Human Physiology

, Volume 39, Issue 4, pp 416–425 | Cite as

Phase and frequency locking of 0.1-Hz oscillations in heart rate and baroreflex control of blood pressure by breathing of linearly varying frequency as determined in healthy subjects

  • A. S. Karavaev
  • A. R. Kiselev
  • V. I. Gridnev
  • E. I. Borovkova
  • M. D. Prokhorov
  • O. M. Posnenkova
  • V. I. Ponomarenko
  • B. P. Bezruchko
  • V. A. Shvartz
Article

Abstract

Functional interaction was studied between the subsystems that ensure autonomic control of the heart rate (HR) and blood pressure (BP) and give rise to 0.1-Hz oscillations in R-R intervals (RRI) and photoplethysmogram (PPG). Twenty-five recordings were obtained from 18- to 32-year-old healthy persons (six women and nineteen men). The RRI and PPG were recorded simultaneously while the respiration rate of a subject in the sitting position increased linearly from 0.05 Hz to 0.25 Hz within 25 min. Phase and frequency locking of 0.1-Hz oscillations by breathing proved to be possible in both RRI and PPG. The intervals of phase and frequency locking of oscillations by respiration differed in duration and relative position. These distinctions suggest that the mechanisms of autonomic 0.1-Hz control of HR and BP are functionally independent.

