Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Beijing, China.
Muscle Nerve. 2021 Feb;63(2):225-230. doi: 10.1002/mus.27106. Epub 2020 Nov 13.
Turns-amplitude, number of small segments (NSS)-activity, and envelope-activity clouds are three methods of electromyography (EMG) interference pattern analysis. Our objective was to evaluate the sensitivity and specificity of each individual cloud analysis and combined clouds analysis to compare with that of quantitative motor unit potential (QMUP) analysis.
A total of 379 muscles from 100 patients were analyzed by both QMUP and clouds analyses. Calculation of sensitivity and specificity was based on the clinical diagnosis as the "gold standard."
For discrimination of abnormal vs normal and neuropathic vs non-neuropathic, combined clouds analysis had greater sensitivity than QMUP analysis and any single cloud analysis, but there were no differences in specificity. For discrimination of myopathic vs non-myopathic, combined clouds analysis and single cloud analysis had greater sensitivity than QMUP analysis, but there were no differences in specificity.
Combined clouds analysis was superior to QMUP and each single cloud analysis for distinguishing normal, myopathic, and neuropathic muscles.
振辐-波数、小节段数（NSS）-活性和包络-活性云是肌电图（EMG）干扰模式分析的三种方法。我们的目的是评估每个单独的云分析和组合云分析的敏感性和特异性，并与定量运动单位电位（QMUP）分析进行比较。
共对 100 例患者的 379 块肌肉进行了 QMUP 和云分析。基于临床诊断作为“金标准”计算敏感性和特异性。
对于异常与正常、神经病变与非神经病变的鉴别，组合云分析的敏感性大于 QMUP 分析和任何单个云分析，但特异性无差异。对于肌病与非肌病的鉴别，组合云分析和单个云分析的敏感性大于 QMUP 分析，但特异性无差异。
组合云分析在区分正常、肌病和神经病变肌肉方面优于 QMUP 和每种单个云分析。
