Controlled test for predictive power of Lyapunov exponents: Their inability to predict epileptic seizures

DOI10.1063/1.1777831zbMath1080.92043OpenAlexW1972901354WikidataQ51635561 ScholiaQ51635561MaRDI QIDQ5705427

Mark G. Frei, Mary Ann F. Harrison, Ivan Osorio, Ying-Cheng Lai

Publication date: 8 November 2005

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/5e08280ef1b0dfd0aa24538a0c0dfd322e96b8e6

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Dynamical systems in biology (37N25) Medical applications (general) (92C50) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45)

Related Items (2)

Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction ⋮ Hyperspherical manifold for EEG signals of epileptic seizures

Cites Work

• Determining Lyapunov exponents from a time series
• Nonlinear prediction of chaotic time series
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• Embedology
• A practical method for calculating largest Lyapunov exponents from small data sets
• Timely detection of dynamical change in scalp EEG signals
• A coupled ordinary differential equation lattice model for the simulation of epileptic seizures
• How often are chaotic saddles nonhyperbolic?
• Dimension, entropy and Lyapunov exponents
• Controlling chaos
• Ergodic theory of chaos and strange attractors
• Crises, sudden changes in chaotic attractors, and transient chaos
• Simple mathematical models with very complicated dynamics
• Quadratic binary programming and dynamical system approach to determine the predictability of epileptic seizures

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