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

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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.

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Notes

  1. Note that for a fair comparison across subjects, the RSA gain was normalized by the standard deviation of the corresponding RP.

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Acknowledgments

This work was supported by the National Institutes of Health (NIH) under Grant R01-HL084502, Grant R01-DA015644, Grant DP1-OD003646, Grant K01-AT00694-01.

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Authors and Affiliations

  1. Applied Signal Processing Group, School of Engineering, The Australian National University, Canberra, Australia

    Sandun Kodituwakku

  2. Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA

    Sandun Kodituwakku, Zhe Chen, Emery N. Brown & Riccardo Barbieri

  3. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Zhe Chen, Emery N. Brown & Riccardo Barbieri

  4. Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA

    Sara W. Lazar

  5. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, 01655, USA

    Premananda Indic

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  1. Sandun Kodituwakku
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  2. Sara W. Lazar
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  3. Premananda Indic
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  4. Zhe Chen
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  6. Riccardo Barbieri
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Correspondence to Riccardo Barbieri.

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Kodituwakku, S., Lazar, S.W., Indic, P. et al. Point process time–frequency analysis of dynamic respiratory patterns during meditation practice. Med Biol Eng Comput 50, 261–275 (2012). https://doi.org/10.1007/s11517-012-0866-z

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  • DOI: https://doi.org/10.1007/s11517-012-0866-z

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