論文

査読有り
2008年2月

Investigation of optimum electrode locations by using an automatized surface electromyography analysis technique

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
  • Ken Nishihara
  • ,
  • Hisashi Kawai
  • ,
  • Toshiaki Gomi
  • ,
  • Miho Terajima
  • ,
  • Yu Chiba

55
2
開始ページ
636
終了ページ
642
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/TBME.2007.912673
出版者・発行元
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Ideintification of the innervation zone is widely used to optimize the accuracy and precision of noninvasive surface electromyography (EMG) signals because the EMG signal is strongly influenced by innervation zones. However, simply structured fusiform muscle, such as biceps brachii muscle, has been employed mainly due to the simplicity with which the propagation from raw EMG signals can be observed. In this study, the optimum electrode location (OEL), free from innervational influence, was investigated by the propagation pattern of action potentials for brachii muscles and more complicated deltoid muscle structures using an automatized signal analysis technique. The technique employed newly developed computer software with additional clinical uses and minimized subjective differences. EMG signals were recorded using surface array electrodes during voluntary isometric contractions obtained from 12 healthy male subjects. Peaks in EMG signals were detected and averaged for each muscle. The propagation patterns and OEL were. examined from biceps brachii muscles for all subjects and from deltoid muscles for seven subjects. The estimated locations were partially confirmed by comparing the root mean squares of the EMG signals. These results show that propagation patterns and OEL could be estimated simply and automatically even from the surface EMG signals of deltoid muscles.

リンク情報
DOI
https://doi.org/10.1109/TBME.2007.912673
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000252622200026&DestApp=WOS_CPL
ID情報
  • DOI : 10.1109/TBME.2007.912673
  • ISSN : 0018-9294
  • Web of Science ID : WOS:000252622200026

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