title: Characterizing nonlinear heartbeat dynamics within a point process framework.
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.
authors.affiliation: Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA. zhechen@neurostat.mgh.harvard.edu
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.forename: Emery N
authors.initials: EN
authors.lastname: Brown
authors.forename: Riccardo
authors.initials: R
authors.lastname: Barbieri
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006333:Heart Failure / Q000175:diagnosis* / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted