Mathematical and Computational Applications, Vol. 10, No. 1, pp. 57-70, 2005. © Association for Scientific Research NEURAL NETWORK CLASSIFICATION OF EEG SIGNALS BY USING AR WITH MLE PREPROCESSING FOR EPILEPTIC SEIZURE DETECTION Abdulhamit Subasi a , M. Kemal Kiymik a* , Ahmet Alkan a , Etem Koklukaya b e-mail : {asubasi, mkemal, aalkan}@ksu.edu.tr;ekaya@sau.edu.tr a Department of Electrical and Electronics Engineering, Kahramanmaraş Sütçü İmam University, 46100 Kahramanmaraş, Turkey. b Department of Electrical and Electronics Engineering, Sakarya University 54187 Sakarya, Turkey. Abstract-The purpose of the work described in this paper is to investigate the use of autoregressive (AR) model by using maximum likelihood estimation (MLE) also interpretation and performance of this method to extract classifiable features from human electroencephalogram (EEG) by using Artificial Neural Networks (ANNs). ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure. It is observed that, ANN classification of EEG signals with AR gives better results and these results can also be used for detecting epileptic seizure. Keywords- EEG, Autoregressive method (AR), Maximum likelihood estimation (MLE), Artificial Neural Networks (ANN). 1. INTRODUCTION EEG signals involve a great deal of information about the function of the brain. But classification and evaluation of these signals are limited. Since there is no definite criterion evaluated by the experts, visual analysis of EEG signals is insufficient. Since routine clinical diagnosis needs to analysis of EEG signals, some automation and computer techniques have been used for this aim. Since the early days of automatic EEG processing, representations based on a Fourier transform have been most commonly applied. This approach is based on earlier observations that the EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands— delta (< 4 Hz), theta (4–8 Hz), alpha (8–14 Hz), and beta (14–30 Hz). Such methods have proved beneficial for various EEG characterizations, but fast Fourier transform (FFT), suffer from large noise sensitivity. Parametric power spectrum estimation methods such as AR, reduces the spectral loss problems and gives better frequency resolution. Also AR method has an advantage over FFT that, it needs shorter duration data records than FFT [1,2]. Also it is faster than Continuos Wavelet transform techniques, especially in real time applications [18]. Numerous other techniques from the theory of signal analysis have been used to obtain representations and extract the features of interest for classification purposes. Neural networks and statistical pattern recognition methods have been applied to EEG analysis. Over the past two decades much research has been done with the use of conventional