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Methods Inf Med 1997; 36(04/05): 294-297
DOI: 10.1055/s-0038-1636859
Original Article
Schattauer GmbH

Detection of “Noisy” Chaos in a Time Series

K. H. Chon
1   Harvard-MIT Health Sciences and Technology, Cambridge, MA, USA
,
J. K. Kanters
2   Department of Internal Medicine, CCU, Elsinore Hospital, Elsinore, Denmark
3   Department of Medical Physiology, University of Copenhagen, Copenhagen, Denmark
,
R. J. Cohen
1   Harvard-MIT Health Sciences and Technology, Cambridge, MA, USA
,
N.-H. Holstein-Rathlou
3   Department of Medical Physiology, University of Copenhagen, Copenhagen, Denmark
› Institutsangaben
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Publikationsverlauf

Publikationsdatum:
19. Februar 2018 (online)

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Abstract:

Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely “noise”. The output from most biological systems is probably the result of both the internal dynamics of the systems, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

Keywords:

Deterministic Chaos - Autoregressive Model - Heart Rate Variability - Lyapunov Exponents
 

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