Keywords

baroreflex instantaneous phase 0.1-Hz oscillations phase synchronization frequency locking controlled respiration R-R intervals photoplethysmogram autonomic control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kiselev, A.R., Gridnev, V.I., Karavaev, A.S., et al., Assessment of a 5-Year Risk of Fatal outcome and Cardiovascular Event in Patients with Acute Myocardial Infarction on the Basis of 0.1-Hz Rhythm Synchronization in the Cardiovascular System, Saratov. Nauch.-Med. Zh., 2010, vol. 6, no. 2, p. 328.Google Scholar
  2. 2.
    Gridnev, V.I., Kiselev, A.R., Posnenkova, O.M., et al., Application of Spectral Analysis of Heart Rhythm Variability for Improvement of Diagnostic Significance of the Load Tests, Vestn. S.-Peterb. Univ. Ser. 11, 2008, no. 2, p. 18.Google Scholar
  3. 3.
    Cooley, R.L., Montano, N., Cogliati, C., et al., Evidence for a Central Origin of the Low-Frequency Oscillation in RR-Interval Variability, Circulation, 1998, no. 98, p. 556.Google Scholar
  4. 4.
    De Boer, R.W., Karemuker, J.M., and Stracker, J., Hemodynamic Fluctuations and Baroreflex Sensitivity in Humans: a Beat-To-Beat Model, Am. J. Physiol., 1987, vol. 253, no. 3, p. 680.Google Scholar
  5. 5.
    Gridnev, V.I., Kiselev, A.R., Kotel’nikova, E.V., et al., Influence of External Periodic Stimuli on Heart Rate Variability in Healthy Subjects and in Coronary Heart Disease Patients, Human Physiol., 2006, vol. 32, no. 5, p. 565.CrossRefGoogle Scholar
  6. 6.
    Higgins, J.L. and Fronek, A., Photoplethysmographic Evaluation of the Relationship Between Skin Reflectance and Skin Blood Volume, J. Biomed. Engineering, 1986, vol. 8, p. 130.CrossRefGoogle Scholar
  7. 7.
    Rhee, S., Yang, B-H., and Asada, H., Theoretical Evaluation of the Influence of Displacement on Finger Photoplethysmography for Wearable Health Monitoring Sensors, in ASME International Mechanical Engineering Congress and Exposition, Symposium on Dynamics, Control, and Design of Biomechanical Systems, 1999.Google Scholar
  8. 8.
    Krupatkin, A.I. and Sidorova, V.V., Lazernaya dopplerovskaya floumetriya mikrotsirkulyatsii krovi. Prakticheskoe rukovodstvo, (Laser Doppler Fluorometry of Blood Microcirculation), Moscow: Meditsina, 2005, p. 18.Google Scholar
  9. 9.
    Ringwood, J.V. and Malpas, S.C., Slow Oscillations in Blood Pressure Via a Nonlinear Feedback Model, Am. J. Physiol., 2001, no. 280, pp. 1105–1115.Google Scholar
  10. 10.
    Ottensen, J.T., Modelling the Dynamical Baroreflex-Feedback Control, Math. Computer Model., 2000, no. 31, p. 167.Google Scholar
  11. 11.
    Kotani, K., Struzik, Z.R., Takamasu, K., et al., Model for Complex Heart Rate Dynamics in Health and Diseases, Phys. Rev. E, 2005, vol. 72, p. 041904.CrossRefGoogle Scholar
  12. 12.
    Malliani, A., Julien, C., Billman, G.E., et al., Cardioscular Variability Is Not An Index of Autonomic Control of Circulation, Am. J. Physiol., 2006, vol. 101, p. 684.Google Scholar
  13. 13.
    Malpas, S.C., Neural Influences on Cardiovascular Variability: Possibilities and Pitfalls, Am. J. Physiol. Heart Circ. Physiol., 2002, vol. 282, no. 1, p. 6.Google Scholar
  14. 14.
    Parati, G., Di Rienzo, M., Castiglioni, P., et. al. Counterpoint: Cardiovascular Variability Is Not an Index of Autonomic Control of Circulation, Am. J. Physiol., 2006, vol. 101, p. 676.Google Scholar
  15. 15.
    Cohen, M.A. and Taylor, J.A., Short-Term Cardiovascular Oscillations in Man: Measuring and Modelling the Physiologies, Am. J. Physiol., 2002, vol. 542, p. 669.CrossRefGoogle Scholar
  16. 16.
    Kiselev, A.R., Gridnev, V.I., Karavaev, A.S., et al., Individual Approach to Administration of Hypotensive Therapy in Patients with Arterial Hypertension on the Basis of Specific Features of the Cardiovascular System Dysfunction, Arter. Giperten., 2011, vol. 17, no. 4, p. 354.Google Scholar
  17. 17.
    Kiselev, A.R., Gridnev, V.I., Karavaev, A.S., et al., The Dynamics of 0.1 Hz Oscillations Synchronization in Cardiovascular System During the Treatment of Acute Myocardial Infarction Patients, Applied Med. Inform., 2011, vol. 28, no. 1, p. 1.Google Scholar
  18. 18.
    Pikovsky, A., Rosenblum, M., and Kurths, J., Synchronization. A Universal Concept in Nonlinear Sciences, Cambridge: Cambridge University Press, 2001.CrossRefGoogle Scholar
  19. 19.
    Granger, C.W.J., Investigating Causal Relations by Econometric Models and Crossspectral Methods, Econometrica, 1969, vol. 37, no. 3, p. 424.CrossRefGoogle Scholar
  20. 20.
    Rosenblum, M.G. and Pikovsky, A.S., Detecting Direction of Coupling in Interacting Oscillators, Phys. Rev. E, 2001, vol. 64, p. 45202.CrossRefGoogle Scholar
  21. 21.
    Niedermeyer, E., Lopes Da Silva F.H., Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Baltimore: Williams and Wilkins, 1993.Google Scholar
  22. 22.
    Smirnov, D.A. and Bezruchko, B.P., Estimation of Interaction Strength and Direction from Short and Noisy Time Series, Phys. Rev. E, 2003, no. 68, p. 046209.Google Scholar
  23. 23.
    Grassberger, P., Schreiber, T., and Schaffrath, C., Non-Linear Time Sequence Analysis, Int. J. Bifurcation Chaos, 1991, vol. 1, p. 521.CrossRefGoogle Scholar
  24. 24.
