A unified point process probabilistic framework to assess heartbeat dynamics and autonomic cardiovascular control.

Zhe Chen,Emery N Brown,Patrick L Purdon,Riccardo Barbieri

doi:10.3389/fphys.2012.00004

Abstract

In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as specific cardiovascular control mechanisms and hemodynamics. Assessment of the model’s statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (baroreflex) sensitivity (BRS), are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second-order non-linearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of non-linearity. We here present a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, non-invasive assessment in clinical practice. We also discuss the limitations and other alternative modeling strategies of our point process approach.

Highlights

Modeling physiological systems by control systems theory, advanced signal processing, and parametric modeling and estimation approaches has been of focal importance in biomedical engineering (Khoo, 1999; Marmarelis, 2004; Xiao et al, 2005; Porta et al, 2009)
Motivated by the importance of quantifying the contribution of non-linearity to heart rate variability (HRV) and the heartbeat dynamics, we have proposed a quantitative index based on the spectrum-bispectrum
Applications of the proposed point process framework to the experimental data led to instantaneous assessment of HRV, respiratory sinus arrhythmia (RSA), baroreflex sensitivity (BRS), and of non-linearity of heartbeat dynamics in healthy subjects under progressive stages of propofol anesthesia (Chen et al, 2009b, 2010b, 2011a)

Summary

Introduction

Modeling physiological systems by control systems theory, advanced signal processing, and parametric modeling and estimation approaches has been of focal importance in biomedical engineering (Khoo, 1999; Marmarelis, 2004; Xiao et al, 2005; Porta et al, 2009). Several authors have demonstrated the feasibility and validity of nonlinear autoregressive models, suggesting that heart rate dynamics studies should put greater emphasis on non-linear analysis (Christini et al, 1995; Chon et al, 1996; Zhang et al, 2004; Jo et al, 2007)

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