Department for Hand, Plastic and Aesthetic Surgery, Ludwig-Maximilian University, Munich, Germany.
Private Practice, New York City, New York.
Facial Plast Surg. 2021 Oct;37(5):614-624. doi: 10.1055/s-0041-1725168. Epub 2021 Mar 5.
The evaluation of neuromodulator treatment outcomes can be performed by noninvasive surface-derived facial electromyography (fEMG) which can detect cumulative muscle fiber activity deep to the skin. The objective of the present study is to identify the most reliable facial locations where the motor unit action potentials (MUAPs) of various facial muscles can be quantified during fEMG measurements. The study population consisted of five males and seven females (31.0 [12.9] years, body mass index of 22.15 [1.6] kg/m). Facial muscle activity was assessed in several facial regions in each patient for their respective muscle activity utilizing noninvasive surface-derived fEMG. Variables of interest were the average root mean square of three performed muscle contractions (= signal) (µV), mean root mean square between those contraction with the face in a relaxed facial expression (= baseline noise) (µV), and the signal to noise ratio (SNR). A total of 1,709 processed fEMG signals revealed one specific reliable location in each investigated region based on each muscle's anatomy, on the highest value of the SNR, on the lowest value for the baseline noise, and on the practicability to position the sensor while performing a facial expression. The results of this exploratory study may help guiding future researchers and practitioners in designing study protocols and measuring individual facial MUAP when utilizing fEMG. The locations presented herein were selected based on the measured parameters (SNR, signal, baseline noise) and on the practicability and reproducibility of sensor placement.
神经调节剂治疗效果的评估可以通过无创的表面衍生面部肌电图（fEMG）进行，该方法可以检测到皮肤深处的累积肌纤维活动。本研究的目的是确定在 fEMG 测量中可以量化各种面部肌肉运动单位动作电位（MUAP）的最可靠的面部位置。研究人群由五名男性和七名女性组成（31.0[12.9]岁，体重指数为 22.15[1.6]kg/m）。在每位患者的各个面部区域中，利用无创的表面衍生 fEMG 评估面部肌肉活动。感兴趣的变量是三次肌肉收缩的平均均方根（=信号）（µV）、面部处于放松表情时两次收缩之间的平均均方根（=基线噪声）（µV）以及信噪比（SNR）。总共处理了 1709 个 fEMG 信号，根据每个肌肉的解剖结构、SNR 的最高值、基线噪声的最低值以及在进行面部表情时放置传感器的可行性，在每个研究区域中确定了一个特定的可靠位置。这项探索性研究的结果可能有助于指导未来的研究人员和从业者在设计研究方案和测量个体面部 MUAP 时利用 fEMG。本文所介绍的位置是基于测量的参数（SNR、信号、基线噪声）以及传感器放置的可操作性和可重复性来选择的。
