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基于表面肌电信号的背部肌肉损伤分类

Classification of back muscle impairment based on the surface electromyographic signal.

作者信息

Roy S H, De Luca C J, Emley M, Oddsson L I, Buijs R J, Levins J A, Newcombe D S, Jabre J F

机构信息

NeuroMuscular Research Center, Boston University, MA 02215, USA.

出版信息

J Rehabil Res Dev. 1997 Oct;34(4):405-14.

PMID:9323644
Abstract

A surface electromyographic (EMG) procedure for classifying muscle impairments in persons with low back pain (LBP) is described. The procedure was studied using a device, the Back Analysis System (BAS), to acquire and process EMG signals from six bilateral muscle sites during sustained isometric contractions designed to progressively fatigue the lower back. Back muscle impairment was determined on the basis of the different ways in which the EMG median frequency parameters change as a function of contraction duration and muscle site. The article describes a series of studies that have been useful in developing an automated procedure for identifying back muscle impairment by comparing individual test results to a normative database. To date, the research results have produced multivariate discriminant functions that have identified two muscle impairment categories associated with deconditioning and imbalances secondary to LBP. We have found that the functions can distinguish individuals with and without LBP with an accuracy of approximately 90%. Other studies are described in which the technique is applied to monitoring changes in muscle performance capability that occur following rehabilitation for LBP. Many of our findings here are also compared to the results of independent studies by others using similar procedures. The need for further research and development of the technique to improve its clinical applicability is also described.

摘要

描述了一种用于对腰痛(LBP)患者的肌肉损伤进行分类的表面肌电图(EMG)程序。使用一种名为背部分析系统(BAS)的设备对该程序进行了研究,以在持续等长收缩过程中从六个双侧肌肉部位采集和处理EMG信号,这些收缩旨在使下背部逐渐疲劳。背部肌肉损伤是根据EMG中位频率参数随收缩持续时间和肌肉部位变化的不同方式来确定的。本文描述了一系列研究,这些研究通过将个体测试结果与标准数据库进行比较,在开发用于识别背部肌肉损伤的自动化程序方面很有用。迄今为止,研究结果产生了多变量判别函数,这些函数识别出了与LBP继发的身体机能下降和失衡相关的两种肌肉损伤类别。我们发现这些函数能够以大约90%的准确率区分有LBP和无LBP的个体。还描述了其他研究,其中该技术被应用于监测LBP康复后肌肉性能能力的变化。我们在此的许多发现也与其他人使用类似程序进行的独立研究结果进行了比较。还描述了进一步研究和开发该技术以提高其临床适用性的必要性。

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