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Journals and Conferences
We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical… (More)
We propose a method to discover couplings in multivariate time series, based on partial mutual information, an information-theoretic generalization of partial correlation. It represents the part of… (More)
For piecewise monotone interval maps we show that Kolmogorov-Sinai entropy can be obtained from order statistics of the values in a generic orbit. A similar statement holds for topological entropy.
The autonomic information flow (AIF) represents the complex communication within the Autonomic Nervous System (ANS). It can be assessed by the mutual information function (MIF) of heart rate… (More)
We propose a method to analyze couplings between two simultaneously measured time series. Our approach is based on conditional mutual sorting information. It is related to other concepts for… (More)
We propose two methods to measure all (linear and nonlinear) statistical dependences in a stationary time series. Presuming ergodicity, the measures can be obtained from eecient numerical algorithms.
The heart rate variability (HRV) is related to several mechanisms of the complex autonomic functioning such as respiratory heart rate modulation and phase dependencies between heart beat cycles and… (More)
The relevance of the complexity of fetal heart rate fluctuations with regard to the classification of fetal behavioural states has not been satisfyingly clarified so far. Because of the short… (More)
This chapter is concerned with two subjects. The rst one is a method of signal preprocessing called ranking. It is of special relevance in nonlinear time series analysis and may cause several… (More)