EMD: A novel technique for the study of tremor time series

  • Eduardo Rocon de Lima
  • A. O. Andrade
  • J. L. Pons
  • P- Kyberd
  • S. J. Nasuto
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 14)

Abstract

This paper introduces the Hilbert Analysis (HA), which is a novel digital signal processing technique, for the investigation of tremor. The HA is formed by two complementary tools, i.e. the Empirical Mode Decomposition (EMD) and the Hilbert Spectrum (HS). In this work we show that the EMD can automatically detect and isolate tremulous and voluntary movements from experimental signals collected from 31 patients with different conditions. Our results also suggest that the tremor may be described by a new class of mathematical functions defined in the HA framework. In a further study, the HS was employed for visualization of the energy activities of signals. This tool introduces the concept of instantaneous frequency in the field of tremor. In addition, it could provide, in a time-frequency-energy plot, a clear visualization of local activities of tremor energy over the time. The HA demonstrated to be very useful to perform objective measurements of any kind of tremor and can therefore be used to perform functional assessment.

Keywords

Tremor time-frequency analysis empirical mode decomposition 

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References

  1. 1.
    E. Rocon et al. (2004), Pathological tremor management: Modelling, compensatory technology and evaluation, Technology and Disability (16)3:18.Google Scholar
  2. 2.
    Akay, Metin (1999), Detection and estimation methods for biomedical signals, Academic Press.Google Scholar
  3. 3.
    Andrade, A.O., Kyberd, P.J. and Taffler, S.D., (2003) Novel Spectral Representation of Electromyographic Signals, Engineering in Medicine and Biology Society-25th AnnualInternational Conference, IEEE(1):2598–2601,2003.Google Scholar
  4. 4.
    Huang, N.E. et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Procedures of Royal Society of London,(454):903–995.MATHCrossRefGoogle Scholar
  5. 5.
    Lawrence M.S. (1999) Computing the Discrete-Time Analytic signal via FFT, IEEE Transactions on signal processing, 47(9):2600–2603.MATHCrossRefGoogle Scholar
  6. 6.
    E. Rocon, A.F. Ruiz, J.L. Pons (2005) On the Use of Rate Gyroscopes for Tremor Sensing in the Human Upper Limb Eurosensors XIX, MP30.Google Scholar
  7. 7.
    J.M. Belda-Lois et al. (2003), Tremor movement analysis techniques: an approach towards ambulatory systems, Asistive Technology-Shaping the future, 827–831.Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2007

Authors and Affiliations

  • Eduardo Rocon de Lima
    • 1
  • A. O. Andrade
    • 2
  • J. L. Pons
    • 1
  • P- Kyberd
    • 3
  • S. J. Nasuto
    • 2
  1. 1.Instituto de Automática IndustrialArganda del ReySpain
  2. 2.University of ReadingReadingUK
  3. 3.University of New BrunswickBrunswickCanada

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