Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators |
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| Affiliation: | 1. Nantes University, Laboratory “Movement, Interactions, Performance” (EA 4334), Nantes, France;2. The University of Queensland, NHMRC Centre of Clinical Research Excellence in Spinal Pain, Injury and Health, School of Health and Rehabilitation Sciences, Brisbane, Australia;3. Institut Universitaire de France (IUF), Paris, France;4. Legs Walking AbilityLab, Shirley Ryan AbilityLab, Chicago, IL, USA;5. Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA;6. Department of Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander University, Erlangen-Nuremberg, 91052 Erlangen, Germany;7. Department of Biomedical Sciences, University of Padova, Padua, Italy;8. Department of Bioengineering, Faculty of Engineering, Imperial College London, UK;9. Department of Clinical and Movement Disorders, Institute of Neurology, University College London, London WC1N 3BG, UK;10. Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Rome, Italy;11. Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia;1. Neuromechanics Laboratory, University of Kansas, Lawrence, KS, United States;2. Neuromuscular Research Laboratory, University of Pittsburg, Pittsburg, PA, United States;3. Applied Neuromuscular Physiology Laboratory, Oklahoma State University, Stillwater, OK, United States;1. Department of Orthodontics, University of Ulm, Albert-Einstein-Allee 11, 89081 Ulm, Germany;2. Department of Prosthodontics, University of Würzburg, Josef-Schneider-Strasse 2, 97080 Würzburg, Germany;3. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova Ulica 17, 2000 Maribor, Slovenia;4. Department of Clinical Physics, Kempenhaeghe Epilepsy and Sleep Center, Sterkselseweg 65, 5591 VE Heeze, the Netherlands;1. Neuromuscular Research Centre & Technology, Department of Bioengineering, Imperial College London, SW7 2AZ London, UK;2. Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy;1. Department of Movement, Human and Health Sciences, University of Rome ‘Foro Italico’, Rome, Italy;2. Department of Bioengineering, Imperial College London, SW7 2AZ London, UK;1. Graduate program in Rehabilitation Science, University of British Columbia, Vancouver, Canada;2. Laboratorio di Ingegneria del Sistema Neuromuscolare (LISiN), Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Italy;3. Escola de Educação Física e Desportos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil;4. Department of Physical Therapy, University of British Columbia, Vancouver, Canada;1. Department of Clinical and Experimental Sciences, Research Centre for Neuromuscular Function and Adapted Physical Activity “Teresa Camplani”, Università degli Studi di Brescia, Brescia, Italy;2. Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, UK;3. School of Sport and Exercise Sciences, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy |
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| 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. |
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| Keywords: | Neural drive Electromyography Decomposition Dataset Editing |
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