Point process time–frequency analysis of dynamic respiratory patterns during meditation practice
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Brown_Point Process Time-Frequency.pdf
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Author(s)
Kodituwakku, Sandun
Lazar, Sara W.
Indic, Premananda
Chen, Zhe
Brown, Emery N.
Barbieri, Riccardo
Date Issued
February 2012
Journal
Medical & Biological Engineering & Computing
Publisher
Springer-Verlag
Citation
Kodituwakku, Sandun, Sara W. Lazar, Premananda Indic, Zhe Chen, Emery N. Brown, and Riccardo Barbieri. “Point Process Time–frequency Analysis of Dynamic Respiratory Patterns During Meditation Practice.” Med Biol Eng Comput 50, no. 3 (March 2012): 261–275.
Version
Author's final manuscript
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 meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11517-012-0866-z
