Dergiler / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2016 / Cilt: 7 - Sayı: 3
Epileptik EG İşaretlerin Aşırı Öğrenme Makineleri ile Sınıflandırılması
- Sayfa
- 481–490
- DOI
- —
Özet
Bu çalışmada Epilepsi tanısı konulmuş hastalardan alınan EEG işaretleri, nöbet öncesi, nöbet anı ve nöbet sonrası olarak sınıflandırılmıştır. EEG işaretleri lineer ve durağan olmayan işaretler olup beynin elektriksel aktivitelerini gösterirler. Nörolojik anormallerde EEG işaretlerin alt bantlarında normal durumdan farklı olarak belirgin değişimler gözlemlenmekte ve bu değişimler nörolojik hastalıkların belirtisi olmaktadır. Epilepsi gibi nörolojik hastalıklarda EEG işaretleri içerisindeki bantlarda normal durumdan farklı olarak bir faz senkronizasyonu ortaya çıkmaktadır. Bu faz eşleşmelerini yüksek dereceden spektral analizi tekniklerinden olan ikiz spektrum analizi ile ortaya çıkararak EEG işareti içerisinden özelikler elde edilebilmektedir. Elde edilen bu özelliklerin bir sınıflandırıcının girişine verilmesi ile epileptik EEG işaretleri sınıflandırılmaktadır. Çalışmada hızlı ve yüksek doğruluk sağlaması açısından sınıflandırıcı olarak aşırı öğrenme makineleri kullanılmıştır. Kullanılan bu yöntem ile %98,60 gibi yüksek bir doğrulukla sınıflandırma gerçekleştirilmiştir. Bu çalışmanın nörologlara epilepsi tanısında yardımcı olacağı düşünülmektedir.
Abstract
In this study, the EEG signals obtained from patients that diagnosed with epilepsy seizure, were classified as before, during and after seizures. EEG signals are the non-linear and non-stationary signals that indicate the electrical activity of the brain. Different from normal situation of the brain, in the abnormal neurological, changes are significantly different in the sub-band of EEG signals, and these changes are signs of neurological disease. Since epilepsy starts the dynamic in the brain changes while the nonlinearity and non-Gaussanity increases in the EEG signal. So, the phase synchronization arises during seizure. During this phase match the features of the EEG signals can be obtained by using bispectrum analysis which is one of the higher order spectral analysis techniques. Bicoherence, as the normalized version of the bispectrum, of EEG signals obtained from eight patients were determined, and quadratic phase coupling (QPC) identified. These features, which is obtained by epileptic EEG signals were fed to the input of the classifier. In terms of providing fast and high accuracy for classification of the EEG signal, the extreme learning machine (ELM) was used. The ELM is a single hidden layer feed-forward neural network. For comparison the artificial neural network (ANN) and support vector machine (SVM) classifiers were also used. In the study, it was shown that the QPCs in the EEG signal increased during epilepsy compared with before epilepsy. This result shows that the complexity and non-Gaussianity increase during epilepsy seizure. By considering the sub-bands of EEG separately, during epilepsy, the ratio of QPC has increased in the low frequency compared to high frequency. In the study it was also shown that the QPC after epilepsy is higher than before epilepsy, however, the QPC after epilepsy is lower than during epilepsy. This suggests that the brain dynamic after epilepsy seizure is more synchronous than before epilepsy seizure. This situation is going on until brain activities became normal. In the study 8 patient’s EEG that were diagnosed with seizure were used. 400 episodes of each pre epilepsy, during epilepsy and after epilepsy were obtained from whole data. A train/test data rate of 50%-50% was used in the classifiers. The test results show that the ELM has higher accuracy than ANN and SVM as shown in the Table 2. By using ELM a high classification accuracy of 98.60% was obtained. For ANN and SVM the test results of %95.33 and %91.25 obtained respectively. Furthermore, it was also shown that the ELM is much faster than ANN and SVM classifiers. This study is thought to help neurologists in the diagnosis of epilepsy.