A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis
Author(s)Chen, Zhe; Purdon, Patrick Lee; Brown, Emery N.; Barbieri, Riccardo
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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.
DepartmentHarvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Proceedings of the 32rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2010
Institute of Electrical and Electronics Engineers
Zhe Chen et al. “A Differential Autoregressive Modeling Approach Within a Point Process Framework for Non-stationary Heartbeat Intervals Analysis.” IEEE, 2010. 3567–3570. Web. ©2010 IEEE.
Final published version
INSPEC Accession Number: 11659977