Characterizing nonlinear heartbeat dynamics within a point process framework

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

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Case-Control Studies
  • Computer Simulation
  • Data Interpretation, Statistical
  • Diagnosis, Computer-Assisted / methods*
  • Electrocardiography / methods*
  • Heart Failure / diagnosis*
  • Heart Failure / physiopathology*
  • Heart Rate
  • Humans
  • Models, Cardiovascular
  • Models, Statistical
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted