Politeknik Dergisi 2147-9429 Gazi Üniversitesi 10.2339/politeknik.1589819 Deep Learning Biomedical Diagnosis Derin Öğrenme Biyomedikal Tanı Multi-Class Detection of Epilepsy Disease with 2D Convolutional Neural Networks Using EEG Signals EEG Sinyallerini Kullanarak 2D Konvolüsyonel Sinir Ağları ile Epilepsi Hastalığının Çok Sınıflı Tespiti https://orcid.org/0009-0009-5346-4574 Geniş Yiğithan GAZİ ÜNİVERSİTESİ https://orcid.org/0000-0002-9887-3808 Akman Aydın Eda GAZİ ÜNİVERSİTESİ 12 04 2025 28 6 1743 1753 11 22 2024 03 19 2025 Copyright © 1998, Politeknik Dergisi 1998 Politeknik Dergisi

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.

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.

 EEG Epilepsi Sürekli Dalgacık Dönüşümü Transfer Öğrenme Derin Öğrenme Konvolüsyonel Sinir Ağları EEG Epilepsy Continuous Wavelet Transform Transfer Learning Deep Learning Convolutional Neural Network 1919B012102339 [1] Bromfield E.B., Cavazos J.E., Sirven J.I., (editors) “An introduction to epilepsy: Basic mechanisms underlying seizures and epilepsy ”, American Epilepsy Society, West Hartford, (2006). [2] WiegartzP. , SeidenbergM. , WoodardA. , GidalB. , Hermann B., “Comorbid psychiatric disorder in chronic epilepsy: recognition and etiology of depression” Neurology, 53: 3-8, (1999). [3] Arunkumar N., KumarK.R., Venkataraman V., “Entropy features for focal EEG and non-focal EEG”, Journal of Computational Science, 27: 440-444, (2018). 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