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Title:Manual editing significantly improves the accuracy of motor unit recruitment threshold and discharge rate assessment from synthetic high-density surface electromyograms
Authors:ID Murks, Nina, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia (Author)
ID Škarabot, Jakob, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Kramberger, Matej, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia (Author)
ID Sedej, Gašper, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia (Author)
ID Valenčič, Tamara, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Connelly, Christopher D., School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Thomason, Haydn, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Divjak, Matjaž, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia (Author)
ID Holobar, Aleš, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia (Author)
Files:.pdf ERK_2025_Murks.pdf (417,15 KB)
MD5: 544F5B9A67DE714DC053ED4C55103305
 
URL https://erk.fe.uni-lj.si/2025/papers/murks(manual_editing).pdf
 
Language:English
Work type:Scientific work
Typology:1.08 - Published Scientific Conference Contribution
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:This study examined the influence of manual editing on the accuracy of motor unit (MU) metrics derived from high-density surface EMG. Seven operators with varying levels of experience manually edited synthetic datasets from the Soleus and Biceps Brachii. We then calculated recruitment threshold (ReTh), derecruitment threshold (DeTh), discharge rate (DR), recruitment discharge rate (ReDR), and derecruitment discharge rate (DeDR) before and after editing. The effects of operator’s experience, contraction level, initial pulse-to-noise ratio (PNR), signal-to-noise ratio (SNR), muscle type, and editing status were analyzed. Manual editing significantly improved accuracy only for ReTh and DR, whereas with other metrics, it only had a minor, insignificant effect. Higher initial PNR consistently reduced errors across all metrics. Contraction level strongly influenced all metrics, whereas muscle type and SNR had minimal impact—SNR was significant only for DeTh, and muscle type only for ReTh. Operator’s experience did not significantly affect any metric. Overall, these results indicate that manual editing is critical for reliable estimation of ReTh and DR, but less essential for DeDR, particularly when initial PNR is high.
Keywords:high-density surface electromyography, motor unit, soleus, biceps brachii, recruitment threshold, discharge rate
Publication status:Not published
Publication version:Preprint, working version of publication, not peer-reviewed
Year of publishing:2025
Number of pages:5
PID:20.500.12556/DKUM-97207 New window
UDC:004.5:61
ISSN on article:2591-0442
COBISS.SI-ID:251377411 New window
Publication date in DKUM:26.02.2026
Views:157
Downloads:3
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a monograph

Title:Zbornik štiriintridesete mednarodne Elektrotehniške in računalniške konference ERK 2025 : Proceedings of the 34th International Electrotechnical and Computer Science Conference ERK 2025
Editors:Andrej Žemva, Andrej Trost
Place of publishing:Portorož
Publisher:Ljubljana : Slovenska sekcija IEEE : Fakulteta za elektrotehniko, 2025
Year of publishing:2025
ISBN:250462467
Conference organizer:Slovenska sekcija IEEE, Fakulteta za elektrotehniko, Ljubljana

Document is financed by a project

Funder:EC - European Commission
Funding programme:HE
Project number:101079392
Name:Hybrid neuroscience based on cerebral and muscular information for motor rehabilitation and neuromuscular disorders
Acronym:HybridNeuro

Funder:UKRI - UK Research and Innovation
Funding programme:Horizon Europe Guarantee
Project number:10052152
Name:Hybrid neuroscience based on cerebral and muscular information for motor rehabilitation and neuromuscular disorders (HybridNeuro)

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:25.09.2025

Secondary language

Language:Slovenian
Keywords:elektromiogrami, motorika mišic


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