Phase space elliptic density feature for epileptic EEG signals classification using metaheuristic optimization method

The electroencephalography (EEG), which is a method for monitoring the brain signals, is a common method used to diagnose the epileptic seizures. In this study, some features are presented for the classification of the brain signals. These features are based on the texture and structure of the brain signals in the phase space representation (PSR). Due to the resonance property, the data are elliptical in the phase space. Therefore, the mentioned features are based on the calculations of the data density in the ellipses. In the first method of feature extraction, the radius values of the ellipses are assumed based on the normal distribution feature. In the two other methods of the presented features, the radius values of the assumed ellipses are calculated by the optimizer. These methods of feature extraction are based on the incremental ellipses and intersecting ellipses, respectively. The density of the data in the assumed ellipses is given to the k-nearest neighbor as a feature to classify the epileptic seizure and seizure-free EEG signals. The intended method was implemented and investigated on two databases of the Bonn university of Germany and the neurology and sleep center of New Delhi. The results indicate that the proposed features are strong tools for separation and diagnosis of this type of signal and have higher accuracy compared to the other classic and updated methods. The extraction speed of the presented features was higher in the test phase compared to the other methods. (C) 2020 Elsevier B.V. All rights reserved.