Literature DB >> 18269999

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

Ken Nishihara1, Hisashi Kawai, Toshiaki Gomi, Miho Terajima, Yu Chiba.   

Abstract

Identification 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.

Entities:  

Mesh:

Year:  2008        PMID: 18269999     DOI: 10.1109/TBME.2007.912673

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  5 in total

1.  Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim.

Authors:  Jose Amezquita-Garcia; Miguel Bravo-Zanoguera; Felix F Gonzalez-Navarro; Roberto Lopez-Avitia; M A Reyna
Journal:  Sensors (Basel)       Date:  2022-05-14       Impact factor: 3.847

2.  A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics.

Authors:  Cynthia Kendell; Edward D Lemaire; Yves Losier; Adam Wilson; Adrian Chan; Bernie Hudgins
Journal:  J Neuroeng Rehabil       Date:  2012-04-26       Impact factor: 4.262

Review 3.  A review of non-invasive techniques to detect and predict localised muscle fatigue.

Authors:  Mohamed R Al-Mulla; Francisco Sepulveda; Martin Colley
Journal:  Sensors (Basel)       Date:  2011-03-24       Impact factor: 3.576

Review 4.  The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury.

Authors:  Morufu Olusola Ibitoye; Eduardo H Estigoni; Nur Azah Hamzaid; Ahmad Khairi Abdul Wahab; Glen M Davis
Journal:  Sensors (Basel)       Date:  2014-07-14       Impact factor: 3.576

5.  Investigation of innervation zone shift with continuous dynamic muscle contraction.

Authors:  Ken Nishihara; Hisashi Kawai; Yu Chiba; Naohiko Kanemura; Toshiaki Gomi
Journal:  Comput Math Methods Med       Date:  2013-06-03       Impact factor: 2.238

  5 in total

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