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

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dc.contributor.authorTurk, Omer
dc.contributor.authorAkpolat, Veysi
dc.contributor.authorVarol, Sefer
dc.contributor.authorAluclu, Mehmet Ufuk
dc.contributor.authorOzerdem, Mehmet Sirac
dc.date.accessioned2024-04-24T17:18:02Z
dc.date.available2024-04-24T17:18:02Z
dc.date.issued2021
dc.departmentDicle Üniversitesien_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.description.sponsorshipDUBAP [Muhendislik. 18.003]; Dicle University Scientific Research Projects Coordinatoren_US
dc.description.sponsorshipThis study was supported by the DUBAP (Muhendislik. 18.003) project. We would like to thank the Dicle University Scientific Research Projects Coordinator for their support.en_US
dc.identifier.doi10.1520/JTE20190626
dc.identifier.endpage2506en_US
dc.identifier.issn0090-3973
dc.identifier.issn1945-7553
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85079573813
dc.identifier.scopusqualityQ3
dc.identifier.startpage2491en_US
dc.identifier.urihttps://doi.org/10.1520/JTE20190626
dc.identifier.urihttps://hdl.handle.net/11468/18560
dc.identifier.volume49en_US
dc.identifier.wosWOS:000685475200022
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherAmer Soc Testing Materialsen_US
dc.relation.ispartofJournal of Testing and Evaluation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEpilepsyen_US
dc.subjectElectroencephalogramen_US
dc.subjectContinuous Wavelet Transformen_US
dc.subjectScalogramen_US
dc.subjectDeep Convolutional Neural Networken_US
dc.subjectGeneralized Epilepsyen_US
dc.subjectPartial Epilepsyen_US
dc.titleClassification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Networken_US
dc.titleClassification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram-Based Convolutional Neural Network
dc.typeArticleen_US

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