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, 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.
Chen
Wei-ting
WT
Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Wang
Zhi-zhong
ZZ
Ren
Xiao-mei
XM
D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004576:Electromyography / Q000379:methods*; D019277:Entropy; D017709:Fractals; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted