Validating motor unit firing patterns extracted by EMG signal decomposition.
Motor unit (MU) firing pattern information can be used clinically or for physiological investigation. It can also be used to enhance and validate electromyographic (EMG) signal decomposition. However, in all instances the validity of the extracted MU firing patterns must first be determined. Two sup...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 6; pp. 649 - 659 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jun2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104572116&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104572116 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2011 vid: 49 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104572116 NLM21042949 2011162614 10.1007/s11517-010-0703-1 NLM21042949 104572116 ppf: 649 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Validating motor unit firing patterns extracted by EMG signal decomposition. aug: au: Parsaei H Nezhad FJ Stashuk DW Hamilton-Wright A Parsaei, Hossein Nezhad, Faezeh Jahanmiri Stashuk, Daniel W Hamilton-Wright, Andrew affil: Systems Design Engineering Department, University of Waterloo, Waterloo, Canada sug: subj: Electromyography Methods Motor Neurons Physiology Signal Processing, Computer Assisted Algorithms Computer Simulation Human Muscle Contraction Physiology Muscle, Skeletal Innervation Muscle, Skeletal Physiology Reproducibility of Results ab: Motor unit (MU) firing pattern information can be used clinically or for physiological investigation. It can also be used to enhance and validate electromyographic (EMG) signal decomposition. However, in all instances the validity of the extracted MU firing patterns must first be determined. Two supervised classifiers that can be used to validate extracted MU firing patterns are proposed. The first classifier, the single/merged classifier (SMC), determines whether a motor unit potential train (MUPT) represents the firings of a single MU or the merged activity of more than one MU. The second classifier, the single/contaminated classifier (SCC), determines whether the estimated number of false-classification errors in a MUPT is acceptable or not. Each classifier was trained using simulated data and tested using simulated and real data. The accuracy of the SMC in categorizing a train correctly is 99% and 96% for simulated and real data, respectively. The accuracy of the SCC is 84% and 81% for simulated and real data, respectively. The composition of these classifiers, their objectives, how they were trained, and the evaluation of their performances using both simulated and real data are presented in detail. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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