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Complexity Changes in Human Wrist Temperature Circadian Rhythms through Ageing

  • R. Marin
  • M. Campos
  • A. Gomariz
  • A. Lopez
  • M. A. Rol
  • J. A. Madrid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

Circadian rhythms are cycles in physiological processes that have a near-daily frequency. The wrist skin temperature has proven to be a good marker of circadian rhythmicity. In this paper we attempt to establish whether complexity changes in human circadian rhythms in ageing can be assessed through phase variability in individual wrist temperature records. To this end, we propose some phase complexity measures that are based on Lempel-Ziv complexity, Approximate Entropy, instantaneous phase, Hilbert transform and a complex continuous wavelet transform. A sample consisting of 53 healthy subjects has been studied. Our experimental results consistently show that a significant decrease in phase complexity happens when ageing.

Keywords

circadian rhythms wrist temperature complexity measures age-dependent changes 

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References

  1. 1.
    Cao, L.: Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena 110(1-2), 43–50 (1997)CrossRefzbMATHGoogle Scholar
  2. 2.
    Cuesta, D., Varela, M., Miró, P., Galdós, P., Abásolo, D., Hornero, R., Aboy, M.: Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy. Medical and Biological Engineering and Computing 45(7), 671–678 (2007)CrossRefGoogle Scholar
  3. 3.
    Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Physical Review A 33(2), 1134–1140 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Gonze, D., Goldbeter, A.: Entrainment versus chaos in a model for a circadian oscillator driven by light-dark cycles. Journal of Statistical Physics 101(1), 649–663 (2000)CrossRefzbMATHGoogle Scholar
  5. 5.
    Hofman, M.A., Swaab, D.F.: Living by the clock: the circadian pacemaker in older people. Ageing Research Reviews 5(1), 33–51 (2006)CrossRefGoogle Scholar
  6. 6.
    Huang, Y.L., Liu, R.Y., Wang, Q.S., Van Someren, E.J.W., Xu, H., Zhou, J.N.: Age-associated difference in circadian sleep-wake and rest-activity rhythms. Physiology & Behavior 76(4-5), 597–603 (2002)CrossRefGoogle Scholar
  7. 7.
    Keylock, C.J.: A wavelet-based method for surrogate data generation. Physica D: Nonlinear Phenomena 225(2), 219–228 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Lempel, A., Ziv, J.: On the Complexity of Finite Sequences. IEEE Transactions on Information Theory 22(1), 75–81 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Nagarajan, R., Szczepanski, J., Wajnryb, E.: Interpreting non-random signatures in biomedical signals with Lempel-Ziv complexity. Physica D: Nonlinear Phenomena 237(3), 359–364 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ortiz-Tudela, E., Martinez-Nicolas, A., Campos, M., Rol, M.Á., Madrid, J.A.: A new integrated variable based on thermometry, actimetry and body position (TAP) to evaluate circadian system status in humans. PLoS Computational Biology 6(11) (2010)Google Scholar
  11. 11.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America 88(6), 2297 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Refinetti, R.: Non-stationary time series and the robustness of circadian rhythms. Journal of Theoretical Biology 227(4), 571–581 (2004)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Refinetti, R., Cornélissen, G., Halberg, F.: Procedures for numerical analysis of circadian rhythms. Biological Rhythm Research 38(4), 275–325 (2007)CrossRefGoogle Scholar
  14. 14.
    Sarabia, J.A., Mondejar, M.T., Marin, R., Campos, M., Rol, M.A., Madrid, J.A.: Caracterizacion del ritmo de temperatura periferica de la mueca, en sujetos monitorizados ambulatoriamente. In: Proceedings de la Tercera Reunion Nacional de la Sociedad Espa ola de Medicina Geriatrica (SEMEG), Oviedo, Spain (2008)Google Scholar
  15. 15.
    Sarabia, J.A., Rol, M.A., Mendiola, P., Madrid, J.A.: Circadian rhythm of wrist temperature in normal-living subjects: A candidate of new index of the circadian system. Physiology & Behavior 95(4), 570–580 (2008)CrossRefGoogle Scholar
  16. 16.
    Schreiber, T., Schmitz, A.: Surrogate time series. Physica D: Nonlinear Phenomena 142(3-4), 346–382 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Shiogai, Y., Stefanovska, A., McClintock, P.V.E.: Nonlinear dynamics of cardiovascular ageing. Physics Reports 488(2-3), 51–110 (2010)CrossRefGoogle Scholar
  18. 18.
    Small, M., Tse, C.K.: Applying the method of surrogate data to cyclic time series. Physica D: Nonlinear Phenomena 164(3-4), 187–201 (2002)CrossRefzbMATHGoogle Scholar
  19. 19.
    Varela, M., Jimenez, L., Fariña, R.: Complexity analysis of the temperature curve: new information from body temperature. European Journal of Applied Physiology 89, 230–237 (2003)CrossRefGoogle Scholar
  20. 20.
    Weinert, D.: Circadian temperature variation and ageing. Ageing Research Reviews 9(1), 51–60 (2010)CrossRefGoogle Scholar
  21. 21.
    Weinert, D., Waterhouse, J.: The circadian rhythm of core temperature: effects of physical activity and aging. Physiology & Behavior 90(2-3), 246–256 (2007)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Luo, X., Small, M.: Detecting chaos in pseudoperiodic time series without embedding. Physical Review E 73(1), 16216 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • R. Marin
    • 1
  • M. Campos
    • 1
  • A. Gomariz
    • 1
  • A. Lopez
    • 1
  • M. A. Rol
    • 2
  • J. A. Madrid
    • 2
  1. 1.Computer Science FacultyUniversity of MurciaSpain
  2. 2.Chronobiology Laboratory, Department of PhysiologyUniversity of MurciaSpain

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