    Mormann, F., Lehnertz, K., David, P., and Elger, C.E., Mean Phase Coherence as a Measure for Phase Synchronization and Its Application to the EEG of Epilepsy Patients, Physica D, 2000, vol. 144, p. 358.CrossRefGoogle Scholar
  25. 25.
    Lachaux, J.P., Rodriguez, E., Martinerie, J., and Varela, F., Measuring Phase-Synchrony in Brain Signals, Human Brain Map, 1999, vol. 8, p. 194.CrossRefGoogle Scholar
  26. 26.
    Ming-Chya Wu and Chin-Kun Hu, Empirical Mode Decomposition and Synchrogram. Approach to Cardio-respiratory Synchronization, Phys. Rev. E., 2006, no. 73, p. 51917.Google Scholar
  27. 27.
    Niedermeyer, E., Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Baltimore: Williams and Wilkins, 1993.Google Scholar
  28. 28.
    Khovanova, N.A. and Khovanov, I.A, Metody analiza vremennykh ryadov. Uchebnoe posobie, (Methods of Time Row Analysis), Kolledzh, 2001, p. 20.Google Scholar
  29. 29.
    Molgaard, H., Sorensen, K.E., and Bjerregaard, P., Circadian Variation and Influence of Risk Factors on Heart Rate Variability in Healthy Subjects, Am. J. Cardiol., 1991, vol. 15, Iss. 8, p. 777.CrossRefGoogle Scholar
  30. 30.
    Sapoznikov, D., Luria, M.H., Mahler, Y., et al., Day vs Night ECG and Heart Rate Variability Patterns in Patients Without Obvious Heart Disease, J. Electrocardiol., 1992, vol. 25, Iss. 3, p. 175.PubMedCrossRefGoogle Scholar
  31. 31.
    Huikuri, H.V., Niemela, M.J., Ojala, S., et al., Circadian Rhythms of Frequency Domain Measures of Heart Rate Variability in Healthy Subjects and Patients with Coronary Artery Disease. Effects of Arousal and Upright Posture, Circulation, 1994, vol. 90, no. 1, p. 121.PubMedCrossRefGoogle Scholar
  32. 32.
    Bernardi, L., Rossi, M., Fratino, P., et al., Relationship between Changes in Human Skin Blood Flow and Autonomic Tone, Microvasc. Res., 1989, vol. 37, p. 16.PubMedCrossRefGoogle Scholar
  33. 33.
    Bernardi, L., Radaelli, A., and Solda, P.L. et al, Autonomic Control of Skin Microvessels. Assessment by Power Spectrum of Photoplethysmographic Waves, Clin. Sci., 1996, vol. 90, p. 345.PubMedGoogle Scholar
  34. 34.
    Baevskii, R.M, Ivanov, G.G., Chireikin, L.V., et al., Analysis of Heart Rhythm Variability When Using Different Electrocardiographic Systems, Vestn. Aritmol., 2001, no. 24, p. 65.Google Scholar
  35. 35.
    Karavaev, A.S., Prokhorov, M.D., Ponomarenko, V.I., et al., Synchronization of Low-Frequency Oscillations in the Human Cardiovascular System, Chaos, 2009, vol. 19, p. 33112.CrossRefGoogle Scholar
  36. 36.
    Ponomarenko, V.I., Gridnev, V.I., and Prokhorov, M.D., Synchronization of Heart Beats and Vascular Tone Rhythm with Respiration, Biomed. Tekhnol. Radioelektronika, 2004, no. 8–9, p. 40.Google Scholar
  37. 37.
    Pikovsky, A., Rosenblum, M., and Kurths, J., Phase Synchronization in Regular and Chaotic Systems, Int. J. Bif. Chaos, 2000, no. 10, p. 2291.Google Scholar
  38. 38.
    Rosenblum, M.G., Pikovsky, A.S., and Schfer, C. Phase Synchronization: From Theory to Data Analysis, Handbook of Biological Physiscs, Moss, F. and Gielen, S., Eds., Elsevier Science 2001, vol. 4, p. 279.CrossRefGoogle Scholar
  39. 39.
    Marple, S.L., Digital Spectral Analysis, Englewood Cliffs: Prentice Hall, 1987.Google Scholar
  40. 40.
    Van der Pol, B., On Relaxation-Oscillations, London Edinburgh Dublin Phil. Mag. J. Sci., 1927, no. 2: 7, p. 987.Google Scholar
  41. 41.
    Bezruchko, B.P. and Smirnov, D.A., Matematicheskoe Modelirovanie I Khaoticheskie Vremennye Ryady (Mathematic Modeling and Chaotic Time Lines), Saratov: Kolledzh, 2005, p. 31.Google Scholar
  42. 42.
    Allen, J., Photoplethysmography and Its Application in Clinical Physiological Measurement, Physiol. Measur., 2007, vol. 28, p. 1.CrossRefGoogle Scholar
  43. 43.
    Stefanovska, A., Bračič, M., and Kvernmo, H.D., Wavelet Analysis of Oscillations in the Peripheral Blood Circulation Measured by Laser Doppler Technique, IEEE Trans Biomed Eng., 1999, vol. 46, p. 1230.PubMedCrossRefGoogle Scholar
  44. 44.
    Krupatkin, A.I., Sidorov, V.V., Merkulov, M.V., et al., Funktsional’naya otsenka perivaskulyarnoi innervatsii konechnostei s pomoshch’yu lazernoi doplerovskoi floumetrii: Posobie dlya vrachei (Functional Assessment of Perivascular Limb Innervation Using Doppler Laser Fluorometry), Moscow: Meditsina, 2004.Google Scholar
  45. 45.
    Kiselev, A.R., Bespyatov, A.B., Kolizhirina, O.M., et al., Internal Synchronization of the Main 0.1-Hz Rhythms in the Autonomic Control of the Cardiovascular System, Human Physiol., 2007, vol. 33, no. 2, p. 188.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2013

Authors and Affiliations

  • A. S. Karavaev
    • 1
  • A. R. Kiselev
    • 2
  • V. I. Gridnev
    • 2
  • E. I. Borovkova
    • 1
  • M. D. Prokhorov
    • 3
  • O. M. Posnenkova
    • 2
  • V. I. Ponomarenko
    • 3
  • B. P. Bezruchko
    • 1
  • V. A. Shvartz
    • 2
  1. 1.Faculty of Nano- and BiotechnologiesSaratov State UniversitySaratovRussia
  2. 2.Saratov Research Institute of CardiologyMinistry of Health of the Russian FederationSaratovRussia
  3. 3.Saratov Branch of the Institute of Radio Engineering and ElectronicsRussian Academy of SciencesSaratovRussia

Personalised recommendations