Medical & Biological Engineering & Computing

, Volume 50, Issue 3, pp 261–275

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

  • Sandun Kodituwakku
  • Sara W. Lazar
  • Premananda Indic
  • Zhe Chen
  • Emery N. Brown
  • Riccardo Barbieri
Original Article

DOI: 10.1007/s11517-012-0866-z

Cite this article as:
Kodituwakku, S., Lazar, S.W., Indic, P. et al. Med Biol Eng Comput (2012) 50: 261. doi:10.1007/s11517-012-0866-z

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.

Keywords

Respiratory sinus arrhythmia Heart rate variability Meditation Point processes Time–frequency analysis 

Copyright information

© International Federation for Medical and Biological Engineering 2012

Authors and Affiliations

  • Sandun Kodituwakku
    • 1
    • 2
  • Sara W. Lazar
    • 4
  • Premananda Indic
    • 5
  • Zhe Chen
    • 2
    • 3
  • Emery N. Brown
    • 2
    • 3
  • Riccardo Barbieri
    • 2
    • 3
  1. 1.Applied Signal Processing Group, School of EngineeringThe Australian National UniversityCanberraAustralia
  2. 2.Neuroscience Statistics Research Laboratory, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  3. 3.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA
  4. 4.Department of Psychiatry, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  5. 5.Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterUSA

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