The wrist extensor muscles have been implicated in a work-related upper limb disorder referred to as non-specific arm pain (NSAP), which has an unknown pathophysiology. The primary objective of this thesis was to perform an electrophysiological evaluation of NSAP to gain a better understanding of the underlying pathophysiology. Secondary objectives were to determine the utility and reliability of the decomposition-based quantitative electromyography (DQEMG) system used to examine NSAP. The utility of the DQEMG system was first tested to determine whether physiological changes in muscles of healthy individuals performing low-level fatiguing contractions could be detected using this approach. Next, the reliability of the outcome variables produced through this system was tested on healthy individuals performing low-level non-fatiguing contractions. A case-control study was then performed using DQEMG to determine whether there were measurable changes in electrophysiological variables that suggest whether NSAP is myopathic or neuropathic in nature. Finally, the case control study was repeated using a less invasive approach of electrophysiological evaluation to determine if this method might be equally useful in determining the pathophysiology of NSAP. Results revealed DQEMG can be effectively and reliably used to detect changes in the physiological characteristics of motor units that accompany fatigue. Specifically, decreases in mean motor unit firing rates along with increases in amplitude, duration, and area parameters of needle- and surface-detected motor unit potentials (MUPs) suggest that recruitment is a main cause of increased electromyographic amplitude parameters with fatigue. Results of the reliability study suggested that DQEMG provides sufficiently consistent results to allow it to be effectively used for quantitative electromyographic (QEMG) analysis. In the first case control study, the QEMG parameters suggested that the underlying pathophysiology in NSAP may be myopathic in nature; specifically, QEMG findings for the NSAP group revealed smaller MUPs compared to the other groups. Lastly, the case control study using spike shape analysis across different levels of isometric wrist extension contractions was deemed to be useful in determining differences among the groups. This research suggests that NSAP may be myopathic in nature, since the NSAP group showed significantly lower mean spike amplitude and mean spike slope values compared to healthy subjects. / Thesis (Ph.D, Rehabilitation Science) -- Queen's University, 2008-08-27 14:53:55.892

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.

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.

Neuromuscular disorders change the underlying structure and function of motor units within a muscle, and are detected using needle electromyography. Currently, inferences about the presence or absence of disease are made subjectively and are largely impression-based. Quantitative electromyography (QEMG) attempts to improve upon the status quo by providing greater levels of precision, objectivity and reproducibility through numeric analysis, however, their results must be transparently presented and explained to be clinically viable. The probabilistic muscle characterization (PMC) model is ideally suited for a clinical decision support system (CDSS) and has many analogues to the subjective analysis currently used. To improve disease characterization performance globally, a hierarchical classification strategy is developed that accounts for the wide range of MUP feature values present at different levels of involvement (LOI) of a disorder. To improve utility, methods for detecting LOI are considered that balance the accuracy in reporting LOI with its clinical utility. Finally, several aggregation methods that represent commonly used human decision-making strategies are considered and evaluated for their suitability in a CDSS. Four aggregation measures (Average, Bayes, Adjusted Bayes, and WMLO) are evaluated, that offer a compromise between two common decision making paradigms: conservativeness (average) and extremeness (Bayes). Standard classification methods have high specificity at a cost of poor sensitivity at low levels of disease involvement, but tend to improve with disease progression. The hierarchical model is able to provide a better balance between low-LOI sensitivity and specificity by providing the classifier with more concise definitions of abnormality due to LOI. Furthermore, a method for detecting two discrete levels of disease involvement (low and high) is accomplished with reasonable accuracy. The average aggregation method offers a conservative decision that is preferred when the quality of the evidence is poor or not known, while the more extreme aggregators such as Bayes rule perform optimally when the evidence is accurate, but underperform otherwise due to outlier values that are incorrect. The methods developed offer several improvements to PMC, by providing a better balance between sensitivity and specificity, through the definition of a clinically useful and accurate measure of LOI, and by understanding conditions for which each of the aggregation measures is better suited. These developments will enhance the quality of decision support offered by QEMG techniques, thus improving the diagnosis, treatment and management of neuromuscular disorders.

Neuromuscular disorders change the underlying structure and function of motor units within a muscle, and are detected using needle electromyography. Currently, inferences about the presence or absence of disease are made subjectively and are largely impression-based. Quantitative electromyography (QEMG) attempts to improve upon the status quo by providing greater levels of precision, objectivity and reproducibility through numeric analysis, however, their results must be transparently presented and explained to be clinically viable. The probabilistic muscle characterization (PMC) model is ideally suited for a clinical decision support system (CDSS) and has many analogues to the subjective analysis currently used. To improve disease characterization performance globally, a hierarchical classification strategy is developed that accounts for the wide range of MUP feature values present at different levels of involvement (LOI) of a disorder. To improve utility, methods for detecting LOI are considered that balance the accuracy in reporting LOI with its clinical utility. Finally, several aggregation methods that represent commonly used human decision-making strategies are considered and evaluated for their suitability in a CDSS. Four aggregation measures (Average, Bayes, Adjusted Bayes, and WMLO) are evaluated, that offer a compromise between two common decision making paradigms: conservativeness (average) and extremeness (Bayes). Standard classification methods have high specificity at a cost of poor sensitivity at low levels of disease involvement, but tend to improve with disease progression. The hierarchical model is able to provide a better balance between low-LOI sensitivity and specificity by providing the classifier with more concise definitions of abnormality due to LOI. Furthermore, a method for detecting two discrete levels of disease involvement (low and high) is accomplished with reasonable accuracy. The average aggregation method offers a conservative decision that is preferred when the quality of the evidence is poor or not known, while the more extreme aggregators such as Bayes rule perform optimally when the evidence is accurate, but underperform otherwise due to outlier values that are incorrect. The methods developed offer several improvements to PMC, by providing a better balance between sensitivity and specificity, through the definition of a clinically useful and accurate measure of LOI, and by understanding conditions for which each of the aggregation measures is better suited. These developments will enhance the quality of decision support offered by QEMG techniques, thus improving the diagnosis, treatment and management of neuromuscular disorders.