High-density surface electromyography provides reliable estimates of motor unit behavior. Issue 6 (June 2016)

• Record Type: Journal Article
• Authors: Martinez-Valdes, E.; Laine, C.M.; Falla, D.; Mayer, F.; Farina, D.
• Is Part Of: Clinical neurophysiology. Volume 127:Issue 6(2016:Jun.)
• Page Start: 2534
• Page End: 2541
• Publication Date: 2016-06
• Subjects: High-density surface EMG -- Motor unit decomposition -- Conduction velocity -- Motor unit discharge rate -- Vastus lateralis -- Vastus medialis; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2015.10.065
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: The reliability of high-density surface electromyography (HDEMG) for the measurement and estimation of motor unit properties was assessed for the first time. HDEMG-derived measures of motor unit behavior provide reliable results across and within sessions. Motor unit decomposition is accurate enough to detect changes in motor unit behavior over a wide range of force levels (from 10% to 70% of maximum force). Abstract: Objective: To assess the intra- and inter-session reliability of estimates of motor unit behavior and muscle fiber properties derived from high-density surface electromyography (HDEMG). Methods: Ten healthy subjects performed submaximal isometric knee extensions during three recording sessions (separate days) at 10%, 30%, 50% and 70% of their maximum voluntary effort. The discharge timings of motor units of the vastus lateralis and medialis muscles were automatically identified from HDEMG by a decomposition algorithm. We characterized the number of detected motor units, their discharge rates, the coefficient of variation of their inter-spike intervals (CoVisi ), the action potential conduction velocity and peak-to-peak amplitude. Reliability was assessed for each motor unit characteristics by intra-class correlation coefficient (ICC). Additionally, a pulse-to-noise ratio (PNR) was calculated, to verify the accuracy of the decomposition. Results: Good to excellent reliability within and between sessions was found for all motor unit characteristics at all force levels (ICCs > 0.8), with the exception of CoVisi that presented poor reliability (ICC < 0.6). PNR was high and similar for both muscles with values ranging between 45.1 and 47.6 dB (accuracy > 95%). Conclusion: Motor unit features can be assessed non-invasively and reliably within and across sessions over a wide range of force levels. Significance: These results suggest that it is possible to characterize motor units in longitudinal intervention studies.