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

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

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