Wavelet based noise removal from EMG signals

Wavelet transform has been applied in this research for removing noise from the surface electromyography signal (SEMG). The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Error. This paper reports on the effectiveness of the wavelet transform applied to th...

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Main Authors:	Chowdhury, Md. Sazzad Hossien, Reaz, Mamun Bin Ibne, Ibrahimy, Muhammad Ibn, Ismail, Ahmad Faris, Mohd-Yasin, Faisal
Format:	Article
Language:	English
Published:	Society for Microelectronics, Electric Components and Materials 2007
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/29533/ http://irep.iium.edu.my/29533/ http://irep.iium.edu.my/29533/1/MIDEM_37%282007%292p94.pdf

id	iium-29533
recordtype	eprints
spelling	iium-295332013-06-18T07:35:51Z http://irep.iium.edu.my/29533/ Wavelet based noise removal from EMG signals Chowdhury, Md. Sazzad Hossien Reaz, Mamun Bin Ibne Ibrahimy, Muhammad Ibn Ismail, Ahmad Faris Mohd-Yasin, Faisal T Technology (General) Wavelet transform has been applied in this research for removing noise from the surface electromyography signal (SEMG). The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Error. This paper reports on the effectiveness of the wavelet transform applied to the SEMG signal as means of removing noise to retrieve information related to muscle contraction and nerve system. Power spectrum analysis has been applied to SEMG signals where mean power frequency was calculated to indicate changes in muscle contraction. Wavelet based noise removal and power spectrum analysis on the EMG signal from the right "biceps brachii" muscle was performed using four wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze SEMG significantly. Results show that WFs Daubechies (db2) provide the best noise removal from the raw SEMG signals among other WFs Daubechies (db6, db8) and orthogonal Meyer. The algorithm is intended for FPGA implementation of portable bio medical equipments to detect neuromuscular disease and muscle fatigue. Society for Microelectronics, Electric Components and Materials 2007-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/29533/1/MIDEM_37%282007%292p94.pdf Chowdhury, Md. Sazzad Hossien and Reaz, Mamun Bin Ibne and Ibrahimy, Muhammad Ibn and Ismail, Ahmad Faris and Mohd-Yasin, Faisal (2007) Wavelet based noise removal from EMG signals. Informacije MIDEM: Journal of Microelectronics, Electronic Components and Materials, 37 (2). pp. 94-97. ISSN 0352-9045 http://www.midem-drustvo.si/journal.htm
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Chowdhury, Md. Sazzad Hossien Reaz, Mamun Bin Ibne Ibrahimy, Muhammad Ibn Ismail, Ahmad Faris Mohd-Yasin, Faisal Wavelet based noise removal from EMG signals
description	Wavelet transform has been applied in this research for removing noise from the surface electromyography signal (SEMG). The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Error. This paper reports on the effectiveness of the wavelet transform applied to the SEMG signal as means of removing noise to retrieve information related to muscle contraction and nerve system. Power spectrum analysis has been applied to SEMG signals where mean power frequency was calculated to indicate changes in muscle contraction. Wavelet based noise removal and power spectrum analysis on the EMG signal from the right "biceps brachii" muscle was performed using four wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze SEMG significantly. Results show that WFs Daubechies (db2) provide the best noise removal from the raw SEMG signals among other WFs Daubechies (db6, db8) and orthogonal Meyer. The algorithm is intended for FPGA implementation of portable bio medical equipments to detect neuromuscular disease and muscle fatigue.
format	Article
author	Chowdhury, Md. Sazzad Hossien Reaz, Mamun Bin Ibne Ibrahimy, Muhammad Ibn Ismail, Ahmad Faris Mohd-Yasin, Faisal
author_facet	Chowdhury, Md. Sazzad Hossien Reaz, Mamun Bin Ibne Ibrahimy, Muhammad Ibn Ismail, Ahmad Faris Mohd-Yasin, Faisal
author_sort	Chowdhury, Md. Sazzad Hossien
title	Wavelet based noise removal from EMG signals
title_short	Wavelet based noise removal from EMG signals
title_full	Wavelet based noise removal from EMG signals
title_fullStr	Wavelet based noise removal from EMG signals
title_full_unstemmed	Wavelet based noise removal from EMG signals
title_sort	wavelet based noise removal from emg signals
publisher	Society for Microelectronics, Electric Components and Materials
publishDate	2007
url	http://irep.iium.edu.my/29533/ http://irep.iium.edu.my/29533/ http://irep.iium.edu.my/29533/1/MIDEM_37%282007%292p94.pdf
first_indexed	2023-09-18T20:43:22Z
last_indexed	2023-09-18T20:43:22Z
_version_	1777409512917958656

Wavelet based noise removal from EMG signals

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