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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Classification of Epileptic and Normal EEG Signals Using Power Spectrum of Sub-bands

Epileptik ve Normal EEG Sinyallerinin Alt Bant Güç Spektrumu Kullanılarak Sınıflandırılması

How to cite: Pehlivan S, Şahin S. Classification of epileptic and normal eeg signals using power spectrum of sub-bands. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2020; 3(1): 6-9. DOI: 10.54856/jiswa.202005095

Full Text: PDF, in English.

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Title: Classification of Epileptic and Normal EEG Signals Using Power Spectrum of Sub-bands

Abstract: The early diagnosis of epilepsy, which affects the lives of many people worldwide, is the first step of treatment to help patients to continue their lives efficiently. Experts have to spend a lot of time and energy to make this diagnosis as quickly and accuratelyaspossible.The aimofthisstudywasto investigatethe capacity of machine learning algorithms to distinguish epileptic and normal signals to develop a system that can automatically diagnose seizures. LabVIEW was used to obtain the sum of EEG sub-band powers which were used as an attribute for both epileptic and normal records. These attributes were classified with different classifiers using Matlab and as a result of the classification, it was concluded that the sub-band power sum can be used as a meaningful attribute in the classification of epileptic and normal EEG signals.

Keywords: LabVIEW; epilepsy; machine learning


Başlık: Epileptik ve Normal EEG Sinyallerinin Alt Bant Güç Spektrumu Kullanılarak Sınıflandırılması

Özet: Dünya genelinde birçok insanın hayatını etkileyen epilepsinin erken teşhisi, hastaların hayatlarına verimli devam edebilmesi için uygulanacak tedavinin ilk adımıdır. Uzmanlar, bu teşhisin en kısa sürede ve en doğru şekilde yapılması için çok fazla zaman ve enerji harcamak zorunda kalmaktadır. Bu çalışmanın amacı, nöbetleri otomatik olarak teşhis edebilen bir sistem geliştirmek için makine öğrenmesi algoritmalarının epileptik ve normal sinyalleri ayırt etme kapasitesini araştırmaktır. LabVIEW, hem epileptik hem normal kayıtlar için bir öznitelik olarak kullanılan EEG alt bant güçlerinin toplamını bulmak için kullanılmıştır. Bu öznitelikler Matlab kullanılarak farklı sınıflandırıcılar ile sınıflandırılmış ve sınıflandırma sonucunda alt bant güç toplamının epileptik ve normal EEG sinyallerinin sınıflandırılmasında anlamlı bir öznitelik olarak kullanılabileceği sonucuna varılmıştır.

Anahtar kelimeler: LabVIEW; epilepsi; makine öğrenmesi


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