Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography
<p>Abstract</p> <p>Background</p> <p>Methods for the calculation and application of quantitative electromyographic (EMG) statistics for the characterization of EMG data detected from forearm muscles of individuals with and without pain associated with repetitive strain...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-02-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
| Online Access: | http://www.jneuroengrehab.com/content/7/1/8 |
| _version_ | 1818562562526019584 |
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| author | McLean Linda Hamilton-Wright Andrew Stashuk Daniel W Calder Kristina M |
| author_facet | McLean Linda Hamilton-Wright Andrew Stashuk Daniel W Calder Kristina M |
| author_sort | McLean Linda |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Methods for the calculation and application of quantitative electromyographic (EMG) statistics for the characterization of EMG data detected from forearm muscles of individuals with and without pain associated with repetitive strain injury are presented.</p> <p>Methods</p> <p>A classification procedure using a multi-stage application of Bayesian inference is presented that characterizes a set of motor unit potentials acquired using needle electromyography. The utility of this technique in characterizing EMG data obtained from both normal individuals and those presenting with symptoms of "non-specific arm pain" is explored and validated. The efficacy of the Bayesian technique is compared with simple voting methods.</p> <p>Results</p> <p>The aggregate Bayesian classifier presented is found to perform with accuracy equivalent to that of majority voting on the test data, with an overall accuracy greater than 0.85. Theoretical foundations of the technique are discussed, and are related to the observations found.</p> <p>Conclusions</p> <p>Aggregation of motor unit potential conditional probability distributions estimated using quantitative electromyographic analysis, may be successfully used to perform electrodiagnostic characterization of "non-specific arm pain." It is expected that these techniques will also be able to be applied to other types of electrodiagnostic data.</p> |
| first_indexed | 2024-12-14T01:05:23Z |
| format | Article |
| id | doaj.art-b8149f8ab0894c8ab4684346db290bdd |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-14T01:05:23Z |
| publishDate | 2010-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-b8149f8ab0894c8ab4684346db290bdd2022-12-21T23:23:01ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032010-02-0171810.1186/1743-0003-7-8Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyographyMcLean LindaHamilton-Wright AndrewStashuk Daniel WCalder Kristina M<p>Abstract</p> <p>Background</p> <p>Methods for the calculation and application of quantitative electromyographic (EMG) statistics for the characterization of EMG data detected from forearm muscles of individuals with and without pain associated with repetitive strain injury are presented.</p> <p>Methods</p> <p>A classification procedure using a multi-stage application of Bayesian inference is presented that characterizes a set of motor unit potentials acquired using needle electromyography. The utility of this technique in characterizing EMG data obtained from both normal individuals and those presenting with symptoms of "non-specific arm pain" is explored and validated. The efficacy of the Bayesian technique is compared with simple voting methods.</p> <p>Results</p> <p>The aggregate Bayesian classifier presented is found to perform with accuracy equivalent to that of majority voting on the test data, with an overall accuracy greater than 0.85. Theoretical foundations of the technique are discussed, and are related to the observations found.</p> <p>Conclusions</p> <p>Aggregation of motor unit potential conditional probability distributions estimated using quantitative electromyographic analysis, may be successfully used to perform electrodiagnostic characterization of "non-specific arm pain." It is expected that these techniques will also be able to be applied to other types of electrodiagnostic data.</p>http://www.jneuroengrehab.com/content/7/1/8 |
| spellingShingle | McLean Linda Hamilton-Wright Andrew Stashuk Daniel W Calder Kristina M Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography Journal of NeuroEngineering and Rehabilitation |
| title | Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography |
| title_full | Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography |
| title_fullStr | Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography |
| title_full_unstemmed | Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography |
| title_short | Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography |
| title_sort | bayesian aggregation versus majority vote in the characterization of non specific arm pain based on quantitative needle electromyography |
| url | http://www.jneuroengrehab.com/content/7/1/8 |
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