TY - GEN
T1 - Analysis of surface electromyography of patients with low back pain based on different movement patterns
AU - Zhou, Fang
AU - Li, Huihui
AU - Song, Gaojun
AU - Du, Wenjing
AU - Peng, Fei
AU - Wang, Lei
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/24
Y1 - 2017/1/24
N2 - The aim of this study was to analyze Surface Electromyography (sEMG) of low back muscles of low back pain (LBP) patients when they performed different movements. We recruited eighteen female LBP patients and eighteen healthy female subjects. They performed forward and backward trunk bending with the maximum voluntary movement, while sEMG data were collected from the multifidus, external oblique and transverse abdominal muscles. We used root mean square (RMS) of sEMG signals and the coefficient of variation (CV) of RMS sEMG signals as a base for analysis. We found significant differences between the two groups in RMS sEMG signals and the CV feature. For all explored muscles, the mean RMS EMG values of the LBP group were lower than those of the healthy group. The CV feature of the LBP group were higher than those of the healthy group for forward bending and lower for backward bending. The reduced values in LBP patients indicate that the activity levels of all explored muscles in LBP patients were lower than those in the healthy group. The result of this study suggested that RMS sEMG signals captured from the low back muscles, and the corresponding CV feature could serve as a base for quantitative assessment of LBP condition. Also, the CV feature can be used to distinguish LBP group and healthy group.
AB - The aim of this study was to analyze Surface Electromyography (sEMG) of low back muscles of low back pain (LBP) patients when they performed different movements. We recruited eighteen female LBP patients and eighteen healthy female subjects. They performed forward and backward trunk bending with the maximum voluntary movement, while sEMG data were collected from the multifidus, external oblique and transverse abdominal muscles. We used root mean square (RMS) of sEMG signals and the coefficient of variation (CV) of RMS sEMG signals as a base for analysis. We found significant differences between the two groups in RMS sEMG signals and the CV feature. For all explored muscles, the mean RMS EMG values of the LBP group were lower than those of the healthy group. The CV feature of the LBP group were higher than those of the healthy group for forward bending and lower for backward bending. The reduced values in LBP patients indicate that the activity levels of all explored muscles in LBP patients were lower than those in the healthy group. The result of this study suggested that RMS sEMG signals captured from the low back muscles, and the corresponding CV feature could serve as a base for quantitative assessment of LBP condition. Also, the CV feature can be used to distinguish LBP group and healthy group.
KW - Coefficient of variation
KW - Low back pain
KW - Root mean square
KW - Surface electromyography
UR - http://www.scopus.com/inward/record.url?scp=85015771115&partnerID=8YFLogxK
U2 - 10.1109/ICInfA.2016.7831993
DO - 10.1109/ICInfA.2016.7831993
M3 - Conference contribution
AN - SCOPUS:85015771115
T3 - 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016
SP - 1154
EP - 1158
BT - 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016
Y2 - 1 August 2016 through 3 August 2016
ER -