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  Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data

Ngamga, E. J., Bialonski, S., Marwan, N., Kurths, J., Geier, C., Lehnertz, K. (2016): Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data. - Physics Letters A, 380, 16, 1419-1425.
https://doi.org/10.1016/j.physleta.2016.02.024

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Ngamga, Eulalie Joelle1, Autor              
Bialonski, S.2, Autor
Marwan, Norbert1, Autor              
Kurths, Jürgen1, Autor              
Geier, C.2, Autor
Lehnertz, K.2, Autor
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings from five epilepsy patients. We employ several statistical techniques to avoid spurious findings due to various influencing factors and due to multiple comparisons and observe precursory structures in three patients. Our findings indicate a high congruence among measures in identifying seizure precursors and emphasize the current notion of seizure generation in large-scale epileptic networks. A final judgment of the suitability for field studies, however, requires evaluation on a larger database.

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 Datum: 2016
 Publikationsstatus: Final veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.physleta.2016.02.024
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 7170
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Health
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
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Titel: Physics Letters A
Genre der Quelle: Zeitschrift, SCI, p3
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Seiten: - Band / Heft: 380 (16) Artikelnummer: - Start- / Endseite: 1419 - 1425 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals398