Using bivariate signal analysis to characterize the epileptic focus: the benefit of surrogates

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• dc.contributor.author Andrzejak, Ralph Gregor
• dc.contributor.author Chicharro Raventós, Daniel
• dc.contributor.author Lehnertz, Klaus
• dc.contributor.author Mormann, Florian
• dc.date.accessioned 2020-02-13T15:22:40Z
• dc.date.available 2020-02-13T15:22:40Z
• dc.date.issued 2011
• dc.description.abstract The disease epilepsy is related to hypersynchronous activity of networks of neurons. While acute epileptic seizures are the most extreme manifestation of this hypersynchronous activity, an elevated level of interdependence of neuronal dynamics is thought to persist also during the seizure-free interval. In multichannel recordings from brain areas involved in the epileptic process, this interdependence can be reflected in an increased linear cross correlation but also in signal properties of higher order. Bivariate time series analysis comprises a variety of approaches, each with different degrees of sensitivity and specificity for interdependencies reflected in lower- or higher-order properties of pairs of simultaneously recorded signals. Here we investigate which approach is best suited to detect putatively elevated interdependence levels in signals recorded from brain areas involved in the epileptic process. For this purpose, we use the linear cross correlation that is sensitive to lower-order signatures of interdependence, a nonlinear interdependence measure that integrates both lower- and higher-order properties, and a surrogate-corrected nonlinear interdependence measure that aims to specifically characterize higher-order properties. We analyze intracranial electroencephalographic recordings of the seizure-free interval from 29 patients with an epileptic focus located in the medial temporal lobe. Our results show that all three approaches detect higher levels of interdependence for signals recorded from the brain hemisphere containing the epileptic focus as compared to signals recorded from the opposite hemisphere. For the linear cross correlation, however, these differences are not significant. For the nonlinear interdependence measure, results are significant but only of moderate accuracy with regard to the discriminative power for the focal and nonfocal hemispheres. The highest significance and accuracy is obtained for the surrogate-corrected nonlinear interdependence measure.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Andrzejak RG, Chicharro D, Lehnertz K, Mormann F. Using bivariate signal analysis to characterize the epileptic focus: the benefit of surrogates. Phys Rev E. 2011 Apr 7;83(4):046203. DOI: 10.1103/PhysRevE.83.046203
• dc.identifier.doi http://dx.doi.org/10.1103/PhysRevE.83.046203
• dc.identifier.issn 2470-0045
• dc.identifier.uri http://hdl.handle.net/10230/43592
• dc.language.iso eng
• dc.publisher American Physical Society
• dc.relation.ispartof Physical Review E. 2011;83:046203.
• dc.rights © American Physical Society. Published article available at https://doi.org/10.1103/PhysRevE.83.046203
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Nonlinear signal analysisen
• dc.subject.keyword Synchronizationen
• dc.subject.keyword Surrogateen
• dc.subject.keyword Electroencephalographic recordingsen
• dc.subject.keyword Epilepsyen
• dc.title Using bivariate signal analysis to characterize the epileptic focus: the benefit of surrogatesen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion