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Multi-dimensional characterization and tracking of motor unit action potentials
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DOI：10.1088/1741-2552/ae5b26.png)
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Abstract
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Objective. Decomposition of high-density surface electromyography (HDsEMG) signals allows identification of individual motor unit firing times and provides a spatiotemporal image of their action potential waveforms. The ability to reliably match and track motor unit action potentials (MUAPs) from the same motor unit across multiple recordings allows changes in their recruitment and firing properties to be identified, however, similarities in MUAP shape can present challenges for reliable tracking. Approach. A new method for matching MUAP waveforms using a multi-dimensional (MD) representation is presented. MUAPs are represented as trajectories in high-dimensional space, where each HDsEMG channel corresponds to a different dimension. Trajectories are compared using MD features to measure the similarity between pairs of MUAP waveforms. Feature reduction and clustering are then used to classify pairs of MUAPs as belonging to the same or different motor units. The ability of the MD method to correctly identify pairs of matching MUAPs was assessed using MUAPs from simulated and experimental datasets and compared with two-dimensional cross-correlation (CC) using a threshold of 0.7, 0.8 or 0.9. Main results. The proposed MD method resulted in significantly higher F1 scores and lower false positive and false negative rates in both simulated and experimental datasets (p < 0.001). Across all datasets examined, the MD method correctly identified a greater number of matching MUAP pairs (89.8 ± 18.4%) compared with the best performing CC threshold (73.3 ± 21.0%). This was accompanied by a 49.6% lower false positive rate for the MD method. Significance. This study demonstrates that MD representations of MUAP trajectories recorded from high density arrays can more accurately identify MUAPs from the same motor unit, improving motor unit tracking compared with traditional correlation based approaches.
Keywords:
Motor unit action potentials
High-density surface electromyography
Multi-dimensional representation
Motor unit tracking
Signal decomposition
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