Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması

Epileptik aktivitelerin saptanması Elektroensflogram (EEG) verilerinin ayrıntılı analizini gerektirir. El ile epileptik aktiviteleri skorlaması hem zor hem de tutarsızdır. Makine öğrenme teknikleri ise el ile skorlamaya göre daha hızlı ve tutarlıdır. Bu nedenle, EEG verilerini sınıflandırmak için etkili bir makine öğrenmesi tekniğine ihtiyaç vardır. Doğrusal olmayan verileri modelleme başarısından dolayı gözetimli öğrenme algoritmalarından Destek Vektör Makineleri(SVM) tercih edilmiştir. Bu başarı ancak uygun çekirdek fonksiyonu seçildiğinde gerçekleşmektedir. Sıklıkla kullanılan çekirdek fonksiyonları linear, polinom ve radyal tabanlı(RBF)’dır. Verilerin doğası önceden bilinmediğinden çekirdek fonksiyonları arasından uygun seçim yapmak zordur. Bu nedenle modeli oluştururken birden fazla çekirdek fonksiyonu kullanılarak aralarından en iyi performansı veren seçilmelidir. Bu çalışmada Bonn üniversitesinden alınan EEG verileri ile 9 farklı sınıflandırma problemi ele alınmıştır. EEG sinyalleri farklı 5 frekans bandında incelenmiş, her frekans bandının standart sapma değerlerinden öznitelik vektörü oluşturulmuştur. Linear, polinom, radyal tabanlı ve Pearson VII(PUK) çekirdek fonksiyonlarının genelleme yetenekleri karşılaştırılmıştır. PUK çekirdek fonksiyonları parametre değerlerinin başarı oranları üzerindeki etkisi de ayrıca incelenmiştir. Çalışmada önerilen model ile öznitelik hesap yükü azaltılmış, boyut azaltım algoritmaları kullanım ihtiyacı ortadan kaldırılmış, daha az işlem yükü oluşturmuştur. PUK çekirdek fonksiyonunun diğer fonksiyonlara göre daha iyi genelleme performansına sahip olduğu sonucuna varılmıştır. İki sınıflı problemlerde %100 başarı oranına ulaşılmıştır.

Epileptic Seizure Classification from EEG Signals with Support Vector Machines

Detection of epileptic activities requires detailed analysis of the electroencephalogram (EEG) data. Scoring manual epileptic activities is both difficult and inconsistent. Machine learning techniques are faster and more consistent than manual scoring. Therefore, there is a need for an effective machine learning technique to classify EEG data. Because of the success of modeling nonlinear data, Support Vector Machines (SVM), which is a supervised learning algorithm, is preferred. This success is achieved only when the appropriate kernel function is selected. Commonly used kernel functions are linear, polynomial and radial based (RBF). Since the nature of the data is not known in advance, it is difficult to make appropriate selection from the kernel functions. For this reason, when creating the model, it should be selected using multiple kernel functions to give the best performance among them. In this study, EEG data from Bonn University and 9 different classification problems are discussed. EEG signals were analyzed in 5 different frequency bands and feature vectors were generated from the standard deviation values of each frequency band. The generalization capabilities of linear, polynomial, radial based and Pearson VII(PUK) kernel functions are compared. The effect of PUK kernel functions parameter values on success rates is also investigated. With the model proposed in the study, processing load was reduced, dimensionality reduction algorithms were eliminated, and less processing load was created. It was concluded that PUK kernel function has better generalization performance than other functions. 100% success rate was achieved in two-class problems.

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Bibtex @araştırma makalesi { politeknik672077, journal = {Politeknik Dergisi}, eissn = {2147-9429}, address = {Gazi Üniversitesi Teknoloji Fakültesi 06500 Teknikokullar - ANKARA}, publisher = {Gazi Üniversitesi}, year = {2022}, volume = {25}, number = {1}, pages = {239 - 249}, doi = {10.2339/politeknik.672077}, title = {Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması}, key = {cite}, author = {Tuncer, Erdem and Doğru Bolat, Emine} }
APA Tuncer, E. & Doğru Bolat, E. (2022). Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması . Politeknik Dergisi , 25 (1) , 239-249 . DOI: 10.2339/politeknik.672077
MLA Tuncer, E. , Doğru Bolat, E. "Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması" . Politeknik Dergisi 25 (2022 ): 239-249 <
Chicago Tuncer, E. , Doğru Bolat, E. "Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması". Politeknik Dergisi 25 (2022 ): 239-249
RIS TY - JOUR T1 - Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması AU - ErdemTuncer, EmineDoğru Bolat Y1 - 2022 PY - 2022 N1 - doi: 10.2339/politeknik.672077 DO - 10.2339/politeknik.672077 T2 - Politeknik Dergisi JF - Journal JO - JOR SP - 239 EP - 249 VL - 25 IS - 1 SN - -2147-9429 M3 - doi: 10.2339/politeknik.672077 UR - Y2 - 2020 ER -
EndNote %0 Politeknik Dergisi Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması %A Erdem Tuncer , Emine Doğru Bolat %T Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması %D 2022 %J Politeknik Dergisi %P -2147-9429 %V 25 %N 1 %R doi: 10.2339/politeknik.672077 %U 10.2339/politeknik.672077
ISNAD Tuncer, Erdem , Doğru Bolat, Emine . "Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması". Politeknik Dergisi 25 / 1 (Mart 2022): 239-249 .
AMA Tuncer E. , Doğru Bolat E. Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması. Politeknik Dergisi. 2022; 25(1): 239-249.
Vancouver Tuncer E. , Doğru Bolat E. Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması. Politeknik Dergisi. 2022; 25(1): 239-249.
IEEE E. Tuncer ve E. Doğru Bolat , "Destek Vektör Makinaları ile EEG Sinyallerinden Epileptik Nöbet Sınıflandırması", , c. 25, sayı. 1, ss. 239-249, Mar. 2022, doi:10.2339/politeknik.672077