Duchêne J, Goubel F
Département de Génie Biologique, URA CNRS 858, Université de Technologie, Compiègne, France.
Eur J Appl Physiol Occup Physiol. 1990;61(1-2):81-7. doi: 10.1007/BF00236698.
Changes in electromyogram (EMG) power spectra were investigated in the triceps surae muscles of two classes of individuals (untrained subjects and athletes) maintaining a plantarflexion torque of 80% of maximal voluntary contraction until exhaustion. A set of 23 parameters describing changes in the frequency content and power of EMG was defined. For most experiments, classical changes were found, indicating a shift of the EMG spectra towards lower frequencies and an increase in the total power of the signals. In 12% of the experiments, alternations in activity between synergistic muscles were found, leading to a large variability in the spectral parameters. After the expression of each experiment in terms of a reduced data matrix and matrix to vector transformations, three methods of discrimination were used to classify subjects with respect to changes in the EMG signal during sustained contraction: (1) evaluation of the most discriminating parameter, (2) principal components analysis, (3) transformation maximizing differences between classes. Method (3) was found to be preferable since it led to good separation of the two classes in a reference group of subjects and a satisfactory projection of each individual from a group of unknowns into the appropriate class. These results suggest using a method such as this for ergonomic or athletic training purposes rather than the usual method of monitoring the frequency shift of the EMG.
研究了两类个体（未经训练的受试者和运动员）在维持最大自主收缩80%的跖屈扭矩直至疲劳时，小腿三头肌肌电图（EMG）功率谱的变化。定义了一组23个描述EMG频率成分和功率变化的参数。在大多数实验中，发现了典型的变化，表明EMG频谱向低频移动，信号总功率增加。在12%的实验中，发现协同肌之间的活动交替，导致频谱参数的较大变异性。在将每个实验表示为简化数据矩阵并进行矩阵到向量变换后，使用三种判别方法对受试者在持续收缩期间EMG信号的变化进行分类：（1）评估最具判别力的参数，（2）主成分分析，（3）最大化类间差异的变换。发现方法（3）更可取，因为它在受试者参考组中能很好地分离两类，并且能将一组未知个体中的每个个体满意地投影到适当的类别中。这些结果表明，为此类人体工程学或运动训练目的，应使用这样的方法，而不是通常监测EMG频率偏移的方法。
