EEG Sinyallerini Kullanarak 2D Konvolüsyonel Sinir Ağları ile Epilepsi Hastalığının Çok Sınıflı Tespiti

Yıl 2025, ERKEN GÖRÜNÜM, 1 - 1

Yiğithan Geniş , Eda Akman Aydın

https://doi.org/10.2339/politeknik.1589819

Öz

Elektroensefalogram (EEG) epilepsi hastalığının teşhisi için önemli bir sinyaldir. Transfer öğrenme, veri boyutlarının model eğitimi için yeterli olmadığı durumlarda, önceden eğitilmiş model ağırlıklarının yeni problemlerde kullanılmasını sağlayan bir tekniktir. Bu çalışmada, transfer öğrenme modelleri sağlıklı gözü açık, sağlıklı gözü kapalı, nöbet anında olmayan hastadan epileptojenik bölgenin karşısından kaydedilmiş, nöbet anında olmayan hastadan epileptojenik bölgeden kaydedilmiş ve nöbet anındaki hastadan epileptojenik bölgeden kaydedilmiş EEG sinyal örneklerinin sınıflandırılması amacıyla kullanılmıştır. Sinyallerin, 2D CNN modelinde kullanılmak üzere zaman-frekans gösterimini elde edebilmek amacıyla Sürekli Dalgacık Dönüşümü (CWT) ile skalogram görüntüleri elde edilerek konvolüsyonel sinir ağı (CNN) için giriş görüntüleri olarak kullanılmıştır. Çalışmanın sonuçları GoogleNet transfer öğrenme modelinin CWT zaman-frekans gösterimi kullanılarak epilepsi teşhisinde en başarılı model olduğunu, önerilen yöntemin beş duruma ait EEG sinyallerini %95.33 doğrulukla ayırt edebildiğini göstermektedir.

Anahtar Kelimeler

EEG, Epilepsi, Sürekli Dalgacık Dönüşümü, Transfer Öğrenme, Derin Öğrenme, Konvolüsyonel Sinir Ağları

Proje Numarası

1919B012102339

Multi-Class Detection of Epilepsy Disease with 2D Convolutional Neural Networks Using EEG Signals

Öz

Electroencephalogram (EEG) is an important signal for the diagnosis of epilepsy. Transfer learning is a technique that allows the use of previously trained model weights in new problems when the data size is not sufficient for model training. In this study, transfer learning models were used to classify EEG signal samples recorded from volunteers relaxed in an awake state with eyes open and eyes closed; recorded from within the epileptogenic zone, and from the opposite of epileptogenic zone during seizure free intervals; and recorded from within the epileptogenic zone during seizure activity. In order to obtain the time-frequency representation of the signals, scalogram images were obtained with Continuous Wavelet Transform (CWT) and used as input images for the convolutional neural network (CNN). The results of the study show that the GoogleNet transfer learning model is the most successful model in the diagnosis of epilepsy with CWT images, and the proposed method can distinguish EEG signals belonging to five conditions with 95.33% accuracy.

Anahtar Kelimeler

EEG, Epilepsy, Continuous Wavelet Transform, Transfer Learning, Deep Learning, Convolutional Neural Network

Proje Numarası

1919B012102339

Kaynakça

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