Methodology for Epilepsy and Epileptic Seizure Recognition using Chaos Analysis of Brain Signals

Abstract

A novel combination of chaotic features and Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for epileptic seizure recognition. The non-linear dynamics of the original EEGs are quantified in the form of the Hurst exponent (H), Correlation dimension (D2), Petrosian Fractal Dimension (PFD), and the Largest lyapunov exponent (?). The process of EEG analysis consists of two phases, namely the qualitative and quantitative analysis. The classification ability of the H, D2, PFD, and ? measures is tested using ANFIS classifier. This method is evaluated with using a benchmark EEG dataset, and qualitative and quantitative results are presented. The inter-ictal EEG-based diagnostic approach achieves 98.6% accuracy with using 4-fold cross validation. Diagnosis based on ictal data is also tested in ANFIS classifier, reaching 98.1% accuracy. Therefore, the method can be successfully applied to both inter-ictal and ictal data.