Abbaspour Sara, Lindén Maria, Gholamhosseini Hamid
School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
School of Engineering, Auckland University of Technology, New Zealand.
Stud Health Technol Inform. 2015;211:91-7.
This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.
本研究旨在提出一种有效的方法，用于从记录自上躯干肌肉的表面肌电图（EMG）信号中自动去除心电图（ECG）伪迹。将小波变换应用于受干扰的表面EMG信号的模拟数据集，以创建多维信号。之后，使用独立成分分析（ICA）从原始EMG信号中分离出ECG伪迹成分。然后通过自动检测算法识别与ECG伪迹对应的成分，并随后使用传统的高通滤波器将其去除。最后，将所提出方法的结果与小波变换、ICA、自适应滤波器和经验模式分解 - ICA方法进行比较。本研究中提出的自动伪迹去除方法成功地从EMG信号中去除了ECG伪迹，信噪比为9.38，同时将原始EMG的失真保持在最低限度。
