Neural Network Classification of EEG Signals by Using AR with MLE Preprocessing for Epileptic Seizure Detection

Abdulhamit Subasi; M. Kemal Kiymik; Ahmet Alkan; Etem Koklukaya

doi:10.3390/mca10010057

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Article

1 April 2005

Neural Network Classification of EEG Signals by Using AR with MLE Preprocessing for Epileptic Seizure Detection

and

Department of Electrical and Electronics Engineering, Kahramanmaraş Sütçü İmam University, 46100 Kahramanmaraş, Turkey

Department of Electrical and Electronics Engineering, Sakarya University 54187 Sakarya, Turkey

Author to whom correspondence should be addressed.

Math. Comput. Appl.2005, 10(1), 57-70;https://doi.org/10.3390/mca10010057

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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)

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