Detection of "noisy" chaos in a time series.
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Detection of "noisy" chaos in a time series. / Chon, K H; Kanters, J K; Cohen, R J; Holstein-Rathlou, N H.
In: Methods of Information in Medicine, Vol. 36, No. 4-5, 1997, p. 294-7.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Detection of "noisy" chaos in a time series.
AU - Chon, K H
AU - Kanters, J K
AU - Cohen, R J
AU - Holstein-Rathlou, N H
N1 - Keywords: Algorithms; Computer Simulation; Heart Rate; Models, Biological; Models, Cardiovascular; Nonlinear Dynamics
PY - 1997
Y1 - 1997
N2 - 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.
AB - 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.
M3 - Journal article
C2 - 9470382
VL - 36
SP - 294
EP - 297
JO - Methods of Information in Medicine
JF - Methods of Information in Medicine
SN - 0026-1270
IS - 4-5
ER -
ID: 8440553