Electrical and Electronics Engineering Dept., Public University of Navarre, Spain.
Electrical and Electronics Engineering Dept., Public University of Navarre, Spain.
Clin Neurophysiol. 2018 Jun;129(6):1170-1181. doi: 10.1016/j.clinph.2018.02.134. Epub 2018 Mar 19.
To evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and pathological muscles.
Three versions of the method (with different window lengths) were compared to three relevant published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained the best performance or a performance not significantly worse than the best were computed.
For signal processing figures of merit, the three versions of the new method performed better (with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the other methods (83.3, 37.5 and 25%).
For the types of normal and pathological muscle studied, the sliding window approach extracted more accurate and reliable MUAP curves than other existing methods.
The new method can be of service in quantitative EMG.
评估一种新提出的基于滑动窗口的运动单位动作电位（MUAP）平均方法的性能，并将其与正常和病理肌肉中相关的已发表方法进行比较。
将三种版本的方法（具有不同的窗口长度）与三种相关的已发表方法在信号分析的基于 merit 图和临床实践中使用的 MUAP 波形参数方面进行比较。对来自正常、肌病、亚急性神经源性和慢性神经源性肌肉的 218 个 MUAP 训练进行了分析。计算了方法获得最佳性能或性能不明显差于最佳性能的案例的百分比分数。
对于信号处理 merit 图，三种版本的新方法的性能更好（分数分别为 100、86.6 和 66.7%），优于其他三种方法（分别为 66.7、25 和 0%）。在 MUAP 波形参数方面，新方法的性能也更好（100、95.8 和 91.7%），优于其他三种方法（83.3、37.5 和 25%）。
对于研究的正常和病理肌肉类型，滑动窗口方法比其他现有方法提取了更准确和可靠的 MUAP 曲线。
新方法可用于定量肌电图。
