Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals

A. Hossen*, G. Deuschl, S. Groppa, U. Heute, M. Muthuraman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson's disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125-9.375 Hz) and band 11 (B11: 15.625-17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

Original languageEnglish
Pages (from-to)461-476
Number of pages16
JournalTechnology and Health Care
Volume28
Issue number5
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • accelerometer signals
  • discrimination
  • EMG
  • essential tremor
  • Parkinsonian tremor
  • Physiological tremor
  • power-spectral density
  • soft-decision technique
  • wavelet-decomposition

ASJC Scopus subject areas

  • Biophysics
  • Bioengineering
  • Information Systems
  • Biomaterials
  • Biomedical Engineering
  • Health Informatics

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