Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework
Author(s)
Chen, Zhe; Brown, Emery N.; Barbieri, Riccardo
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Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.
Date issued
2010-06Department
Harvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers
Citation
Zhe Chen, E.N. Brown, and R. Barbieri. “Characterizing Nonlinear Heartbeat Dynamics Within a Point Process Framework.” Biomedical Engineering, IEEE Transactions on 57.6 (2010): 1335-1347. © 2011 IEEE.
Version: Final published version
Other identifiers
INSPEC Accession Number: 11340778
ISSN
0018-9294