Analysis of kinematic data in pathological tremor with the Hilbert-Huang transform

J. A. Gallego*, E. Rocon, A. D. Koutsou, Jose L Pons

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper presents analysis of kinematic data of tremor patients while performing different tasks with Ensemble Empirical Mode Decomposition (EEMD), a novel noise-assisted data analysis method. EEMD automatically separates raw kinematic data into three components: 1) noise from various sources, 2) tremulous movement, and 3) voluntary movement. Comparison of this technique with other decomposition methods such as recursive forth and back filters or Empirical Mode Decomposition (EMD) shows a better performance; EEMD separation of tremor diminishes EMD error in a 45.2 % (mean error 0.041 0.036 rad/s). Moreover, postprocessing of EEMD separated tremor allows the calculation of the Hilbert spectrum, a high resolution time-energy-frequency distribution that improves analysis of tremors.

Original languageEnglish (US)
Title of host publication2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
Pages80-83
Number of pages4
DOIs
StatePublished - 2011
Event2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 - Cancun, Mexico
Duration: Apr 27 2011May 1 2011

Publication series

Name2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011

Other

Other2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
CountryMexico
CityCancun
Period4/27/115/1/11

ASJC Scopus subject areas

  • Neuroscience(all)

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