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In vivo cardiac phase response curve elucidates human respiratory heart rate variability

Author

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  • Björn Kralemann

    (Institut für Pädagogik, Christian-Albrechts-Universität zu Kiel)

  • Matthias Frühwirth

    (Human Research Institute of Health Technology and Prevention Research)

  • Arkady Pikovsky

    (University of Potsdam)

  • Michael Rosenblum

    (University of Potsdam)

  • Thomas Kenner

    (Institute of Physiology, Medical University)

  • Jochen Schaefer

    (International Institute for Theoretical Cardiology)

  • Maximilian Moser

    (Human Research Institute of Health Technology and Prevention Research
    Institute of Physiology, Medical University)

Abstract

Recovering interaction of endogenous rhythms from observations is challenging, especially if a mathematical model explaining the behaviour of the system is unknown. The decisive information for successful reconstruction of the dynamics is the sensitivity of an oscillator to external influences, which is quantified by its phase response curve. Here we present a technique that allows the extraction of the phase response curve from a non-invasive observation of a system consisting of two interacting oscillators—in this case heartbeat and respiration—in its natural environment and under free-running conditions. We use this method to obtain the phase-coupling functions describing cardiorespiratory interactions and the phase response curve of 17 healthy humans. We show for the first time the phase at which the cardiac beat is susceptible to respiratory drive and extract the respiratory-related component of heart rate variability. This non-invasive method for the determination of phase response curves of coupled oscillators may find application in many scientific disciplines.

Suggested Citation

  • Björn Kralemann & Matthias Frühwirth & Arkady Pikovsky & Michael Rosenblum & Thomas Kenner & Jochen Schaefer & Maximilian Moser, 2013. "In vivo cardiac phase response curve elucidates human respiratory heart rate variability," Nature Communications, Nature, vol. 4(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms3418
    DOI: 10.1038/ncomms3418
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    Cited by:

    1. Björn R H Blomqvist & Richard P Mann & David J T Sumpter, 2018. "Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-23, May.
    2. Eddie Nijholt & Jorge Luis Ocampo-Espindola & Deniz Eroglu & István Z. Kiss & Tiago Pereira, 2022. "Emergent hypernetworks in weakly coupled oscillators," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. Bezruchko, B.P. & Ponomarenko, V.I. & Smirnov, D.A. & Sysoev, I.V. & Prokhorov, M.D., 2021. "Class-oriented techniques for reconstruction of dynamics from time series," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).

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