Supported by NIH Grant R01-HL084502
Heart rate is a moment-to-moment indicator of
cardiovascular integrity measured on every physical examination. Heart rate is
also monitored continuously in patients under anesthesia, during surgery, in
those treated in an intensive care unit and in fetuses during labor. Heart rate
variability is an important quantitative marker of cardiovascular regulation by
the autonomic nervous system that is widely used in research studies, as well
as in clinical practice to diagnose both cardiovascular and non-cardiovascular
diseases to track its progression and to assess the efficacy of therapies. The
measurement and interpretation of heart rate and heart rate variability depend
critically on how these quantities are computed from the time-series of R-wave
events on the electrocardiogram. While the design of algorithms to compute
heart rate and to assess heart rate variability is an active area of research,
none of the current approaches considers the natural point process structure of
human heart beats, together with the physiology underlying the generation of
the discrete, biological events.
In the first point process model implemented, we derive an explicit probability model for heart rate under the assumption that the stochastic properties of the R-R intervals are governed by an inverse Gaussian renewal model. We estimate the time-varying inverse Gaussian parameters by local maximum likelihood, and assess model goodness-of-fit by Kolmogorov-Smirnov tests based on the time-rescaling theorem. We illustrate our new definitions in an analysis of human heart beat intervals from ten healthy subjects undergoing a tilt table experiment. We report instantaneous heart rate variance signal estimates and show that they provide different information from that in the instantaneous heart rate signal estimates. Our framework gives a more physiologically sound representation of the stochastic structure in heart rate than those provided by current definitions and analysis methods. This work has been published in the American Journal of Physiology: Heart and Circulatory Physiology (Barbieri et al., 2005).
Based on the model above, we also implemented an adaptive point process procedure to estimate instantaneous time-variant heart rate variability indices, and we demonstrated the ability of our method to track instantaneous dynamics in autonomic regulation of the cardiovascular system in the same tilt table protocol. The adaptive algorithm can update the heart rate variability estimates at any time resolution, obviating the need for interpolation, and can track fast dynamics by considering only the actual information at each time step. The algorithm is easy to implement for on-line analysis of heart rate variability in the intensive care unit, operating room or labor and delivery suits. The dynamics of our indices of heart rate variability may be useful in characterizing normal and pathological conditions of cardiovascular control and regulation. The adaptive algorithm has been published in IEEE Transaction on Biomedical Engineering (Barbieri et al., 2006).Our paradigm offers a new set of tools to study autonomic regulation of the cardiovascular system in both research and clinical settings. Current work is focusing on more complex history dependence models for human heart beat generation, and on more complex representations incorporating the point process framework into models of cardiovascular system control and autonomic regulation. In particular, this can be achieved studying cardiovascular control with extensions of these methods to state-space representations of the autonomic nervous system, as well as inclusion of other cardiovascular variables such as arterial blood pressure, central venous pressure, and respiration. More detailed informations about our algorithm and demo programs can be found here.