Year 2020, Volume 25 , Issue 3, Pages 1431 - 1444 2020-12-31

CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS
Sonlu Dürtü Yanıtı Filtresi ve Yapay Sinir Ağları Eğitim Algoritmaları tabanlı Epileptik EEG Sinyalinin Sınıflandırılması

Şengül BAYRAK [1] , Eylem YÜCEL DEMİREL [2] , Rüya ŞAMLI [3]


The electroencephalogram is a powerful tool for understanding the electrical activities of the brain. The automatic and accurate classification of extracranial and intracranial electroencephalogram signals are significant for the evaluation of epilepsy. Electroencephalogram signals contain significant characteristic information about epileptic brain waves. However, the electroencephalogram signals are easily disrupted by the artifacts polluting. This study proposed a clinical decision support system to extract significant epilepsy-related spectral features from the electroencephalogram signal. The artifact-free electroencephalogram signals features were obtained from the Kaiser window based on Finite Impulse Filter. The extracted features were modeled by the Artificial Neural Networks Back Propagation training algorithms which are Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The algorithms' classification performances were compared by the accuracy rates. The experiment results show that compared with the Artificial Neural Networks Back Propagation training algorithms, the performance of the Levenberg-Marquardt is better from the point of accuracy rate which achieves a satisfying classification accuracy of 83.01% for extracranial and intracranial electroencephalogram signals.
Elektroansefalogram beyinin elektriksel aktivitelerini anlamak için güçlü bir araçtır. Ekstrakranial ve intrakranial elektroansefalogram sinyallerinin otomatik ve doğru sınıflandırılması epilepsinin değerlendirilmesi için önemlidir. Elektroansefalogram sinyali, epileptik beyin dalgası hakkında önemli karakteristik bilgi içermektedir. Fakat elektroansefalogram sinyali artefakt kirleticiler tarafından kolaylıkla bozulmaktadır. Bu çalışma, elektroansefalogram sinyalinden epilepsi hakkında önemli spektral özellikleri çıkarmak amacıyla klinik bir karar destek sistemi önermektedir. Artefakttan arındırılmış elektroansefalogram sinyal özellikleri, Kaiser penceresi tabanlı Sonlu Dürtü Yanıtı filtresinden elde edilmiştir. Yapay Sinir Ağları Geri Yayılım eğitim algoritmalarından Levenberg-Marquardt, Bayesian Düzenlenmesi ve Ölçekli Konjugat Gradyan algoritmalarına çıkarılan özellikler uygulanarak modellenmiştir. Algoritmaların sınıflandırma performansları doğruluk oranlarına göre karşılaştırılmıştır. Deneysel sonuçlar, Yapay Sinir Ağları Geri Yayılma eğitim algoritmaları ile yapılan deneyler karşılaştırıldığında, Levenberg-Marquardt algoritması ekstrakranial ve intrakranial elektroansefalogram sinyali için %83,01'lik tatmin edici bir sınıflandırma doğruluğu ile diğer algoritmalara göre daha iyi doğruluk oranı verdiğini gösterir.
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Primary Language en
Subjects Computer Science, Artifical Intelligence
Journal Section Research Articles
Authors

Orcid: 0000-0002-4114-4305
Author: Şengül BAYRAK (Primary Author)
Institution: HALIC UNIVERSITY
Country: Turkey


Orcid: 0000-0003-1979-8860
Author: Eylem YÜCEL DEMİREL
Institution: İSTANBUL ÜNİVERSİTESİ - CERRAHPAŞA
Country: Turkey


Orcid: 0000-0002-8723-1228
Author: Rüya ŞAMLI
Institution: İSTANBUL ÜNİVERSİTESİ - CERRAHPAŞA
Country: Turkey


Supporting Institution The Scientific Technological Research Council of Turkey (TÜBİTAK)
Project Number 118E682
Dates

Application Date : June 18, 2020
Acceptance Date : September 20, 2020
Publication Date : December 31, 2020

Bibtex @research article { uumfd754577, journal = {Uludağ University Journal of The Faculty of Engineering}, issn = {2148-4147}, eissn = {2148-4155}, address = {}, publisher = {Bursa Uludağ University}, year = {2020}, volume = {25}, pages = {1431 - 1444}, doi = {10.17482/uumfd.754577}, title = {CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS}, key = {cite}, author = {Bayrak, Şengül and Yücel Demirel, Eylem and Şamlı, Rüya} }
APA Bayrak, Ş , Yücel Demirel, E , Şamlı, R . (2020). CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS . Uludağ University Journal of The Faculty of Engineering , 25 (3) , 1431-1444 . DOI: 10.17482/uumfd.754577
MLA Bayrak, Ş , Yücel Demirel, E , Şamlı, R . "CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS" . Uludağ University Journal of The Faculty of Engineering 25 (2020 ): 1431-1444 <https://dergipark.org.tr/en/pub/uumfd/issue/57911/754577>
Chicago Bayrak, Ş , Yücel Demirel, E , Şamlı, R . "CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS". Uludağ University Journal of The Faculty of Engineering 25 (2020 ): 1431-1444
RIS TY - JOUR T1 - CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS AU - Şengül Bayrak , Eylem Yücel Demirel , Rüya Şamlı Y1 - 2020 PY - 2020 N1 - doi: 10.17482/uumfd.754577 DO - 10.17482/uumfd.754577 T2 - Uludağ University Journal of The Faculty of Engineering JF - Journal JO - JOR SP - 1431 EP - 1444 VL - 25 IS - 3 SN - 2148-4147-2148-4155 M3 - doi: 10.17482/uumfd.754577 UR - https://doi.org/10.17482/uumfd.754577 Y2 - 2020 ER -
EndNote %0 Uludağ University Journal of The Faculty of Engineering CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS %A Şengül Bayrak , Eylem Yücel Demirel , Rüya Şamlı %T CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS %D 2020 %J Uludağ University Journal of The Faculty of Engineering %P 2148-4147-2148-4155 %V 25 %N 3 %R doi: 10.17482/uumfd.754577 %U 10.17482/uumfd.754577
ISNAD Bayrak, Şengül , Yücel Demirel, Eylem , Şamlı, Rüya . "CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS". Uludağ University Journal of The Faculty of Engineering 25 / 3 (December 2020): 1431-1444 . https://doi.org/10.17482/uumfd.754577
AMA Bayrak Ş , Yücel Demirel E , Şamlı R . CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS. UUJFE. 2020; 25(3): 1431-1444.
Vancouver Bayrak Ş , Yücel Demirel E , Şamlı R . CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS. Uludağ University Journal of The Faculty of Engineering. 2020; 25(3): 1431-1444.
IEEE Ş. Bayrak , E. Yücel Demirel and R. Şamlı , "CLASSIFICATION OF EPILEPTIC EEG SIGNALS BASED ON FINITE IMPULSE RESPONSE FILTER AND ARTIFICIAL NEURAL NETWORKS TRAINING ALGORITHMS", Uludağ University Journal of The Faculty of Engineering, vol. 25, no. 3, pp. 1431-1444, Dec. 2021, doi:10.17482/uumfd.754577