Farina Dario, Zagari Domenico, Gazzoni Marco, Merletti Roberto
Bioengineering Center, Department of Electronics, Politechnic of Turin, Corso Duca degli Abruzzi 24, Turin 10129, Italy.
Muscle Nerve. 2004 Feb;29(2):282-91. doi: 10.1002/mus.10547.
The aim of this study was to assess the reproducibility of muscle-fiber conduction velocity (MFCV) estimates obtained from multichannel surface electromyographic (EMG) recordings. Surface EMG signals were collected with a matrix of 61 electrodes during isometric, submaximal (50% of the maximal voluntary contraction torque) contractions of the biceps brachii muscle. Conduction velocity was estimated using multichannel maximum likelihood techniques. Reproducibility of MFCV estimates was assessed varying the number of signals (two to seven) used for the estimate and the distance between detection points (5-30 mm). Intraclass correlation coefficient (ICC) of both initial MFCV values and their rates of change with fatigue increased when increasing number of signals and distance between detection points. ICC of initial MFCV was negative using two signals for MFCV estimate, and it increased to approximately 75% with six to seven signals. Thus, reproducibility of MFCV estimates may be improved significantly using advanced multichannel estimation methods with respect to classic two-channel techniques.
本研究的目的是评估从多通道表面肌电图(EMG)记录中获得的肌纤维传导速度(MFCV)估计值的可重复性。在肱二头肌等长次最大收缩(最大自主收缩扭矩的50%)期间,用61个电极的矩阵采集表面EMG信号。使用多通道最大似然技术估计传导速度。通过改变用于估计的信号数量(2至7个)和检测点之间的距离(5 - 30毫米)来评估MFCV估计值的可重复性。随着信号数量和检测点之间距离的增加,初始MFCV值及其疲劳变化率的组内相关系数(ICC)均增加。使用两个信号进行MFCV估计时,初始MFCV的ICC为负,而使用6至7个信号时,ICC增加到约75%。因此,相对于传统的双通道技术,使用先进的多通道估计方法可能会显著提高MFCV估计值的可重复性。