A new and fast approach towards sEMG decomposition.
The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of pres...
| Published in: | Medical & Biological Engineering & Computing Vol. 51; no. 5; pp. 593 - 606 |
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| Main Authors: | , , , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
May2013
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109855810&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109855810 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2013 vid: 51 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109855810 NLM23329211 2012084099 10.1007/s11517-012-1029-y NLM23329211 109855810 ppf: 593 ppct: 13 formats: fmt: @attributes: type: P tig: atl: A new and fast approach towards sEMG decomposition. aug: au: Gligorijevic, Ivan van Dijk, Johannes P Mijovic, Bogdan Van Huffel, Sabine Blok, Joleen H De Vos, Maarten Gligorijević, Ivan Mijović, Bogdan affil: Department of Electrical Engineering, SCD-SISTA, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium sug: subj: Electromyography Methods Signal Processing, Computer Assisted Action Potentials Physiology Adult Algorithms Female Male Motor Neurons Physiology Muscle Contraction Physiology Muscle, Skeletal Physiology Young Adult Adult: 19-44 years Female Male ab: The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using shapes detected on these channels, the hierarchical clustering algorithm as reported by Quian Quiroga et al. (Neural Comput 16:1661-1687, 2004) is extended for multichannel data in order to obtain the motor unit action potential (MUAP) signatures. After this first step, more motor unit firings are obtained using the extracted signatures by a novel demixing technique. In this demixing stage, we propose a time-efficient solution for the general convolutive system that models the motor unit firings on the HD-sEMG grid. We constrain this system by using the extracted signatures as prior knowledge and reconstruct the firing patterns in a computationally efficient way. The algorithm performance is successfully verified on simulated data containing up to 20 different MUAP signatures. Moreover, we tested the method on real low contraction recordings from the lateral vastus leg muscle by comparing the algorithm's output to the results obtained by manual analysis of the data from two independent trained operators. The proposed method showed to perform about equally successful as the operators. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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