Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes

Abstract

Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.

Fatiguing Effects on the Multi-Scale Entropy of Surface Electromyography in Children with Cerebral Palsy

“…Sample entropy (SampEn), originally proposed by Richman and Moorman [17], is an effective measure of the complexity of short time series, with wide applications in analyzing non-linear and non-stationary biomedical signals including EMG [21,35,36]. To calculate the sample entropy of a scalar time series tx 1 , x 2 , .…”

Classification of surface electromyogram signals based on directed acyclic graphs and support vector machines

“…The twelve time-and frequency-domain features included maximum, singular value, average energy, VAR, standard deviation, and WL of wavelet coefficients and wavelet packet coefficients [19][20][21][22][23][24][25][26]. The three nonlinear dynamic features were entropy of wavelet coefficients, entropy of wavelet packet coefficients, and maximum of Lyapunov exponent [27][28][29]. We made use of wavelet base sym3 to decompose the sEMG signal into three layers by wavelet decomposition and wavelet packet decomposition.…”

Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control

“…A number of feature extraction methods were utilized in the literature, these include the mean absolute value (MAV), Rami waveform length (WL), sloop sign changes (SSC), and number of zero crossings (ZC) [4]; fast Fourier transform (FFT) [5], wavelets and wavelet packet transform (WPT) [6,7]; cepstral coefficients (CC), Willison amplitude (WAMP) [3]; sample entropy (ENT) [8]; and the autoregressive (AR) model parameters [9]. Phinyomark et al compared 50 feature extraction methods for EMG pattern recognition [10].…”