Yazdani Shayan, Azghani Mahmood Reza, Sedaaghi Mohammad Hossein
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Department of Biomechanics, Faculty of Mechanical Engineering, Sahand University of Technology, Tabriz, Iran.
Australas Phys Eng Sci Med. 2017 Sep;40(3):575-584. doi: 10.1007/s13246-017-0564-0. Epub 2017 Jul 21.
This paper presents a new method to remove electrocardiogram (ECG) interference from electromyogram (EMG). This interference occurs during the EMG acquisition from trunk muscles. The proposed algorithm employs progressive image denoising (PID) algorithm and ensembles empirical mode decomposition (EEMD) to remove this type of interference. PID is a very recent method that is being used for denoising digital images mixed with white Gaussian noise. It detects white Gaussian noise by deterministic annealing. To the best of our knowledge, PID has never been used before, in the case of EMG and ECG separation or in other 1D signal denoising applications. We have used it according to this fact that amplitude of the EMG signal can be modeled as white Gaussian noise using a filter with time-variant properties. The proposed algorithm has been compared to the other well-known methods such as HPF, EEMD-ICA, Wavelet-ICA and PID. The results show that the proposed algorithm outperforms the others, on the basis of three evaluation criteria used in this paper: Normalized mean square error, Signal to noise ratio and Pearson correlation.
本文提出了一种从肌电图（EMG）中去除心电图（ECG）干扰的新方法。这种干扰在从躯干肌肉采集肌电图期间出现。所提出的算法采用渐进图像去噪（PID）算法和总体经验模态分解（EEMD）来去除此类干扰。PID是一种最近用于对混合有高斯白噪声的数字图像进行去噪的方法。它通过确定性退火来检测高斯白噪声。据我们所知，PID以前从未在肌电图和心电图分离的情况下或其他一维信号去噪应用中使用过。基于肌电信号的幅度可以使用具有时变特性的滤波器建模为高斯白噪声这一事实，我们使用了该算法。所提出的算法已与其他知名方法进行了比较，如高通滤波器（HPF）、总体经验模态分解 - 独立成分分析（EEMD - ICA）、小波 - 独立成分分析（Wavelet - ICA）和PID。结果表明，基于本文使用的三个评估标准：归一化均方误差、信噪比和皮尔逊相关性，所提出的算法优于其他算法。
