.identifier.wosWOS:000472660100021
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofBrain Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectContinuous wavelet transformen_US
dc.subjectConvolutional neural networken_US
dc.subjectEEGen_US
dc.subjectEpilepsyen_US
dc.subjectScalogramen_US
dc.titleEpilepsy detection by using scalogram based convolutional neural network from eeg signalsen_US
dc.titleEpilepsy detection by using scalogram based convolutional neural network from eeg signals
dc.typeArticleen_US

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