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DC Field | Value | Language |
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dc.contributor.author | Abel, E | - |
dc.contributor.author | Meng, H | - |
dc.contributor.author | Forster, A | - |
dc.contributor.author | Holder, D | - |
dc.date.accessioned | 2011-12-09T10:59:37Z | - |
dc.date.available | 2011-12-09T10:59:37Z | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | IEEE Transactions on Biomedical Engineering, 53(2): 219 - 225, Feb 2006 | en_US |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.other | http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1580827 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/6058 | - |
dc.description | This is the post-print version of the Article - Copyright @ 2006 IEEE | en_US |
dc.description.abstract | Clinical electromyography (EMG) interference pattern (IP) signals can reveal more diagnostic information than their constituents, the motor unit action potentials (MUAPs). Singularities and irregular structures typically characterize the mathematically defined content of information in signals. In this paper, a wavelet transform method is used to detect and quantify the singularity characteristics of EMG IP signals using the Lipschitz exponent (LE) and measures derived from it. The performance of the method is assessed in terms of its ability to discriminate healthy, myopathic and neuropathic subjects and how it compares with traditionally used Turns Analysis (TA) methods and a method recently developed by the authors, interscale wavelet maximum (ISWM). Highly significant intergroup differences were found using the LE method. Most of the singularity measures have a performance similar to that of ISWM and considerably better than that of TA. Some measures such as the ratio of the mean LE value to the number of singular points in the signal have considerably superior performance to both methods. These findings add weight to the view that wavelet analysis methods offer an effective way forward in the quantitative analysis of EMG IP signal to assist the clinician in the diagnosis of neuromuscular disorders. | en_US |
dc.description.sponsorship | This work was funded by the Engineering and Physical Sciences Research Council, Swindon, U.K. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | DSP | en_US |
dc.subject | Signal Processing | en_US |
dc.subject | EMG | en_US |
dc.subject | Embedded Systems | en_US |
dc.title | Singularity characteristics of needle EMG IP signals | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/TBME.2005.862548 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel (Active) | - |
pubs.organisational-data | /Brunel/Brunel (Active)/School of Engineering & Design | - |
pubs.organisational-data | /Brunel/Brunel Active Staff | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Engineering and Design | - |
pubs.organisational-data | /Brunel/Brunel Active Staff/School of Engineering and Design/Electronic and Computer Engineering | - |
Appears in Collections: | Electronic and Electrical Engineering Publications Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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singularityTBME2.pdf | 193.51 kB | Adobe PDF | View/Open |
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