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A chaos-ANFIS approach is presented for analysis of EEG signals fo r 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 (λ). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H and λ measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitativedoi:10.5815/ijisa.2013.06.05 fatcat:tyebhxgmgbgfrmwmymaqzgw7ra