Clinical characterization of muscle using quantitative electromyographic signal analysis

神经肌肉疾病改变所涉及的肌肉的结构和组织及其运动单位（MU）的补充。从受影响的肌肉适当检测的肌电图（EMG）信号的特征可以反映存在的疾病的类型和严重程度。信号特征的正确解释可以帮助诊断，治疗和管理。目前，EMG信号被主观地和定性地评估为与神经或肌肉疾病、慢性和严重性一致的特征。这种评估是主观的，并且部分地高度依赖于检查医生的技能和经验，并且可以跟踪渐进变化的程度是有限的。定量EMG（QEMG）分析允许更客观、一致和灵敏的电生理表征。然而，很少有应用程序能够在临床上获取QEMG数据存在，更重要的是，一种方法，用于完成解释QEMG数据的关键步骤，以产生神经肌肉系统的表征是目前不可用的。因此，拟议的研究的目标是产生一个集成的系统，用于EMG信号采集，QEMG分析和电生理表征，用于常规使用的EMG诊所。此外，将为每个表征提供全面和直观的依据以及有效的置信度测量，以支持改进的临床决策。新系统的新方面将包括：采集、分析和解释的集成;信号采集的简便性和一致性;所提供的QEMG数据的有效性;以及电生理表征的价值。除了促进QEMG数据的采集和质量，这项工作的主要意义将是改善QEMG数据的解释，这将有助于更好地诊断，治疗和管理神经肌肉疾病。此外，证明在EMG诊所中容易地完成这些研究的能力将增加该方法被转化为其他临床EMG机器以产生广泛影响的可能性。

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.