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Automatika : journal for control, measurement, electronics, computing and communications, Vol. 59 No. 3-4, 2018.

Original scientific paper

https://doi.org/10.1080/00051144.2018.1553669

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

S. Ostojić ; Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb Zagreb, Croatia
S. Peharec ; Polyclinic Peharec, Pula, Croatia
V. Srhoj-Egekher ; Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb Zagreb, Croatia
M. Cifrek ; Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb Zagreb, Croatia

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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

Hrčak ID:

225224

URI

https://hrcak.srce.hr/225224

Publication date:

12.12.2018.

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