低负荷作业水平颈肩肌肉骨骼疾患的肌电信号特征研究

摘要

探讨低负荷作业水平下颈肩肌肉骨骼疾患的特征性肌电信号.使用北欧肌肉骨骼疾患问卷将24名缝纫女工分为症状组和无症状组.采用表面肌电图技术(sEMG)连续记录两组女工在缝纫操作时双侧颈竖脊肌(LCES和RCES)、双侧上斜方肌(LUT和RUT)200 min的肌电信号.肌电的时域指标以电活性(EA)表示,频域指标为中位频率(MF),并计算每分钟EA随时间变化的斜率(EA slope)和每分钟MF随时间变化的斜率(MF slope).通过频谱振幅联合分析(JASA),统计测试肌肉EA slope与MF slope交点在JASA图中的频数和时间赋值,结果表明无症状组测试肌肉EA slope与MF slope交点在JASA图各象限分布均匀,差异无显著性；症状组在JASA图各象限分布频数的差异有显著性(x2=46.08,P＜0.01).两组工人不同肌肉在各象限中分布:LCES和RCES在第2象限(x2LCES=5.35,P＜0.05；x2RCES=9.36,P ＜0.01)和第4象限(x2LCES=5.63,P＜0.05；x2RCES=5.93,P＜0.01)中频数分布差异有显著性.LUT和RUT在这两个象限中频数分布差异无显著性.EA slope与MF slope的交点在JASA图中时间赋值分析表明,无症状组肌电在JASA图4个象限点的分布差异经方差分析显示无显著性；症状组则差异有显著性( FLCES=2.29,P＜0.05；FRCRS=4.02,P＜0.01；FLUT=3.39,P ＜0.05；FRUT=4.98,P ＜0.01).两组EA slope与MF slope交点在JASA图4个象限中时间赋值分布有显著性差异(F=5.43,P＜0.01).振幅指标和频谱指标的斜率交点在JASA图第4象限分布不均,是颈肩部肌肉骨骼疾患肌电信号的特征性变化.%The aim of this work is to identify and quantify the feature of electromyographic signals of work-related musculoskeletal disorders of neck-shoulder using sEMG. Twenty four of female sewing machine operators were divided into two groups using Nordic musculoskeletal disorder questionnaire. The sEMG signals of bilateral cervical erector spine (LCES/RCES) and bilateral upper trapezuis (LUT/RUT) were monitored and recorded during the whole operating time (200 min). Results showed the distribution of joint points for EA slope and MF slope intersections was equivalent in asymptomatic group and uneven in symptomatic group,and in symptomatic group the frequency of crossing in second and fourth quadrant were more than that of in the first and third quadrant (X2=46. 80,P < 0. 01 ) ,According to ANOVAs analysis,the differences of four quadrants were significant ( FLCES = 2. 29,P < 0. 05; FRCES = 4. 02,P < 0. 01; FLUT = 3. 39,P < 0. 05; FRUT= 4. 98 ,P < 0. 01) in symptomatic group,and the difference of time assignment distribution in the fourth quadrant between two groups were statistical significant (F = 5. 43 ,P <0. 01). The distribution of intersection point of EA slope and MF slope in the fourth quadrant diagram was uneven,which might be contributed by the sEMG signal signatures of neck-shoulder musculoskeletal disorders.