首页> 外文期刊>Electromyography and Clinical Neurophysiology: International Bimonthly Review >Comparison of F-waves of motor unit action potentials activated during voluntary contraction.
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Comparison of F-waves of motor unit action potentials activated during voluntary contraction.

机译:主动收缩期间激活的运动单位动作电位的F波比较。

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The system for classifying F-waves was developed to study the properties of F-wave and to compare single motor unit (MU) F-waves with motor unit action potentials (MUAPs) activated during voluntary contraction. The F-waves evoked by submaximal stimulation as well as the EMG signals during voluntary contraction at 6 levels of 10-100% of maximum voluntary contraction (MVC) were measured in the tibialis anterior muscles of 3 healthy volunteers. Nine channel F-wave waveforms in a selected electrode array were classified using a template-matching method. After the detection procedure of MUAPs in voluntary EMG signals, the MUAPs were also classified by the same method. Most of the F-waves (88.4%) were composed of a single MUAP. The numbers of MU classified from single MU F-waves in 3 subjects were 12, 12 and 15, and the numbers of MU classified from the voluntary EMG signals at 6 contraction levels were 20, 27 and 24, respectively. A total of 26 single MU F-waves were identified with the MUs extracted from the data during voluntary contractions. The results suggest that the F-waves are composed of a population of the MUs, which are recruited at a wide range of contraction levels. The classification procedures of F-waves and voluntary EMG signals have made it possible to recognize the same MU in both signals and to analysis the firing thresholds of F-waves.
机译:开发了用于对F波进行分类的系统,以研究F波的特性,并将单个运动单位(MU)F波与在自愿收缩过程中激活的运动单位动作电位(MUAP)进行比较。在3名健康志愿者的胫骨前肌中测量了次最大刺激引起的F波以及自主收缩期间的EMG信号,其水平为最大自主收缩(MVC)的10-100%的6个水平。使用模板匹配方法对所选电极阵列中的九个通道F波波形进行分类。在对自愿肌电信号中的MUAP进行检测之后,也用相同的方法对MUAP进行分类。大多数F波(88.4%)由单个MUAP组成。在3个受试者中,从单个MU F波分类的MU数分别为12、12和15,从6个收缩水平的自愿EMG信号分类的MU数分别为20、27和24。在自愿收缩期间,从数据中提取的MU总共识别出26个单MU F波。结果表明,F波由大量的MU组成,这些MU的收缩水平范围很广。 F波和自愿EMG信号的分类程序使得可以在两个信号中识别相同的MU并分析F波的触发阈值。

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