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

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 high...

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Main Authors:	Hussain, Mohammed Sazzad, Reaz, Mamun Ibn, Mohd-Yasin, Faisal, Ibrahimy, Muhammad Ibn
Format:	Article
Language:	English
Published:	Blackwell Publishing Ltd 2009
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/5972/ http://irep.iium.edu.my/5972/ http://irep.iium.edu.my/5972/ http://irep.iium.edu.my/5972/1/Electromygraphy_Signal_2009.pdf

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recordtype	eprints
spelling	iium-59722011-11-21T22:05:47Z http://irep.iium.edu.my/5972/ Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction Hussain, Mohammed Sazzad Reaz, Mamun Ibn Mohd-Yasin, Faisal Ibrahimy, Muhammad Ibn TK7885 Computer engineering 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. Blackwell Publishing Ltd 2009 Article PeerReviewed application/pdf en http://irep.iium.edu.my/5972/1/Electromygraphy_Signal_2009.pdf 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 http://www.wiley.com/bw/journal.asp?ref=0266-4720 10.1111/j.1468-0394.2008.00483.x
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Hussain, Mohammed Sazzad Reaz, Mamun Ibn Mohd-Yasin, Faisal Ibrahimy, Muhammad Ibn Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
description	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.
format	Article
author	Hussain, Mohammed Sazzad Reaz, Mamun Ibn Mohd-Yasin, Faisal Ibrahimy, Muhammad Ibn
author_facet	Hussain, Mohammed Sazzad Reaz, Mamun Ibn Mohd-Yasin, Faisal Ibrahimy, Muhammad Ibn
author_sort	Hussain, Mohammed Sazzad
title	Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
title_short	Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
title_full	Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
title_fullStr	Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
title_full_unstemmed	Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
title_sort	electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction
publisher	Blackwell Publishing Ltd
publishDate	2009
url	http://irep.iium.edu.my/5972/ http://irep.iium.edu.my/5972/ http://irep.iium.edu.my/5972/ http://irep.iium.edu.my/5972/1/Electromygraphy_Signal_2009.pdf
first_indexed	2023-09-18T20:14:48Z
last_indexed	2023-09-18T20:14:48Z
_version_	1777407716124262400

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

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