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EMG signal decomposition using wavelet transformation with respect to different wavelet and a comparative study

Published: 24 November 2009 Publication History

Abstract

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development and modern Human Computer Interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. For properly analyze EMG signal there need good quality of decomposition so that it can reveal the total characteristics of EMG signals. Because EMG signal is Non-Stationary signal so it needs such a method that can decompose non-stationary signal thus wavelet decomposition is a good choice for this type. There are different types of wavelet available. Henceforth, it is necessary that proper attempt should be taken to choice the best one. Here analyses of EMG Signals were made by Various Wavelet Decomposition method with different types of wavelet and it illustrates the comparative study on best possible energy localization in the time-scale plane in order to show the performance. Thus we can choice the right one. The EMG signals used for this analysis - were found both from locally collected as well as from www.emglab.net[5] which provides EMG signal related raw data and other facilities.

References

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Cram, JR; Kasman, GS; Holtz, J. Introduction to Surface Electromyography. Aspen Publishers Inc.; Gaithersburg, Maryland, 1998.
[2]
Micera, S; Vannozzi, G; Sabatini, AM; Dario, P. Improving detection of muscle activation intervals. IEEE Engineering in Medicine and Biology Magazine. 2001;20(6):38--46.
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Lanyi, X; Adler, A. An improved method for muscle activation detection during gait. Canadian Conference of Electrical and Computer Engineering. 2004;1:357--360.
[4]
Merlo, A; Farina, D. A Fast and Reliable Technique for Muscle Activity Detection from Surface EMG Signals. IEEE Trans Biomed Eng. 2003;50(3):316--323. doi: 10.1109/TBME.2003.808829.
[5]
http://www.emglab.net/emglab/Signals/S001/S001.php? diagnosis=Normal&patient=01&muscle=Generic
[6]
Fang, J; Agarwal, GC; Shahani, BT. Decomposition of EMG signals by wavelet spectrum matching. Procedures of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1997; Chicago, IL, USA. pp. 1253--1256.
[7]
Yamada, R; Ushiba, J; Tomita, Y; Masakado, Y. Decomposition of Electromyographic Signal by Principal Component Analysis of Wavelet Coefficient. IEEE EMBS Asian-Pacific Conference on Biomedical Engineering 2003; Keihanna, Japan. pp. 118--119.
[8]
Laterza, F; Olmo, G. Analysis of EMG signals by means of the matched wavelet transform. Electronics Letters. 1997;33(5):357--359.
[9]
Pattichis, CS; Pattichis, MS. Time-scale analysis of motor unit action potentials. IEEE Trans Biomed Eng. 1999;46(11):1320--1329. doi: 10.1109/10.797992.
[10]
Kumar, DK; Pah, ND; Bradley, A. Wavelet analysis of surface electromyography to determine muscle fatigue. IEEE Trans Neural Syst Rehabil Eng. 2003;11(4):400--406. doi: 10.1109/TNSRE.2003.819901.
[11]
Amin, MG. Time-frequency spectrum analysis and estimation for nonstationary random processes. Time-Frequency Signal Analysis Methods and Applications, Ed: B. Boashash, Longman Chesire 1992; Melbourne, Australia, pp. 208--232.

Cited By

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  • (2022)Implementation of Feature Extraction of Neuro Muscular EMG Signal2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)10.1109/ICATIECE56365.2022.10047002(1-5)Online publication date: 16-Dec-2022

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  1. EMG signal decomposition using wavelet transformation with respect to different wavelet and a comparative study

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              cover image ACM Other conferences
              ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
              November 2009
              1479 pages
              ISBN:9781605587103
              DOI:10.1145/1655925
              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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              Published: 24 November 2009

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              1. EMG signal analysis
              2. non-stationary signal
              3. signal decomposition
              4. wavelet decomposition

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              • (2022)Implementation of Feature Extraction of Neuro Muscular EMG Signal2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE)10.1109/ICATIECE56365.2022.10047002(1-5)Online publication date: 16-Dec-2022

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