Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals

Rajaguru, Harikumar; Prabhakar, Sunil Kumar

ISBN 10: 3960671229 / ISBN 13: 9783960671220

Published by Anchor Academic Publishing, 2017

About this Item

pp. 52.

Seller Inventory # 26378451241

Bibliographic Details

• Title: Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals
• Publisher: Anchor Academic Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 3960671229
• ISBN 13: 9783960671220
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the principle of Bayes' rule in which it is the probability based theorem. The results of PSO, hybrid PSO and Bayesian classifier are calculated and their performance is analyzed using performance index, quality value, cost function and classification rate in calculating the epileptic risk level from EEG.