Format

Send to

Choose Destination
IEEE Trans Biomed Eng. 2010 Jun;57(6):1335-47. doi: 10.1109/TBME.2010.2041002. Epub 2010 Feb 17.

Characterizing nonlinear heartbeat dynamics within a point process framework.

Author information

1
Neuroscience Statistics Research Laboratory, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA. zhechen@neurostat.mit.edu

Abstract

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.

PMID:
20172783
PMCID:
PMC2952361
DOI:
10.1109/TBME.2010.2041002
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for IEEE Engineering in Medicine and Biology Society Icon for PubMed Central
Loading ...
Support Center