Somnologie - Schlafforschung und Schlafmedizin

, Volume 18, Issue 4, pp 243–251 | Cite as

Ereignisbasierte Charakterisierung kardiovaskulärer Interaktionen während des Schlafs

Kardiorespiratorische Koordination und Ensemble-Kopplungsspuren
  • A. Müller
  • M. Riedl
  • T. Penzel
  • J. Kurths
  • N. Wessel
Schwerpunkt

Zusammenfassung

Die Untersuchung verschiedener Ereignisse wie Apnoen, Hypopnoen und diverser Arousals während des Schlafs spielt eine zentrale Rolle in der Diagnostik und im Verstehen von Schlafstörungen und evtl. Folgeerkrankungen. Häufig wird nur das Auftreten dieser Ereignisse betrachtet, statt diese im Zusammenhang mit anderen kardiovaskulären Größen zu untersuchen und die jeweiligen Interaktionen dieser Größen untereinander zu charakterisieren. Mit dem Koordigramm und den Ensemble-Kopplungsspuren werden 2 neue Methoden vorgestellt, die genau das ermöglichen sollen. Anhand einer Fallstudie eines Probanden mit häufig auftretenden Arousals werden die Möglichkeiten der neuen Werkzeuge zur Quantifizierung der autonomen Reaktion auf Störungen des Schlafs demonstriert. In einer Reanalyse von Patienten mit obstrukiver Schlafapnoe wird weiterhin die diagnostische Relevanz der kardiorespiratorischen Koordination zur Risikoabschätzung einer entstehenden Hypertonie gezeigt.

Schlüsselwörter

Arousal Kopplungsanalyse Schlafstadien Schlafstörungen Physiologische kardiovaskuläre Reaktionen 

Event-based characterization of cardiovascular interactions during sleep

Cardiorespiratory coordination and ensemble coupling traces

Abstract

The analysis of events such as apneas, hypopneas, and various types of arousals during sleep plays a central role when diagnosing and trying to understand sleep-related disorders and possible sequelae. Often, only the occurrence of these events is regarded instead of putting them into context with other cardiovascular variables and characterizing their mutual interactions. In this article, we present two new methods that allow for such an analysis: the coordigram and the ensemble symbolic-coupling traces. Through a case study of a subject with frequent arousals, the potential of the new tools for quantifying the autonomic response to sleep disturbances is shown. Furthermore, in a reanalysis of patients suffering from obstructive sleep apnea, the diagnostic relevance of cardiorespiratory coordination for risk stratification of an emerging hypertension is demonstrated.

Keywords

Arousal Coupling analysis Sleep stages Sleep disorders Cardiovascular physiological phenomena 

