title: Characterization of surface EMG signals using improved approximate entropy.
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.
authors.affiliation: Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Wei-ting
authors.initials: WT
authors.lastname: Chen
authors.forename: Zhi-zhong
authors.initials: ZZ
authors.lastname: Wang
authors.forename: Xiao-mei
authors.initials: XM
authors.lastname: Ren
mesh_terms: 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