Automatic Classification for Neural Signals in Epilepsy Using Artificial Neural Network

EasyChair Preprint 249

Abstract

Abstract. Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a compare for classification of   seizure EEG signals with different method. The proposed method is based on the empirical mode decomposition (EMD), the artificial neural network (ANN) and Support Vector Machine (SVM). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The Second-order difference plot (SODP) of IMFs provides elliptical structure. The 89% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. In ANN we check out the Training and Testing results for discriminate seizure-free EEG signals from the epileptic seizure EEG signals.

Keyphrases: Artificial Neural Networks (ANN), Electroencephalogram (EEG), Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Second-order difference plot (SODP)

@booklet{EasyChair:249,
  author    = {Neeraj Kumar and Hasmat Malik},
  title     = {Automatic Classification for Neural Signals in Epilepsy Using Artificial Neural Network},
  doi       = {10.29007/lfrr},
  howpublished = {EasyChair Preprint 249},
  year      = {EasyChair, 2018}}