Kombine Derin Öğrenme Tabanlı Epileptik Nöbet Teşhisi

Abstract

Epilepsi hastalığı yaygın nörolojik hastalıklardan bir tanesi olarak öne çıkmaktadır. Epilepsi hastalığının teşhisinde elektroensefalografi (EEG) kullanılarak beynin sinirsel aktivitesi gözlemlenir ve bu da epilepsi hastalığının teşhisine olanak sağlar. Günümüzde genel olarak biyolojik sinyallerden hastalık teşhisinde klasik makine öğrenmesi yöntemleri sıklıkla kullanılmakla birlikte son yıllarda derin öğrenme yapıları ön plana çıkmaktadır. Derin öğrenme ağları sinyallerden özellik çıkarımına gerek duymaması, özellikler için ek bir çaba gerektirmemesi, insan kaynaklı hesaplama hatalarının önüne geçmesi ve zaman kaybının önüne geçmesi açısından klasik makine öğrenmesi yöntemlerine göre daha avantajlı bir konuma gelmektedir. Bu çalışmada, zaman serisi EEG sinyalini, zaman-frekans bileşenlerini temsil edecek görüntüleri ve ham EEG sinyallerinin sayısal değerlerini kullanarak epilepsi nöbet aktivitesini otomatik bir şekilde tespit eden kombine bir derin öğrenme modeli üzerine çalışılmıştır. Çalışmada Bonn Üniversitesinin halka açık epilepsi veri seti kullanılmıştır. Veri seti sağlıklı ve epilepsi hastası insanlardan kaydedilen A,B,C,D,E şeklinde etiketlenmiş EEG kayıtlarını içermektedir. Bu çalışmada EGG sinyallerinin zaman dizisini ve zamana bağlı EEG sinyallerinin zaman-frekans-görüntü dönüşümlerini kullanarak kombine bir model ortaya koyulmuştur. Sinyalleri görüntülere dönüştürmede CWT ve STFT yöntemleri kullanılmıştır. Oluşturulan modelin CNN girdilerinde STFT görüntüleri kullanıldığında ikili sınıflandırma için %99.47 doğruluk oranı elde edilmiştir. CWT görüntüleri ile ise %99.27 doğruluk oranına ulaşılmıştır. Elde edilen model, EEG verilerinde epilepsi nöbet aktivitesinin olup olmadığını yüksek başarı ile tespit edebilmektedir.

Keywords

• Epilepsi
• Evrişimli Sinir Ağı
• Yinelenen Sinir Ağı
• EEG
• Epileptik nöbet teşhisi

Combined Deep Learning Based Epileptic Seizure Diagnosis

Abstract

Epilepsy stands out as one of the common neurological diseases. In the diagnosis of epilepsy, the neural activity of the brain is observed using electroencephalography (EEG), which allows the diagnosis of epilepsy disease. Although classical machine learning methods are frequently used in the diagnosis of diseases from biological signals, deep learning structures have come to the fore in recent years. Deep learning networks are in a more advantageous position than classical machine learning methods in terms of not requiring feature extraction from signals, requiring no additional effort for features, preventing human-induced computational errors, and preventing time loss. In this study, a combined deep learning model that automatically detects epileptic seizure activity using images representing the time-frequency components of the time domain EEG signal and numerical values of the raw EEG signals was studied. The public epilepsy dataset of the University of Bonn was used in the study. The dataset includes EEG recordings labelled as A, B, C, D, E recorded from healthy and epileptic people. In this study, we made a combined model using the time sequence of EGG signals and time-frequency-image transformations of time-dependent EEG signals. We used CWT and STFT to convert signals to images. We achieved 99.47% accuracy for binary classification when we used STFT images in the CNN inputs of our model. With CWT images, we performed an accuracy rate of 99.27%. The model obtained can detect with high success whether there is epileptic seizure activity in EEG data.

Keywords

• Epilepsy
• Convolutional Neural Network
• Recurrent Neural Network
• EEG
• Epileptic Seizure Diagnos

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