EVIDENCE CONSISTENT WITH DETERMINISTIC CHAOS IN HUMAN CARDIAC DATA: SURROGATE AND NONLINEAR DYNAMICAL MODELING

DOI10.1142/S0218127408020197zbMath1144.92009OpenAlexW2045380744MaRDI QIDQ3520761

Michael Small, Junfeng Sun, Yi Zhao

Publication date: 26 August 2008

Published in: International Journal of Bifurcation and Chaos (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218127408020197

zbMATH Keywords

surrogate data methodnonlinear modelingcardiac time seriesPPS algorithm

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Dynamical systems in biology (37N25) Physiology (general) (92C30) Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10) Physiological flow (92C35)

Related Items (6)

Implementing high-order chaos control scheme for cardiac conduction model with pathological rhythms ⋮ Deterministic models for simulating electrocardiographic signals ⋮ Response of the parameters of a neural network to pseudoperiodic time series ⋮ Quasiperiodicity route to chaos in cardiac conduction model ⋮ SYNCHRONIZATION OF CHAOTIC OSCILLATORS BY MEANS OF GENERALIZED PROPORTIONAL INTEGRAL OBSERVERS ⋮ Testing for Linear and Nonlinear Gaussian Processes in Nonstationary Time Series

Cites Work

• Testing for nonlinearity in time series: the method of surrogate data
• Determining Lyapunov exponents from a time series
• Measuring the strangeness of strange attractors
• Complex dynamics underlying the human electrocardiogram
• Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems
• Correlation dimension estimation from electrocardiograms
• Surrogate time series.
• Applying the method of surrogate data to cyclic time series
• On the evidence of deterministic chaos in ECG: Surrogate and predictability analysis
• Identifying deterministic signals in simulated gravitational wave data: algorithmic complexity and the surrogate data method
• On the Complexity of Finite Sequences

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