Characterization of surface EMG signals using improved approximate entropy

Chen, Wei‐Ting; Wang, Zhizhong; Ren, Xiaomei

doi:10.1631/jzus.2006.b0844

Cited by 35 publications

(10 citation statements)

References 20 publications

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“…In addition, the muscles under 40˝angle need more motor units to maintain and adjust the discharge of the muscle fibers or reinforces the shorter fibers [55], as a result, the firing of the motor units and the discharge of muscle fibers might be more complex than that under 180˝angle. The conclusion is consistently fitted with the literature as Chen et al [56] reported. This supports the hypothesis that the firing of the motor units and the discharge of the muscle fibers play a direct role in modulating the contraction of muscle groups to generate different motor patterns.…”

Section: Discussionsupporting

confidence: 88%

Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

Chen

Xie

Liu

et al. 2016

Entropy

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An efficient analytical method for electromyogram (EMG) signals is of great significance to research the inherent mechanism of a motor-control system. In this paper, we proposed an improved approach named wavelet-packet-based local band spectral entropy (WP-LBSE) by introducing the concept of frequency band local-energy (ELF) into the wavelet packet entropy, in order to characterize the time-varying complexity of the EMG signals in the local frequency band. The EMG data were recorded from the biceps brachii (BB) muscle and triceps brachii (TB) muscle at 40˝, 100˝and 180ő f elbow flexion by 10 healthy participants. Significant differences existed among any pair of the three patterns (p < 0.05). The WP-LBSE values of the EMG signals in BB muscle and TB muscle demonstrated a decreased tendency from 40˝to 180˝of elbow flexion, while the distributions of spectral energy were decreased to a stable state as time periods progressed under the same pattern. The result of this present work is helpful to describe the time-varying complexity characteristics of the EMG signals under different joint angles, and is meaningful to research the dynamic variation of the activated motor units and muscle fibers in the motor-control system.

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Section: Discussionsupporting

confidence: 88%

Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

Chen

Xie

Liu

et al. 2016

Entropy

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“…Chaos is a complex motion which is always restricted in limited areas, extremely sensitive to initial values, long-term unpredictable, with track never repeated, fractal dimension and strange attractors. Amount of study demonstrated that Center Nervous System (CNS) generates chaotic firing patterns of action potentials; and a variety of physiological potential nerve signals, including EEG, ECG and EMG, etc., originated from CNS, have shown some degree of chaotic behavior, too [16][17][18][19][20][21][22][23][24][25][26][27][28]. Some nonlinear analysis methods are applied to the analysis of EMG.…”

Section: Chaotic Theory Analysis Methodsmentioning

confidence: 99%

Application of the Chaos Theory in the Analysis of EMG on Patients with Facial Paralysis

Xiong

Zhao

Han

et al. 2014

Robot Intelligence Technology and Applications 2

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Abstract. Surface electromyography (sEMG) has been widely applied to disease diagnosis, pathologic analysis and rehabilitation evaluation. It is the nonlinear summation of the electrical activity of the motor units in a muscle and can reflect the state of neuromuscular function. Traditional linear and statistical analysis methods have some significant limitations due to the short-term stationary and lower signal-noise ratio of sEMG. In this paper, we introduce chaotic analysis into the field of sEMG process to investigate the hidden nonlinear characteristics of sEMG of patients with facial paralysis. sEMG on the bilateral masseter, levator labii superioris and frontalis of the 21 patients is recorded. Chaotic analysis is employed to extract new features, including correlation dimension, Lyapunov exponent, approximate entropy and so on. We discover the maximum Lyapunov exponents are all greater than 0, indicating that sEMG is a chaotic signal; correlation dimensions of sEMG on healthy sides are all smaller than that of diseased sides; and inversely, the approximate entropies of healthy sides are all greater than that of diseased sides. Consequently, chaotic analysis can provide a new insight into the complexity of the EMG and may be a vital indicator of diagnosis and recovery assessment of facial paralysis.

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“…This is done either by halfwave or full-wave rectification of the signal. A full-wave rectification method was applied to preserve the energy of the signal [25,[29][30][31][32][33][34], and the expression for the method is given in Eq. (1).…”

Section: Rectification Processmentioning

confidence: 99%

“…(2), where t j and t i denote the lower and upper bounds of the part of the signal to be integrated, respectively. The above expression represents the area below the absolute value of the signal curve at time T = t i -t j [30][31][32][33][34][35].…”

Section: Signal Energymentioning

confidence: 99%

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

Taşar¹,

Gülten²

2017

Modern Fuzzy Control Systems and Its Applications

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In recent years, researchers have conducted many studies on the design and control of prosthesis devices that take the place of a missing limb. Functional ability of prosthesis hands that mimic biological hand functions increases depending on the number of independent finger movements possible. From this perspective, in this study, six different finger movements were given to a prosthesis hand via bioelectrical signals, and the functionality of the prosthesis hand was increased. Bioelectrical signals were recorded by surface electromyography for four muscles with the help of surface electrodes. The recorded bioelectrical signals were subjected to a series of preprocessing and feature extraction processes. In order to create meaningful patterns of motion and an effective cognitive interaction network between the human and the prosthetic hand, fuzzy logic classification algorithms were developed. A five-fingered and 15-jointed prosthetic hand was designed via SolidWorks, and a prosthetic prototype was produced by a 3D printer. In addition, prosthetic hand simulator was designed in Matlab/SimMechanics. Pattern control of both the simulator and the prototype hand in real time was achieved. Position control of motors connected to each joint of the prosthetic hand was provided by a PID controller. Thus, an effective cognitive communication network established between the user, and the real-time pattern control of the prosthesis was provided by bioelectrical signals.

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Characterization of surface EMG signals using improved approximate entropy

Cited by 35 publications

References 20 publications

Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

Application of the Chaos Theory in the Analysis of EMG on Patients with Facial Paralysis

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

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