Farina D, Crosetti A, Merletti R
Centro di Bioingegneria, Department of Electronics, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
IEEE Trans Biomed Eng. 2001 Jan;48(1):66-77. doi: 10.1109/10.900250.
As more and more intramuscular electromyogram (EMG) decomposition programs are being developed, there is a growing need for evaluating and comparing their performances. One way to achieve this goal is to generate synthetic EMG signals having known features. Features of interest are: the number of channels acquired (number of detection surfaces), the number of detected motor unit action potential (MUAP) trains, their time-varying firing rates, the degree of shape similarity among MUAPs belonging to the same motor unit (MU) or to different MUs, the degree of MUAP superposition, the MU activation intervals, the amount and type of additive noise. A model is proposed to generate one or more channels of intramuscular EMG starting from a library of real MUAPs represented in a 16-dimensional space using their Associated Hermite expansion. The MUAP shapes, regularity of repetition rate, degree of superposition, activation intervals, etc. may be time variable and are described quantitatively by a number of parameters which define a stochastic process (the model) with known statistical features. The desired amount of noise may be added to the synthetic signal which may then be processed by the decomposition algorithm under test to evaluate its capability of recovering the signal features.
随着越来越多的肌内肌电图（EMG）分解程序被开发出来，对其性能进行评估和比较的需求日益增长。实现这一目标的一种方法是生成具有已知特征的合成EMG信号。感兴趣的特征包括：采集的通道数（检测表面的数量）、检测到的运动单位动作电位（MUAP）序列的数量、它们随时间变化的发放率、属于同一运动单位（MU）或不同运动单位的MUAP之间的形状相似程度、MUAP叠加程度、MU激活间隔、加性噪声的量和类型。提出了一种模型，该模型从使用其关联埃尔米特展开在16维空间中表示的真实MUAP库开始生成一个或多个肌内EMG通道。MUAP的形状、重复率的规律性、叠加程度、激活间隔等可能随时间变化，并由一些参数定量描述，这些参数定义了一个具有已知统计特征的随机过程（模型）。可以将所需量的噪声添加到合成信号中，然后可以由正在测试的分解算法对其进行处理，以评估其恢复信号特征的能力。
