We introduce an algorithm for automatic identification of true positive (TP) and false positive (FP) spikes in the motor unit spike train, identified by blind source separation (BSS) of high-density surface electromyograms (HDsEMG). The algorithm selects predefined number of spikes, so called witnesses, from identified spike train. The other spikes in the spike train are called test spikes and are classified into TP or FP spikes by our algorithm. For this purpose, the algorithm constructs as many motor unit filters as there are test spikes, using the information from all the witnesses and each individual test spike. Afterwards, it applies each motor unit filter to HDsEMG to get new estimate of MU spike train for each selected test spike and calculates previously introduced Pulse-to-Noise Ratio (PNR) on preselected witnesses in this new spike train. When accumulated over all the test spikes, these PNR values exhibit bimodal distribution with the peak at lower PNR values representing FPs and the peak at higher PNR values representing TPs. Therefore, FPs and TPs can be discriminated by applying computationally efficient segmentation algorithm to corresponding PNR values. We also propose and mutually compare different witness selection strategies and show that selection of about 40 spikes with maximal amplitude in the identified spike train minimizes the selection of FPs as witnesses and maximizes the TP vs. FP discrimination power. In our tests on 20 s long experimental HDsEMG signals from biceps brachii muscle the number of FPs decreased from 23.9 ± 4.7 to 4.1 ± 4.4 when the proposed algorithm was used.

中文翻译：

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根据高密度表面肌电图自动识别各个运动单元的放电准确性


我们引入了一种自动识别运动单位尖峰序列中真阳性（TP）和假阳性（FP）尖峰的算法，通过高密度表面肌电图（HDsEMG）的盲源分离（BSS）来识别。该算法从识别的尖峰序列中选择预定义数量的尖峰，即所谓的“见证者”。尖峰序列中的其他尖峰称为测试尖峰，并通过我们的算法分为 TP 或 FP 尖峰。为此，该算法使用来自所有目击者和每个单独测试尖峰的信息构建与测试尖峰一样多的运动单元过滤器。然后，它将每个运动单元滤波器应用于 HDsEMG，以获得每个选定测试尖峰的 MU 尖峰序列的新估计，并计算之前在该新尖峰序列中预选见证人上引入的脉噪比 (PNR)。当在所有测试峰值上累积时，这些 PNR 值呈现双峰分布，较低 PNR 值处的峰值代表 FP，较高 PNR 值处的峰值代表 TP。因此，可以通过对相应的 PNR 值应用计算高效的分割算法来区分 FP 和 TP。我们还提出并相互比较了不同的见证人选择策略，并表明在已识别的尖峰序列中选择约 40 个具有最大振幅的尖峰可以最大限度地减少 FP 作为见证人的选择，并最大化 TP 与 FP 的区分能力。在我们对来自肱二头肌的 20 秒长实验 HDsEMG 信号进行的测试中，当使用所提出的算法时，FP 的数量从 23.9 ± 4.7 减少到 4.1 ± 4.4。