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临床定量肌电图综述。

A review of clinical quantitative electromyography.

作者信息

Farkas Charles, Hamilton-Wright Andrew, Parsaei Hossein, Stashuk Daniel W

机构信息

Department of Systems Design Engineering, University of Waterloo, Canada.

出版信息

Crit Rev Biomed Eng. 2010;38(5):467-85. doi: 10.1615/critrevbiomedeng.v38.i5.30.

Abstract

Information regarding the morphology of motor unit potentials (MUPs) and motor unit firing patterns can be used to help diagnose, treat, and manage neuromuscular disorders. In a conventional electromyographic (EMG) examination, a clinician manually assesses the characteristics of needle-detected EMG signals across a number of distinct needle positions and forms an overall impression of the condition of the muscle. Such a subjective assessment is highly dependent on the skills and level of experience of the clinician, and is prone to a high error rate and operator bias. Quantitative methods have been developed to characterize MUP waveforms using statistical and probabilistic techniques that allow for greater objectivity and reproducibility in supporting the diagnostic process. In this review, quantitative EMG (QEMG) techniques ranging from simple reporting of numeric MUP values to interpreted muscle characterizations are presented and reviewed in terms of their clinical potential to improve status quo methods. QEMG techniques are also evaluated in terms of their suitability for use in a clinical decision support system based on previously established criteria. Aspects of prototype clinical decision support systems are then presented to illustrate some of the concepts of QEMG-based decision making.

摘要

有关运动单位电位(MUPs)形态和运动单位放电模式的信息可用于帮助诊断、治疗和管理神经肌肉疾病。在传统的肌电图(EMG)检查中,临床医生会手动评估在多个不同针电极位置检测到的针电极肌电信号的特征,并对肌肉状况形成总体印象。这种主观评估高度依赖于临床医生的技能和经验水平,并且容易出现高错误率和操作者偏差。已经开发出定量方法,使用统计和概率技术来表征MUP波形,从而在支持诊断过程中实现更高的客观性和可重复性。在本综述中,介绍并回顾了从简单报告MUP数值到解释性肌肉特征描述的定量肌电图(QEMG)技术,依据其改善现有方法的临床潜力进行评估。还根据先前确立的标准,评估了QEMG技术在临床决策支持系统中的适用性。接着介绍了原型临床决策支持系统的各个方面,以阐明基于QEMG决策的一些概念。

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