Differentiating patients with radiculopathy from chronic low back pain patients by single surface EMG parameter

Abstract

The classification potential of surface electromyographic (EMG) parameters needs to be explored beyond classification of subjects onto low back pain subjects and control subjects. In this paper, a classification model based on surface EMG parameter is introduced to differentiate low back pain patients with radiculopathy from chronic low back pain (CLBP) patients and control subjects. A variant of the Roman chair was used to perform static contractions, where subject's own upper body weight was used to induce muscle fatigue in low back muscles. Surface EMG signals were recorded over the paraspinal muscles at L1–L2 and L4–L5 interspace level. As a descriptor of spectral changes, the median frequency of the power spectrum (MDF) was estimated by use of Hilbert–Huang transform. Student's t-test detected that regression line slope of the median frequency is significantly different (p < 0.05) only between low back pain patients with radiculopathy and other two groups. There was no significant difference between CLBP patients and control subjects. The achieved overall accuracy of the implemented decision tree classification model was at best 86.8%. The results suggest possibility of differentiating low back pain patients to subgroups depending on clinical symptoms.

Keywords

• Biomedical signal processing
• classification
• electromyography
• Hilbert–Huang transform
• low back pain
• radiculopathy