Xi Xugang, Zhang Yan, Zhao Yunbo, She Qingshan, Luo Zhizeng
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Rev Sci Instrum. 2019 Mar;90(3):035003. doi: 10.1063/1.5057725.
Surface electromyogram (sEMG) signals are physiological signals that are widely applied in certain fields. However, sEMG signals are frequently corrupted by noise, which can lead to catastrophic consequences. A novel scheme based on complementary ensemble empirical mode decomposition (CEEMD), improved interval thresholding (IT), and component correlation analysis is developed in this study to reduce noise contamination. To solve the problem of losing desired information from sEMG, an sEMG signal is first decomposed using CEEMD to obtain intrinsic mode functions (IMFs). Subsequently, IMFs are selected via component correlation analysis, which is a measure used to select relevant modes. Thus, each selected IMF is modified through improved IT. Finally, the sEMG signal is reconstructed using the processed and residual IMFs. Root-mean-square error (RMSE) and signal-to-noise ratio (SNR) are introduced as evaluation criteria for the sEMG signal from the standard database. With SNR varying from 1 dB to 25 dB, the proposed method increases SNR by at least 1 dB and reduces RMSE compared with stationary wavelet transform and other denoising algorithms based on empirical mode decomposition. Moreover, the proposed method is applied to hand motion recognition. Results show that the rate of the denoised sEMG signal is higher than that of the raw sEMG signal.
表面肌电图（sEMG）信号是在某些领域中广泛应用的生理信号。然而，sEMG信号经常受到噪声干扰，这可能导致灾难性后果。本研究提出了一种基于互补总体经验模态分解（CEEMD）、改进的区间阈值法（IT）和分量相关性分析的新方案，以减少噪声污染。为了解决sEMG中丢失期望信息的问题，首先使用CEEMD对sEMG信号进行分解，以获得本征模态函数（IMF）。随后，通过分量相关性分析选择IMF，这是一种用于选择相关模态的度量。因此，每个选定的IMF通过改进的IT进行修正。最后，使用处理后的和剩余的IMF重建sEMG信号。引入均方根误差（RMSE）和信噪比（SNR）作为来自标准数据库的sEMG信号的评估标准。当SNR在1 dB到25 dB之间变化时，与平稳小波变换和其他基于经验模态分解的去噪算法相比，该方法将SNR提高了至少1 dB，并降低了RMSE。此外，该方法应用于手部运动识别。结果表明，去噪后的sEMG信号的识别率高于原始sEMG信号。
