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Title: Development of surface electromyographic spectral analysis techniques for assessing paraspinal muscle function
Author: Oliver, Christopher William
ISNI:       0000 0001 3457 4991
Awarding Body: University of London
Current Institution: University College London (University of London)
Date of Award: 1995
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In every industrialised society, back pain is the most common musculoskeletal ailment and is the most costly disease process in the working age population. Back pain is a difficult disease to classify and is even harder to objectively measure. In assessing back pain it may be easier to assess function rather than pain. If the deficient anatomical site can be tested, it will be more likely that reproducible information may emerge. It is thought that deconditioned and weakened muscles are associated with back pain. To test lumbar muscle dysfunction ideally the system should be isolated as much as possible, and the test system should have minimal artefacts. To objectively measure function of the lumbar paraspinal muscles a regulated isometric stress testing was used with simultaneous recording of surface electromyograms. Signal analysis of the filtered and digitised signal was then processed by fast Fourier transformation. From the processed signal, power spectrum, median frequency and halfwidth were plotted. Sampling and smoothing software programs were used to produce three-dimensional images representing time on the X axis, frequency of motor unit firing on the V axis and signal amplitude on the Z axis. The signal amplitude was a different colour on a two-dimensional spectrogram plot of time versus frequency, producing a colour 'contour map' of the data. These graphical representations demonstrated the dynamic changes of signal amplitude and frequency with time. Reliability and repeatability studies were performed at two isometric loads. To objectively measure the spectrogram data artificial intelligence neural networks were implemented. Normal subjects and back pain sufferers in this study were shown to demonstrate statistically different power spectra and median frequencies. Spectral colour maps and neural networks showed apparent differences between chronic back pain and normal subjects. Artificial intelligence appeared to be good objective method of measuring paraspinal electromyogram power spectra. The spectral colour maps appeared to reflect altered motor unit firing rates and recruitment patterns. These new methods of objectively measuring lumbar function could have clinical application in assessing back pain patients.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Back pain