MEDLINE Journals

    Representation of time-varying nonlinear systems with time-varying principal dynamic modes.

    Authors
    Zhong Y, Jan KM, Ju KH, et al. 
    Source
    IEEE Trans Biomed Eng 2007 Nov; 54(11) :1983-92.
    Abstract

    System identification of nonlinear time-varying (TV) systems has been a daunting task, as the number of parameters required for accurate identification is often larger than the number of data points available, and scales with the number of data points. Further, a 3-D graphical representation of TV second-order nonlinear dynamics without resorting to taking slices along one of the four axes has been a significant challenge to date. In this paper, we present a TV principal dynamic mode (TVPDM) method which overcomes these deficiencies. The TVPDM, by design, reduces one dimension, and by projecting PDM coefficients onto a set of basis functions, both nonstationary and nonlinear dynamics can be characterized. Another significant advantage of the TVPDM is its ability to discriminate the signal from noise dynamics, and provided that signal dynamics are orthogonal to each other, it has the capability to separate them. The efficacy of the proposed method is demonstrated with computer simulation examples comprised of various forms of nonstationarity and nonlinearity. The application of the TVPDM to the human heart rate and arterial blood pressure data during different postures is also presented and the results reveal significant nonstationarity even for short-term data recordings. The newly developed method has the potential to be a very useful tool for characterizing nonlinear TV systems, which has been a significant, challenging problem to date.

    Mesh
    Algorithms
    Artifacts
    Computer Simulation
    Data Interpretation, Statistical
    Models, Biological
    Nonlinear Dynamics
    Principal Component Analysis
    Time Factors
    Language

    eng

    Pub Type(s)
    Journal Article Research Support, N.I.H., Extramural
    PubMed ID

    18018693

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