Time-Varying Cardiovascular Oscillations
Signals derived from the human cardiovascular system (CVS) are exceptionally complex, being time-varying, noisy, and of necessarily limited duration. Yet an appropriate analysis of them may be expected to yield detailed information about the dynamics of the underlying physiological processes. A new approach to the analysis and modelhng of CVS signals is proposed. It combines decomposition of the signals into principal modes and a novel method of parameter identification in nonlinear stochastic systems based on Bayesian inference. The scheme is tested on a noisy Van der Pol oscillator, for which it yields rapid convergence and correct inference of the known parameters. Preliminary applications to CVS data are discussed.
KeywordsProbability Density Function Empirical Mode Decomposition Oscillatory Component Observe Time Series Nonlinear Stochastic System
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