Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction

Autores
Blanco, Susana Alicia Ana; Garay, Arturo; Coulombie, Diego
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Under the hypothesis that the uncontrolled neuronal synchronization propagates recruiting more and more neurons, the aim is to detect its onset as early as possible by signal analysis. This synchronization is not noticeable just by looking at the EEG, so mathematical tools are needed for its identification. Objective. The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure. Materials and Methods. Invasive ictal records were used. We measured the Fourier spectrum entropy of the electroencephalographic signals 4 to 32 minutes before the attack in low, medium and high frequencies. Results. The high-frequency band shows a markedly rate of increase of the entropy, with positive slopes and low correlation coefficient. The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes. The mid-frequency band showed both positive and negative slopes with low correlation. Conclusions. The entropy in the high frequencies could be predictor, because it shows changes in the previous moments of the attack. Its main problem is the variability, which makes it difficult to set the threshold that ensures an adequate prediction.
Fil: Blanco, Susana Alicia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Belgrano. Facultad de Ingenieria; Argentina
Fil: Garay, Arturo. Centro de Educación Médica e Investigaciones Clínicas; Argentina
Fil: Coulombie, Diego. Universidad Nacional de la Matanza. Instituto de Investigación y Desarrollo; Argentina
Materia
EPILEPSY
DETACTION
SIGNAL PROCESSING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/3764
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure PredictionBlanco, Susana Alicia AnaGaray, ArturoCoulombie, DiegoEPILEPSYDETACTIONSIGNAL PROCESSINGhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Under the hypothesis that the uncontrolled neuronal synchronization propagates recruiting more and more neurons, the aim is to detect its onset as early as possible by signal analysis. This synchronization is not noticeable just by looking at the EEG, so mathematical tools are needed for its identification. Objective. The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure. Materials and Methods. Invasive ictal records were used. We measured the Fourier spectrum entropy of the electroencephalographic signals 4 to 32 minutes before the attack in low, medium and high frequencies. Results. The high-frequency band shows a markedly rate of increase of the entropy, with positive slopes and low correlation coefficient. The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes. The mid-frequency band showed both positive and negative slopes with low correlation. Conclusions. The entropy in the high frequencies could be predictor, because it shows changes in the previous moments of the attack. Its main problem is the variability, which makes it difficult to set the threshold that ensures an adequate prediction.Fil: Blanco, Susana Alicia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Belgrano. Facultad de Ingenieria; ArgentinaFil: Garay, Arturo. Centro de Educación Médica e Investigaciones Clínicas; ArgentinaFil: Coulombie, Diego. Universidad Nacional de la Matanza. Instituto de Investigación y Desarrollo; ArgentinaHindawi Publishing Corporation2013-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/3764Blanco, Susana Alicia Ana; Garay, Arturo; Coulombie, Diego; Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction; Hindawi Publishing Corporation; ISRN Neurology; 2013; 5-2013; 287327-2873272090-5505enginfo:eu-repo/semantics/altIdentifier/url/http://www.hindawi.com/isrn/neurology/2013/287327/10.1155/2013/287327info:eu-repo/semantics/altIdentifier/doi/10.1155/2013/287327info:eu-repo/semantics/altIdentifier/issn/2090-5505info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:09:36Zoai:ri.conicet.gov.ar:11336/3764instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:09:36.531CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
title Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
spellingShingle Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
Blanco, Susana Alicia Ana
EPILEPSY
DETACTION
SIGNAL PROCESSING
title_short Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
title_full Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
title_fullStr Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
title_full_unstemmed Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
title_sort Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction
dc.creator.none.fl_str_mv Blanco, Susana Alicia Ana
Garay, Arturo
Coulombie, Diego
author Blanco, Susana Alicia Ana
author_facet Blanco, Susana Alicia Ana
Garay, Arturo
Coulombie, Diego
author_role author
author2 Garay, Arturo
Coulombie, Diego
author2_role author
author
dc.subject.none.fl_str_mv EPILEPSY
DETACTION
SIGNAL PROCESSING
topic EPILEPSY
DETACTION
SIGNAL PROCESSING
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Under the hypothesis that the uncontrolled neuronal synchronization propagates recruiting more and more neurons, the aim is to detect its onset as early as possible by signal analysis. This synchronization is not noticeable just by looking at the EEG, so mathematical tools are needed for its identification. Objective. The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure. Materials and Methods. Invasive ictal records were used. We measured the Fourier spectrum entropy of the electroencephalographic signals 4 to 32 minutes before the attack in low, medium and high frequencies. Results. The high-frequency band shows a markedly rate of increase of the entropy, with positive slopes and low correlation coefficient. The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes. The mid-frequency band showed both positive and negative slopes with low correlation. Conclusions. The entropy in the high frequencies could be predictor, because it shows changes in the previous moments of the attack. Its main problem is the variability, which makes it difficult to set the threshold that ensures an adequate prediction.
Fil: Blanco, Susana Alicia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Belgrano. Facultad de Ingenieria; Argentina
Fil: Garay, Arturo. Centro de Educación Médica e Investigaciones Clínicas; Argentina
Fil: Coulombie, Diego. Universidad Nacional de la Matanza. Instituto de Investigación y Desarrollo; Argentina
description Under the hypothesis that the uncontrolled neuronal synchronization propagates recruiting more and more neurons, the aim is to detect its onset as early as possible by signal analysis. This synchronization is not noticeable just by looking at the EEG, so mathematical tools are needed for its identification. Objective. The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure. Materials and Methods. Invasive ictal records were used. We measured the Fourier spectrum entropy of the electroencephalographic signals 4 to 32 minutes before the attack in low, medium and high frequencies. Results. The high-frequency band shows a markedly rate of increase of the entropy, with positive slopes and low correlation coefficient. The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes. The mid-frequency band showed both positive and negative slopes with low correlation. Conclusions. The entropy in the high frequencies could be predictor, because it shows changes in the previous moments of the attack. Its main problem is the variability, which makes it difficult to set the threshold that ensures an adequate prediction.
publishDate 2013
dc.date.none.fl_str_mv 2013-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/3764
Blanco, Susana Alicia Ana; Garay, Arturo; Coulombie, Diego; Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction; Hindawi Publishing Corporation; ISRN Neurology; 2013; 5-2013; 287327-287327
2090-5505
url http://hdl.handle.net/11336/3764
identifier_str_mv Blanco, Susana Alicia Ana; Garay, Arturo; Coulombie, Diego; Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction; Hindawi Publishing Corporation; ISRN Neurology; 2013; 5-2013; 287327-287327
2090-5505
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.hindawi.com/isrn/neurology/2013/287327/10.1155/2013/287327
info:eu-repo/semantics/altIdentifier/doi/10.1155/2013/287327
info:eu-repo/semantics/altIdentifier/issn/2090-5505
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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