Performance Analysis of Cat Swarm Optimization with Soft Discriminant Classifier for Diagnosis of Epilepsy using EEG Signals

A seizure caused by epilepsy is characterized by the rapid excitation of a significant number of neuronal cells in quick succession. Patients have a great deal of difficulties as a result of an unanticipated anomalous function that occurs in their brains. Due to the neurons' high rate of electrical discharge, the usual bodily functions are greatly perturbed. Electroencephalography (EEG), a visual representation of these electrical brain movements, is used to record them. In this study, dimensionality reduction and feature extraction algorithms are used to minimize the dimensionality of EEG data. The Power Spectral Density (PSD) and Singular Value Decomposition (SVD) algorithms are employed to lower dimensionality. The Cat Swarm Optimization (CSO) algorithm is employed as a feature extraction method. Softmax Discriminant classifier is used to identify epilepsy. Results show that the when PSD with CSO is identified with soft discriminant classifier model gives the best accuracy of 97.19%. When SVD with CSO is identified with soft discriminant classifier model gives the accuracy of 95.55%.