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首页> 外文期刊>International urogynecology journal and pelvic floor dysfunction >Characterization of the motor units of the external anal sphincter in pregnant women with multichannel surface EMG
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Characterization of the motor units of the external anal sphincter in pregnant women with multichannel surface EMG

机译:多通道表面肌电图孕妇的肛门外括约肌运动单位的表征

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Introduction and hypothesis: Locating the innervation zones (IZs) of the external anal sphincter (EAS) is helpful to obstetricians to identify areas particularly vulnerable to episiotomy in pregnant women. The aim was to investigate the motor unit (MU) properties of the EAS during voluntary contractions. Methods: Electromyographic signals were detected, from 478 pregnant women, by means of an intra-anal cylindrical probe carrying a circumferential array of 16 electrodes. The signals were decomposed into the constituent MU action potential trains and 5,947 templates were extracted and analyzed in order to identify the IZ position. Results: MUs innervated at one end are concentrated in the dorsal portion of the sphincter, while MUs innervated in the middle are distributed symmetrically in the left and right portions of the EAS. The angular propagation velocity was estimated for each MU resulting in 260±45 rad/s, corresponding to 1.8 m/s on the probe surface and to about 4 m/s at a radial depth of 10 mm from the probe surface. Conclusions: A novel method for identification and classification of MUs of the EAS is proposed and applied to a large-scale study. It is possible to distinguish MUs of the EAS in a minimally invasive way and identify their IZs. This information should be used to plan episiotomies and minimize risks of EAS denervation.
机译:引言和假设:定位肛门外括约肌(EAS)的神经支配区(IZs)有助于产科医生确定孕妇特别容易进行会阴切开术的区域。目的是研究自愿收缩期间EAS的运​​动单位(MU)特性。方法:通过带有16个电极圆周排列的肛门内圆柱探针,从478名孕妇中检测到肌电信号。将信号分解为组成的MU动作电位序列,并提取和分析5947个模板以识别IZ位置。结果:一端支配的MUs集中在括约肌的背侧部分,而中间支配的MUs对称地分布在EAS的左右两侧。估计每个MU的角传播速度,结果为260±45 rad / s,对应于探针表面上的1.8 m / s,并且在距探针表面10 mm的径向深度处约为4 m / s。结论:提出了一种用于EAS MU的识别和分类的新方法,并将其应用于大规模研究。可以用微创方式区分EAS的MU,并识别其IZ。该信息应用于计划癫痫发作并最小化EAS失神经的风险。

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