Abstract
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.
Highlights
C OMPUTER-AIDED motor unit (MU) identification from surface electromyograms is a fast-developing discipline
We introduce the algorithm that allows for cost-efficient discrimination of true positive (TP) and false positive (FP) firings in the MU spike train, identified by the Convolution Kernel Compensation (CKC) or any other blind source separation (BSS) method
We extended the previously proposed Pulse-to-Noise Ratio (PNR) metric [16] and demonstrated for the first time that we can use it for the assessment of identification accuracy of each individual MU firing
Summary
C OMPUTER-AIDED motor unit (MU) identification from surface electromyograms (sEMG) is a fast-developing discipline. Being based on the estimates of the average energy of pulses and noise, the PNR is an asymptotic measure of accuracy and requires sufficient number of spikes (typically 40 or more) and baseline noise samples to reliably estimate the quality of MU identification [16]. As such it is not suitable for assessment of the single MU firing identification accuracy. We extend the existing algorithms for the segmentation of MU spike trains and provide them with the measure of quality at the level of each individual spike This supports significant improvement of the current HDsEMG decomposition as it reduces the need for manual inspection and editing of decomposition results. Both improvements are essential for expediting the transfer of EMG decomposition from the controlled laboratory environments to the clinical and rehabilitation practice
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Schedule a callMore From: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
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