%0 Journal Article
%T Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition
%A S. A. Hosseini
%A M-R. Akbarzadeh-T
%A M-B. Naghibi-Sistani
%J International Journal of Intelligent Systems and Applications
%D 2013
%I MECS Publisher
%R 10.5815/ijisa.2013.06.05
%X A chaos-ANFIS approach is presented for analysis of EEG signals for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the hurst exponent (H) and largest lyapunov exponent (¦Ë). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H and ¦Ë measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. Our inter-ictal EEG based diagnostic approach achieves 97.4% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 96.9% accuracy. Therefore, our method can be successfully applied to both inter-ictal and ictal data.
%K ANFIS
%K EEG
%K Hurst Exponent
%K Lyapunov Exponent
%U http://www.mecs-press.org/ijisa/ijisa-v5-n6/v5n6-5.html