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Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_4 | Pages 23 - 23
1 Jan 2013
Coxon A Farmer S Greenough C
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Introduction

ECG contamination of paraspinal EMG measurements is a known issue (1,2), with several proposed methods of correction(3,4). In addition to this some question remains to how much of an effect this contamination actually has on the EMG recordings.

Methods

From a population of 455 previously recorded EMG datasets, 33 severely contaminated sets of data were selected. These 33 datasets were analysed to produce the Half-Width, RMS, RMS Slope, RMS Intercept, MF Slope, and MF Intercept variables.

The Independent Component Analysis method was used to separate the EMG data into a series of additive subcomponents which allowed the removal of ECG contamination whilst preserving underlying EMG. The subcomponents were then reintegrated to produce the original EMG signal, minus the contamination.

The resultant signal data were analysed to produce the same outcome variables so a comparison could be made.


Orthopaedic Proceedings
Vol. 94-B, Issue SUPP_I | Pages 36 - 36
1 Jan 2012
Coxon A Farmer S Greenough C
Full Access

Introduction

It has previously been reported (1,2,3) that EMG signals from the lumbar spine are highly prone to contamination by ECG artefacts. It has also been reported that Independent Component Analysis is a suitable method for extracting this contamination (4).

Methods

EMG data was recorded from 192 subjects across two years (initial contact, 12 months and 24 months). The data were analysed and the spectral half-widths calculated.

The ICA method was then applied to the original raw data. As the power spectrum of ECG runs from 0-20Hz the resultant spectra were analysed to calculate which of them had the most signal energy below 20Hz. A high band pass filter was used to remove all signal data below 20Hz from this independent component.

This method was chosen as there was signal data present in the chosen spectrum above 20Hz which would be EMG data. Removing data only below 20Hz preserved this EMG data.

The components were then re-integrated and re-analysed to calculate the new half-widths. These new half-widths were compared with the originals to generate the results.


Orthopaedic Proceedings
Vol. 94-B, Issue SUPP_I | Pages 40 - 40
1 Jan 2012
Coxon A Farmer S Watson P Murray M Roper H Kaid L Greenough C
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Introduction

Previous work(1) has suggested that Spectral Colour Mapping (SCM) may have potential as an objective measurement tool for analysing Electromyography (EMG) data from spinal muscles, but the production and analysis of these maps is a complex undertaking. It would be beneficial for a system to create these maps and be useable with a minimum of training.

Methods

EMG data was recorded from 192 subjects across two years (initial contact, 12 months and 24 months). The data were analysed and SCMs produced. The 30 second test data was split into 30 one second epochs. Colour values were scaled to the individual data set maximum and divided into 12 bands according to frequency strength at a particular point. Median Frequency values were calculated for each epoch and a line of best fit added to the colour map to further aid the diagnosis process.

Maps with faulty recordings were excluded and 20 data sets from each group (BP and no BP) selected at random. Four observers were given only 5 minutes instruction and then asked to indicate whether they thought each map belonged to the LBP or no LBP group.


Orthopaedic Proceedings
Vol. 93-B, Issue SUPP_IV | Pages 485 - 485
1 Nov 2011
Coxon A Farmer S Greenough C
Full Access

Introduction: It has previously been reported(1,2,3) that EMG signals from the lumbar spine are highly prone to contamination by ECG artefacts. As the ECG spectrum overlaps an area of interest in the EMG spectrum this has obvious implications for the accurate analysis of EMG data.

Methods: EMG data was recorded from 192 subjects across two years (initial contact, 12 months and 24 months). When a moving average filter was applied to this raw data an obvious ECG trace could be observed in the case of a large proportion of the tests. The application of a Fast Fourier Transform on this raw data demonstrated a large low frequency spike, with little known correlation to lumbar muscle spectral characteristics, but highly indicative of an ECG signal.

As multiple source signals were recorded per test, the Independent Component Analysis technique was able to be used to split the EMG raw signal into statistically independent components. This technique is designed to take the multiple signal inputs, and convert them into multiple outputs, where the inputs are distinguishable by electrode location; the outputs are distinguishable by signal biological origin.

Results: Upon extraction, one of the signal traces showed a clear ECG trace. The Fourier Transform of this trace showed the low frequency spike, with no other signal components present. The Fourier Transform of the EMG trace showed the original EMG graph, with no low frequency peak. Specific spatial information has been exchanged for a much cleaner signal.

Conflicts of Interest: None

Source of Funding: None


Orthopaedic Proceedings
Vol. 90-B, Issue SUPP_III | Pages 491 - 492
1 Aug 2008
Coxon A Farmer S Greenough C
Full Access

Introduction: It has previously been reported that EMG variables recorded from the lumbar spinal muscles may be recorded reproducibly, are able to discriminate low back pain subjects from normal volunteers and are predictive of future back pain. At present, however, an experienced operator is required to acquire the signals and to determine the value of some variables. This has hindered the transfer of the technique from the laboratory to the clinical setting.

Methods: The EMG signal is subjected to a Fast Fourier Transform and a power spectrum is produced. An Expert System has been developed to examine this power spectrum. In accordance to a rule base several variables are generated including the half width. The error analysis can detect a number of possible errors of recording that can affect test results and unusual traces are flagged for further consideration. In some defined cases a correction is automatically applied.

Results: The Standard error between tshe manually generated half width and the automatically calculated value is 30%. Using the automated system 5% of subjects were found to change classification from normal to at risk. The sensitivity and specificity of detecting recording errors was 0.5 and 0.4 respectively. Work is ongoing.

Conclusions: The new system has reduced data set analysis from days to minutes, thus many different methods of analysis can be compared and contrasted readily. The automatic calculation of half width and other variables has brought clinical usage one step closer, and allow EMG analysis to provide a useful tool for monitoring treatment and measuring outcome.