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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23453
Ver los metadatos del registro completo
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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 |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/23453 |
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http://sedici.unlp.edu.ar/handle/10915/23453 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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