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多部位肌电图幅度估计

Multiple site electromyograph amplitude estimation.

作者信息

Clancy E A, Hogan N

机构信息

Department of Electrical Engineering and Computer Science, Massachusetts of Technology, Cambridge 02139.

出版信息

IEEE Trans Biomed Eng. 1995 Feb;42(2):203-11. doi: 10.1109/10.341833.

Abstract

Temporal whitening of individual surface electromyograph (EMG) waveforms and spatial combination of multiple recording sites have separately been demonstrated to improve the performance of EMG amplitude estimation. This investigation combined these two techniques by first whitening, then combining the data from multiple EMG recording sites to form an EMG amplitude estimate. A phenomenological mathematical model of multiple sites of the surface EMG waveform, with analytic solution for an optimal amplitude estimate, is presented. Experimental surface EMG waveforms were then sampled from multiple sites during nonfatiguing, constant-force, isometric contractions of the biceps or triceps muscles, over the range of 10-75% maximum voluntary contraction. A signal-to-noise ratio (SNR) was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Results showed that SNR performance: 1) increased with the number of EMG sites, 2) was a function of the sampling frequency, 3) was predominantly invariant to various methods of determining spatial uncorrelation filters, 4) was not sensitive to the intersite correlations of the electrode configuration investigated, and 5) was best at lower levels of contraction. A moving average root mean square estimator (245-ms window) provided an average +/- standard deviation (A +/- SD) SNR of 10.7 +/- 3.3 for single site unwhitened recordings. Temporal whitening and four combined sites improved the A +/- SD SNR to 24.6 +/- 10.4. On one subject, eight whitened combined sites were achieved, providing an A +/- SD SNR or 35.0 +/- 13.4.

摘要

个体表面肌电图(EMG)波形的时间白化以及多个记录部位的空间组合,已分别被证明可提高EMG幅度估计的性能。本研究将这两种技术结合起来,首先对白化,然后将来自多个EMG记录部位的数据进行组合,以形成EMG幅度估计。提出了表面EMG波形多个部位的现象学数学模型,并给出了最优幅度估计的解析解。然后在肱二头肌或肱三头肌进行非疲劳、恒力等长收缩过程中,从多个部位采集实验表面EMG波形,收缩范围为最大自主收缩的10%-75%。从每个幅度估计中计算信噪比(SNR)(估计值围绕平均值的偏差被视为噪声)。结果表明,SNR性能:1)随EMG部位数量增加而提高;2)是采样频率的函数;3)对于确定空间不相关滤波器的各种方法基本不变;4)对所研究电极配置的部位间相关性不敏感;5)在较低收缩水平时最佳。对于单部位未白化记录,移动平均均方根估计器(245毫秒窗口)提供的平均±标准差(A±SD)SNR为10.7±3.3。时间白化和四个组合部位将A±SD SNR提高到24.6±10.4。在一名受试者身上,实现了八个白化组合部位,提供的A±SD SNR为35.0±13.4。

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