Wavelet $p$-Leader Non Gaussian Multiscale Expansions for Heart Rate Variability Analysis in Congestive Heart Failure Patients

Abstract

Results obtained for a large cohort of subjects suggest that the novel non Gaussian indices might robustly quantify crucial information for clinical risk stratification in CHF patients.

Heartbeat Dynamics Analysis under Cold-Pressure Test using Wavelet p-Leader Non-Gaussian Multiscale Expansions

“…In conclusion, our study exploits the recently proposed wavelet p-leaders MF expansion [13] as a powerful tool to detect non-Gaussian properties in heartbeat series, particularly during CPT elicitations, and supports the important role of inhomogeneous point-process interpolation prior to MF estimates.…”

“…Data were recorded from 24 healthy right-handed volunteers undergoing a sympathovagal elicitation through CPT, comparing results with respect to resting conditions. From the best of our knowledge, the investigation of multifractal estimates on heartbeat dynamics following a CPT is a novelty of this study, especially considering the recently proposed non-Gaussian multiscale representation from wavelet p-leaders [13].…”

“…3) Non-Gaussian Multiscale Representation: Building on (5), [13] proposed the multiscale non-Gaussian expansion…”

“…where the moments q i can be of low order their precise choice to tune the sensitivity of L 2P q (j) to different natures of departure from Gaussian, see [13] for details. Moreover, since C 1 (j) does not appear in (7), L 2P q (j) quantifies only nonlinear data properties.…”

“…More recent state of the art MF analyses rely on discrete wavelet transforms and include the so-called wavelet leader multifractal formalisms [11] and its generalization using p-leaders [12]. Recently, it has been proposed to use such wavelet p-leader analyses as the basis for defining a family of nonlinear indices characterizing non-Gaussian dynamics [13], without the need of a strict power-law models.…”