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Predictability of 120s-long posturographic signals was investigated by the method of forecasting. We used signals from 32 young healthy participants registered with both open and closed eyes. Influence of the window width and the embedding dimension on predictability was studied. Our results indicate existence of a deterministic component in the posturographic signal. Predictability decreases with the increasing window width ranging from 0.24s to 3.84s. An increase of the embedding dimension, while keeping the window width constant and an adequate decrease of the lag is proposed as a method that could better estimate the saturation dimension of the medium- and high-dimensional signals. Moreover, we have found that the signals representing swaying in the anterio-posterior direction have a higher predictability than those representing lateral swinging and that closing the eyes deteriorates predictability of the posturographic signals.
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Tom
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71--83
Opis fizyczny
Bibliogr. 16 poz., tab., wykr.
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autor
autor
- Departament of Biophysics, K. M. University of Medical Sciences of Sciences of Poznań, Fredry 10, 61-701 Poznań, Poland, kmichalak@amp.edu.pl
Bibliografia
- 1. Motta M., Spano A., Neri M., Schillaci G., Corteloni C., Andermarcher E., Gamberini F. Rizolli G.: The specificity and sensitivity of computerized posturography in study of postural unbalance in the elderly. Arch. Gerontol. Geriatr. Suppl. 1991, 2, 127-132.
- 2. Prieto T.E., Myklebust J.B., Hoffmann R.G., Lovett E.G. Myklebust B.M.: Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Trans. Biomed. Eng. 1996, 43, 9, 956-66.
- 3. Kennel M.B. Isabelle S.: Method to distinguish chaos from colored noise to determine embedding parameters. Phys. Rev. A 1992, 46, 6, 3111-3118.
- 4. Casdagli M.: Nonlinear prediction of chaotic time series. Physica D 1989, 35, 335-356.
- 5. Sugihara G. May R.M.: Nonlinear forecasting as way of distinguishing chaos from measurement error in time series. Nature 1990, 344, 734-741.
- 6. Farmer J.D. Sidorovich J.J.: Predicting chaotic time series. Physics Letters 1987, 59, 8, 845-848.
- 7. Tsonis A.A. Elsner J.B.: Nonlinear prediction as way of distinguishing chaos from random fractal sequences. Nature 1992, 358, 217-220.
- 8. Hernandez J.L., Valdez J.L., Biscay R., Jimenez J.C. Valdez P.: EEG predictability: adequacy of non-linear forecasting methods. International Journal of Bio-Medical Computing 1995, 38, 197-206.
- 9. Myklebust J.B., Prieto T. Myclebust B.: Evaluation of nonlinear dynamics in postural steadiness time series. Ann. Biomed. Eng. 1995, 23, 711-719.
- 10. Gagey P.M., Martinerie J.M., Pezard L. Benaim C.: L'equilibre statique est controle par un system dynamique non-lineaire. Ann. Otolaryngol. Chir. Cervicofac. 1998, 115, 161-168.
- 11. Blinowska K.J. Malinowski M.: Non-linear and linear forecasting of the EEG time series. Biol. Cybern. 1991, 66, 159-165.
- 12. Michalak K.P. Jaśkowski P.: Dimensional complexity of posturographic signal: II. Optimization of embedding parameters: window width, lag and embedding dimension. Current Topics in Biphysics 2003, 27, 1, 27-36.
- 13. Michalak K.P. Jaśkowski P.: Dimensional complexity of posturographic signals: I optimization of frequency sampling and recording time. Current Topics in Biophysics 2002, 26, 2, 235-244.
- 14. Pritchard D. Duke D.W.: Measuring chaos in the brain: a tutorial review of nonlinear dynamical EEG analysis. International Journal of Neuroscience 1992, 67, 31-80.
- 15. Rapp P.E., Albano A.M., Schmah T.I. Farwell L.A.: Filtered noise can mimic low-dimensional chaotic attractors. Phys. Rev. E 1993, 47, 4, 2289-2297.
- 16. Rapp P.E., Albano A.M., Zimmerman I.D. Jimenez-Montano M.A.: Phase-randomized surrogates can produce spurious identifications of non-random structure. Phys. Lett. A 1994, 192, 27-33.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-article-BPZ1-0043-0046