Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction

M. S. Hussain, Md. Mamun Ibne Reaz, F. Mohd-Yasin, M. I. Ibrahimy

Research output: Contribution to journalArticle

67 Citations (Scopus)

Abstract

Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet, it is possible to remove interference noise (denoise) effectively in order to analyse the SEMG. Daubechies wavelets (db2, db4, db5, db6, db8), symmlet (sym4, sym5) and the orthogonal Meyer (dmey) wavelet can efficiently remove noise from the recorded SEMG signals. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square difference and signal-to-noise ratio values. Results for both root mean square difference and signal-to-noise ratio show that wavelet db2 performs denoising best out of the wavelets. Furthermore, the higher order statistics method is applied for SEMG signal analysis because of its unique properties when applied to random time series, such as parameter estimation, testing of Gaussianity and linearity, deterministic and non-deterministic signal detection etc. Gaussianity and linearity tests as part of higher order statistics are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. According to the results, the SEMG signal becomes less Gaussian and more linear with increased force.

Original languageEnglish
Pages (from-to)35-48
Number of pages14
JournalExpert Systems
Volume26
Issue number1
DOIs
Publication statusPublished - Feb 2009

Fingerprint

Higher-order Statistics
Higher order statistics
Electromyography
Signal Analysis
Signal analysis
Muscle
Wavelet transforms
Wavelet Transform
Contraction
Wavelets
Denoising
Linearity
Mean Square
Meyer Wavelet
Roots
Daubechies Wavelet
Signal to noise ratio
Noise Removal
Signal Detection
Parameter Estimation

Keywords

  • Denoising
  • Electromyography
  • Higher order statistics
  • Motor unit
  • Muscle contraction
  • Wavelet

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Theoretical Computer Science
  • Computational Theory and Mathematics

Cite this

Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. / Hussain, M. S.; Ibne Reaz, Md. Mamun; Mohd-Yasin, F.; Ibrahimy, M. I.

In: Expert Systems, Vol. 26, No. 1, 02.2009, p. 35-48.

Research output: Contribution to journalArticle

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