Adaptive combination methods of autoregressive parameters for epileptic EEG signals classification

摘要

Abstract: Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. This paper illustrates the use of adaptive combination autoregressive parameters for the feature extraction. The multilayer perceptron neural network is selected for the classification of electroencephalogram signals (EEG). Five types of EEG signals (Normal (A, B), Interictal (C, D), and Ictal (E) from Bonn University) were classified with the accuracy of 97.66% by the adaptive combination autoregressive parameters.