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• W2010067247 abstract "Among nonlinear dynamical invariants, determination of the largest Lyapunov exponent is well suited to positive identification of chaos in observed time series. When analyzing the dynamics of biomedical series, such as an electro-encephalogram (EEG), model-based methods should be used. Moreover, in the absence of any well founded theoretical model, and because of unexplained variability in the data, candidate models must provide for a stochastic component. Here we use nonlinear autoregressive stochastic modeling to estimate the dominant Lyapunov exponent in an EEG series and compute confidence intervals from surrogate data. The results are found to differ from those of approaches which aim at deleting noise prior to analysis." @default.
• W2010067247 created "2016-06-24" @default.
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• W2010067247 date "2001-06-28" @default.
• W2010067247 modified "2023-09-27" @default.
• W2010067247 title "Estimating Lyapunov exponents in biomedical time series" @default.
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• W2010067247 doi "https://doi.org/10.1103/physreve.64.010902" @default.
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