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Nonlinear statistical modeling and model discovery for cardiorespiratory data.

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Nonlinear statistical modeling and model discovery for cardiorespiratory data. / Luchinsky, Dmitry G.; Millonas, M. M.; Smelyanskiy, V. N. et al.
In: Physical Review E, Vol. 72, No. 2, 08.2005, p. 021905.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Luchinsky, DG, Millonas, MM, Smelyanskiy, VN, Pershakova, A, Stefanovska, A & McClintock, PVE 2005, 'Nonlinear statistical modeling and model discovery for cardiorespiratory data.', Physical Review E, vol. 72, no. 2, pp. 021905. https://doi.org/10.1103/PhysRevE.72.021905

APA

Luchinsky, D. G., Millonas, M. M., Smelyanskiy, V. N., Pershakova, A., Stefanovska, A., & McClintock, P. V. E. (2005). Nonlinear statistical modeling and model discovery for cardiorespiratory data. Physical Review E, 72(2), 021905. https://doi.org/10.1103/PhysRevE.72.021905

Vancouver

Luchinsky DG, Millonas MM, Smelyanskiy VN, Pershakova A, Stefanovska A, McClintock PVE. Nonlinear statistical modeling and model discovery for cardiorespiratory data. Physical Review E. 2005 Aug;72(2):021905. doi: 10.1103/PhysRevE.72.021905

Author

Luchinsky, Dmitry G. ; Millonas, M. M. ; Smelyanskiy, V. N. et al. / Nonlinear statistical modeling and model discovery for cardiorespiratory data. In: Physical Review E. 2005 ; Vol. 72, No. 2. pp. 021905.

Bibtex

@article{7de81a0f537e47968a22fef22e0334a4,
title = "Nonlinear statistical modeling and model discovery for cardiorespiratory data.",
abstract = "We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.",
keywords = "cardiovascular system, pneumodynamics, statistical analysis, stochastic processes, nonlinear dynamical systems",
author = "Luchinsky, {Dmitry G.} and Millonas, {M. M.} and Smelyanskiy, {V. N.} and A. Pershakova and Aneta Stefanovska and McClintock, {Peter V. E.}",
year = "2005",
month = aug,
doi = "10.1103/PhysRevE.72.021905",
language = "English",
volume = "72",
pages = "021905",
journal = "Physical Review E",
issn = "1539-3755",
publisher = "American Physical Society",
number = "2",

}

RIS

TY - JOUR

T1 - Nonlinear statistical modeling and model discovery for cardiorespiratory data.

AU - Luchinsky, Dmitry G.

AU - Millonas, M. M.

AU - Smelyanskiy, V. N.

AU - Pershakova, A.

AU - Stefanovska, Aneta

AU - McClintock, Peter V. E.

PY - 2005/8

Y1 - 2005/8

N2 - We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.

AB - We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.

KW - cardiovascular system

KW - pneumodynamics

KW - statistical analysis

KW - stochastic processes

KW - nonlinear dynamical systems

U2 - 10.1103/PhysRevE.72.021905

DO - 10.1103/PhysRevE.72.021905

M3 - Journal article

VL - 72

SP - 021905

JO - Physical Review E

JF - Physical Review E

SN - 1539-3755

IS - 2

ER -