title: A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis.
abstract: Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions.
authors.affiliation: Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. zhechen@mit.edu
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.forename: Patrick L
authors.initials: PL
authors.lastname: Purdon
authors.forename: Emery N
authors.initials: EN
authors.lastname: Brown
authors.forename: Riccardo
authors.initials: R
authors.lastname: Barbieri
mesh_terms: D000465:Algorithms; D017704:Baroreflex / Q000502:physiology*; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D012044:Regression Analysis; D013269:Stochastic Processes; D018667:Tilt-Table Test / Q000379:methods*