Characterizing nonlinear heartbeat dynamics within a point process framework

Author

Chen, Zhe ; Brown, Emery N. ; Barbieri, Riccardo

Author_Institution

Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, 02114, USA

fYear

2008

fDate

20-25 Aug. 2008

Firstpage

2781

Lastpage

2784

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.

Keywords

Cardiology; Control systems; Heart beat; Heart rate; Heart rate variability; Humans; Kernel; Nonlinear dynamical systems; Nonlinear systems; Power system modeling; Algorithms; Case-Control Studies; Computer Simulation; Data Interpretation, Statistical; Diagnosis, Computer-Assisted; Electrocardiography; Heart Failure; Heart Rate; Humans; Models, Cardiovascular; Models, Statistical; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Conference_Location

Vancouver, BC

ISSN

1557-170X

Print_ISBN

978-1-4244-1814-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2008.4649779

Filename

4649779