DocumentCode
52840
Title
Multi-Scale Surface Electromyography Modeling to Identify Changes in Neuromuscular Activation With Myofascial Pain
Author
Ching-Fen Jiang ; Yu-Ching Lin ; Nan-Ying Yu
Author_Institution
Dept. of Biomed. Eng., I-Shou Univ., Kaohsiung, Taiwan
Volume
21
Issue
1
fYear
2013
fDate
Jan. 2013
Firstpage
88
Lastpage
95
Abstract
To solve the limitations in using the conventional parametric measures to define myofascial pain, a 3-D multi-scale wavelet energy variation graph is proposed as a way to inspect the pattern of surface electromyography (SEMG) variation between the dominant and nondominant sides at different frequency scales during a muscle contraction cycle and the associated changes with the upper-back myofascial pain. The model was developed based on the property of the wavelet energy of the SEMG signal revealing the degree of correspondence between the shape of the motor unit action potential and the wavelet waveform at a certain scale in terms of the frequency band. The characteristic pattern of the graph for each group (30 normal and 26 patient subjects) was first derived and revealed the dominant-hand effect and the changes with myofascial pain. Through comparison of individual graphs across subjects, we found that the graph pattern reveals a sensitivity of 53.85% at a specificity of 83.33% in the identification of myofascial pain. The changes in these patterns provide insight into the transformation between different fiber recruitment, which cannot be explored using conventional SEMG features. Therefore, this multi-scale analysis model could provide a reliable SEMG features to identify myofascial pain.
Keywords
discrete wavelet transforms; electromyography; neuromuscular stimulation; 3D multiscale wavelet energy variation graph; SEMG signal; dominant-hand effect; fiber recruitment; frequency band; motor unit action potential; multiscale analysis model; multiscale surface electromyography; muscle contraction cycle; neuromuscular activation; upper-back myofascial pain; wavelet waveform; Electromyography; Neuromuscular; Pain; Recruitment; Shape; Wavelet transforms; Myofascial pain (MF); surface electromyography (SEMG); wavelet energy; Adult; Algorithms; Computer Simulation; Electromyography; Female; Fibromyalgia; Humans; Male; Middle Aged; Models, Neurological; Models, Statistical; Muscle Contraction; Muscle, Skeletal; Neuromuscular Junction; Reproducibility of Results; Sensitivity and Specificity; Synaptic Transmission;
fLanguage
English
Journal_Title
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1534-4320
Type
jour
DOI
10.1109/TNSRE.2012.2211618
Filename
6327369
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