IEEE Trans Neural Syst Rehabil Eng. 2020 Aug;28(8):1836-1845. doi: 10.1109/TNSRE.2020.3002792. Epub 2020 Jun 16.
The objective of this study is to explore the diagnostic decision and sensitivity of the surface electromyogram (EMG) clustering index (CI) with respect to post-stroke motor unit (MU) alterations through a simulation approach by the existing motor neuron pool model and surface EMG model. In the simulation analysis, three patterns of diagnostic decisions were presented in 24 groups representing eight types in three degrees of MU alterations. Specifically, the CI decision exhibited an abnormally increased pattern for five types, an abnormally decreased pattern for two types, and an invariant pattern for one type. Furthermore, the CI diagnostic decision was found to be highly sensitive to three types because a 50% degree of alteration in these types resulted in a distinct deviation of 2.5 in the CI Z-score. The mixed CI patterns were confirmed in experimental data collected from the paretic muscles of 14 subjects with stroke, as compared to the healthy muscles of 10 control subjects. Given the simulation results as a guideline, the CI diagnostic decision could be interpreted from general neural or muscular changes into specific MU changes (in eight types). This can further promote clinical applications of the convenient surface EMG tool in examining and monitoring paretic muscle changes toward customized stroke rehabilitation.
本研究旨在通过现有的运动神经元池模型和表面肌电模型的仿真方法，探讨表面肌电图（EMG）聚类指数（CI）对脑卒中后运动单位（MU）变化的诊断决策和敏感性。在仿真分析中，针对 8 种类型中的 8 种程度的 MU 变化，24 组共呈现了 3 种诊断决策模式。具体而言，CI 决策表现出 5 种异常增加模式、2 种异常减少模式和 1 种不变模式。此外，CI 诊断决策对 3 种类型高度敏感，因为这 3 种类型的改变程度达到 50%时，CI Z 分数的偏差就会明显偏离 2.5。与 10 名对照受试者的健康肌肉相比，从 14 名脑卒中患者的瘫痪肌肉中收集到的实验数据中证实了混合 CI 模式。基于模拟结果作为指导，CI 诊断决策可以从一般神经或肌肉变化推断为特定 MU 变化（8 种类型）。这将进一步促进方便的表面肌电图工具在检查和监测瘫痪肌肉变化方面的临床应用，以实现针对脑卒中患者的个性化康复。
