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

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4 Author(s)
Zhe Chen ; Med. Sch., Neurosci. Stat. Res. Lab., Harvard Univ., Boston, MA, USA ; Purdon, P.L. ; Brown, E.N. ; Barbieri, R.

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.

Published in:

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Date of Conference:

Aug. 31 2010-Sept. 4 2010