A novel approach to automatically quantify the level of coincident activity between EMG and MMG signals.
Although previous studies have highlighted both similarities and differences between the timing of electromyography (EMG) and mechanomyography (MMG) activities of muscles, there is no method to systematically quantify the temporal alignment between corresponding EMG and MMG signals. We proposed a no...
| Publicado en: | Journal of Electromyography & Kinesiology Vol. 41; pp. 34 - 41 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Aug2018
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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=130303193&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130303193 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10506411 JIX jtl: Journal of Electromyography & Kinesiology issn: 10506411 maglogo: N pubinfo: dt: Aug2018 vid: 41 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 130303193 130303193 NLM29738937 130303193 10.1016/j.jelekin.2018.04.001 NLM29738937 130303193 ppf: 34 ppct: 7 formats: tig: atl: A novel approach to automatically quantify the level of coincident activity between EMG and MMG signals. aug: au: Plewa, Katherine Samadani, Ali Orlandi, Silvia Chau, Tom affil: Holland Bloorview Kids Rehabilitation Hospital, Canada sug: subj: Electromyography Methods Female Male Algorithms Muscle, Skeletal Physiology Adult Gait Lower Extremity Physiology Human Adult: 19-44 years Female Male ab: Although previous studies have highlighted both similarities and differences between the timing of electromyography (EMG) and mechanomyography (MMG) activities of muscles, there is no method to systematically quantify the temporal alignment between corresponding EMG and MMG signals. We proposed a novel method to determine the level of coincident activity in quasi-periodic MMG and EMG signals. The method optimizes 3 muscle-specific parameters: amplitude threshold, window size and minimum percent of EMG and MMG overlap using a particle swarm optimization algorithm to maximize the agreement (balanced accuracy) between electrical and mechanical muscle activity. The method was applied to bilaterally recorded EMG and MMG signals from 4 lower limb muscles per side of 25 pediatric participants during self-paced gait. Mean balanced accuracy exceeded 75% for all muscles except the lateral gastrocnemius, where EMG and MMG misalignment was notable (56% balanced accuracy). The proposed method can be applied to the criterion-driven comparison of simultaneously recorded myographic signals from two different measurement modalities during a motor task. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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