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

Hussain, Mohammed Sazzad and Reaz, Mamun Ibn and Mohd-Yasin, Faisal and Ibrahimy, Muhammad Ibn (2009) Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. Expert Systems: The Journal of Knowledge Engineering, 26 (1). pp. 35-48. ISSN 0266-4720

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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.

Item Type:	Article (Journal)
Additional Information:	4637/5972
Uncontrolled Keywords:	electromyography, motor unit, muscle contraction, wavelet, denoising, higher order statistics
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr Muhammad Ibrahimy
Date Deposited:	01 Nov 2011 11:37
Last Modified:	22 Nov 2011 06:05
URI:	http://irep.iium.edu.my/id/eprint/5972

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