Characterization of surface EMG signals using improved approximate entropy / 浙江大学学报(英文版)(B辑:生物医学和生物技术)
Journal of Zhejiang University. Science. B
;
(12): 844-848, 2006.
Article
in English
| WPRIM
| ID: wpr-251846
ABSTRACT
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Signal Processing, Computer-Assisted
/
Pattern Recognition, Automated
/
Cluster Analysis
/
Data Interpretation, Statistical
/
Models, Statistical
/
Nonlinear Dynamics
/
Fractals
/
Entropy
/
Electromyography
Type of study:
Risk factors
Limits:
Humans
Language:
English
Journal:
Journal of Zhejiang University. Science. B
Year:
2006
Type:
Article
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