School of Rehabilitation Therapy, Queen's University, Kingston, Ontario, Canada.
J Neuroeng Rehabil. 2010 Feb 15;7:8. doi: 10.1186/1743-0003-7-8.
Methods for the calculation and application of quantitative electromyographic (EMG) statistics for the characterization of EMG data detected from forearm muscles of individuals with and without pain associated with repetitive strain injury are presented.
A classification procedure using a multi-stage application of Bayesian inference is presented that characterizes a set of motor unit potentials acquired using needle electromyography. The utility of this technique in characterizing EMG data obtained from both normal individuals and those presenting with symptoms of "non-specific arm pain" is explored and validated. The efficacy of the Bayesian technique is compared with simple voting methods.
The aggregate Bayesian classifier presented is found to perform with accuracy equivalent to that of majority voting on the test data, with an overall accuracy greater than 0.85. Theoretical foundations of the technique are discussed, and are related to the observations found.
Aggregation of motor unit potential conditional probability distributions estimated using quantitative electromyographic analysis, may be successfully used to perform electrodiagnostic characterization of "non-specific arm pain." It is expected that these techniques will also be able to be applied to other types of electrodiagnostic data.
本文提出了一种计算和应用定量肌电图（EMG）统计数据的方法，用于描述与重复性劳损相关的疼痛和无疼痛个体前臂肌肉中检测到的 EMG 数据。
本文提出了一种使用贝叶斯推理多阶段应用的分类程序，用于描述使用针式肌电图获得的一组运动单位电位。探讨并验证了该技术在描述正常个体和出现“非特异性手臂疼痛”症状个体的 EMG 数据中的有效性。比较了贝叶斯技术与简单投票方法的效果。
发现所提出的综合贝叶斯分类器在测试数据上的性能与多数投票相当，整体准确率大于 0.85。本文讨论了该技术的理论基础，并与观察结果相关联。
使用定量肌电图分析估计的运动单位电位条件概率分布的聚合，可以成功地用于执行“非特异性手臂疼痛”的电诊断特征描述。预计这些技术也将能够应用于其他类型的电诊断数据。
