Network modeling and analysis of lumbar muscle surface EMG signals during flexion-extension in individuals with and without low back pain

In this paper, we propose modeling the activity coordination network between lumbar muscles using surface electromyography (sEMG) signals and performing the network analysis to compare the lumbar muscle coordination patterns between patients with low back pain (LBP) and healthy control subjects. Ten healthy subjects and eleven LBP patients were asked to perform flexion-extension task, and the sEMG signals were recorded. Both the subject-level and the group-level PC(fdr) algorithms are applied to learn the sEMG coordination networks with the error-rate being controlled. The network features are further characterized in terms of network symmetry, global efficiency, clustering coefficient and graph modules. The results indicate that the networks representing the normal group are much closer to the order networks and clearly exhibit globally symmetric patterns between the left and right sEMG channels. While the coordination activities between sEMG channels for the patient group are more likely to cluster locally and the group network shows the loss of global symmetric patterns. As a complementary tool to the physical and anatomical analysis, the proposed network analysis approach allows the visualization of the muscle coordination activities and the extraction of more informative features from the sEMG data for low back pain studies.