Literatur

  1. 1.
    Andrzejak RG, Ledberg A, Deco G (2006) Detecting event-related time-dependent directional couplings. N J Phys 8(6)Google Scholar
  2. 2.
    Ashkenazy Y, Lewkowicz M, Levitan J et al (2001) Scale-specific and scale-independent measures of heart rate variablity as risk indicators. Europhys Lett 53(6):709–715CrossRefGoogle Scholar
  3. 3.
    Blasius B, Huppert A, Stone L (1999) Complex dynamics and phase synchronization in spatially extended ecological systems. Nature 399:354–359CrossRefPubMedGoogle Scholar
  4. 4.
    Cammarota C, Rogora E (2006) Spectral and symbolic analysis of heart rate data during the tilt test. Phys Rev E Stat Nonlin Soft Matter Phys 74:042903CrossRefPubMedGoogle Scholar
  5. 5.
    Cysarz D, Bettermann H, Lange S et al (2004) A quantitative comparison of different methods to detect cardiorespiratory coordination during night-time sleep. Biomed Eng Online. doi:10.1186/1475-925X-3-44Google Scholar
  6. 6.
    Dempsey JA, Veasey SC, Morgan JB, O’Donnel CP (2010) Pathophysiology of sleep apnea. Physiol Rev 90:47–112CrossRefPubMedCentralPubMedGoogle Scholar
  7. 7.
    Galletly DC, Larsen PD (1997) Cardioventilatory coupling during anaesthesia. Br J Anaesth 79:35–40CrossRefPubMedGoogle Scholar
  8. 8.
    Glass L (2001) Synchronization and rhythmic processes in physiology. Nature 410:277–284CrossRefPubMedGoogle Scholar
  9. 9.
    Iber C, Ancoli-Israel S, Chesson A, Quan SF (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Am Acad Sleep MedGoogle Scholar
  10. 10.
    Ivanov PC, Nunes Amaral LA, Goldberger AL, Stanley HE (1998) Stochastic feedback and the regulation of biological rhythms. Europhys Lett 43(4):363–368CrossRefPubMedGoogle Scholar
  11. 11.
    Laude D, Elghozi JL, Girard A et al (2004) Comparison of various techniques used to estimate spontaneous baroreflex sensitivity (the EuroBaVar study). Am J Physiol Regul Integr Comp Physiol 286:R226CrossRefPubMedGoogle Scholar
  12. 12.
    Martini M, Kranz TA, Wagner T, Lehnertz K (2011) Inferring directional interactions from transient signals with symbolic transfer entropy. Phys Rev E Stat Nonlin Soft Matter Phys 83:011919CrossRefPubMedGoogle Scholar
  13. 13.
    Milton JG, Cabrera JL, Ohira T (2008) Unstable dynamical systems: delays, noise and control. Europhys Lett 83:48001CrossRefGoogle Scholar
  14. 14.
    Müller A, Riedl M, Wessel N et al (2012) Methoden zur Analyse kardiorespiratorischer und kardiovaskulärer Kopplungen. Somnologie 16:24–31CrossRefGoogle Scholar
  15. 15.
    Müller A, Riedl M, Penzel T et al (2013) Coupling analysis of transient cardiovascular dynamics. Biomed Tech (Berl) 58(2):131–139Google Scholar
  16. 16.
    Penzel T, Riedl M, Gapelyuk A et al (2012) Effect of CPAP therapy on daytime cardiovascular regulations in patients with obstructive sleep apnea. Comput Biol Med 42:328–334CrossRefPubMedGoogle Scholar
  17. 17.
    Porta A, Baselli G, Rimoldi O et al (2000) Assessing baroreflex gain from spontaneous variability in conscious dogs: role of causality and respiration. Am J Physiol Heart Circ Physiol 279:2558–2567Google Scholar
  18. 18.
    Raschke F (1986) Coordination in the circulatory and respiratory systems. Temporal Disorder in Human Oscillatory Systems. Springer, BerlinGoogle Scholar
  19. 19.
    Remmers JE (2005) A century of control of breathing. Am J Respir Crit Care Med 172(1):6–11CrossRefPubMedGoogle Scholar
  20. 20.
    Riedl M, Müller A, Krämer JF et al (2014) Cardio-respiratory coordination increases during sleep apnea. PLOS One 9(4):e93866CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Rosenblum MG, Kurths J, Pikovsky A et al (1998) Synchronization in noisy systems and cardiorespiratory interaction. IEEE Eng Med Biol Mag 17(6):46–53CrossRefPubMedGoogle Scholar
  22. 22.
    Schäfer C, Rosenblum MG, Kurths J, Abel HH (1998) Heartbeat synchronized with ventilation. Nature 392:239–240CrossRefPubMedGoogle Scholar
  23. 23.
    Suhrbier A, Riedl M, Malberg H et al (2010) Cardiovascular regulation during sleep quantified by symbolic coupling traces. Chaos 20(4):045124CrossRefPubMedGoogle Scholar
  24. 24.
    Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology (1996) Heart rate variability. Eur Heart J 17:354–381CrossRefGoogle Scholar
  25. 25.
    Wagner T, Fell J, Lehnertz K (2010) The detection of transient directional couplings based on phase synchronization. New J Phys 12:053031CrossRefGoogle Scholar
  26. 26.
    Wessel N, Suhrbier A, Riedl M et al (2009) Detection of time-delayed interactions in biosignals using symbolic coupling traces. Eur Phys Lett 87:10004CrossRefGoogle Scholar
  27. 27.
    Ying-Cheng L, Kostelich EJ (2002) Detectability of dynamical coupling from delay-coordinate embedding of scalar time series. Phys Rev E Stat Nonlin Soft Matter Phys 66:036217CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • A. Müller
    • 1
  • M. Riedl
    • 1
  • T. Penzel
    • 2
  • J. Kurths
    • 1
    • 3
    • 4
  • N. Wessel
    • 1
  1. 1.Institut für Physik, Kardiovaskuläre PhysikHumboldt-Universität zu BerlinBerlinDeutschland
  2. 2.Interdisziplinäres Schlafmedizinisches ZentrumCharité BerlinBerlinDeutschland
  3. 3.Potsdam-Institut für KlimafolgenforschungPotsdamDeutschland
  4. 4.Institute for Complex Systems and Mathematical BiologyUniversity of AberdeenAberdeenUK

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