Journal ArticleDOI
Practical implementation of nonlinear time series methods: The TISEAN package.
TLDR
In this paper, the authors describe the implementation of methods of nonlinear time series analysis which are based on the paradigm of deterministic chaos and present a variety of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation.Abstract:
We describe the implementation of methods of nonlinear time series analysis which are based on the paradigm of deterministic chaos. A variety of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation, and nonlinearity testing are discussed with particular emphasis on issues of implementation and choice of parameters. Computer programs that implement the resulting strategies are publicly available as the TISEAN software package. The use of each algorithm will be illustrated with a typical application. As to the theoretical background, we will essentially give pointers to the literature. (c) 1999 American Institute of Physics.read more
Citations
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Journal ArticleDOI
Surrogate time series
Thomas Schreiber,Andreas Schmitz +1 more
TL;DR: Specific as well as more general approaches to constrained randomisation, providing a full range of examples, and some implementational aspects of the realisation of these methods in the TISEAN software package are discussed.
Journal ArticleDOI
Protein Disorder Prediction: Implications for Structural Proteomics
TL;DR: DisEMBL is a computational tool for prediction of disordered/unstructured regions within a protein sequence that has developed parameters based on several alternative definitions and introduced a new one based on the concept of "hot loops," i.e., coils with high temperature factors.
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Synchronization and rhythmic processes in physiology
TL;DR: Molecular and physical techniques combined with physiological and medical studies are addressing questions concerning the dynamics of physiological rhythms and are transforming the understanding of the rhythms of life.
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EEG dynamics in patients with Alzheimer's disease
TL;DR: EEG abnormalities of AD patients are characterized by slowed mean frequency, less complex activity, and reduced coherences among cortical regions, suggesting that the EEG has utility as a valuable tool for differential and early diagnosis of AD.
Journal ArticleDOI
Nonlinear multivariate analysis of neurophysiological signals
TL;DR: This work describes the multivariate linear methods most commonly used in neurophysiology and shows that they can be extended to assess the existence of nonlinear interdependence between signals and describes nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization.
References
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Book
Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Journal ArticleDOI
Measuring the Strangeness of Strange Attractors
TL;DR: In this paper, the correlation exponent v is introduced as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise, and algorithms for extracting v from the time series of a single variable are proposed.
Journal ArticleDOI
Ergodic theory of chaos and strange attractors
Jean-Pierre Eckmann,David Ruelle +1 more
TL;DR: A review of the main mathematical ideas and their concrete implementation in analyzing experiments can be found in this paper, where the main subjects are the theory of dimensions (number of excited degrees of freedom), entropy (production of information), and characteristic exponents (describing sensitivity to initial conditions).
Journal ArticleDOI
Independent coordinates for strange attractors from mutual information.
TL;DR: In this paper, the mutual information I is examined for a model dynamical system and for chaotic data from an experiment on the Belousov-Zhabotinskii reaction.