基于FastICA和通道间相关的表面肌电信号分解研究

Research on Decomposition of Surface EMG Signals Based on FastICA and Channel Correlation

目的为了对神经肌肉疾病进行相关的研究和临床上诊断治疗,探索新的和有效的表面肌电(surface EMG,sEMG)信号分解方法。方法首先用FastICA求解混矩阵,然后对测量信号矩阵进行变换,再用通道间相关性分解s EMG信号。结果经过仿真和真实信号进行测试,分解信噪比为0 d B的第一组信号时,以平均95.6%的准确率分解出20个运动单元(motor unit,MU);分解信噪比为20 d B,且参与发放的MU更多,发放频率更高的第二组信号时,以平均98.4%的准确率分解出29个MU;分解真实信号时,得到的平均MU个数为14.2,并用"二源法"进行评测,两组中分解出相同MU的比例为80%,且相同MU发放时刻的平均重合率为95.1%。结论这种结合Fast ICA和通道间相关的方法能以较高的准确率实现s EMG信号的有效分解。

Objective A new and effective method for decomposing surface electromyography（ s EMG） signals was explored for the relevant research,diagnosis and treatment of clinical neuromuscular diseases. Methods The Fast ICA method was employed to obtain the de-mixed matrix which was then used to transform the matrix of measurement. At last,the s EMG signals were decomposed by utilizing the channel correlations. Results Two groups of simulation signals and one group of real s EMG signals were tested and the results showed that for the first group of simulation signals with 0d B SNR,an average of 20 motor units were extracted with an average accuracy of 95. 6%; While for the second group of simulation signals with 20 d B SNR,an average of 29 motor units were extracted with an average accuracy of 98. 4%. As to the real s EMG signals,an average of 14. 2motor units were extracted. A ＂two-source＂test was further conducted to evaluate the performance of the proposed method,it showed that the proportion of the same MU extracted by both groups was 80%,and the average coincidence rate of the same MU was 95. 1%. Conclusion The combination of Fast ICA and channel correlation method can effectively decompose the surface EMG signals with high accuracy.