Academic Journal
Multi-dimensional characterization and tracking of motor unit action potentials.
| Τίτλος: | Multi-dimensional characterization and tracking of motor unit action potentials. |
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| Συγγραφείς: | McManus L; Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland., Liegey J; Neuromuscular Systems Lab, School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland., Lowery MM; Neuromuscular Systems Lab, School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland. |
| Πηγή: | Journal of neural engineering [J Neural Eng] 2026 Apr 16; Vol. 23 (2). Date of Electronic Publication: 2026 Apr 16. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004- |
| Ιατρικοί όροι (MeSH): | Action Potentials*/physiology , Electromyography*/methods , Motor Neurons*/physiology , Muscle, Skeletal*/physiology , Recruitment, Neurophysiological*/physiology, Humans ; Algorithms |
| Περίληψη: | 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. (Creative Commons Attribution license.) |
| Contributed Indexing: | Keywords: first dorsal interosseous; high-density surface EMG; motor unit action potential; motor unit decomposition; motor unit tracking; multi-dimensional |
| Entry Date(s): | Date Created: 20260402 Date Completed: 20260714 Latest Revision: 20260714 |
| Update Code: | 20260714 |
| DOI: | 10.1088/1741-2552/ae5b26 |
| PMID: | 41927003 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1741-2552 |
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| DOI: | 10.1088/1741-2552/ae5b26 |