Biomedical Engineering Department, the University of Isfahan, Isfahan, Iran.
J Neural Eng. 2011 Dec;8(6):066002. doi: 10.1088/1741-2560/8/6/066002. Epub 2011 Oct 6.
The aim of this study was to assess the accuracy of the convolution kernel compensation (CKC) method in decomposing high-density surface EMG (HDsEMG) signals from the pennate biceps femoris long-head muscle. Although the CKC method has already been thoroughly assessed in parallel-fibered muscles, there are several factors that could hinder its performance in pennate muscles. Namely, HDsEMG signals from pennate and parallel-fibered muscles differ considerably in terms of the number of detectable motor units (MUs) and the spatial distribution of the motor-unit action potentials (MUAPs). In this study, monopolar surface EMG signals were recorded from five normal subjects during low-force voluntary isometric contractions using a 92-channel electrode grid with 8 mm inter-electrode distances. Intramuscular EMG (iEMG) signals were recorded concurrently using monopolar needles. The HDsEMG and iEMG signals were independently decomposed into MUAP trains, and the iEMG results were verified using a rigorous a posteriori statistical analysis. HDsEMG decomposition identified from 2 to 30 MUAP trains per contraction. 3 ± 2 of these trains were also reliably detected by iEMG decomposition. The measured CKC decomposition accuracy of these common trains over a selected 10 s interval was 91.5 ± 5.8%. The other trains were not assessed. The significant factors that affected CKC decomposition accuracy were the number of HDsEMG channels that were free of technical artifact and the distinguishability of the MUAPs in the HDsEMG signal (P < 0.05). These results show that the CKC method reliably identifies at least a subset of MUAP trains in HDsEMG signals from low force contractions in pennate muscles.
本研究旨在评估卷积核补偿（CKC）方法在分解羽状股二头肌长头高密度表面肌电（HDsEMG）信号中的准确性。尽管 CKC 方法已经在平行纤维肌肉中得到了充分评估，但有几个因素可能会阻碍其在羽状肌肉中的性能。即，羽状和平行纤维肌肉的 HDsEMG 信号在可检测的运动单位（MU）数量和 MUAP 的空间分布方面存在很大差异。在这项研究中，使用具有 8mm 电极间距的 92 通道电极网格，在低力自愿等长收缩期间从 5 名正常受试者记录单极表面肌电图信号。同时使用单极针记录肌内肌电图（iEMG）信号。将 HDsEMG 和 iEMG 信号分别分解为 MUAP 列车，并用严格的后验统计分析验证 iEMG 结果。HDsEMG 分解在每次收缩中识别出 2 到 30 个 MUAP 列车。其中 3 ± 2 个列车也可以通过 iEMG 分解可靠地检测到。在选定的 10 秒间隔内，这些常见列车的测量 CKC 分解准确性为 91.5 ± 5.8%。其他列车未进行评估。影响 CKC 分解准确性的显著因素是无技术伪影的 HDsEMG 通道数量和 HDsEMG 信号中 MUAP 的可分辨性（P < 0.05）。这些结果表明，CKC 方法可靠地识别了羽状肌肉低力收缩时 HDsEMG 信号中至少一部分 MUAP 列车。
