Performance Analysis of Lion Optimization Algorithm with Hybrid Classifier for Epilepsy Detection

Abstract

The anatomical elements and the actions are amazing for the nervous system, but the human brain is susceptible to more neurological conditions, and epilepsy is one of such abnormalities. In medical parlance, a person is said to have the disease known as epilepsy if they have recurring seizures. In this study, Lion Optimization Algorithm (LOA) is employed to reduce the features dimensionality from EEG outputs. Following this, the reduced records are evaluated with the use of a hybrid learning approach that combines a Gaussian Mixture Model (GMM) with an Expectation Maximization (EM) technique. Results indicate that an average accuracy of 91% is achieved when the LOA features is identified using GMM with EM.