Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, N2L 3G1 Canada.
IEEE Trans Neural Syst Rehabil Eng. 2013 Mar;21(2):265-74. doi: 10.1109/TNSRE.2012.2218287. Epub 2012 Sep 27.
A system to resolve an intramuscular electromyographic (EMG) signal into its component motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters of motor units (MUs), such as their motor unit potential (MUP) templates and mean firing rates, are of interest. The system filters an EMG signal, detects MUPs, and clusters and classifies the detected MUPs into MUPTs. Clustering is partially based on the K-means algorithm, and the supervised classification is implemented using a certainty-based algorithm. Both clustering and supervised classification algorithms use MUP shape and MU firing pattern information along with signal dependent assignment criteria to obtain robust performance across a variety of EMG signals. During classification, the validity of extracted MUPTs are determined using several supervised classifiers; invalid trains are corrected and the assignment threshold for each train is adjusted based on the estimated validity (i.e., adaptive classification). Performance of the developed system in terms of accuracy (A(c)), assignment rate (A(r)), correct classification rate (CC(r)) , and the error in estimating the number of MUPTs represented in the set of detected MUPs (E(NMUPTs)) was evaluated using 32 simulated and 30 real EMG signals comprised of 3-11 and 3-15 MUPTs, respectively. The developed system, with average CC(r) of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CC(r) of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of E(NMUPTs), the new system, with average E(NMUPTs) of 0.3 and 0.2 for simulated and real data respectively, was better able to estimate the number of MUPTs represented in a set of detected MUPs than the previous system, with average E(NMUPTs) of 2.2 and 0.8 for simulated and real data respectively. For both the simulated and real data used, variations in A(c), A(r), and E(NMUPTs) for the newly developed system were lower than for the previous system, which demonstrates that the new system can successfully adjust the assignment criteria based on the characteristics of a given signal to achieve robust performance across a wide variety of EMG signals, which is of paramount importance for successfully promoting the clinical application of EMG signal decomposition techniques.
一种用于将肌内肌电图（EMG）信号分解为其组成的运动单位电位（MUP）的系统被提出。该系统主要用于临床应用，其中几个运动单位（MU）的生理参数，如 MU 的模板和平均放电率等，都是很感兴趣的。该系统对 EMG 信号进行滤波、检测 MUP 并对检测到的 MUP 进行聚类和分类成 MUPT。聚类部分基于 K 均值算法，而有监督的分类则使用基于可信度的算法来实现。聚类和有监督分类算法都使用 MUP 形状和 MU 放电模式信息以及信号相关的分配标准来获得各种 EMG 信号下的鲁棒性能。在分类过程中，使用多个有监督分类器来确定提取的 MUPT 的有效性；无效的列车被纠正，并且根据估计的有效性（即自适应分类）调整每个列车的分配阈值。使用 32 个模拟和 30 个真实 EMG 信号来评估所开发系统的性能，这些信号分别由 3-11 和 3-15 个 MUPT 组成。所开发的系统，对于模拟数据的平均准确率（A(c)）为 86.4%，对于真实数据的平均准确率（A(c)）为 96.4%，优于之前开发的 EMG 分解系统，对于模拟数据的平均准确率（A(c)）为 71.6%，对于真实数据的平均准确率（A(c)）为 89.7%，分别提高了 14.7%和 6.7%。在估计检测到的 MUP 集合中代表的 MUPT 数量方面，新系统的平均误差（E（NMUPTs））为 0.3 和 0.2，分别用于模拟和真实数据，优于之前系统的平均误差（E（NMUPTs））为 2.2 和 0.8，分别用于模拟和真实数据。对于使用的模拟和真实数据，新系统的 A(c)、A(r)和 E（NMUPTs）的变化都低于之前的系统，这表明新系统可以根据给定信号的特征成功调整分配标准，从而在各种 EMG 信号下实现稳健的性能，这对于成功推广 EMG 信号分解技术的临床应用至关重要。
