Military Institute of Health Service, Department of Neurology, 128 Szaserow St., 00-909 Warsaw, Poland.
Muscle Nerve. 2012 Jul;46(1):63-9. doi: 10.1002/mus.23286.
Electrophysiological studies of human motor units can use various electromyographic techniques. Together with the development of new techniques for analysis and processing of bioelectric signals, motor unit action potential (MUAP) wavelet analysis represents an important change in the development of electromyographic techniques.
The proposed approach involves isolating single MUAPs, computing their scalograms, taking the maximum values of the scalograms in 5 selected scales, and averaging across MUAPs to give a single five-dimensional feature vector per muscle. After Support Vector Machine analysis, the feature vector is reduced to a single decision parameter that allows the subject to be assigned to 1 of 3 groups: myogenic, healthy, or neurogenic. The software is available as freeware.
MUAP wavelet analysis yielded consistent results for the diagnostic index and muscle classification, with only 7 incorrect classifications out of a total of 1,015 samples.
This proposed approach provides a sensitive and reliable method for evaluating and characterizing MUAPs.
人类运动单位的电生理研究可以使用各种肌电图技术。随着生物电信号分析和处理新技术的发展,运动单位动作电位(MUAP)子波分析代表了肌电图技术发展的重要变化。
该方法涉及分离单个 MUAP,计算它们的标度图,在 5 个选定的尺度中取标度图的最大值,并对 MUAP 进行平均,以获得每个肌肉的单个五维特征向量。在支持向量机分析之后,特征向量被简化为单个决策参数,允许将受试者分配到 3 个组之一:肌源性、健康或神经源性。该软件可作为免费软件使用。
MUAP 子波分析为诊断指标和肌肉分类提供了一致的结果,总共 1015 个样本中只有 7 个错误分类。
该方法为评估和描述 MUAP 提供了一种敏感可靠的方法。