基于非线性能量算子的表面肌电信号MUAP发放检测

采用基于平滑非线性能量算子(smoothed nonlinear energy operator, SNEO)的方法对表面肌电(surface EMG, SEMG)信号运动单位动作电位(motor unit action potential, MUAP)的发放信息进行检测,提出一种能较精确确定MUAP发放数目的阈值检测方法.利用这些方法分别对肌肉轻度收缩和中度收缩时的SEMG信号进行了MUAP发放检测实验,结果表明,对于轻度收缩时的SEMG信号,本文的方法十分有效,而对中度收缩时的SEMG信号也能获得比较满意的检测结果.     

利用运动单位发放信息建立多通道信息的表面肌电信号分解

目的基于多通道信息的表面肌电(surfaceelectromyographic,sEMG)信号分解有助于弥补单通道分解时空间和发放信息不足的缺点.本文提出利用运动单位(motorunit,MU)的发放信息建立多通道 sEMG信号中属于同一 MU的模板映射关系,实现多导信号的信息互补,从而提高分解的准确率.方法对四导仿真信号先分别进行单通道分解,然后利用各通道之间的发放信息建立模板映射关系进行多通道分解.结果仿真实验结果显示单通道分解准确率平均为75%,多通道分解准确率为88%,表明利用MU发放信息建立模板映射关系进行sEMG信号分解能够提高分解有效性.结论将该方法应用于真实信号分解,也能有效得到 MU的波形和发放信息.     

Synthesising of a motor unit potential based on the sequential firing of muscle fibres

A model for the summation of the fibre potentials in a motor unit is presented. A possible mechanism for the recruitment sequence of the various fibres of a motor unit is developed on the basis of anatomical and physiological findings supplemented with a mathematical approach making it possible to take into consideration the spatial and temporal distribution of various fibre potentials. In addition to the commonly accepted parameters of a motor unit potential, stress is laid on the relative position of the recording electrode with respect to the structure of the motor unit and the location of the motor point of the muscle. The model is evaluated both with dipole and dielectric attenuation approaches. The sub-unit concept of a motor unit is critically examined in the light of the results of the model. Attention has been focused on the motor units of the brachial biceps, but the flexibility of the approach also makes it possible to consider motor units of other muscles. The procedure for such generalisations and the respective limitations are discussed. The model provides a unified approach to the diversity of the parameters of the motor unit potential.     

Skill and selection bias has least influence on motor unit action potential firing rate/frequency

INTRODUCTION: Motor unit action potential (MUAP) rise time in quantitative electromyography (QEMG) using multi-MUAP analysis may influence neuromuscular disease diagnosis. OBJECTIVE: To demonstrate in QEMG that MUAP firing rate/frequency (FR) is the parameter least dependent on electrodiagnostician skill (EDXc) skill and selection bias. METHODS AND MATERIALS: During minimal contraction (< 6 MUAPs/4.8 s analysis period) of tibialis anterior, 20 MUAPs were selected for QEMG at each of the 3 insertion sites. There were three EDXcs (professor & two fellows), each an independent MUAP selector + EMG machine itself (EMGM), for a total of four independent MUAP selectors. EDXc and selector order variation at each site involved the following. At insertion site1, during EMG by EDXc 1, the MUAP selector order was EDXc 1, EMGM, EDXc 2 and EDXc 3. At the following two sites, the previous 1st selector was sequentially rotated to become the last selector during EMG by the subsequent EDXc. The protocol was repeated in two subjects. MUAP inclusion criteria by EDXcs was MUAP rise time < 1 ms by visual inspection and, by the EMGM, any and all MUAPs during the 4.8 s analysis period. RESULTS: The reproducibility of measurements between MUAP selectors for mean frequency was satisfactory (correlation coefficient for inter-EDXc was 0.57, and 0.66 between the most experienced EDXc and EMGM. Inter- EDXc correlation coefficient was poor for amplitude (0.10), duration (0.34), size index (0.35), phases (-0.13) and turns (-0.22). Of the MUAPs collected by EMGM, the mean amplitude was smaller and mean duration was longer than those collected by the other 3 selectors with no significant differences for the other parameters. CONCLUSIONS: FR was the only satisfactorily reproduced parameter from QEMG of MUAPs between selectors, and least dependent on skill and/or selection bias. From this, FR appears a relatively objective parameter, while other parameters appear unduly influenced by EDXc skill and/or MUAP selection bias.     

