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

Textbook 2017 47 Pages

Engineering - Computer Engineering

Summary

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.

Details

Pages
47
Type of Edition
Erstausgabe
Year
2017
ISBN (eBook)
9783960676225
ISBN (Book)
9783960671220
File size
839 KB
Language
English
Catalog Number
v354269
Institution / College
Bannari Amman Institute of Technology – Department of ECE
Grade
Tags
Particle swarm optimization PSO EEG Bayes Electroencephalogram Hybrid PSO Epileptic risk Epilepsy Bayesian classifier

Authors

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