Cervical EMG profile differences between patients of neck pain and control.

PURPOSE The objective of this study was to investigate EMG signals of cervical muscles in five directional efforts from chronic neck pain patients and compare them with those of the healthy controls to discern differences between patients and controls with respect to strength and EMG characteristics. METHOD Seventeen male and 17 female idiopathic and non-specific chronic neck pain patients without any diagnosed pathologies or prior surgery in the age group 18-65 years were recruited into the study. The controls consisted of 30 male and 33 female subjects with no history of neck pain in the past 12 months. Both patients and controls performed the experimental activities of flexion, left anterolateral flexion, left lateral flexion, left posterolateral extension and extension. The patients exerted to their 20% maximum voluntary contraction (MVC), pain threshold and pain tolerance levels in three separate contractions. Similarly, the control subjects exerted to their 20% MVC, 60% MVC and MVC in random order. The descriptive statistics for strength, normalised peak EMG, median frequency (MF), 10% frequency bands and their power were calculated. Eight levels of wavelet decomposition and their coefficients were calculated and subjected to principal component analysis. These variables were subjected to analysis of variance and regression analysis to distinguish between patients and controls. The full wave rectified linear envelope detected EMG of patients and controls were plotted against time to reveal pattern differences. RESULTS There was a lack of significant difference in the MF of the two samples indicating that the muscle conduction velocity was not disturbed by the pain. Significant differences were also found in 10 percentile frequency bands between patients and controls (p < 0.05). The wavelet decomposition with principal component analysis revealed that patients and controls could be identified as such 100% of the time at 20% MVC; and, patients and controls could be identified correctly 100% and 90% of the time respectively at pain threshold/60% MVC. CONCLUSION Thus, a combination of EMG spectral frequency banding and wavelet decomposition with regression can be used to distinguish chronic pain patients from controls.