Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection

Ezgi Özer; Ahmet Furkan Kınataş; Emine Yiğit; Hamza Demir; Alper Birinci

doi:10.29130/dubited.1745675

TR EN

Epileptik Nöbet Bölgesi Tespiti için Grid Arama Algoritması Kullanılarak Farklı Hibrit Öğrenme Algoritmalarının Değerlendirilmesi

Abstract

Bu makalede, epileptik nöbetlerin erken teşhisi için Elektroensefalogram (EEG) verilerini doğru bir şekilde sınıflandırmada etkili bir yöntem sunulmaktadır. Önerilen süreç esasen istatistiksel veriyi, ayrık dalgacık dönüşümlerini (DWT), makine öğrenmesi algoritmalarını ve özellik seçme tekniklerini bağımsız olarak hibritleştirilmiştir. DWT kullanımıyla, otomatik çok çözünürlüklü sinyal işleme yaklaşımı, doğru bir sınıflandırma performansını garantilemek için EEG sinyallerini değişen pencere boyutlarına sahip ayrıntılı parçalara böldükten sonra ayrıntı ve yaklaşıklık katsayılarına ayrıştırılmıştır. Sinyallerdeki doğrusal olmayan ve dinamik örüntüleri tanımlayan bu katsayılardan istatistiksel gizli özellikler çıkarılmıştır. Önemli unsurları vurgularken özellik matrisinin boyutunu azaltmak için özellik seçme teknikleri kullanılmıştır. Giriş matrislerini sınıflandırmak için farklı sınıflandırıcı yapıları geliştirilmiştir. Tüm sınıflandırıcılar için, ızgara arama teknikleri kullanılarak optimum hiperparametreler elde edilmiştir. Modelin performansını değerlendirmek için sınıflandırmaya ilişkin performans metrikleri hesaplanmıştır. Ayrıca, EEG sinyallerini ayırt etmek için en önemli frekans bantları tespit edilmiştir. Analizde, önerilen prosedürü epileptik davranışları doğru bir şekilde tespit etme açısından diğer yaklaşımlarla karşılaştırmak için Bonn Üniversitesi veri tabanından bir kıyaslama veri seti kullanılmıştır. Sonuçlar, önerilen yaklaşımın EEG sinyallerini sınıflandırmada performans metrikleri ve bilgi kriterleri açısından daha sağlam modeller tahmin edebileceğini göstermiştir.

Keywords

Özellik Çıkarımı, Özellik Seçimi, Izgara Arama, Makine Öğrenmesi, Sinyal İşleme

Project Number

1919B012303343

Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection

Abstract

In this paper, an effective method for accurately classifying Electroencephalogram (EEG) data for the early identification of epileptic seizures is presented. The suggested process essentially hybridizes several statistical data, discrete wavelet transformations (DWT), machine learning algorithms, and feature selection techniques independently. The automated multi-resolution signal processing approach decomposes EEG signals into detail and approximation coefficients after splitting them into detailed parts with varying window sizes using DWT. Statistical latent features are extracted from these coefficients that describe the nonlinear and dynamical patterns in the signals. Feature selection techniques were used to reduce the dimension of the feature matrix while highlighting the important elements. Different classifier structures were developed to classify input matrices. For all classifiers, the optimal hyperparameters were found using grid search techniques. Performance metrics for classification were calculated to assess the model's performance. Also, the most important frequency bands were detected to distinguish EEG signals. In the analysis, to compare the proposed procedure with the other approaches in terms of detecting the epileptic behaviors correctly, a benchmark data set from the University of Bonn database was used. The results showed that the proposed approach can estimate more robust models concerning performance metrics and information criteria in classifying EEG signals.

Keywords

Feature extraction, Feature selection, Grid search, Machine learning, Signal processing

Supporting Institution

This study was carried out within the scope of a project supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under the 2209-A Research Project Support Programme for Undergraduate Students (Project No: 1919B012303343).

Project Number

1919B012303343

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

The authors would like to thank TÜBİTAK for its financial support.

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Details

Primary Language

English

Subjects

Supervised Learning, Machine Learning Algorithms, Classification Algorithms

Journal Section

Research Article

Authors

Ezgi Özer ^*
0000-0003-1567-2216
Türkiye

Ahmet Furkan Kınataş
0009-0004-9616-3652
Türkiye

Emine Yiğit
0009-0006-3802-1482
Türkiye

Hamza Demir
0009-0004-3222-6542
Türkiye

Alper Birinci
0009-0003-0983-2532
Türkiye

Publication Date

April 19, 2026

Submission Date

July 18, 2025

Acceptance Date

January 2, 2026

Published in Issue

Year 2026 Volume: 14 Number: 2

DOI

https://doi.org/10.29130/dubited.1745675

IZ

https://izlik.org/JA85SX53EW

APA

Özer, E., Kınataş, A. F., Yiğit, E., Demir, H., & Birinci, A. (2026). Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection. Duzce University Journal of Science and Technology, 14(2), 324-339. https://doi.org/10.29130/dubited.1745675

AMA

1.Özer E, Kınataş AF, Yiğit E, Demir H, Birinci A. Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection. DUBİTED. 2026;14(2):324-339. doi:10.29130/dubited.1745675

Chicago

Özer, Ezgi, Ahmet Furkan Kınataş, Emine Yiğit, Hamza Demir, and Alper Birinci. 2026. “Evaluating Different Hybrid Learning Algorithms Using Grid Search Algorithm for Epileptic Seizure Zone Detection”. Duzce University Journal of Science and Technology 14 (2): 324-39. https://doi.org/10.29130/dubited.1745675.

EndNote

Özer E, Kınataş AF, Yiğit E, Demir H, Birinci A (April 1, 2026) Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection. Duzce University Journal of Science and Technology 14 2 324–339.

IEEE

[1]E. Özer, A. F. Kınataş, E. Yiğit, H. Demir, and A. Birinci, “Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection”, DUBİTED, vol. 14, no. 2, pp. 324–339, Apr. 2026, doi: 10.29130/dubited.1745675.

ISNAD

Özer, Ezgi - Kınataş, Ahmet Furkan - Yiğit, Emine - Demir, Hamza - Birinci, Alper. “Evaluating Different Hybrid Learning Algorithms Using Grid Search Algorithm for Epileptic Seizure Zone Detection”. Duzce University Journal of Science and Technology 14/2 (April 1, 2026): 324-339. https://doi.org/10.29130/dubited.1745675.

JAMA

1.Özer E, Kınataş AF, Yiğit E, Demir H, Birinci A. Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection. DUBİTED. 2026;14:324–339.

MLA

Özer, Ezgi, et al. “Evaluating Different Hybrid Learning Algorithms Using Grid Search Algorithm for Epileptic Seizure Zone Detection”. Duzce University Journal of Science and Technology, vol. 14, no. 2, Apr. 2026, pp. 324-39, doi:10.29130/dubited.1745675.

Vancouver

1.Ezgi Özer, Ahmet Furkan Kınataş, Emine Yiğit, Hamza Demir, Alper Birinci. Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection. DUBİTED. 2026 Apr. 1;14(2):324-39. doi:10.29130/dubited.1745675

Epileptik Nöbet Bölgesi Tespiti için Grid Arama Algoritması Kullanılarak Farklı Hibrit Öğrenme Algoritmalarının Değerlendirilmesi

Abstract

Keywords

Project Number

Evaluating Different Hybrid Learning Algorithms using Grid Search Algorithm for Epileptic Seizure Zone Detection

Abstract

Keywords

Supporting Institution

Project Number

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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