Point process time-frequency analysis of dynamic respiratory patterns during meditation practice.

Medical & Biological Engineering & Computing
Sandun KodituwakkuRiccardo Barbieri

Abstract

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure-derived heart beat series (pulse intervals, PIs) are modeled as an inverse Gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. The model is statistically validated using Kolmogorov-Smirnov goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditat...Continue Reading

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Citations

Nov 17, 2012·BMC Cardiovascular Disorders·Paolo MelilloLeandro Pecchia
Mar 5, 2014·IEEE Journal of Biomedical and Health Informatics·Paolo MelilloLeandro Pecchia
Jun 9, 2016·Scientific Reports·Joseph WielgoszRichard J Davidson
Nov 12, 2018··Nguyen-Thinh Le, Nguyen-Thinh Le

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