Instantaneous monitoring of heart rate variability

Most of the currently accepted approaches to compute heart rate and assess heart rate variability operate on interpolated, continuous-valued heart rate signals, thereby ignoring the underlying discrete structure of human heart beats. To overcome this limitation, we model the stochastic structure of heart beat intervals as a history-dependent, inverse Gaussian process and derive from it an explicit probability density describing heart rate and heart rate variability. We estimate the parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit using Q-Q plot analyses. We apply our model in an analysis of human heart beat intervals from a tilt-table experiment. Our results suggest that the new definitions of heart rate and heart rate variability convey different information than other conventional indices, both in time and frequency domains, and may have important implications for research studies of cardiovascular and autonomic regulation.