A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis

• Main Authors: Chen, Zhe (Contributor), Purdon, Patrick Lee (Contributor), Brown, Emery N. (Contributor), Barbieri, Riccardo (Contributor)
• Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers, 2012-04-20T15:06:56Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Institutes of Health (U.S.) (Grant R01-HL084502)
National Institutes of Health (U.S.) (Grant K25-NS05758)
National Institutes of Health (U.S.) (Grant DP2-OD006454)
National Institutes of Health (U.S.) (Grant DP1-OD003646)
National Institutes of Health (U.S.) (Grant CRC UL1 RR025758)