Detection of "noisy" chaos in a time series.

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Standard

Detection of "noisy" chaos in a time series. / Chon, K H; Kanters, J K; Cohen, R J; Holstein-Rathlou, N H.

I: Methods of Information in Medicine, Bind 36, Nr. 4-5, 1997, s. 294-7.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Chon, KH, Kanters, JK, Cohen, RJ & Holstein-Rathlou, NH 1997, 'Detection of "noisy" chaos in a time series.', Methods of Information in Medicine, bind 36, nr. 4-5, s. 294-7.

APA

Chon, K. H., Kanters, J. K., Cohen, R. J., & Holstein-Rathlou, N. H. (1997). Detection of "noisy" chaos in a time series. Methods of Information in Medicine, 36(4-5), 294-7.

Vancouver

Chon KH, Kanters JK, Cohen RJ, Holstein-Rathlou NH. Detection of "noisy" chaos in a time series. Methods of Information in Medicine. 1997;36(4-5):294-7.

Author

Chon, K H ; Kanters, J K ; Cohen, R J ; Holstein-Rathlou, N H. / Detection of "noisy" chaos in a time series. I: Methods of Information in Medicine. 1997 ; Bind 36, Nr. 4-5. s. 294-7.

Bibtex

@article{c6956350abf111ddb5e9000ea68e967b,
title = "Detection of {"}noisy{"} chaos in a time series.",
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.",
author = "Chon, {K H} and Kanters, {J K} and Cohen, {R J} and Holstein-Rathlou, {N H}",
note = "Keywords: Algorithms; Computer Simulation; Heart Rate; Models, Biological; Models, Cardiovascular; Nonlinear Dynamics",
year = "1997",
language = "English",
volume = "36",
pages = "294--7",
journal = "Methods of Information in Medicine",
issn = "0026-1270",
publisher = "Schattauer",
number = "4-5",

}

RIS

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

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