Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear predict...

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Autores: Naro, Daniel, Rummel, Christian, Schindler, Kaspar A., Andrzejak, Ralph Gregor
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2014
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/43638
Acceso en línea:http://hdl.handle.net/10230/43638
http://dx.doi.org/10.1103/PhysRevE.90.032913
Access Level:acceso abierto
Palabra clave:Nonlinear signal analysis
Determinism
Electroencephalographic recordings
Epilepsy
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spelling Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability scoreNaro, DanielRummel, ChristianSchindler, Kaspar A.Andrzejak, Ralph GregorNonlinear signal analysisDeterminismElectroencephalographic recordingsEpilepsyThe rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).R.G.A. acknowledges Grant No. FIS-2010-18204 of the Spanish Ministry of Education and Science and funding from the Volkswagen Foundation. K.S. and C.R. are grateful for support by the Swiss National Science Foundation (Projects No. SNF 320030-122010 and No. 33CM30-124089).American Physical Society202020202014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/43638http://dx.doi.org/10.1103/PhysRevE.90.032913reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésPhysical Review E. 2014;90(3):032913http://hdl.handle.net/10230/42940info:eu-repo/grant/Agreement/ES/3PN/FIS2010-18204© American Physical Society. Published article available at https://journals.aps.org/pre/pdf/10.1103/PhysRevE.90.032913info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/436382026-06-12T07:21:37Z
dc.title.none.fl_str_mv Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
title Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
spellingShingle Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
Naro, Daniel
Nonlinear signal analysis
Determinism
Electroencephalographic recordings
Epilepsy
title_short Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
title_full Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
title_fullStr Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
title_full_unstemmed Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
title_sort Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score
dc.creator.none.fl_str_mv Naro, Daniel
Rummel, Christian
Schindler, Kaspar A.
Andrzejak, Ralph Gregor
author Naro, Daniel
author_facet Naro, Daniel
Rummel, Christian
Schindler, Kaspar A.
Andrzejak, Ralph Gregor
author_role author
author2 Rummel, Christian
Schindler, Kaspar A.
Andrzejak, Ralph Gregor
author2_role author
author
author
dc.subject.none.fl_str_mv Nonlinear signal analysis
Determinism
Electroencephalographic recordings
Epilepsy
topic Nonlinear signal analysis
Determinism
Electroencephalographic recordings
Epilepsy
description The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).
publishDate 2014
dc.date.none.fl_str_mv 2014
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/10230/43638
http://dx.doi.org/10.1103/PhysRevE.90.032913
url http://hdl.handle.net/10230/43638
http://dx.doi.org/10.1103/PhysRevE.90.032913
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Physical Review E. 2014;90(3):032913
http://hdl.handle.net/10230/42940
info:eu-repo/grant/Agreement/ES/3PN/FIS2010-18204
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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 15,811543

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