Wavelet based noise removal from EMG signals


Hussain, M. S. and Reaz, M. B. I. and Ibrahimy, M. I. and Ismail, A. F. and Mohd-Yasin, F. (2007) Wavelet based noise removal from EMG signals. INFORMACIJE MIDEM-JOURNAL OF MICROELECTRONICS ELECTRONIC COMPONENTS AND MATERIALS, 37 (2). pp. 94-97.

Full text not available from this repository.


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.

Item Type: Article
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 29 Sep 2011 04:06
Last Modified: 29 Sep 2011 04:06
URII: http://shdl.mmu.edu.my/id/eprint/3053


Downloads per month over past year

View ItemEdit (login required)