Key result
A novel Bayesian dynamical inference method successfully characterized nonlinear cardiorespiratory dynamics and estimated coupling parameters directly from univariate blood pressure time-series data.
A novel Bayesian dynamical inference method can successfully characterize nonlinear cardiorespiratory dynamics and coupling parameters directly from univariate blood pressure time-series data.
May facilitate noninvasive cardiorespiratory coupling assessment from blood pressure; leaves open clinical validation before adoption.
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.
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Luchinsky et al. (2005) studied Cardiorespiratory dynamics. Bayesian dynamical inference method was evaluated. A novel Bayesian dynamical inference method successfully characterized nonlinear cardiorespiratory dynamics and estimated coupling parameters directly from univariate blood pressure time-series data.
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