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Using Laguerre expansion within point-process models of heartbeat dynamics: A comparative study

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4 Author(s)
Gaetano Valenza ; Neuroscience Statistics Research Laboratory, Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA ; Luca Citi ; Enzo Pasquale Scilingo ; Riccardo Barbieri

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.

Published in:

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

Aug. 28 2012-Sept. 1 2012