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Empirical mode decomposition: a novel technique for the study of tremor time series

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Abstract

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

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References

  1. Akay M (1994) Biomedical signal processing, 1st edn. Academic, Sandiego

    Google Scholar 

  2. Akay M (1999) Detection and estimation methods for biomedical signals. Academic, San Diego

    Google Scholar 

  3. Andrade AO (2005) Decomposition and analysis of electromyographic signals. PhD thesis, University of Reading, Reading

  4. Andrade AO, Kyberd PJ, Taffler SD (2003) A novel spectral representation of electromyographic signals. In: Leder RS (ed) Engineering in medicine and biology society—25th annual international conference, Cancun, Mexico, vol 1, IEEE, pp 2598–2601

  5. Ang WT (2004) Active Control compensation in Handheld Instrument for Microsurgery. PhD thesis, Johns Hopkins University

  6. Anouti A, Koller W (1998) Tremor disorders: diagnosis and management. West J Med 162(6):523–530

    Google Scholar 

  7. Belda-Lois JM, Sanchez-Lacuesta J, Vivas-Broseta MJ, Rocon E, Bueno L, Pons JL (2003) Tremor movement analysis techniques: an approach towards ambulatory systems. Assistive technology—shaping the future, pp 827–831

  8. Debnath L, Mikusinski P (1999) Introduction to Hilbert spaces with applications, 2nd edn. Academic, San Diego

    MATH  Google Scholar 

  9. Edwards R, Beuter A (1999) Using time domain characteristics to discriminate physiologic and parkinsonian tremors. J Clin Neurophysiol 17(1):87–100

    Article  Google Scholar 

  10. Edwards R, Beuter A (1999) Using time frequency characteristics to discriminate physiologic and parkinsonian tremors. J Clin Neurophysiol 16:484–494

    Article  Google Scholar 

  11. Elble RJ (1997) The pathophysiology of tremor. In: Watts RL, Koller WC (eds) Movement disorders: neurologic principles and practice. McGraw-Hill, New York, pp 405–417

    Google Scholar 

  12. Elble RJ, Koller WC (1990) Tremor. The Johns Hopkins University Press, Baltimore

    Google Scholar 

  13. Elble RJ (2003) Characteristics of physiologic tremor in young and elderly adults. Clin Neurophysiol 114:624–635

    Article  Google Scholar 

  14. Fahn S, Tolosa E, Marin C (1998) Clinical rating scale for tremor. In: Tolosa E, Jankovic J (eds) Parkinson’s disease and movement disorders. Urban & Schwarzenberg, Baltimore

    Google Scholar 

  15. Gantert C, Honerkamp J, Timmer J (1992) Analysing the dynamics of tremor time series. Biol Cybern 66:479–484

    Article  MATH  Google Scholar 

  16. Gao JB, Wen-wen T (2002) Pathological tremors as diffusional processes. Biol Cybern 86:263–270

    Article  MATH  Google Scholar 

  17. Huang NE, Chern CC, Huang K, Salvino LW, Long SR, Fan KL (2001) A new spectral representation of earthquake data: Hilbert spectral analysis of station TCU129, Chi-Chi, Taiwan, 21 September 1999. Bull Seismol Soc Am 91(5):1310–1338

    Article  Google Scholar 

  18. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Roy Soc Lond 454:903–995

    Article  MATH  MathSciNet  Google Scholar 

  19. Jain SS, Kirshblum SC (1993) Movement disorders, including tremors. In: Joel A, DeLisa JB (eds) Rehabilitation medicine: principles and practice, vol 2. Lippincott, Philadelphia

  20. Kapandji IA (1983) The physiology of the joints: upper limb, vol 1. Churchill Livingstone, London

  21. Marple LS Jr (1999) Computing the discrete-time "analytic" signal via fft. IEEE Trans Signal Process 47(9):2600–2603

    Article  MATH  Google Scholar 

  22. Moreno JC, Freriks B, Thorsteinsson F, Sanchez J, Pons JL (2004) Intelligent knee–ankle–foot orthosis: the gait project approach. Rehabilitation sciences in the new millenium—challenge for multidisciplinary research, pp 271–274

  23. Nechyba MC (1998) Learning and validation of human control strategies. PhD thesis, Johns Hopkins University, Baltimore

  24. Riviere C (1981) Adaptive suppression of tremor for improved human–machine control. PhD thesis, Johns Hopkins University, Baltimore

  25. Rocon E, Belda-Lois JM, Sanchez-Lacuesta JJ, Pons JL (2004) Pathological tremor management: modelling,compensatory technology and evaluation. Technol Disabil 16:3–18

    Google Scholar 

  26. Rocon E, Ruiz AF, Pons JL (2005) On the use of rate gyroscopes for tremor sensing in the human upper limb. In: Proceedings of the international conference eurosensors XIX, p MP30

  27. Timmer J, Gantert C, Deuschl G, Honerkamp J (1993) Characteristics of hand tremor time series. Biol Cybern 70:75–80

    Article  MATH  Google Scholar 

  28. Timmer J, Haubler S, Lauk M, Lucking CH (1998) Pathological tremors: deterministic chaos or nonlinear stochastic oscillators? Biol Cybern 78:349–357

    Article  MATH  Google Scholar 

  29. Timmer J, Lauk M, Deuschl G (1998) Cross-spectral analysis of physiological tremor and muscle activity. I. Theory and application to unsynchronized electromyogram. Biol Cybern 78:349–357

    Article  MATH  Google Scholar 

  30. Tong K, Granat MH (1999) A practical gait analysis system using gyroscopes. Med Eng Phys 21:87–94

    Article  Google Scholar 

  31. Viosca E, Peydro MF, Puchol A, Soler C, Prat J, Corts A, Sanchez J, Belda JM, Lafuente R, Poveda R (1999) Gufa de uso y prescripci=n de productos ortoprotTsicos a medida. IBV—Instituto Biomecánico de Valencia

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Authors and Affiliations

  1. Instituto de Automática Industrial, Carretera Campo Real, km 0.200, La Poveda—Arganda del Rey, 28500, Madrid, Spain

    Eduardo Rocon de Lima & José Luis Pons

  2. University of Reading, Reading, UK

    Adriano O. Andrade & Slawomir J. Nasuto

  3. University of New Brunswick, Fredericton, NB, Canada

    Peter Kyberd

Authors
  1. Eduardo Rocon de Lima
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  2. Adriano O. Andrade
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  3. José Luis Pons
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  4. Peter Kyberd
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  5. Slawomir J. Nasuto
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Correspondence to Eduardo Rocon de Lima.

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de Lima, E.R., Andrade, A.O., Pons, J.L. et al. Empirical mode decomposition: a novel technique for the study of tremor time series. Med Bio Eng Comput 44, 569–582 (2006). https://doi.org/10.1007/s11517-006-0065-x

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  • DOI: https://doi.org/10.1007/s11517-006-0065-x

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