Laboratorio de Medios e Interfases, Departamento de Bioingeniería, Universidad Nacional de Tucumán, Consejo Nacional de Investigaciones Científicas y Técnicas, San Miguel de Tucumán, Argentina.
Biomed Eng Online. 2010 Nov 12;9:72. doi: 10.1186/1475-925X-9-72.
Electromyographic signals can be used in biomedical engineering and/or rehabilitation field, as potential sources of control for prosthetics and orthotics. In such applications, digital processing techniques are necessary to follow efficient and effectively the changes in the physiological characteristics produced by a muscular contraction. In this paper, two methods based on information theory are proposed to evaluate the processing techniques.
These methods determine the amount of information that a processing technique is able to extract from EMG signals. The processing techniques evaluated with these methods were: absolute mean value (AMV), RMS values, variance values (VAR) and difference absolute mean value (DAMV). EMG signals from the middle deltoid during abduction and adduction movement of the arm in the scapular plane was registered, for static and dynamic contractions. The optimal window length (segmentation), abduction and adduction movements and inter-electrode distance were also analyzed.
Using the optimal segmentation (200 ms and 300 ms in static and dynamic contractions, respectively) the best processing techniques were: RMS, AMV and VAR in static contractions, and only the RMS in dynamic contractions. Using the RMS of EMG signal, variations in the amount of information between the abduction and adduction movements were observed.
Although the evaluation methods proposed here were applied to standard processing techniques, these methods can also be considered as alternatives tools to evaluate new processing techniques in different areas of electrophysiology.
肌电图信号可用于生物医学工程和/或康复领域，作为假肢和矫形器控制的潜在来源。在这些应用中，需要数字处理技术来有效地跟踪肌肉收缩产生的生理特征变化。本文提出了两种基于信息论的方法来评估处理技术。
这些方法确定了处理技术能够从肌电图信号中提取的信息量。用这些方法评估的处理技术有：绝对平均值（AMV）、均方根值（RMS）、方差值（VAR）和差分绝对值平均值（DAMV）。在肩胛骨平面中记录了手臂外展和内收运动时三角肌中部的肌电图信号，用于静态和动态收缩。还分析了最佳窗口长度（分段）、外展和内收运动以及电极间距离。
使用最佳分段（静态收缩时为 200ms 和 300ms，动态收缩时为 200ms 和 300ms），最佳处理技术为：静态收缩时的 RMS、AMV 和 VAR，动态收缩时仅为 RMS。使用肌电图信号的 RMS 值，观察到外展和内收运动之间信息量的变化。
尽管这里提出的评估方法已应用于标准处理技术，但这些方法也可以被视为评估电生理学不同领域新处理技术的替代工具。
