Review: A Survey of performance and techniques for automatic epilepsy detection

Autores
Orosco, Lorena Liliana; Garces Correa, Maria Agustina; Laciar Leber, Eric
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Epilepsy is a chronic neurological disorder of the brain that affects around 50 million people worldwide. The early detection of epileptic seizures using electroencephalogram (EEG) signals is a useful tool for several applications in epilepsy diagnosis. Many techniques have been developed for unscrambling the underlying features of seizures present in EEGs. This article reviews the seizure detection algorithms developed in the last decade. In general terms, techniques based on the wavelet transform, entropy, tensors, empirical mode decomposition, chaos theory, and dynamic analysis are surveyed in the field of epilepsy detection. A performance comparison of the reviewed algorithms is also conducted. The needs for a reliable practical implementation are highlighted and some future prospectives in the area are given. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow precise and quick diagnoses. Finally, the lack of standardization of the methods in the epileptic seizure detection field is an emerging problem that has to be solved to allow homogenous comparisons of detector performance.
Fil: Orosco, Lorena Liliana. 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: 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: Laciar Leber, Eric. 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
Materia
Epilepsy
Seizure Detection Algorithm
Performance
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:11336/26634
Acceder
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spelling Review: A Survey of performance and techniques for automatic epilepsy detectionOrosco, Lorena LilianaGarces Correa, Maria AgustinaLaciar Leber, EricEpilepsySeizure Detection AlgorithmPerformanceEpilepsy is a chronic neurological disorder of the brain that affects around 50 million people worldwide. The early detection of epileptic seizures using electroencephalogram (EEG) signals is a useful tool for several applications in epilepsy diagnosis. Many techniques have been developed for unscrambling the underlying features of seizures present in EEGs. This article reviews the seizure detection algorithms developed in the last decade. In general terms, techniques based on the wavelet transform, entropy, tensors, empirical mode decomposition, chaos theory, and dynamic analysis are surveyed in the field of epilepsy detection. A performance comparison of the reviewed algorithms is also conducted. The needs for a reliable practical implementation are highlighted and some future prospectives in the area are given. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow precise and quick diagnoses. Finally, the lack of standardization of the methods in the epileptic seizure detection field is an emerging problem that has to be solved to allow homogenous comparisons of detector performance.Fil: Orosco, Lorena Liliana. 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: 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: Laciar Leber, Eric. 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; ArgentinaInstitute of Biomedical Engineering2013-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/26634Orosco, Lorena Liliana; Garces Correa, Maria Agustina; Laciar Leber, Eric; Review: A Survey of performance and techniques for automatic epilepsy detection; Institute of Biomedical Engineering; Journal of Medical and Biological Engineering; 33; 6; 12-2013; 526-5371609-09852199-4757CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.jmbe.org.tw/index.php?action=archives2&no=2027info:eu-repo/semantics/altIdentifier/doi/10.5405/jmbe.1463info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:07Zoai:ri.conicet.gov.ar:11336/26634instacron: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 09:33:07.521CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Review: A Survey of performance and techniques for automatic epilepsy detection
title Review: A Survey of performance and techniques for automatic epilepsy detection
spellingShingle Review: A Survey of performance and techniques for automatic epilepsy detection
Orosco, Lorena Liliana
Epilepsy
Seizure Detection Algorithm
Performance
title_short Review: A Survey of performance and techniques for automatic epilepsy detection
title_full Review: A Survey of performance and techniques for automatic epilepsy detection
title_fullStr Review: A Survey of performance and techniques for automatic epilepsy detection
title_full_unstemmed Review: A Survey of performance and techniques for automatic epilepsy detection
title_sort Review: A Survey of performance and techniques for automatic epilepsy detection
dc.creator.none.fl_str_mv Orosco, Lorena Liliana
Garces Correa, Maria Agustina
Laciar Leber, Eric
author Orosco, Lorena Liliana
author_facet Orosco, Lorena Liliana
Garces Correa, Maria Agustina
Laciar Leber, Eric
author_role author
author2 Garces Correa, Maria Agustina
Laciar Leber, Eric
author2_role author
author
dc.subject.none.fl_str_mv Epilepsy
Seizure Detection Algorithm
Performance
topic Epilepsy
Seizure Detection Algorithm
Performance
dc.description.none.fl_txt_mv Epilepsy is a chronic neurological disorder of the brain that affects around 50 million people worldwide. The early detection of epileptic seizures using electroencephalogram (EEG) signals is a useful tool for several applications in epilepsy diagnosis. Many techniques have been developed for unscrambling the underlying features of seizures present in EEGs. This article reviews the seizure detection algorithms developed in the last decade. In general terms, techniques based on the wavelet transform, entropy, tensors, empirical mode decomposition, chaos theory, and dynamic analysis are surveyed in the field of epilepsy detection. A performance comparison of the reviewed algorithms is also conducted. The needs for a reliable practical implementation are highlighted and some future prospectives in the area are given. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow precise and quick diagnoses. Finally, the lack of standardization of the methods in the epileptic seizure detection field is an emerging problem that has to be solved to allow homogenous comparisons of detector performance.
Fil: Orosco, Lorena Liliana. 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: 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: Laciar Leber, Eric. 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
description Epilepsy is a chronic neurological disorder of the brain that affects around 50 million people worldwide. The early detection of epileptic seizures using electroencephalogram (EEG) signals is a useful tool for several applications in epilepsy diagnosis. Many techniques have been developed for unscrambling the underlying features of seizures present in EEGs. This article reviews the seizure detection algorithms developed in the last decade. In general terms, techniques based on the wavelet transform, entropy, tensors, empirical mode decomposition, chaos theory, and dynamic analysis are surveyed in the field of epilepsy detection. A performance comparison of the reviewed algorithms is also conducted. The needs for a reliable practical implementation are highlighted and some future prospectives in the area are given. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow precise and quick diagnoses. Finally, the lack of standardization of the methods in the epileptic seizure detection field is an emerging problem that has to be solved to allow homogenous comparisons of detector performance.
publishDate 2013
dc.date.none.fl_str_mv 2013-12
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/11336/26634
Orosco, Lorena Liliana; Garces Correa, Maria Agustina; Laciar Leber, Eric; Review: A Survey of performance and techniques for automatic epilepsy detection; Institute of Biomedical Engineering; Journal of Medical and Biological Engineering; 33; 6; 12-2013; 526-537
1609-0985
2199-4757
CONICET Digital
CONICET
url http://hdl.handle.net/11336/26634
identifier_str_mv Orosco, Lorena Liliana; Garces Correa, Maria Agustina; Laciar Leber, Eric; Review: A Survey of performance and techniques for automatic epilepsy detection; Institute of Biomedical Engineering; Journal of Medical and Biological Engineering; 33; 6; 12-2013; 526-537
1609-0985
2199-4757
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.jmbe.org.tw/index.php?action=archives2&no=2027
info:eu-repo/semantics/altIdentifier/doi/10.5405/jmbe.1463
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
dc.publisher.none.fl_str_mv Institute of Biomedical Engineering
publisher.none.fl_str_mv Institute of Biomedical Engineering
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 13.070432

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