Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction
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Publication:3529633
DOI10.1063/1.2137623zbMath1144.37405OpenAlexW2036801659WikidataQ48591365 ScholiaQ48591365MaRDI QIDQ3529633
Matthias Winterhalder, Ariane Schad, Armin Brandt, Jens Timmer, Björn Schelter, Andreas Schulze-Bonhage, Thomas Maiwald
Publication date: 14 October 2008
Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)
Full work available at URL: https://semanticscholar.org/paper/0bf9eab2b33eec0145e91ccbea6686007232799a
Related Items (6)
NONLINEAR TIME SERIES ANALYSIS IN EPILEPSY ⋮ Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients ⋮ Proposing a two-level stochastic model for epileptic seizure genesis ⋮ Synaptic plasticity based model for epileptic seizures ⋮ Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers ⋮ Identification and verification of chaotic dynamics in a missile system from experimental time series
Cites Work
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- Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients
- The synchronization of chaotic systems
- Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic
- Fundamentals of synchronization in chaotic systems, concepts, and applications
- Synchronization in chaotic systems
- Controlled test for predictive power of Lyapunov exponents: Their inability to predict epileptic seizures
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