dc.contributor.author | Pino, Lou Joseph | |
dc.date.accessioned | 2009-01-21 21:14:47 (GMT) | |
dc.date.available | 2009-01-21 21:14:47 (GMT) | |
dc.date.issued | 2009-01-21T21:14:47Z | |
dc.date.submitted | 2008 | |
dc.identifier.uri | http://hdl.handle.net/10012/4214 | |
dc.description.abstract | Objectives: Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disease process. A clinical decision support system (CDSS) for the electrophysiological characterization of muscles by analyzing motor unit potentials (MUPs) was developed to assist physicians and researchers with the diagnosis, treatment & management of neuromuscular disorders and analyzed against criteria for use in a clinical setting.
Methods: Quantitative MUP data extracted from various muscles from control subjects and patients from a number of clinics was used to compare the sensitivity, specificity, and accuracy of a number of different clinical decision support methods. The CDSS developed in this work known as AMC-PD has three components: MUP characterization using Pattern Discovery (PD), muscle characterization by taking the average of MUP characterizations and calibrated muscle characterizations.
Results: The results demonstrated that AMC-PD achieved higher accuracy than conventional means and outlier analysis. Duration, thickness and number of turns were the most discriminative MUP features for characterizing the muscles studied in this work.
Conclusions: AMC-PD achieved higher accuracy than conventional means and outlier analysis. Muscle characterization performed using AMC-PD can facilitate the determination of “possible”, “probable”, or “definite” levels of disease whereas the conventional means and outlier methods can only provide a dichotomous “normal” or “abnormal” decision. Therefore, AMC-PD can be directly used to support clinical decisions related to initial diagnosis as well as treatment and management over time. Decisions are based on facts and not impressions giving electromyography a more reliable role in the diagnosis, management, and treatment of neuromuscular disorders. AMC-PD based calibrated muscle characterization can help make electrophysiological examinations more accurate and objective. | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.subject | decision support | en |
dc.subject | pattern recognition | en |
dc.subject | quantitative electromyography | en |
dc.subject | motor unit potentials | en |
dc.title | Neuromuscular Clinical Decision Support using Motor Unit Potentials Characterized by 'Pattern Discovery' | en |
dc.type | Doctoral Thesis | en |
dc.pending | false | en |
dc.subject.program | System Design Engineering | en |
uws-etd.degree.department | Systems Design Engineering | en |
uws-etd.degree | Doctor of Philosophy | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |