Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction

Bjoern Schelter, Matthias Winterhalder, Thomas Maiwald, Armin Brandt, Ariane Schad, Andreas Schulze-Bonhage, Jens Timmer

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140 Citations (Scopus)

Abstract

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory. (C) 2006 American Institute of Physics.

Original languageEnglish
Article number013108
Number of pages10
JournalChaos
Volume16
Issue number1
DOIs
Publication statusPublished - Mar 2006

Cite this

Schelter, B., Winterhalder, M., Maiwald, T., Brandt, A., Schad, A., Schulze-Bonhage, A., & Timmer, J. (2006). Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. Chaos, 16(1), [013108]. https://doi.org/10.1063/1.2137623