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Conf Proc IEEE Eng Med Biol Soc. 2008;2008:2781-4. doi: 10.1109/IEMBS.2008.4649779.

Characterizing nonlinear heartbeat dynamics within a point process framework.

Author information

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

Abstract

Heartbeat intervals are known to have nonlinear and non-stationary dynamics. In this paper, we propose a nonlinear Volterra-Wiener expansion modeling of human heartbeat dynamics within a point process framework. Inclusion of second-order nonlinearity allows us to estimate dynamic bispectrum. The proposed probabilistic model was examined with two recorded heartbeat interval data sets. Preliminary results show that our model is beneficial to characterize the inherent nonlinearity of the heartbeat dynamics.

PMID:
19163282
PMCID:
PMC2644067
DOI:
10.1109/IEMBS.2008.4649779
[Indexed for MEDLINE]
Free PMC Article

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