基于自适应阈值处理的表面肌电信号小波去噪研究

Surface Electromyogram Denoising Using Adaptive Wavelet Thresholding

针对表面肌电(sEMG)信号信噪比较低的问题,本文在Donoho通用阈值法的基础上,采用了一种基于自适应阈值处理的小波去噪方法。相对于通用阈值法,这种方法可以根据sEMG信号的不同信噪比自适应地调整阈值,更有效地去除噪声、减小信号的失真,从而提高信噪比。仿真和真实sEMG信号实验均论证了这种方法的优越性。

Surface electromyogram （sEMG） may have low signal to noise ratios. An adaptive wavelet thresholding technique was developed in this study to remove noise contamination from sEMG signals. Compared with conventional wavelet thresholding methods, the adaptive approach can adjust thresholds based on different signal to noise ratios of the processed signal, thus effectively removing noise contamination and reducing distortion of the EMG signal. The advantage of the developed adaptive thresholding method was demonstrated using simulated and experimental sEMG recordings.