Temporal Correlation in Phrenic Neural Activity

  • Bernard Hoop
  • William L. Krause
  • Homayoun Kazemi
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 450)


Neural activity which gives rise to eupnea fluctuates in a complex manner. Apparently “noisy” variations in activity of the phrenic nerve may display a fractal scaling relationship. Fractal scaling in eupnea is the consequence of physical and chemical processes acting over short time scales at the cellular level, and which are correlated with similar processes acting simultaneously over longer time scales. Specifically, variations in phrenic neural bursts may not be independent random fluctuations or entirely due to short-range influences4, but may exhibit temporal correlation indicative of fractal scaling. West and Deering20 have demonstrated that fractal processes are essentially unresponsive to error and very tolerant of variability in the physiological environment. In this view, eupnea with its concomitant stability to error from a broad spectrum of inputs must have the error-tolerant properties of fractals.


Power Spectral Density Temporal Correlation Phrenic Nerve Detrended Fluctuation Analysis Fractal Scaling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bassingthwaighte, J. B., L. S. Liebovitch, and B. J. West, Fractal Physiology. New York: Oxford, 1994.CrossRefGoogle Scholar
  2. 2.
    Beagle, J. L., B. Hoop, and H. Kazemi. Phrenic nerve response to glutamate antagonist microinjection in the ventral medulla. In: Advances in Control and Modeling of Ventilation, edited by R. Hughson, D. A. Cunningham, and J. Duffin., New York: Plenum, 1998, (this volume).Google Scholar
  3. 3.
    Bianchi, A. L., M. Denavit-Saubie, and J. Champagnat. Central control of breathing in mammals: neuronal circuitry, membrane properties, and neurotransmitters. Physiol. Rev. 75, 1–45, 1995.PubMedGoogle Scholar
  4. 4.
    Bruce, E. N. Temporal variations in the pattern of breathing. J. Appl. Physiol. 80: 1079–1087, 1996.PubMedCrossRefGoogle Scholar
  5. 5.
    Donaldson, G. C. The chaotic behavior of resting human respiration. Respir. Physiol. 88: 313–321, 1992.PubMedCrossRefGoogle Scholar
  6. 6.
    Feder, J. Fractals. New York: Plenum, 1988, pp. 180–181.Google Scholar
  7. 7.
    Flandrin, P. On the spectrum of fractional Brownian motions. IEEE Trans. Infor. Theor. 35: 197–199, 1989.CrossRefGoogle Scholar
  8. 8.
    Hausdorff, J. M., C. -K. Peng, Z. Ladin, J. Y. Wei, and A. L. Goldberger. Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. J. Appl. Physiol. 78: 349–358, 1995.PubMedGoogle Scholar
  9. 9.
    Hausdorff, J. M., and C. -K. Peng. Multi-scaled randomness: a source of l/f noise in biology. Physical Review E 54: 2154–2157, 1996.CrossRefGoogle Scholar
  10. 10.
    Hoop, B., M. D. Burton, H. Kazemi, and L. S. Liebovitch. Correlation in stimulated respiratory neural noise. CHAOS 5: 609–612, 1995.PubMedCrossRefGoogle Scholar
  11. 11.
    Hoop, B., M. D. Burton, and H. Kazemi. Fractal noise in breathing. In: Bioengineering Approaches to Pulmonary Physiology and Medicine, edited by M. C. K. Khoo, New York: Plenum, 1996, pp. 161–173.CrossRefGoogle Scholar
  12. 12.
    Hughson, R. L., Y. Yamamoto, J. -O. Fortrat, R. Leask, and M. S. Fofana. Possible fractal and/or chaotic breathing patterns in resting humans. In: Bioengineering Approaches to Pulmonary Physiology and Medicine, edited by M. C. K. Khoo, New York: Plenum, 1996, pp. 187–196.CrossRefGoogle Scholar
  13. 13.
    Peng, C. -K., S. Havlin, H. E. Stanley, and A. L. Goldberger. Quantification of scaling exponents and crossover phenomena in nonstationary hearbeat time series. CHAOS 5: 82–87, 1995.PubMedCrossRefGoogle Scholar
  14. 14.
    Sammon, M., J. R. Romaniuk, and E. N. Bruce. Bifurcations of the respiratory pattern produced with phasic vagal stimulation in the rat. J. Appl. Physiol. 75: 912–926, 1993.PubMedGoogle Scholar
  15. 15.
    Schepers, H. E., J. H. G. M van Beek, and J. B. Bassingthwaighte. Four methods to estimate the fractal dimension from self-affine signals. IEEE Eng. Med Biol Mag. 11 (2): 57–64, 71, 1992.CrossRefGoogle Scholar
  16. 16.
    Szeto, H. H, P. Y. Cheng, J. A. Decena, Y. Cheng, D. Wu, and G. Dwyer. Fractal properties in fetal breathing dynamics. Am. J. Physiol. 263: R141–R147, 1992.Google Scholar
  17. 17.
    Tuck, S. A., Y. Yamamoto, and R. L. Hughson. The effects of hypoxia and hyperoxia on the 1/f nature of breath-by-breath ventilatory variability. In: Modelling and Control of Ventilation, edited by S. J. G. Semple and L. Adams. New York: Plenum, 1995, pp. 297–302.CrossRefGoogle Scholar
  18. 18.
    Viswanathan, G. M, C. -K. Peng, H. E. Stanley, and A. L. Goldberger. Deviations from uniform power law scaling in nonstationary time series. Physical Review E 55: 845–849, 1997.CrossRefGoogle Scholar
  19. 19.
    Voss, R. F. Random fractal forgeries. In: Fundamental Algorithms in Computer Graphics, edited by R. A. Earnshaw, Berlin: Springer, pp. 805–835, 1985.CrossRefGoogle Scholar
  20. 20.
    West, B. J. and W. Deering. Fractal physiology for physicists: Levy Statistics. Physics Reports 246: 2–100, 1994.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Bernard Hoop
    • 1
  • William L. Krause
    • 1
  • Homayoun Kazemi
    • 1
  1. 1.Pulmonary and Critical Care Unit, Medical Services, Harvard Medical SchoolMassachusetts General HospitalBostonUSA

Personalised recommendations