Pilkar Rakesh B, Yarossi Mathew, Forrest Gail
Kessler Foundation, West Orange, NJ 07052, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1847-50. doi: 10.1109/EMBC.2012.6346311.
Rectification of surface EMGs during electrical stimulations (ES) is still a problem to be solved. The broad band frequency components of ES artifact overlap with the EMG spectrum, make this task challenging. In this study, we investigate the potential use of empirical mode decomposition (EMD) method to remove the stimulus artifact from surface EMGs collected during such applications. We hypothesize that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic modes which can be used to isolate ES artifact. Basic EMD is tested on two signals - ES induced EMG and EMG of voluntary contractions added with simulated ES signal. The algorithm isolates the EMG from ES artifact with considerable success. Further, the EMD method along with the energy operator -TKEO gives even better representation of the EMG signal. However, some high frequency data was lost during reconstruction process. Hence, there is further need to investigate the relationship between the EMD parameters and stimulus artifact properties so that the algorithm can be optimized to reconstruct pure artifact free EMG signal with minimum lost of data.
在电刺激（ES）期间对表面肌电图进行校正仍是一个有待解决的问题。ES伪迹的宽带频率成分与肌电图频谱重叠，使得这项任务具有挑战性。在本研究中，我们研究了经验模态分解（EMD）方法在去除此类应用过程中采集的表面肌电图中刺激伪迹方面的潜在用途。我们假设EMD算法为将肌电信号分解为具有物理意义的本征模态提供了一个合适的平台，这些本征模态可用于分离ES伪迹。在两个信号上测试了基本的EMD——ES诱发的肌电图以及添加了模拟ES信号的自主收缩肌电图。该算法相当成功地从ES伪迹中分离出了肌电图。此外，EMD方法与能量算子——TKEO一起能更好地表示肌电信号。然而，在重建过程中一些高频数据丢失了。因此，进一步研究EMD参数与刺激伪迹特性之间的关系是有必要的，以便优化算法以重建无伪迹的纯肌电信号，同时使数据丢失最少。
