Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Network

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dc.authorid0000-0002-0060-1880en_US
dc.contributor.authorTürk, Ömer
dc.contributor.authorAkpolat, Veysi
dc.contributor.authorVarol, Sefer
dc.contributor.authorAluçlu, Mehmet Ufuk
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2021-10-25T13:36:06Z
dc.date.available2021-10-25T13:36:06Z
dc.date.issued2020en_US
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractDuring the supervisory activities of the brain, the electrical activities of nerve cell clusters produce oscillations. These complex biopotential oscillations are called electroencephalogram (EEG) signals. Certain diseases, such as epilepsy, can be detected by measuring these signals. Epilepsy is a disease that manifests itself as seizures. These seizures manifest themselves in different characteristics. These different characteristics divide epilepsy seizure types into two main groups: generalized and partial epilepsy. This study aimed to classify different types of epilepsy from EEG signals. For this purpose, a scalogram-based, deep learning approach has been developed. The utilized classification process had the following main steps: the scalogram images were obtained by using the continuous wavelet transform (CWT) method. So, a one-dimension EEG time series was converted to a two-dimensional time-frequency data set in order to extract more features. Then, the increased dimension data set (CWT scalogram images) was applied to the convolutional neural network (CNN) as input patterns for classifying the images. The EEG signals were taken from Dicle University, Neurology Clinic of Medical School. This data consisted of four classes: healthy brain waves, generalized preseizure, generalized seizure, and partial epilepsy brain waves. With the proposed method, the average accuracy performance of three of the EEG records' classes (healthy, generalized preseizure, and generalized seizure), and that of all four classes of EEG records were 90.16 % (± 0.20) and 84.66 % (± 0.48). According to these results, regarding the specific accuracy ratings of the recordings, the healthy EEG records scored 91.29 %, generalized epileptic seizure records were at 96.50 %, partial seizure EEG records scored 89.63 %, and the preseizure EEG records had a 90.44 % rating. The results of the proposed method were compared to the results of both similar studies and conventional methods. As a result, the performance of the proposed method was found to be acceptable.en_US
dc.identifier.citationTürk, Ö., Akpolat, V., Varol, S., Aluçlu, M. U., & Özerdem, M. S. (2020). Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram–Based Convolutional Neural Network. In Journal of Testing and Evaluation (Vol. 49, Issue 4, p. 20190626). ASTM International. https://doi.org/10.1520/jte20190626en_US
dc.identifier.doi10.1520/JTE20190626en_US
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85079573813en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1520/jte20190626
dc.identifier.urihttps://www.webofscience.com/wos/woscc/full-record/WOS:000685475200022?AlertId=d383397b-4355-449e-9419-70f9e0e77c15&SID=F1vcqQId99jRM5IHv6Z
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85079573813&origin=resultslist&sort=plf-f&src=s&sid=a62039e6e5deaf63037f308703c11573&sot=b&sdt=b&sl=24&s=DOI%2810.1520%2fJTE20190626%29&relpos=0&citeCnt=0&searchTerm=
dc.identifier.urihttps://hdl.handle.net/20.500.12514/2905
dc.identifier.volume49en_US
dc.identifier.wosWOS:000685475200022en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherASTM Internationalen_US
dc.relation.ispartofIn Journal of Testing and Evaluationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectContinuous wavelet transform; Deep convolutional neural network; Electroencephalogram; Epilepsy; Generalized epilepsy; Partial epilepsy; Scalogramen_US
dc.titleClassification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Networken_US
dc.typeArticleen_US

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