Testing the null hypothesis of the nonexistence of a preseizure state

A rapidly growing number of studies deals with the prediction of epileptic seizures. For this purpose, various techniques derived from linear and nonlinear time series analysis have been applied to the electroencephalogram of epilepsy patients. In none of these works, however, the performance of the...

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Authors: Andrzejak, Ralph Gregor, Mormann, Florian, Kreuz, Thomas, Rieke, Christoph, Kraskov, Alexander, Elger, Christian E., Lehnertz, Klaus
Format: article
Status:Published version
Publication Date:2003
Country:España
Institution:Universitat Pompeu Fabra
Repository:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/43635
Online Access:http://hdl.handle.net/10230/43635
http://dx.doi.org/10.1103/PhysRevE.67.010901
Access Level:Open access
Keyword:Nonlinear signal analysis
Electroencephalographic recordings
Epilepsy
Seizure prediction
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spelling Testing the null hypothesis of the nonexistence of a preseizure stateAndrzejak, Ralph GregorMormann, FlorianKreuz, ThomasRieke, ChristophKraskov, AlexanderElger, Christian E.Lehnertz, KlausNonlinear signal analysisElectroencephalographic recordingsEpilepsySeizure predictionNonlinear signal analysisElectroencephalographic recordingsEpilepsySeizure predictionA rapidly growing number of studies deals with the prediction of epileptic seizures. For this purpose, various techniques derived from linear and nonlinear time series analysis have been applied to the electroencephalogram of epilepsy patients. In none of these works, however, the performance of the seizure prediction statistics is tested against a null hypothesis, an otherwise ubiquitous concept in science. In consequence, the evaluation of the reported performance values is problematic. Here, we propose the technique of seizure time surrogates based on a Monte Carlo simulation to remedy this deficit.C.E.E., T.K., K.L., F.M., and C.R. acknowledge support from the Deutsche Forschungsgemeinschaft.American Physical Society202020202003info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/43635http://dx.doi.org/10.1103/PhysRevE.67.010901reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésPhysical Review E. 2003;67:010901© American Physical Society. Published article available at dx.doi.org/10.1103/PhysRevE.67.010901info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/436352026-06-12T07:21:37Z
dc.title.none.fl_str_mv Testing the null hypothesis of the nonexistence of a preseizure state
title Testing the null hypothesis of the nonexistence of a preseizure state
spellingShingle Testing the null hypothesis of the nonexistence of a preseizure state
Andrzejak, Ralph Gregor
Nonlinear signal analysis
Electroencephalographic recordings
Epilepsy
Seizure prediction
Nonlinear signal analysis
Electroencephalographic recordings
Epilepsy
Seizure prediction
title_short Testing the null hypothesis of the nonexistence of a preseizure state
title_full Testing the null hypothesis of the nonexistence of a preseizure state
title_fullStr Testing the null hypothesis of the nonexistence of a preseizure state
title_full_unstemmed Testing the null hypothesis of the nonexistence of a preseizure state
title_sort Testing the null hypothesis of the nonexistence of a preseizure state
dc.creator.none.fl_str_mv Andrzejak, Ralph Gregor
Mormann, Florian
Kreuz, Thomas
Rieke, Christoph
Kraskov, Alexander
Elger, Christian E.
Lehnertz, Klaus
author Andrzejak, Ralph Gregor
author_facet Andrzejak, Ralph Gregor
Mormann, Florian
Kreuz, Thomas
Rieke, Christoph
Kraskov, Alexander
Elger, Christian E.
Lehnertz, Klaus
author_role author
author2 Mormann, Florian
Kreuz, Thomas
Rieke, Christoph
Kraskov, Alexander
Elger, Christian E.
Lehnertz, Klaus
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Nonlinear signal analysis
Electroencephalographic recordings
Epilepsy
Seizure prediction
Nonlinear signal analysis
Electroencephalographic recordings
Epilepsy
Seizure prediction
topic Nonlinear signal analysis
Electroencephalographic recordings
Epilepsy
Seizure prediction
Nonlinear signal analysis
Electroencephalographic recordings
Epilepsy
Seizure prediction
description A rapidly growing number of studies deals with the prediction of epileptic seizures. For this purpose, various techniques derived from linear and nonlinear time series analysis have been applied to the electroencephalogram of epilepsy patients. In none of these works, however, the performance of the seizure prediction statistics is tested against a null hypothesis, an otherwise ubiquitous concept in science. In consequence, the evaluation of the reported performance values is problematic. Here, we propose the technique of seizure time surrogates based on a Monte Carlo simulation to remedy this deficit.
publishDate 2003
dc.date.none.fl_str_mv 2003
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/43635
http://dx.doi.org/10.1103/PhysRevE.67.010901
url http://hdl.handle.net/10230/43635
http://dx.doi.org/10.1103/PhysRevE.67.010901
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Physical Review E. 2003;67:010901
dc.rights.none.fl_str_mv © American Physical Society. Published article available at dx.doi.org/10.1103/PhysRevE.67.010901
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © American Physical Society. Published article available at dx.doi.org/10.1103/PhysRevE.67.010901
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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