Impact of Motor Unit Action Potential Components on the Motor Unit Identification from Dynamic High-Density Surface Electromyograms

被引:0
|
作者
Glaser, V. [1 ]
Holobar, A. [1 ]
机构
[1] Univ Maribor, Fac Elect Engn & Comp Sci, Smetanova 17, SLO-2000 Maribor, Slovenia
关键词
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
We assessed the impact of different motor unit action potential (MUAP) components in dynamic muscle contractions on decomposition of high-density surface electromyograms (hdEMG). In particular, hypothesis that nontravelling MUAP components, originating from the tendon regions, are less sensitive to changes in geometry of fusiform muscles than travelling MUAP components has been tested on synthetic monopolar hdEMG signals. The latter have been decomposed by previously introduced Convolution Kernel Compensation (CKC) method, using five different sections of simulated MUAPs for motor unit identification. Accuracy of decomposition results increased significantly when motor units were identified from the nontravelling MUAP components, compared to the results obtained from travelling components. Average motor unit identification sensitivity increased from 67.4%+/- 15.7% to 81.3%+/- 11.3% and false alarm rate decreased from 0.75%+/- 1.21% to 0.20%+/- 0.24%. Results confirmed that non-travelling MUAP components are discriminative enough to reliably identify motor units from hdEMG and less sensitive to geometric changes of fusiform muscles during dynamic muscle contractions than travelling MUAP components.
引用
收藏
页码:90 / 93
页数:4
相关论文
共 50 条
  • [1] Motor Unit Identification From High-Density Surface Electromyograms in Repeated Dynamic Muscle Contractions
    Glaser, Vojko
    Holobar, Ales
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (01) : 66 - 75
  • [2] Automatic Identification of Individual Motor Unit Firing Accuracy From High-Density Surface Electromyograms
    Urh, Filip
    Holobar, Ales
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 28 (02) : 419 - 426
  • [3] On the Impact of Spike Segmentation on Motor Unit Identification in Dynamic Surface Electromyograms
    Glaser, V.
    Holobar, A.
    [J]. 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2017, : 430 - 433
  • [4] Novel Method for Accuracy Assessment of Individual Motor Unit Firing Identification from High-Density Surface Electromyograms
    Urh, Filip
    Holobar, Ales
    [J]. 2018 IEEE-EMBS CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES (IECBES), 2018, : 108 - 111
  • [5] Motor unit filter prelearning strategies for decomposition of compound muscle action potentials in high-density surface electromyograms
    Holobar, A.
    Francic, A.
    [J]. 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, 2020, : 3465 - 3468
  • [6] A Novel Measure of Motor Unit Action Potential Variability in Nonstationary Surface Electromyograms
    Glaser, Vojko
    Holobar, Ales
    [J]. CONVERGING CLINICAL AND ENGINEERING RESEARCH ON NEUROREHABILITATION II, VOLS 1 AND 2, 2017, 15 : 123 - 127
  • [7] Real-Time Motor Unit Identification From High-Density Surface EMG
    Glaser, Vojko
    Holobar, Ales
    Zazula, Damjan
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2013, 21 (06) : 949 - 958
  • [8] Motor unit tracking with high-density surface EMG
    Maathuis, Ellen M.
    Drenthen, Judith
    van Dijk, Johannes P.
    Visser, Gerhard H.
    Blok, Joleen H.
    [J]. JOURNAL OF ELECTROMYOGRAPHY AND KINESIOLOGY, 2008, 18 (06) : 920 - 930
  • [9] On the Prediction of Motor Unit Filter Changes in Blind Source Separation of High-Density Surface Electromyograms During Dynamic Muscle Contractions
    Kramberger, Matej
    Holobar, Ales
    [J]. IEEE ACCESS, 2021, 9 : 103533 - 103540
  • [10] Cross-Examination of Motor Unit Pulses Improves the Accuracy of Motor Unit Identification from High-Density EMG
    Urh, F.
    Holobar, A.
    [J]. CONVERGING CLINICAL AND ENGINEERING RESEARCH ON NEUROREHABILITATION III, 2019, 21 : 1136 - 1140