144 research outputs found
The human ECG - nonlinear deterministic versus stochastic aspects
We discuss aspects of randomness and of determinism in electrocardiographic
signals. In particular, we take a critical look at attempts to apply methods of
nonlinear time series analysis derived from the theory of deterministic
dynamical systems. We will argue that deterministic chaos is not a likely
explanation for the short time variablity of the inter-beat interval times,
except for certain pathologies. Conversely, densely sampled full ECG recordings
possess properties typical of deterministic signals. In the latter case,
methods of deterministic nonlinear time series analysis can yield new insights.Comment: 6 pages, 9 PS figure
Nonlinear projective filtering I: Background in chaos theory
We derive a locally projective noise reduction scheme for nonlinear time
series using concepts from deterministic dynamical systems, or chaos theory. We
will demonstrate its effectiveness with an example with known deterministic
dynamics and discuss methods for the verification of the results in the case of
an unknown deterministic system.Comment: 4 pages, PS figures, needs nolta.st
Nonlinear projective filtering I: Application to real time series
We discuss applications of nonlinear filtering of time series by locally
linear phase space projections. Noise can be reduced whenever the error due to
the manifold approximation is smaller than the noise in the system. Examples
include the real time extraction of the fetal electrocardiogram from abdominal
recordings.Comment: 4 pages, PS figures, needs nolta.st
Practical implementation of nonlinear time series methods: The TISEAN package
Nonlinear time series analysis is becoming a more and more reliable tool for
the study of complicated dynamics from measurements. The concept of
low-dimensional chaos has proven to be fruitful in the understanding of many
complex phenomena despite the fact that very few natural systems have actually
been found to be low dimensional deterministic in the sense of the theory. In
order to evaluate the long term usefulness of the nonlinear time series
approach as inspired by chaos theory, it will be important that the
corresponding methods become more widely accessible. This paper, while not a
proper review on nonlinear time series analysis, tries to make a contribution
to this process by describing the actual implementation of the algorithms, and
their proper usage. Most of the methods require the choice of certain
parameters for each specific time series application. We will try to give
guidance in this respect. The scope and selection of topics in this article, as
well as the implementational choices that have been made, correspond to the
contents of the software package TISEAN which is publicly available from
http://www.mpipks-dresden.mpg.de/~tisean . In fact, this paper can be seen as
an extended manual for the TISEAN programs. It fills the gap between the
technical documentation and the existing literature, providing the necessary
entry points for a more thorough study of the theoretical background.Comment: 27 pages, 21 figures, downloadable software at
http://www.mpipks-dresden.mpg.de/~tisea
Testing for Chaos in Deterministic Systems with Noise
Recently, we introduced a new test for distinguishing regular from chaotic
dynamics in deterministic dynamical systems and argued that the test had
certain advantages over the traditional test for chaos using the maximal
Lyapunov exponent.
In this paper, we investigate the capability of the test to cope with
moderate amounts of noisy data. Comparisons are made between an improved
version of our test and both the ``tangent space'' and ``direct method'' for
computing the maximal Lyapunov exponent. The evidence of numerical experiments,
ranging from the logistic map to an eight-dimensional Lorenz system of
differential equations (the Lorenz 96 system), suggests that our method is
superior to tangent space methods and that it compares very favourably with
direct methods
Divergence Measure Between Chaotic Attractors
We propose a measure of divergence of probability distributions for
quantifying the dissimilarity of two chaotic attractors. This measure is
defined in terms of a generalized entropy. We illustrate our procedure by
considering the effect of additive noise in the well known H\'enon attractor.
Comparison of two H\'enon attractors for slighly different parameter values,
has shown that the divergence has complex scaling structure. Finally, we show
how our approach allows to detect non-stationary events in a time series.Comment: 9 pages, 6 figure
Nonlinear analysis of bivariate data with cross recurrence plots
We use the extension of the method of recurrence plots to cross recurrence
plots (CRP) which enables a nonlinear analysis of bivariate data. To quantify
CRPs, we develop further three measures of complexity mainly basing on diagonal
structures in CRPs. The CRP analysis of prototypical model systems with
nonlinear interactions demonstrates that this technique enables to find these
nonlinear interrelations from bivariate time series, whereas linear correlation
tests do not. Applying the CRP analysis to climatological data, we find a
complex relationship between rainfall and El Nino data
Assessing coupling dynamics from an ensemble of time series
Finding interdependency relations between (possibly multivariate) time series
provides valuable knowledge about the processes that generate the signals.
Information theory sets a natural framework for non-parametric measures of
several classes of statistical dependencies. However, a reliable estimation
from information-theoretic functionals is hampered when the dependency to be
assessed is brief or evolves in time. Here, we show that these limitations can
be overcome when we have access to an ensemble of independent repetitions of
the time series. In particular, we gear a data-efficient estimator of
probability densities to make use of the full structure of trial-based
measures. By doing so, we can obtain time-resolved estimates for a family of
entropy combinations (including mutual information, transfer entropy, and their
conditional counterparts) which are more accurate than the simple average of
individual estimates over trials. We show with simulated and real data that the
proposed approach allows to recover the time-resolved dynamics of the coupling
between different subsystems
Measuring Information Transfer
An information theoretic measure is derived that quantifies the statistical
coherence between systems evolving in time. The standard time delayed mutual
information fails to distinguish information that is actually exchanged from
shared information due to common history and input signals. In our new
approach, these influences are excluded by appropriate conditioning of
transition probabilities. The resulting transfer entropy is able to distinguish
driving and responding elements and to detect asymmetry in the coupling of
subsystems.Comment: 4 pages, 4 Figures, Revte
Quantification of depth of anesthesia by nonlinear time series analysis of brain electrical activity
We investigate several quantifiers of the electroencephalogram (EEG) signal
with respect to their ability to indicate depth of anesthesia. For 17 patients
anesthetized with Sevoflurane, three established measures (two spectral and one
based on the bispectrum), as well as a phase space based nonlinear correlation
index were computed from consecutive EEG epochs. In absence of an independent
way to determine anesthesia depth, the standard was derived from measured blood
plasma concentrations of the anesthetic via a pharmacokinetic/pharmacodynamic
model for the estimated effective brain concentration of Sevoflurane. In most
patients, the highest correlation is observed for the nonlinear correlation
index D*. In contrast to spectral measures, D* is found to decrease
monotonically with increasing (estimated) depth of anesthesia, even when a
"burst-suppression" pattern occurs in the EEG. The findings show the potential
for applications of concepts derived from the theory of nonlinear dynamics,
even if little can be assumed about the process under investigation.Comment: 7 pages, 5 figure
- …