Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators. (June 2021)

• Record Type: Journal Article
• Authors: Hug, François; Avrillon, Simon; Del Vecchio, Alessandro; Casolo, Andrea; Ibanez, Jaime; Nuccio, Stefano; Rossato, Julien; Holobar, Aleš; Farina, Dario
• Is Part Of: Journal of electromyography and kinesiology. Volume 58(2021)
• Publication Date: 2021-06
• Subjects: Neural drive -- Electromyography -- Decomposition -- Dataset -- Editing; Electromyography -- Periodicals; Kinesiology -- Periodicals; Electromyography -- Periodicals; Movement -- physiology -- Periodicals; Muscles -- physiology -- Periodicals; Électromyographie -- Périodiques; Cinésiologie -- Périodiques; Electromyography; Kinesiology; Electronic journals; Periodicals; 616.740757
• DOI: 10.1016/j.jelekin.2021.102548
• Languages: English
• ISSNs: 1050-6411

Abstract

Abstract: There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119–134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons × 11 contractions/muscles × 4–28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.