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Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression

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dc.contributor.author Quintero Rincón, Antonio
dc.contributor.author Flugelman, Máximo
dc.contributor.author Prendes, Jorge
dc.contributor.author D'Giano, Carlos
dc.date.accessioned 2019-11-08T15:12:09Z
dc.date.available 2019-11-08T15:12:09Z
dc.date.issued 2019
dc.identifier.citation Quintero Rincón A, Flugelman M, Prendes J, D’Giano C. Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression. Rev Argent Bioing. 2019;23(2):17-24 en_US
dc.identifier.uri http://revista.sabi.org.ar/index.php/revista/issue/view/14
dc.identifier.uri https://repositorio.fleni.org.ar/handle/123456789/103
dc.description.abstract Seizure detection plays a central role in most aspects of epilepsy care. Understanding the complex epileptic signals system is a typical problem in electroencephalographic (EEG) signal processing. This problem requires different analysis to reveal the underlying behavior of EEG signals. An example of this is the non-linear dynamic: mathematical tools applied to biomedical problems with the purpose of extracting features or quantifying EEG data. In this work, we studied epileptic seizure detection independently in each brain rhythms from a multilevel 1D wavelet decomposition followed by the independent component analysis (ICA) representation of multivariate EEG signals. Next, the largest Lyapunov exponents (LLE) and their scaling given by its ± standard deviation are estimated in order to obtain the vectors to be used during the training and classification stage. With this information, a logistic regression classification is proposed with the aim of discriminating between seizure and non-seizure. Preliminary experiments with 99 epileptic events suggest that the proposed methodology is a powerful tool for detecting seizures in epileptic signals in terms of classification accuracy, sensitivity and specificity en_US
dc.language.iso eng en_US
dc.publisher Sociedad Argentina de Bioingeniería en_US
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Largest Lyapunov Exponents en_US
dc.subject Exponente de Lyapunov en_US
dc.subject Logistic Models en_US
dc.subject Modelos Logísticos en_US
dc.subject Electroencephalography en_US
dc.subject Electroencefalografía en_US
dc.title Study on epileptic seizure detection in EEG signals using largest Lyapunov exponents and logistic regression en_US
dc.type info:eu-repo/semantics/publishedVersion
dc.type info:eu-repo/semantics/article en_US
dc.description.fil Fil: Quintero Rincón, Antonio. Fleni; Argentina.
dc.description.fil Fil: Flugelman, Máximo. Instituto Tecnológico de Buenos Aires. Departamento de Bioingeniería; Argentina.
dc.description.fil Fil: Prendes, Jorge. University of Toulouse, Institut de Recherche en Informatique de Toulouse; Francia.
dc.description.fil Fil: D'Giano, Jorge. Fleni. Centro Integral de Epilepsia y Unidad de Monitoreo de Videoelectroencefalografía; Argentina.
dc.relation.ispartofVOLUME 23
dc.relation.ispartofNUMBER 2
dc.relation.ispartofPAGINATION 17-24
dc.relation.ispartofCOUNTRY Argentina
dc.relation.ispartofCITY Buenos Aires
dc.relation.ispartofTITLE Revista Argentina de Bioingeniería
dc.relation.ispartofISSN 2591-376X
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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