Real-Time Motor Unit Identification From High-Density Surface EMG
Real-Time Motor Unit Identification From High-Density Surface EMG
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DOI：
10.1109/tnsre.2013.2247631
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发表时间：
2013-11-01
影响因子：
4.9
通讯作者：
Zazula, Damjan
中科院分区：
文献类型：
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作者：
Glaser, Vojko;Holobar, Ales;Zazula, Damjan
这项研究致力于实时在线分解高密度表面肌电图 (EMG)。所提出的方法基于先前发布的卷积核补偿（CKC）技术，并共享相同的分解范式，即运动单元动作电位的补偿和运动单元（MU）放电的直接识别。之前发布的 CKC 版本以批处理模式运行，需要大约 10 秒的 EMG 信号，与此相反，实时实现从初始化阶段对大约 3 秒的 EMG 信号进行批处理开始，并在新的 EMG 样本块可用时继续迭代更新 MU 放电估计器。其与之前验证的 CKC 批量版本和渐近贝叶斯最优线性最小均方误差 (LMMSE) 估计器的详细比较表明，所有三种技术在识别的 MU 放电方面具有高度一致性。在信噪比为 20 dB 的合成表面 EMG 的情况下，识别 MU 放电的平均灵敏度为 98%。在实验 EMG 的情况下，实时 CKC 在最初 5 秒的 EMG 记录后完全收敛，平均而言，实时和批量 CKC 与 90% 的 MU 放电一致。实时 CKC 识别的 MU 比批量版本略少（实验 EMG，平均 4 个 MU，而批量 CKC 识别 5 个 MU），但在常规个人计算机上每秒多通道表面 EMG 只需要 0.6 秒的处理时间。
This study addresses online decomposition of high-density surface electromyograms (EMG) in real time. The proposed method is based on the previously published Convolution Kernel Compensation (CKC) technique and shares the same decomposition paradigm, i.e., compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires similar to 10 s of EMG signal, the real-time implementation begins with batch processing of similar to 3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal linear minimum mean square error (LMMSE) estimator demonstrates high agreement in identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98%. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90% of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.