CleanEMG——使用自适应最小二乘算法估计表面肌电图中的电力线干扰。
CleanEMG--power line interference estimation in sEMG using an adaptive least squares algorithm.
作者信息
Fraser G D, Chan A D C, Green J R, Abser N, MacIsaac D
机构信息
Department of Systems & Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7941-4. doi: 10.1109/IEMBS.2011.6091958.
This paper presents an adaptive least squares algorithm for estimating the power line interference in surface electromyography (sEMG) signals. The algorithm estimates the power line interference, without the need for a reference input. Power line interference can be removed by subtracting the estimate from the original sEMG signal. The algorithm is evaluated with simulated sEMG based on its ability to accurately estimate power line interference at different frequencies and at various signal-to-noise ratios. Power line estimates produced by the algorithm are accurate for signal-to-noise ratios below 15 dB (SNR estimation error at 15 dB is 14.7995 dB + 1.6547 dB).
本文提出了一种自适应最小二乘算法，用于估计表面肌电图（sEMG）信号中的电力线干扰。该算法无需参考输入即可估计电力线干扰。通过从原始sEMG信号中减去估计值，可以去除电力线干扰。基于该算法在不同频率和各种信噪比下准确估计电力线干扰的能力，使用模拟sEMG对其进行了评估。对于信噪比低于15 dB的情况，该算法产生的电力线估计值是准确的（15 dB时的SNR估计误差为14.7995 dB + 1.6547 dB）。
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