Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion artifacts etc. Currently, Empirical Mode Decomposition (EMD) and its modifications such as Ensemble EMD (EEMD), and Complementary EEMD (CEEMD) are used to decompose EMG into a series of Intrinsic Mode Functions (IMFs). The denoised EMG can be obtained from the selected IMFs. Statistical methods are used to select the signal dominant IMFs to reconstruct the denoised signal. In this work, a novel procedure is proposed to automatically separate noisy IMFs from the original sEMG signal. For this purpose, Permutation Entropy (PE) is employed in EEMD sifting process called Partly EEMD (PEEMD), to separate the noisy IMFs from the original sEMG signal according to the preset PE threshold. PEEMD decomposes the original signal into various modes according to a preset PE threshold and the denoised signal is reconstructed from resultant IMFs. The PEEMD denoising procedure is applied on the experimental sEMG data collected from eight subjects, that include six various upper limb movement classes. The proposed denoising procedure achieved an improved denoising performance in comparison with EMD, EEMD, and CEEMD. An alternate measure called Sample Entropy (SE) is also used in place of PE, for the automated sifting process as a comparison. Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), and Reconstruction Error (RE) parameters are used to evaluate the denoising performance. The results, averaged across eight subjects, demonstrate that the proposed denoising procedure outperforms the state-of-the-art EMD techniques in terms of these performance measures on the experimentally collected sEMG data samples.

表面肌电（sEMG）信号是肌肉激活信号，在肌肉诊断、康复、假肢和言语等方面有应用。然而，它们会受到电源线干扰（PLI）、运动伪影等噪声的影响。目前、经验模态分解 (EMD) 及其改进版本，例如集成 EMD (EEMD) 和互补 EEMD (CEEMD)，用于将 EMG 分解为一系列本征模态函数 (IMF)。去噪 EMG 可以从选定的 IMF 中获得。统计方法用于选择信号主导 IMF 来重建去噪信号。在这项工作中，提出了一种新的程序来自动将噪声 IMF 从原始 sEMG 信号中分离出来。为此，在称为部分 EEMD (PEEMD) 的 EEMD 筛选过程中采用排列熵 (PE)，根据预设的 PE 阈值从原始 sEMG 信号中分离出噪声 IMF。PEEMD 根据预设的 PE 阈值将原始信号分解为各种模式，并根据结果 IMF 重建去噪信号。PEEMD 去噪程序应用于从八名受试者收集的实验 sEMG 数据，其中包括六种不同的上肢运动类别。与 EMD、EEMD 和 CEEMD 相比，所提出的去噪程序实现了改进的去噪性能。还使用一种称为样本熵 (SE) 的替代测量方法来代替 PE，用于自动筛选过程作为比较。信噪比（SNR）、均方根误差（RMSE）和重建误差（RE）参数用于评估去噪性能。八个受试者的平均结果表明，就实验收集的 sEMG 数据样本的性能测量而言，所提出的去噪程序优于最先进的 EMD 技术。