Name
Abstract | ||
|---|---|---|
This study was designed to evaluate the suitability of nonlinear recurrence quantification analysis (RQA) in assessing electromyograph (EMG) signals during dynamic exercise. RQA has been proven to be effective in analyzing nonstationary signals. The subject group consisted of 19 male patients diagnosed with low back pain. EMG signals were recorded from left and right paraspinal muscles during isoinertial exercise both before and after 12 weeks of regimented physical therapy. Autorecurrence analysis was performed between the left and right EMG signals individually, and cross-recurrence analysis was performed on the left-right EMG pairs. Spectral analysis of the EMG signals was employed as an independent, objective measure of fatigue. Increase in the RQA variable % determinism during the 90-s dynamic tests was found to be a good marker for fatigue. Before physical therapy, this nonlinear marker revealed simultaneous increases in motor unit recruitment within each pool and between left and right pools. After physical therapy, the motor unit recruitment was less within and between pools, indicative of increased fatigue resistance. Finally, fatigue resistance (less increase in % determinism) correlated well with subjective scores of pain relief. Taken together, these latter results indicate that recurrence analysis may be useful in charting the efficacy of a specific exercise therapy program in reducing low back pain by elevating the fatigue threshold. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/s00422-004-0474-6 | Biological Cybernetics |
Keywords | Field | DocType |
Fatigue,Physical Therapy,Fatigue Resistance,Exercise Therapy,Paraspinal Muscle | Increased fatigue,Exercise therapy,Paraspinal Muscle,Fatigue resistance,Physical medicine and rehabilitation,Spectral analysis,Recurrence quantification analysis,Mathematics,Low back pain,Motor unit recruitment | Journal |
Volume | Issue | ISSN |
90 | 5 | 0340-1200 |
Citations | PageRank | References |
4 | 1.00 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiwei Liu | 1 | 4 | 1.00 |
| Markku Kankaanpää | 2 | 33 | 6.17 |
| Joseph P. Zbilut | 3 | 48 | 10.79 |
| Charles L. Webber Jr. | 4 | 13 | 4.99 |