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

Rajaguru, Harikumar; Prabhakar, Sunil Kumar

 
9783960671220: Comprehensive Analysis of Swarm Based Classifiers and Bayesian Based Models for Epilepsy Risk Level Classification from EEG Signals
Softcover
ISBN 10: 3960671229 ISBN 13: 9783960671220
Publisher: Anchor Academic Publishing, 2017

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.

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Publisher
Anchor Academic Publishing
Publication date
2017
Language
English
ISBN 10 
3960671229
ISBN 13 
9783960671220
Binding
Paperback
Number of pages
52