Analysis and Classification of Epilepsy Stages with Genetic Programming

Book Chapters

  1. Arturo Sotelo, Enrique Guijarro, Leonardo Trujillo, Luis Coria and Yuliana Martínez. Analysis and Classification of Epilepsy Stages with Genetic Programming. In Oliver Schütze, Carlos A Coello Coello, Alexandru-Adrian Tantar, Emilia Tantar, Pascal Bouvry, Pierre Del Moral and Pierrick Legrand (eds.). EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing series, volume 175, Springer Berlin Heidelberg, 2013, pages 57-70. URL, DOI BibTeX

    @incollection{,
    	year = 2013,
    	isbn = "978-3-642-31518-3",
    	booktitle = "EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II",
    	volume = 175,
    	series = "Advances in Intelligent Systems and Computing",
    	editor = "Schütze, Oliver and Coello Coello, Carlos A. and Tantar, Alexandru-Adrian and Tantar, Emilia and Bouvry, Pascal and Del Moral, Pierre and Legrand, Pierrick",
    	doi = "10.1007/978-3-642-31519-0_4",
    	title = "Analysis and Classification of Epilepsy Stages with Genetic Programming",
    	url = "http://dx.doi.org/10.1007/978-3-642-31519-0_4",
    	publisher = "Springer Berlin Heidelberg",
    	keywords = "Epilepsy Diagnosis; Genetic Programming; Classification",
    	author = "Sotelo, Arturo and Guijarro, Enrique and Trujillo, Leonardo and Coria, Luis and Martínez, Yuliana",
    	pages = "57-70"
    }
    

Abstract

Epilepsy is a widespread disorder that affects many individuals worldwide. For this reason much work has been done to develop computational systems that can facilitate the analysis and interpretation of the signals generated by a patients brain during the onset of an epileptic seizure. Currently, this is done by human experts since computational methods cannot achieve a similar level of performance. This paper presents a Genetic Programming (GP) based approach to analyze brain activity captured with Electrocorticogram (ECoG). The goal is to evolve classifiers that can detect the three main stages of an epileptic seizure. Experimental results show good performance by the GP-classifiers, evaluated based on sensitivity, specificity, prevalence and likelihood ratio. The results are unique within this domain, and could become a useful tool in the development of future treatment methods.

Published in
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II
Advances in Intelligent Systems and Computing
Pages 57-70
Volume 175
http://link.springer.com/chapter/10.1007%2F978-3-642-31519-0_4
Copyright
2012
ISSN
2194-5357
ISBN
978-3-642-31519-0

 

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