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Title:Identifikacija gruč neločljivih motoričnih enot iz večkanalnih površinskih elektromiogramov dvoglave nadlahtne mišice
Authors:ID Kutoš, Leon, Laboratorij za sistemsko programsko opremo, Fakulteta za elektrotehniko, računalništvo in informatiko, Univerza v Mariboru, Slovenija (Author)
ID Škarabot, Jakob, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK (Author)
ID Holobar, Aleš, Laboratorij za sistemsko programsko opremo, Fakulteta za elektrotehniko, računalništvo in informatiko, Univerza v Mariboru, Slovenija (Author)
Files:.pdf Identifikacija_gruc_nelocljivih_motoricnih_enot_iz_veckanalnih_povrsinskih_elektromiogramov_dvoglavne_nadlahtne_misice_-_Kutos_et_al.pdf (1,01 MB)
MD5: 45D88D3F5C890A4ACC833F0EB8C5FFF3
 
Language:Slovenian
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:We analyzed the capability of previously introduced Convolution Kernel Compensation (CKC) method to identify clusters of motor units (MUs) that share similar motor unit action potentials (MUAPs) and, therefore, cannot be mutually discriminated by the decomposition of high-density surface electromyograms (hdEMG). The tests were performed on biceps brachii muscle because hdEMG decomposition yields relatively small number of individual MUs in this muscle. In this study, we analyzed how many MUs of biceps brachii get merged into the same spike train by the CKC method due to MUAP similarity and what are the sensitivity and precision of MU discharge identification in merged spike trains. We compared these metrics with the identification of individual MUs in both synthetic and experimental hdEMG. In synthetic hdEMG with 20 dB noise, the number of identified MUs increased from 5.2 ± 2.8 (individual MUs) to 16.4±8.4 MUs when merged MU spike trains were taken into consideration, in addition to individual MUs. Discharges of individual MUs were identified with sensitivity of 77.0±15.8 % and precision of 86.1±25.4 %, whereas the merg ed MUs were identified with sensitivity of 79.6±15.7 % and precision of 97.4±11.8 %. Similar results were observed also for noiseless hdEMG. In experimental hdEMG signals from biceps brachii muscle of two young healthy individuals, the number of identified MUs increased from 7.5±2.4 (individual MUs) to 21.9±17.6 MUs (merged MU spike trains). Individual MUs were identified with sensitivity of 83.3±15.9 % and precision of 80.0±19.0 %, whereas when considering also merged MUs, the sensitivity and precision of MU discharge identification increased to 82.3±22.4 % and 94.9±11.4 %, respectively. In conclusion, the merged MU spike trains obtained by hdEMG decompositions carry important information about the activity of skeletal muscles and can be used to increase the number of MUs identified from hdEMG.
Keywords:površinski elektromiogram (EMG), motorična enota, vlak impulzov, zlivanje motoričnih enot, biceps brachii
Publication version:Version of Record
Publication date:28.09.2023
Year of publishing:2023
Number of pages:4
PID:20.500.12556/DKUM-88846 New window
UDC:004.9
ISSN on article:2591-0442
COBISS.SI-ID:167880451 New window
Publication date in DKUM:30.05.2024
Views:416
Downloads:21
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a proceedings

Title:Mednarodna Elektrotehniška in računalniška konferenca ERK 2023
Conference organizer:Fakulteta za elektrotehniko, Univerza v Ljubljani, Slovenija

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0041
Name:Računalniški sistemi, metodologije in inteligentne storitve

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

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:29.05.2024

Secondary language

Language:English
Title:Identification of clusters of inseparable motor units from high-density electromyograms of biceps brachii muscle
Abstract:We analyzed the capability of previously introduced Convolution Kernel Compensation (CKC) method to identify clusters of motor units (MUs) that share similar motor unit action potentials (MUAPs) and, therefore, cannot be mutually discriminated by the decomposition of high-density surface electromyograms (hdEMG). The tests were performed on biceps brachii muscle because hdEMG decomposition yields relatively small number of individual MUs in this muscle. In this study, we analyzed how many MUs of biceps brachii get merged into the same spike train by the CKC method due to MUAP similarity and what are the sensitivity and precision of MU discharge identification in merged spike trains. We compared these metrics with the identification of individual MUs in both synthetic and experimental hdEMG. In synthetic hdEMG with 20 dB noise, the number of identified MUs increased from 5.2 ± 2.8 (individual MUs) to 16.4±8.4 MUs when merged MU spike trains were taken into consideration, in addition to individual MUs. Discharges of individual MUs were identified with sensitivity of 77.0±15.8 % and precision of 86.1±25.4 %, whereas the merg ed MUs were identified with sensitivity of 79.6±15.7 % and precision of 97.4±11.8 %. Similar results were observed also for noiseless hdEMG. In experimental hdEMG signals from biceps brachii muscle of two young healthy individuals, the number of identified MUs increased from 7.5±2.4 (individual MUs) to 21.9±17.6 MUs (merged MU spike trains). Individual MUs were identified with sensitivity of 83.3±15.9 % and precision of 80.0±19.0 %, whereas when considering also merged MUs, the sensitivity and precision of MU discharge identification increased to 82.3±22.4 % and 94.9±11.4 %, respectively. In conclusion, the merged MU spike trains obtained by hdEMG decompositions carry important information about the activity of skeletal muscles and can be used to increase the number of MUs identified from hdEMG.
Keywords:surface high density electromyogram (HDEMG), motor unit, spike train, motor unit merging, biceps brachii


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