Automatic detection of epileptic seizures in long-term EEG records

Autores
Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Epilepsy is a neurological disorder which affects nearly 1.5% of the world's total population. Trained physicians and neurologists visually scan the long-term electroencephalographic (EEG) records to identify epileptic seizures. It generally requires many hours to interpret the data. Therefore, tools for quick detection of seizures in long-term EEG records are very useful. This study proposes an algorithm to help detect seizures in long-term iEEG based on low computational costs methods using Spectral Power and Wavelet analysis. The detector was tested on 21 invasive intracranial EEG (iEEG) records. A sensitivity of 85.39% was achieved. The results indicate that the proposed method detects epileptic seizures in long-term iEEG records successfully. Moreover, the algorithm does not require long processing time due to its simplicity. This feature will allow significant time reduction of the visual inspection of iEEG records performed by the specialists.
Fil: Garces Correa, Maria Agustina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Orosco, Lorena Liliana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Diez, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Materia
Eeg Frequency Bands
Epilepsy
Intracranial Eeg Records (Ieeg)
Power Spectrum
Wavelet Decomposition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/38013
Acceder
id CONICETDig_4b0729bf3a524fb4b6506f9c152ca650
oai_identifier_str oai:ri.conicet.gov.ar:11336/38013
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automatic detection of epileptic seizures in long-term EEG recordsGarces Correa, Maria AgustinaOrosco, Lorena LilianaDiez, Pablo FedericoLaciar Leber, EricEeg Frequency BandsEpilepsyIntracranial Eeg Records (Ieeg)Power SpectrumWavelet Decompositionhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Epilepsy is a neurological disorder which affects nearly 1.5% of the world's total population. Trained physicians and neurologists visually scan the long-term electroencephalographic (EEG) records to identify epileptic seizures. It generally requires many hours to interpret the data. Therefore, tools for quick detection of seizures in long-term EEG records are very useful. This study proposes an algorithm to help detect seizures in long-term iEEG based on low computational costs methods using Spectral Power and Wavelet analysis. The detector was tested on 21 invasive intracranial EEG (iEEG) records. A sensitivity of 85.39% was achieved. The results indicate that the proposed method detects epileptic seizures in long-term iEEG records successfully. Moreover, the algorithm does not require long processing time due to its simplicity. This feature will allow significant time reduction of the visual inspection of iEEG records performed by the specialists.Fil: Garces Correa, Maria Agustina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Orosco, Lorena Liliana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Diez, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaPergamon-Elsevier Science Ltd2015-02info: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/11336/38013Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric; Automatic detection of epileptic seizures in long-term EEG records; Pergamon-Elsevier Science Ltd; Computers In Biology And Medicine; 57; 2-2015; 66-730010-4825CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2014.11.013info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S001048251400331Xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:39:29Zoai:ri.conicet.gov.ar:11336/38013instacron: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:34982025-09-29 10:39:29.426CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automatic detection of epileptic seizures in long-term EEG records
title Automatic detection of epileptic seizures in long-term EEG records
spellingShingle Automatic detection of epileptic seizures in long-term EEG records
Garces Correa, Maria Agustina
Eeg Frequency Bands
Epilepsy
Intracranial Eeg Records (Ieeg)
Power Spectrum
Wavelet Decomposition
title_short Automatic detection of epileptic seizures in long-term EEG records
title_full Automatic detection of epileptic seizures in long-term EEG records
title_fullStr Automatic detection of epileptic seizures in long-term EEG records
title_full_unstemmed Automatic detection of epileptic seizures in long-term EEG records
title_sort Automatic detection of epileptic seizures in long-term EEG records
dc.creator.none.fl_str_mv Garces Correa, Maria Agustina
Orosco, Lorena Liliana
Diez, Pablo Federico
Laciar Leber, Eric
author Garces Correa, Maria Agustina
author_facet Garces Correa, Maria Agustina
Orosco, Lorena Liliana
Diez, Pablo Federico
Laciar Leber, Eric
author_role author
author2 Orosco, Lorena Liliana
Diez, Pablo Federico
Laciar Leber, Eric
author2_role author
author
author
dc.subject.none.fl_str_mv Eeg Frequency Bands
Epilepsy
Intracranial Eeg Records (Ieeg)
Power Spectrum
Wavelet Decomposition
topic Eeg Frequency Bands
Epilepsy
Intracranial Eeg Records (Ieeg)
Power Spectrum
Wavelet Decomposition
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Epilepsy is a neurological disorder which affects nearly 1.5% of the world's total population. Trained physicians and neurologists visually scan the long-term electroencephalographic (EEG) records to identify epileptic seizures. It generally requires many hours to interpret the data. Therefore, tools for quick detection of seizures in long-term EEG records are very useful. This study proposes an algorithm to help detect seizures in long-term iEEG based on low computational costs methods using Spectral Power and Wavelet analysis. The detector was tested on 21 invasive intracranial EEG (iEEG) records. A sensitivity of 85.39% was achieved. The results indicate that the proposed method detects epileptic seizures in long-term iEEG records successfully. Moreover, the algorithm does not require long processing time due to its simplicity. This feature will allow significant time reduction of the visual inspection of iEEG records performed by the specialists.
Fil: Garces Correa, Maria Agustina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Orosco, Lorena Liliana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Diez, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
description Epilepsy is a neurological disorder which affects nearly 1.5% of the world's total population. Trained physicians and neurologists visually scan the long-term electroencephalographic (EEG) records to identify epileptic seizures. It generally requires many hours to interpret the data. Therefore, tools for quick detection of seizures in long-term EEG records are very useful. This study proposes an algorithm to help detect seizures in long-term iEEG based on low computational costs methods using Spectral Power and Wavelet analysis. The detector was tested on 21 invasive intracranial EEG (iEEG) records. A sensitivity of 85.39% was achieved. The results indicate that the proposed method detects epileptic seizures in long-term iEEG records successfully. Moreover, the algorithm does not require long processing time due to its simplicity. This feature will allow significant time reduction of the visual inspection of iEEG records performed by the specialists.
publishDate 2015
dc.date.none.fl_str_mv 2015-02
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/11336/38013
Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric; Automatic detection of epileptic seizures in long-term EEG records; Pergamon-Elsevier Science Ltd; Computers In Biology And Medicine; 57; 2-2015; 66-73
0010-4825
CONICET Digital
CONICET
url http://hdl.handle.net/11336/38013
identifier_str_mv Garces Correa, Maria Agustina; Orosco, Lorena Liliana; Diez, Pablo Federico; Laciar Leber, Eric; Automatic detection of epileptic seizures in long-term EEG records; Pergamon-Elsevier Science Ltd; Computers In Biology And Medicine; 57; 2-2015; 66-73
0010-4825
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2014.11.013
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S001048251400331X
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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
_version_ 1844614420291911680
score 13.070432

Publicaciones similares