Clinical characterization of muscle using quantitative electromyographic signal analysis

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Neuromuscular disorders change the structure and organization of the muscles involved and their complement of motor units (MUs). Characteristics of electromyographic (EMG) signals suitably detected from affected muscles can reflect the type and severity of disorder present. Proper interpretation of signal characteristics can assist with diagnosis, treatment and management. Currently, EMG signals are subjectively and qualitatively assessed for features consistent with nerve or muscle disease, chronicity, and severity. This assessment is subjective, and in part, highly dependent on the skill and experience of the examining physician and the extent to which progressive changes can be tracked is limited. Quantitative EMG (QEMG) analysis allows for a more objective, consistent and sensitive electrophysiological characterization. However, few applications capable of clinically acquiring QEMG data exist and more importantly a method for completing the crucial step of interpreting QEMG data to produce a characterization of the neuromuscular system is currently not available. The objectives of the proposed research are therefore to produce an integrated system for EMG signal acquisition, QEMG analysis and electrophysiological characterization for routine use in an EMG clinic. In addition, a comprehensive and intuitive rationale, and a valid measure of confidence will be provided for each characterization to support improved clinical decisions. Novel aspects of the new system will include: the integration of acquisition, analysis and interpretation; the ease and consistency of signal acquisition, the validity of the QEMG data provided; and the value of the electrophysiological characterization. Beyond facilitating the acquisition and quality of QEMG data, the main significance of this work will be improved interpretation of QEMG data which will contribute to better diagnosis, treatment and management of neuromuscular disorders. Furthermore, demonstrating an ability to readily complete these studies in an EMG clinic will increase the likelihood of the methods being translated to other clinical EMG machines for widespread impact.