自动分解肌电图运动单位动作电位各参数的分析和临床意义

目的：探讨运动单位动作电位（ＭＵＡＰ）各参数评价神经肌肉疾病的临床意义。方法：采用肌电图仪,以常规同心圆针电极记录,对７１例健康成人和４３例神经肌肉疾病患者的肱二头肌或／和胫前肌进行自动分解肌电图（ＡＤＥＭＧ）检测,共收集ＭＵＡＰ８３５６个,测定ＭＵＡＰ的５个参数：波幅、时限、转折数、发放率、棘波间隔变异系数（ＣＩＶ）。结果：１５例肌源性疾病患者表现为每一记录部位的ＭＵＡＰ数目增加;肱二头肌ＭＵＡＰ的波幅、时限、转折数减低、发放率增加,ＣＩＶ无改变;胫前肌ＭＵＡＰ的各参数无显著性改变。２８例神经源性疾病患者表现为每一记录部位的ＭＵＡＰ数目减少;ＭＵＡＰ的波幅、转折数及ＣＩＶ增加,时限及发放率无显著性增加。结论：ＡＤＥＭＧ检测的ＭＵＡＰ各参数对于评价神经肌肉疾病有重要的临床意义。     

运动单位数目测定在神经源性疾病中的价值

目的：探讨运动单位数目测定(MUNE)对神经肌肉疾病的诊断价值。方法：应用Viking IV型肌电图仪中的“统计学方法”对85例健康人和56例神经源性病变患者的趾短伸肌和鱼际肌进行测定。记录和刺激电极均为表面电极。结果：对照组的结果显示可重复性好,≤60岁时,运动单位(MU)数与年龄、性别和左右侧无关;＞60岁时,MU数显著减少。56例患者均表现MU数减少,可重复性高于对照组,并且随着病情的好转,功能性MU数增加。结论：MUNE是显示肌肉MU丧失的一种简单、非创伤性的快速方法,是检测神经源性疾病的理想手段之一。     

Model for decomposition of the motor unit action potential 2 Analysis of simulated motor unit action potentials

A model for decomposition of the motor unit action potential (MUAP) which finds an optimal fit of a set of simulated single-fibre action potentials (SSFAPs) to the original MUAP is tested. The composition of SSFAPs which best produces the MUAP is assumed to carry information about the actual distribution of single-fibre action potentials generating the MUAP. Two methods are derived from the model. The first makes use of a fixed set of SSFAPs. In the second method, a gradually expanding set of SSFAPs is built, using a sequence of crosscorrelation, optimal reconstruction and subtraction. In the paper MUAPs are constructed under various well defined conditions. The MUAPs are decomposed by the two methods, and the results are compared with traditional MUAP parameters. Under these conditions, the model obtains parameters with closer biological connections compared with traditional measures.     

对小儿脑性瘫痪精细运动功能评估量表的应用评价

目的探讨精细运动功能评估量表（FMFM）的敏感性、实用性。方法收集45例脑瘫患儿随机分为A、B、C3组,每组各15例,治疗前后使用FMFM进行评估。组A和组B患儿根据评估结果制定康复计划,综合康复治疗后分别于90d、30d后再次评估。组C患儿康复不参考测试结果,常规综合康复治疗90d后再次评估。结果1．各组治疗前后FMFM量化评估对比均有显著差异（P〈0．05）;2．组A与组B治疗前后FMFM改变量进行比较,组A较组B明显（P〈0．05）;3．组A与组C治疗前后FMFM改变量进行比较,组A较组C明显（P〈0．05）。结论FMFM能客观反映疗效、有较高灵敏性和一定的临床康复指导意义,可做为一种可信评估方法推广使用。     

Model for decomposition of the motor unit action potential 1 The algorithm

A model for decomposition of the motor unit action potential (MUAP) into its constituent single-fibre action potentials is presented. It finds an optimal fit of a set of simulated single-fibre action potentials (SSFAPs) to the MUAP. The SSFAPs are assumed to originate from muscle fibres at different distances from the electrode, having various delays in time. Two methods for decomposition of the MUAP are derived from this model: first, that the MUAP is decomposed into a fixed set of SSFAPs; and secondly that the MUAP is decomposed into an adaptive, expanding set of SSFAPs. In the second method three steps are used repeatedly. First, the MUAP is cross-correlated with a collection of four SSFAPs. Then the most similar SSFAPs are used to reconstruct the original MUAP. The reconstruction thus obtained is subtracted from the original MUAP to detect activity not yet imitated. This difference (‘residual’) is again used for cross-correlation, restarting in step 1. After a suitable number of iterations, the MUAP is optimally imitated by a set of SSFAPs. The set of SSFAPs, obtained as described, is assumed to give information about underlying anatomical and physiological data (such as fibre number, fibre density, impulse dispersion) of the motor unit under study.