Pairwise attribute noise detection algorithm for detecting noise in surface electromyography recordings

dc.contributor.advisorMacIsaac, Dawn
dc.contributor.advisorParker, Philip
dc.contributor.advisorFleming, Michael
dc.contributor.authorPhillips, Gillian
dc.date.accessioned2023-03-01T16:16:26Z
dc.date.available2023-03-01T16:16:26Z
dc.date.issued2016
dc.date.updated2023-03-01T15:01:06Z
dc.description.abstractThe focus of this work was to modify an existing algorithm originally designed for data mining software metrics and evaluate its usefulness as a quality assessment tool for surface electromyography (sEMG) signals. The pairwise attribute noise detection algorithm (PANDA) was configured to distinguish between clean and noisy sEMG signals. Multiple testing was performed to find the most effective configuration for contamination detection. Data contaminated with power line interference, motion artifact, saturation and combinations of the three were studied. Both simulated and recorded data were used in the configuration and testing stages of this work. PANDA was found to be able to detect low levels of contamination (SNRs of 3 – 17 dB, depending on the type of noise) with high sensitivity (100%). After verifying PANDA’s effectiveness, the algorithm was compared to a one-class support vector machine (SVM) designed for the same purpose. For all types of noise, PANDA was more sensitive than the SVM.
dc.description.copyright© Gillian Phillips, 2016
dc.formattext/xml
dc.format.extentix, 76 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/13220
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titlePairwise attribute noise detection algorithm for detecting noise in surface electromyography recordings
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.fullnameMaster of Science in Engineering
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.Sc.E.

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