Detection and prediction of epileptic seizures: a patient's case study

Autores
Verdes, Pablo Fabián; Stefan, Hermann; Deco, Gustavo; Obradovic, Dragan; Dubé, Louis J.; Hopfengaertner, Ruediger
Año de publicación
2000
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Human epilepsy is a disease characterized by sudden, unprovoked, recurrent seizures accompanied by pathological electrical activity in the brain, and is frequently resistant to drug treatment. The ability to anticipate the onset of these incapacitating episodes would -hopefully- permit clinical interventions and avoid the serious consequences they may provoque. In this work we first consider the problem of detection of the onset of an epileptic seizure, comparing linear and non-linear techniques of time series analysis applied to electro-encephalogram recordings against onset times determined clinically. Automatic detection would be useful for fast seizure recognition which is of importance for further diagnostic procedures. The second, more ambitious goal is to foresee the ocurrence of an upcoming seizure, exploiting the widely conjectured "decrease in complexity" associated with ictal episodes. Roughly speaking, we monitor changes in time-varying windowed estimates of different magnitudes characterizing the brain's intrinsic dynamics. We face these problems for five seizures belonging to a single patient, using two strategies of brain activity reconstruction: single and multiple-channel delay embedding of the dynamics. We have found that the studied approaches successfully reflect the non-stationary character of ictal episodes, and seizure onsets were clearly accussed. For prediction, the criteria employed in the determination of clinical onset times appeared crucial.
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
epileptic seizure
nonlinear time series analysis
detection
prediction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23453

id SEDICI_c1c70614b4e77824fa77098b34911f34
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23453
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Detection and prediction of epileptic seizures: a patient's case studyVerdes, Pablo FabiánStefan, HermannDeco, GustavoObradovic, DraganDubé, Louis J.Hopfengaertner, RuedigerCiencias Informáticasepileptic seizurenonlinear time series analysisdetectionpredictionHuman epilepsy is a disease characterized by sudden, unprovoked, recurrent seizures accompanied by pathological electrical activity in the brain, and is frequently resistant to drug treatment. The ability to anticipate the onset of these incapacitating episodes would -hopefully- permit clinical interventions and avoid the serious consequences they may provoque. In this work we first consider the problem of detection of the onset of an epileptic seizure, comparing linear and non-linear techniques of time series analysis applied to electro-encephalogram recordings against onset times determined clinically. Automatic detection would be useful for fast seizure recognition which is of importance for further diagnostic procedures. The second, more ambitious goal is to foresee the ocurrence of an upcoming seizure, exploiting the widely conjectured "decrease in complexity" associated with ictal episodes. Roughly speaking, we monitor changes in time-varying windowed estimates of different magnitudes characterizing the brain's intrinsic dynamics. We face these problems for five seizures belonging to a single patient, using two strategies of brain activity reconstruction: single and multiple-channel delay embedding of the dynamics. We have found that the studied approaches successfully reflect the non-stationary character of ictal episodes, and seizure onsets were clearly accussed. For prediction, the criteria employed in the determination of clinical onset times appeared crucial.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2000-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23453enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:26Zoai:sedici.unlp.edu.ar:10915/23453Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:26.857SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Detection and prediction of epileptic seizures: a patient's case study
title Detection and prediction of epileptic seizures: a patient's case study
spellingShingle Detection and prediction of epileptic seizures: a patient's case study
Verdes, Pablo Fabián
Ciencias Informáticas
epileptic seizure
nonlinear time series analysis
detection
prediction
title_short Detection and prediction of epileptic seizures: a patient's case study
title_full Detection and prediction of epileptic seizures: a patient's case study
title_fullStr Detection and prediction of epileptic seizures: a patient's case study
title_full_unstemmed Detection and prediction of epileptic seizures: a patient's case study
title_sort Detection and prediction of epileptic seizures: a patient's case study
dc.creator.none.fl_str_mv Verdes, Pablo Fabián
Stefan, Hermann
Deco, Gustavo
Obradovic, Dragan
Dubé, Louis J.
Hopfengaertner, Ruediger
author Verdes, Pablo Fabián
author_facet Verdes, Pablo Fabián
Stefan, Hermann
Deco, Gustavo
Obradovic, Dragan
Dubé, Louis J.
Hopfengaertner, Ruediger
author_role author
author2 Stefan, Hermann
Deco, Gustavo
Obradovic, Dragan
Dubé, Louis J.
Hopfengaertner, Ruediger
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
epileptic seizure
nonlinear time series analysis
detection
prediction
topic Ciencias Informáticas
epileptic seizure
nonlinear time series analysis
detection
prediction
dc.description.none.fl_txt_mv Human epilepsy is a disease characterized by sudden, unprovoked, recurrent seizures accompanied by pathological electrical activity in the brain, and is frequently resistant to drug treatment. The ability to anticipate the onset of these incapacitating episodes would -hopefully- permit clinical interventions and avoid the serious consequences they may provoque. In this work we first consider the problem of detection of the onset of an epileptic seizure, comparing linear and non-linear techniques of time series analysis applied to electro-encephalogram recordings against onset times determined clinically. Automatic detection would be useful for fast seizure recognition which is of importance for further diagnostic procedures. The second, more ambitious goal is to foresee the ocurrence of an upcoming seizure, exploiting the widely conjectured "decrease in complexity" associated with ictal episodes. Roughly speaking, we monitor changes in time-varying windowed estimates of different magnitudes characterizing the brain's intrinsic dynamics. We face these problems for five seizures belonging to a single patient, using two strategies of brain activity reconstruction: single and multiple-channel delay embedding of the dynamics. We have found that the studied approaches successfully reflect the non-stationary character of ictal episodes, and seizure onsets were clearly accussed. For prediction, the criteria employed in the determination of clinical onset times appeared crucial.
I Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description Human epilepsy is a disease characterized by sudden, unprovoked, recurrent seizures accompanied by pathological electrical activity in the brain, and is frequently resistant to drug treatment. The ability to anticipate the onset of these incapacitating episodes would -hopefully- permit clinical interventions and avoid the serious consequences they may provoque. In this work we first consider the problem of detection of the onset of an epileptic seizure, comparing linear and non-linear techniques of time series analysis applied to electro-encephalogram recordings against onset times determined clinically. Automatic detection would be useful for fast seizure recognition which is of importance for further diagnostic procedures. The second, more ambitious goal is to foresee the ocurrence of an upcoming seizure, exploiting the widely conjectured "decrease in complexity" associated with ictal episodes. Roughly speaking, we monitor changes in time-varying windowed estimates of different magnitudes characterizing the brain's intrinsic dynamics. We face these problems for five seizures belonging to a single patient, using two strategies of brain activity reconstruction: single and multiple-channel delay embedding of the dynamics. We have found that the studied approaches successfully reflect the non-stationary character of ictal episodes, and seizure onsets were clearly accussed. For prediction, the criteria employed in the determination of clinical onset times appeared crucial.
publishDate 2000
dc.date.none.fl_str_mv 2000-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23453
url http://sedici.unlp.edu.ar/handle/10915/23453
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844615813764481024
score 13.070432