Systems Design Engineering Department, University of Waterloo, Waterloo, Canada.
Med Biol Eng Comput. 2011 Jun;49(6):649-58. doi: 10.1007/s11517-010-0703-1. Epub 2010 Nov 2.
Motor unit (MU) firing pattern information can be used clinically or for physiological investigation. It can also be used to enhance and validate electromyographic (EMG) signal decomposition. However, in all instances the validity of the extracted MU firing patterns must first be determined. Two supervised classifiers that can be used to validate extracted MU firing patterns are proposed. The first classifier, the single/merged classifier (SMC), determines whether a motor unit potential train (MUPT) represents the firings of a single MU or the merged activity of more than one MU. The second classifier, the single/contaminated classifier (SCC), determines whether the estimated number of false-classification errors in a MUPT is acceptable or not. Each classifier was trained using simulated data and tested using simulated and real data. The accuracy of the SMC in categorizing a train correctly is 99% and 96% for simulated and real data, respectively. The accuracy of the SCC is 84% and 81% for simulated and real data, respectively. The composition of these classifiers, their objectives, how they were trained, and the evaluation of their performances using both simulated and real data are presented in detail.
运动单位（MU）放电模式信息可用于临床或生理研究。它还可以用于增强和验证肌电图（EMG）信号分解。然而，在所有情况下，都必须首先确定提取的 MU 放电模式的有效性。本文提出了两种可用于验证提取的 MU 放电模式的监督分类器。第一个分类器，即单/合并分类器（SMC），用于确定运动单位电位序列（MUPT）是否代表单个 MU 的放电，还是多个 MU 的合并活动。第二个分类器，即单/污染分类器（SCC），用于确定 MUPT 中的误分类错误估计数量是否可接受。这两个分类器都是使用模拟数据进行训练，然后使用模拟和真实数据进行测试。SMC 对模拟和真实数据的分类准确性分别为 99%和 96%。SCC 的准确性分别为 84%和 81%。本文详细介绍了这些分类器的组成、目标、训练方法以及使用模拟和真实数据评估其性能的方法。
