Embedding Parameters Based Distinguish between Normal and Epileptic EEG Using Artificial Neural Network

Abstract

The embedding parameters of electroencephalogram (EEG) time series, i.e., the embedding dimension and delay time, are used together as the input features of artificial neural network for distinguishing between normal and epileptic EEG time series. Cao’s method and mutual information method are applied for computing the embedding dimension and delay time of normal and epileptic EEG time series, respectively. The probabilistic neural network (PNN) is used in this paper for distinguishing between normal and epileptic EEG time series. The results of the simulation show that the overall accuracy as high as 100% can be achieved by using the method proposed in this paper, and that the accuracy obtained based on the both parameters is better than that obtained based on each of the two parameters respectively.