Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
Med Eng Phys. 2013 Apr;35(4):537-42. doi: 10.1016/j.medengphy.2012.10.009. Epub 2012 Dec 11.
Surface electromyogram (EMG) is often corrupted by three types of noises, i.e. power line interference (PLI), white Gaussian noise (WGN), and baseline wandering (BW). A novel framework based primarily on empirical mode decomposition (EMD) was developed to reduce all the three noise contaminations from surface EMG. In addition to regular EMD, the ensemble EMD (EEMD) was also examined for surface EMG denoising. The advantages of the EMD based methods were demonstrated by comparing them with the traditional digital filters, using signals derived from our routine electrode array surface EMG recordings. The experimental results demonstrated that the EMD based methods achieved better performance than the conventional digital filters, especially when the signal to noise ratio of the processed signal was low. Among all the examined methods, the EEMD based approach achieved the best surface EMG denoising performance.
表面肌电图（EMG）经常受到三种类型的噪声的干扰，即电力线干扰（PLI）、白高斯噪声（WGN）和基线漂移（BW）。提出了一种基于经验模态分解（EMD）的新框架，以减少表面 EMG 中的所有三种噪声污染。除了常规的 EMD 之外，还检查了集合经验模态分解（EEMD）用于表面 EMG 去噪。通过将源自我们常规电极阵列表面 EMG 记录的信号与传统数字滤波器进行比较，证明了基于 EMD 的方法的优势。实验结果表明，基于 EMD 的方法比传统数字滤波器具有更好的性能，尤其是在处理信号的信噪比较低时。在所检查的所有方法中，基于 EEMD 的方法实现了最佳的表面 EMG 去噪性能。
