Using Laguerre expansion within point-process models of heartbeat dynamics: a comparative study.
Point-process models have been recognized as a distinguished tool for the instantaneous assessment of heartbeat dynamics. Although not thoroughly linked to the physiology, nonlinear models also yield a more accurate quantification of cardiovascular control dynamics. Here, we propose a Laguerre expansion of the linear and nonlinear Wiener-Volterra kernels in order to account for the nonlinear and non-gaussian information contained in the ECG-derived heartbeat series while using a reduced number of parameters. Within an Inverse-Gaussian probability model, up to quadratic nonlinearities were considered to continuously estimate the dynamic spectrum and bispectrum. Results performed on 10 subjects undergoing a stand-up protocol show that this novel methodology improves on the algorithmic performances and, at the same time, more accurately characterizes sympatho-vagal changes to posture.
Neuroscience Statistics Research Laboratory, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA. g.valenza@ieee.org
Gaetano
G
Valenza
Luca
L
Citi
Enzo Pasquale
EP
Scilingo
Riccardo
R
Barbieri
D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008955:Models, Cardiovascular; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted