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Nonlinear analysis of posturographic data

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Abstract

The aim of this work is to determine whether postural sway can be well described by nonlinear deterministic modelling. Since the results of nonlinear analysis depend on experimental data processing, emphasis was given to the assessment of a proper methodology to process posturographic data. Centre of Pressure (CoP) anterior-posterior (AP) displacements (stabilogram) were obtained by static posturography tests performed on control subjects. A nonlinear determinism test was applied to investigate the nature of data. A nonlinear filtering method allowed us to estimate properly the parameters of the nonlinear model without altering signal dynamics. The largest Lyapunov exponent (LLE) was estimated to quantify the chaotic behaviour of postural sway. LLE values were found to be positive although close to zero. This suggests that postural sway derives from a process exhibiting weakly chaotic dynamics.

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References

  1. Abarbanel HDI, Brown R, Sidorowich JJ, Tsimiring LS (1993) The analysis of observed chaotic data in physical systems. Rev Mod Phys 65:1331–1392

    Article  Google Scholar 

  2. Badii R, Brogli G, Derighetti B, Ravani M (1988) Dimension increase in filtered chaotic signals. Phys Rev Lett 60(11):979–982

    Article  Google Scholar 

  3. Baratto L, Morasso P, Re C, Spada G (2002) A new look at posturographic analysis in the clinical context: sway-density versus other parameterization techniques. Motor Control 6:248–273

    Google Scholar 

  4. Baselli G, Cerutti S, Porta A, Signorini MG (2002) Short and long term non-linear analysis of RR variability series. Med Eng Phys 24(1):21–32

    Article  Google Scholar 

  5. Blaszczyk JW, Klonowski W (2001) Postural stability and fractal Dynamics. Acta Neurobiol Exp 61:105–112

    Google Scholar 

  6. Collins JJ, De Luca CJ (1994) Random walking during quiet standing. Phys Rev Lett 73(5):764–767

    Article  Google Scholar 

  7. Doyle TLA, Dugan EL, Humphries B, Newton RU (2004) Discriminating between elderly and young using a fractal dimension analysis of centre of pressure. Int J Med Sci 1(1):11–20

    Google Scholar 

  8. Doyle TL, Newton RU, Burnett AF (2005) Reliability of traditional and fractal dimension measures of quiet stance center of pressure in young, healthy people. Arch Phys Med Rehabil 86:2034–2040

    Article  Google Scholar 

  9. Fioretti S, Guidi M, Ladislao L, Ghetti G (2004) Analysis and reliability of posturographic parameters in Parkinson patients at an early stages.In: Proc 26th Ann Intern Conf IEEE EMBS, San Francisco, Sept 1–5, pp 651–654

  10. Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33:134–140

    Article  Google Scholar 

  11. Grassberger P, Hegger R, Kantz H, Schaffrath C, Schreiber T (1993) On noise reduction methods for chaotic data. Chaos 3:127–141

    Article  MATH  Google Scholar 

  12. Hegger R, Kantz H (1999) Practical implementation of nonlinear time series methods: the TISEAN package. Chaos 9(2):413–435

    Article  MATH  Google Scholar 

  13. Kantz H, Schreiber T Nonlinear time series analysis (2004) Cambridge University, Second edition

  14. Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 45:3403–3411

    Article  Google Scholar 

  15. Kostelich EJ, Yorke JA (1990) Noise reduction: finding the simplest dynamical system consistent with the data. Physica D 41:183–196

    Article  MATH  Google Scholar 

  16. Murata A, Iwase H (1998) Chaotic analysis of body sway. In: Proc 20th Ann Intern Conf IEEE Eng in Med and Biol Society, Hong Kong 20, n.3

  17. Myklebust JB, Prieto T, Myklebust B (1995) Evaluation of nonlinear dynamics in postural steadiness time series. Ann Biomed Eng 23:711–719

    Article  Google Scholar 

  18. Pascolo PB, Barazza F, Carniel R (2006) Considerations on the application of the chaos paradigm to describe the postural sway. Chaos Solitons Fractals 27:1339–1346

    Article  MATH  Google Scholar 

  19. Pascolo PB, Marini A, Carniel R, Barazza F (2005) Posture as a chaotic system and an application to the Parkinson’s disease. Chaos Solitons Fractals 24:1343–1346

    Article  MATH  Google Scholar 

  20. Prieto TE, Myklebust JB, Hoffmann RG, Lovett EG, Myklebust BM (1996) Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Trans Biomed Eng 43(9):956–966

    Article  Google Scholar 

  21. Riley MA, Balasubramaniam R, Turvey MT (1999) Recurrence quantification analysis of postural fluctuations. Gait Posture 9:65–78

    Article  Google Scholar 

  22. Sabatini AM (2000) Analysis of postural sway using entropy measures of signal complexity. Med Biol Eng Comput 38(6):617–24

    Article  Google Scholar 

  23. Sasaki O, Gagey PM, Ouaknine AM, Martinerie J, Le Van Quyen M, Toupet M, L’Heritier A (2001) Nonlinear analysis of orthostatic posture in patients with vertigo or balance disorders. Neurosci Res 4:185–192

    Article  Google Scholar 

  24. Schreiber T, Schmitz A (2000) Surrogate time series. Physica D 142:346–382

    Article  MATH  Google Scholar 

  25. Takens F (1981) Detecting strange attractors in turbulence in dynamical systems and turbulence lecture notes in mathematics 898. Springer, Berlin 366–381

    Google Scholar 

  26. Van Emmerik RE, van Wegen EE (2002) On the functional aspects of variability in postural control. Exerc Sport Sci Rev 30(4):177–183

    Article  Google Scholar 

  27. Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time series. Physica D 16:285–317

    Article  MATH  Google Scholar 

  28. Yamada N (1995) Chaotic swaying of the upright posture. Human Mov Sci 14:711–26

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to thank Dr. P.Pace and G. Ghetti, P.T., for the use of the Posture and Movement Analysis Laboratory at I.N.R.C.A. Hospital, Ancona; prof. M.G. Signorini, Milan Polytechnic, for her useful suggestions; the authors of the following software packages: TISEAN (TIme SEries ANalysis) Software package and online documentation, by R. Hegger, H. Kantz, and T. Schreiber, available at: http://www.mpipksdresden.mpg.de∼tisean/Tisean_2.1; NDT (Nonlinear Dynamics Toolbox, Georgia Institute of Technology Applied Chaos) Software package and online documentation, by J. Reiss, available at: http://www.physics.gatech.edu/chaos/research/NDT.html; VRA (Visual Recurrence Analysis) Software package and online documentation, by E. Kononov, available at http://home.netcom.com/∼eugenek/download.html.

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Correspondence to Sandro Fioretti.

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Ladislao, L., Fioretti, S. Nonlinear analysis of posturographic data. Med Bio Eng Comput 45, 679–688 (2007). https://doi.org/10.1007/s11517-007-0213-y

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  • DOI: https://doi.org/10.1007/s11517-007-0213-y

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