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Characterization of surface EMG signals using improved approximate entropy

An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, w... Full description

Journal Title: Journal of Zhejiang University SCIENCE B 2006, Vol.7(10), pp.844-848
Main Author: Chen, Wei-ting
Other Authors: Wang, Zhi-zhong , Ren, Xiao-mei
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1673-1581 ; E-ISSN: 1862-1783 ; DOI: 10.1631/jzus.2006.B0844
Link: http://dx.doi.org/10.1631/jzus.2006.B0844
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recordid: springer_jour10.1631/jzus.2006.B0844
title: Characterization of surface EMG signals using improved approximate entropy
format: Article
creator:
  • Chen, Wei-ting
  • Wang, Zhi-zhong
  • Ren, Xiao-mei
subjects:
  • Surface EMG (sEMG) signal
  • Nonlinear analysis
  • Approximate entropy (ApEn)
  • Fractal dimension
  • R318.04
ispartof: Journal of Zhejiang University SCIENCE B, 2006, Vol.7(10), pp.844-848
description: An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.
language: eng
source:
identifier: ISSN: 1673-1581 ; E-ISSN: 1862-1783 ; DOI: 10.1631/jzus.2006.B0844
fulltext: fulltext
issn:
  • 1862-1783
  • 18621783
  • 1673-1581
  • 16731581
url: Link


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subjectSurface EMG (sEMG) signal ; Nonlinear analysis ; Approximate entropy (ApEn) ; Fractal dimension ; R318.04
descriptionAn improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.
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abstractAn improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.
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pubZhejiang University Press
doi10.1631/jzus.2006.B0844
pages844-848
date2006-10