Evrişimli Sinir Ağları Kullanılarak Epileptik Nöbet Tespiti: CHB-MIT Scalp EEG Veri Kümesi Üzerinde Çoklu Sınıflandırma

Year 2025, Volume: 18 Issue: 1, 11 - 18, 26.06.2025

Meltem Kurt Pehlivanoğlu , Eren Çalbay , Ömer Emircan Ayvaz , Yunus Emre Kirci

https://doi.org/10.54525/bbmd.1526069

Abstract

Epileptik nöbet tespiti, epilepsinin yönetimi ve tedavisinde kritik bir görevdir ve doğru ve etkili tanı araçlarının geliştirilmesini gerektirir. Bu çalışma, CHB-MIT Scalp EEG veri kümesinden yararlanarak, kafa derisi EEG kayıtlarından epileptik nöbetleri tespit etmek için Evrişimsel Sinir Ağları (CNN) kullanan modeller sunar. Analizimizin sağlamlığını artırmak için, epilepsi teşhisi konmuş farklı sayıda denekle bu veri kümesinin birden fazla versiyonunu oluşturduk. Ek olarak, model performansı üzerindeki etkilerini değerlendirmek için temel ve gelişmiş olarak adlandırılan iki tür etiketleme tekniği kullandık. Nöbet tespitindeki etkinliklerini belirlemek için özellikle ResNet50, ResNet101, VGG16, InceptionV3, Xception ve DenseNet121 olmak üzere CNN tabanlı modeller kullandık. Bu çalışma, bu alandaki üç-sınıf sınıflandırma problemini ele alan ilk çalışmadır. DenseNet121 modeli, %85,52'lik bir F1 puanı ile en yüksek performansı elde etmiştir.

Keywords

EEG , Epilepsi , Atak Tespiti , CNN , Derin Öğrenme

Supporting Institution

TÜBİTAK

Project Number

1919B012327866

Thanks

Bu çalışma TÜBİTAK 2209-A - Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında 1919B012327866 numaralı proje kapsamında desteklenmiştir.

Epileptic Seizure Detection Using Convolutional Neural Networks: Multi-class Classification on the CHB-MIT Scalp EEG Dataset

Abstract

The detection of epileptic seizures is a critical task in the management and treatment of epilepsy, requiring the development of accurate and effective diagnostic tools. This study presents models using Convolutional Neural Networks (CNN) to detect epileptic seizures from scalp EEG recordings, leveraging the CHB-MIT Scalp EEG dataset. To enhance the robustness of the analysis, multiple versions of this dataset were created with a different number of subjects diagnosed with epilepsy. Additionally, two types of labeling techniques, referred to as basic and advanced, were employed to evaluate their impact on model performance. CNN-based models, particularly ResNet50, ResNet101, VGG16, InceptionV3, Xception, and DenseNet121, were used to assess their effectiveness in seizure detection. This study is the first to address the three-class classification problem specifically with the CHB-MIT Scalp EEG dataset. The DenseNet121 model achieved the highest performance with an F1 score of 85.52%.

Keywords

Epilepsy , Seizure Detection , CNN , Deep learning , EEG

Project Number

1919B012327866

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