Ma Shihan, Lv Bo, Lin Chuang, Sheng Xinjun, Zhu Xiangyang
IEEE J Biomed Health Inform. 2021 Jan;25(1):47-58. doi: 10.1109/JBHI.2020.2987528. Epub 2021 Jan 5.
Surface electromyography (EMG) signals are inevitably contaminated by various noise components, including powerline interference (PLI), baseline wandering (BW), and white Gaussian noise (WGN). These noises directly degrade the efficiency of EMG processing and affect the accuracy and robustness of further applications. Currently, most of the EMG filters only target one category of noise. Here, we propose a novel filter to remove all three types of noise. The noisy EMG signal is first decomposed into an ensemble of band-limited modes using variational mode decomposition (VMD). Each category of noise is located within specific modes and is separately removed in sub-bands. In particular, WGN is suppressed by soft thresholding with a noise level-dependent threshold. The denoising performance was assessed from simulated and experimental signals using three performance metrics: the root mean square error ([Formula: see text]), the improvement in signal-to-noise ratio ([Formula: see text]), and the percentage reduction in the correlation coefficient ( η). Other methods, including traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method, were examined for comparison. The proposed method achieved the best performance to remove BW or WGN. It also effectively reduced PLI noise when the signal-to-noise ratio (SNR) was low. The SNR was improved by 18.6, 19.2, and 8.0 dB for EMG signals corrupted with PLI, BW, and WGN at -6 dB SNR, respectively. The experimental results illustrated that noise was completely removed from resting states, and obvious spikes were distinguished from action states. For two of the ten subjects, the improved SNR reached 20 dB. This study explores the special characteristics of VMD and demonstrates the feasibility of using the VMD-based filter to denoise EMG signals. The proposed filter is efficient at removing three categories of noise and can be used for any application that requires EMG signal filtering at the preprocessing stage, such as gesture recognition and EMG decomposition.
表面肌电图（EMG）信号不可避免地会受到各种噪声成分的污染，包括电力线干扰（PLI）、基线漂移（BW）和白高斯噪声（WGN）。这些噪声直接降低了EMG处理的效率，并影响进一步应用的准确性和鲁棒性。目前，大多数EMG滤波器仅针对一类噪声。在此，我们提出一种新型滤波器来去除所有三种类型的噪声。首先使用变分模态分解（VMD）将有噪声的EMG信号分解为一组带限模态。每类噪声位于特定模态内，并在子带中分别去除。特别地，通过使用与噪声水平相关的阈值进行软阈值处理来抑制WGN。使用三个性能指标从模拟和实验信号评估去噪性能：均方根误差（[公式：见正文]）、信噪比的改善（[公式：见正文]）和相关系数的降低百分比（η）。还研究了其他方法进行比较，包括传统的无限脉冲响应（IIR）滤波器、经验模态分解（EMD）方法和总体经验模态分解（EEMD）方法。所提出的方法在去除BW或WGN方面表现最佳。当信噪比（SNR）较低时，它还能有效降低PLI噪声。对于在-6 dB SNR下被PLI、BW和WGN污染的EMG信号，SNR分别提高了18.6、19.2和8.0 dB。实验结果表明，在静息状态下噪声被完全去除，并且在动作状态下能区分出明显的尖峰。对于十名受试者中的两名，改善后的SNR达到20 dB。本研究探索了VMD的特殊特性，并证明了使用基于VMD的滤波器对EMG信号进行去噪的可行性。所提出的滤波器在去除三类噪声方面效率很高，可用于任何在预处理阶段需要对EMG信号进行滤波的应用，如手势识别和EMG分解。
