Classification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on fourier transform

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dc.contributor.authorAl-Azzawi, Athar Hussein Ali
dc.contributor.authorAl-Jumaili, Saif
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.contributor.authorDuru, Adil Deniz
dc.date.accessioned2023-01-08T08:24:23Z
dc.date.available2023-01-08T08:24:23Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalıen_US
dc.description.abstractEpilepsy classification techniques are one of the areas that are still under searching till now as long as there is no specific method for detection seizures. The brain consists of more than 100 billion nerves that generate electrical activity. These activities are recorded using an Electroencephalogram (EEG) by electrodes attached to the scalp. EEG is considered a big footstep in the medical and technical field where it allows the detection of brain disorders. However, this paper aims to identify the most efficient classification algorithm for classifying EEG signals of epileptic seizures. Therefore, we applied two classification techniques namely Support Vector Machine (SVM) and k-Nearest Neighbors (KNN), which rely on the features extracted from the data by the Fast Fourier Transform (FFT) method. The results show SVM obtained the highest accuracy value compared to KNN, accurate scores were 99.5% and 99%, respectively.en_US
dc.identifier.citationAl-azzawi, A. H. A. L., Al-jumaili, S., Ibrahim, A. A., Duru, A. D. (2022). Classification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on Fourier Transform. In AIP Conference Proceedings (Vol. 2499, No. 1, p. 020003). AIP Publishing LLC.en_US
dc.identifier.isbn9780735442863
dc.identifier.scopus2-s2.0-85144015403
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3175
dc.identifier.volume2499en_US
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Azzawi, Athar Hussein Ali
dc.institutionauthorAl-Jumaili, Saif
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherAmerican Institute of Physics Inc.en_US
dc.relation.ispartofAIP Conference Proceedings
dc.relation.isversionof10.1063/5.0105034en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectroencephalogram (EEG)en_US
dc.subjectFast Fourier Transform (FFT)en_US
dc.subjectK-Nearest Neighbors (KNN)en_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.titleClassification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on fourier transform
dc.typeConference Object

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