EEG SİNYALLERİ İLE EPİLEPSİ KRİZİNİN TAHMİNLENMESİNDE RASSAL ORMAN ALGORİTMASI İLE HİPER PARAMETRE OPTİMİZASYONUN UYGULANMASI

Year 2021, Volume: 3 Issue: 2, 189 - 203, 28.02.2021

Fatih Yılmaz , Mustafa Cem Kasapbaşı

Abstract

Dünyadaki 50 milyondan fazla kişiden oluşan tüm nüfusun yaklaşık % 1'i epilepsi ve epileptik nöbetlerden etkilenmektedir (Litt, Echauz 2002) (Kandel ve ark., 2000). Epileptik nöbetler, beynin elektriksel aktivitesindeki bir rahatsızlıktan kaynaklanır. Epilepsi nöbetinin saptanması genellikle elektroensefalografik (EEG) sinyal incelendikten sonra uzman görüşü tarafından gerçekleştirilir. Bu manuel bir süreçtir ve büyük ölçüde doktorun uzmanlığına dayanır. Bu nedenle, doktorların daha az hatayla teşhis koymasına yardımcı olmak için otomatik tanı veya yardım sistemleri gereklidir. Bu çalışmada, epileptik nöbetlerin varlığını sınıflandırmak için iyi bilinen (Andrzejak ve ark. 2001) bir veri kümesi kullanılmıştır. Veri setinin farklı konfigürasyonları literatürde bir kısmı Lojistik Regresyon, Dalgacık yöntemi, Karar Ağacı, Destek Vektör Makinesi, Yoğun Sinir Ağları, vb. birçok veri madenciliği ve makine öğrenme algoritması ile incelenmiştir. İyi tanı beklentisini karşılamak için Rassal Orman kullanılarak sınıflandırma modeli geliştirilmiştir ve sonuçlar aynı veri seti üzerinde incelenen farklı yöntemlerle karşılaştırılmıştır. Çalışılan deneylerin bazı vakalarında %99,78 oranında doğruluk, %99,95 özgüllük ve %99,61 hassasiyet elde edilmiştir ve sonuçlar modelinin başarılı şekilde sınıflandırdığını göstermektedir.

Keywords

EEG, Hiper Parametre Optimizasyonu, Makine öğrenmesi

IMPLEMENTATION OF HYPER PARAMETER OPTIMIZATION WITH RANDOM FOREST ALGORITHM FOR THE ESTIMATION OF THE EPILEPTIC SEIZURES WITH EEG SIGNALS

Abstract

About %1 of the whole population of the world which constitutes more than 50 million people are affected by epilepsy and epileptic seizures (Litt, Echauz 2002) (Kandel ve ark., 2000). Epileptic seizures are caused by a disturbance in the electrical activity of the brain. Detecting epileptic seizure is generally carried out by the expert opinion after examining the electroencephalographic (EEG) signal. This is a manual process and heavily relies on the expertise of the physician. Therefore automated diagnosis or aiding systems are required to assist physicians to diagnose with fewer errors. In this study, a well known (Andrzejak et al. 2001) dataset is used for classifying the existence of epileptic seizures. Different configurations of the data set have been studied with many data mining and machine learning algorithms in the literatüre, some of which are Logistic Regression, Wavelet Method, Decision Tree, Support Vector Machine, Dense Neural networks, etc.. In this study, a classification model was developed by using Random Forest to meet the good diagnosis expectation, and results were compared with different methods studied on the same data set. In some cases of the studied experiments above 99,78 percent of accuracy, 99,95% specificity, and 99,61% sensitivity are obtained, indicating a good sign of classification model.

Keywords

EEG, Hyper Parameter Optimization,, Machine Learning

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