A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence

"This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering...

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Detalles Bibliográficos
Autores principales: Quintero-Rincón, Antonio, Pereyra, Marcelo, D'Giano, Carlos, Batatia, Hadj, Risk, Marcelo
Formato: Ponencias en Congresos acceptedVersion
Lenguaje:Inglés
Publicado: 2019
Materias:
ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
EPILEPSIA
MODELOS MATEMATICOS
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/1751
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-1751
record_format dspace
spelling I32-R138-123456789-17512022-12-07T14:13:48Z A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence Quintero-Rincón, Antonio Pereyra, Marcelo D'Giano, Carlos Batatia, Hadj Risk, Marcelo ELECTROENCEFALOGRAFIA PROCESAMIENTO DE SEÑALES ESTADISTICA EPILEPSIA MODELOS MATEMATICOS "This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure." 2019-09-12T17:11:58Z 2019-09-12T17:11:58Z 2017-10 Ponencias en Congresos info:eu-repo/semantics/acceptedVersion 1680-0737 http://ri.itba.edu.ar/handle/123456789/1751 en info:eu-repo/semantics/altIdentifier/doi/10.1007/978-981-10-4086-3_4 info:eu-repo/grantAgreement/EPSRC/EP/D063485/1/UK. Swindon info:eu-repo/grantAgreement/ITBA/ITBACyT/AR. Ciudad de Buenos Aires info:eu-repo/grantAgreement/FLENI/Protocolo/07/15/AR. Ciudad de Buenos Aires application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
EPILEPSIA
MODELOS MATEMATICOS
spellingShingle ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
EPILEPSIA
MODELOS MATEMATICOS
Quintero-Rincón, Antonio
Pereyra, Marcelo
D'Giano, Carlos
Batatia, Hadj
Risk, Marcelo
A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
topic_facet ELECTROENCEFALOGRAFIA
PROCESAMIENTO DE SEÑALES
ESTADISTICA
EPILEPSIA
MODELOS MATEMATICOS
description "This paper presents a statistical signal processing method for the characterization of EEG of patients suffering from epilepsy. A statistical model is proposed for the signals and the Kullback-Leibler divergence is used to study the differences between Seizure/Non-Seizure in patients suffering from epilepsy. Precisely, EEG signals are transformed into multivariate coefficients through multilevel 1D wavelet decomposition of different brain frequencies. The generalized Gaussian distribution (GGD) is shown to model precisely these coefficients. Patients are compared based on the analytical development of Kullback-Leibler divergence (KLD) of their corresponding GGD distributions. The method has been applied to a dataset of 18 epileptic signals of 9 patients. Results show a clear discrepancy between Seizure/Non-Seizure in epileptic signals, which helps in determining the onset of the seizure."
format Ponencias en Congresos
acceptedVersion
author Quintero-Rincón, Antonio
Pereyra, Marcelo
D'Giano, Carlos
Batatia, Hadj
Risk, Marcelo
author_facet Quintero-Rincón, Antonio
Pereyra, Marcelo
D'Giano, Carlos
Batatia, Hadj
Risk, Marcelo
author_sort Quintero-Rincón, Antonio
title A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_short A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_full A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_fullStr A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_full_unstemmed A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence
title_sort visual eeg epilepsy detection method based on a wavelet statistical representation and the kullback-leibler divergence
publishDate 2019
url http://ri.itba.edu.ar/handle/123456789/1751
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