Decision Support for Clinical Neurophysiology

肌肉和神经的紊乱改变了相关肌肉的结构和组织。从受累肌肉适当检测到的临床肌电(EMG)信号的特征反映了目前存在的障碍的程度和类型。目前，医生在主观上和定性上评估肌电信号特征，以支持特定疾病的诊断、治疗和管理。这项评估高度依赖于医生的技能和经验。此外，对参与的严重性和跟踪渐进变化的敏感度的评估是有限的。定量肌电图(QEMG)包括对肌电信号的检测和分析，以计算神经肌肉特征的统计数据。QEMG可以提高神经肌肉评估的特异性和敏感性，并跟踪与特定治疗或管理制度相关的纵向变化。然而，目前需要一种方法来完成解释QEMG数据以产生神经肌肉特征这一关键步骤。 我们目前正在进行的研究计划的主要目标如下： 1.开发和评估可辅助解释QEMG结果的方法。 2.引入新的量化统计，更有效地反映神经肌肉结构和生理。 在临床可行的肌电信号分解系统的基础上，将开发和评估新的基于统计学的模式发现技术，该技术用于提取潜在信息并通过创建神经肌肉特征来促进对QEMG结果的解释，所述QEMG数据包含关于被研究肌肉的结构、组织和操作状态的信息。这些描述将用语言术语表示。他们的理由将很容易理解，他们的统计基础将能够得到检验。此外，还将提供神经肌肉特征的统计置信度的有效测量。通过方便地解释QEMG数据，开发的系统将极大地增加其有用性，并最终实现其临床应用。

Disorders of muscle and nerve change the structure and organization of the muscles involved. Characteristics of clinical electromyographic (EMG) signals suitably detected from affected muscles reflect the degree and type of disorder present. Physicians currently, subjectively and qualitatively, assess EMG signal characteristics to support diagnosis, treatment and management of specific disorders. This assessment is highly dependent on physician skill and experience. Furthermore, assessment of the severity of involvement and the sensitivity with which progressive changes can be tracked is limited. Quantitative electromyography (QEMG) involves the detection and analysis of EMG signals to calculate statistics for neuromuscular characterization. QEMG can improve the specificity and sensitivity of neuromuscular assessments and track longitudinal changes associated with specific treatment or management regimes. However, a method for completing the crucial step of interpreting QEMG data to produce a neuromuscular characterization is currently needed. The current major objectives of our ongoing research program are as follows: 1. To develop and evaluate methods that can assist in the interpretation of QEMG results. 2. Introduce new quantitative statistics that more effectively reflect neuromuscular structure and physiology. Based on clinically viable EMG signal decomposition systems that provide QEMG data that contain information regarding the structural, organizational and operational state of the muscles under study, novel statistically-based pattern discovery techniques for extracting the underlying information and facilitating the interpretation of QEMG results by creating a neuromuscular characterization will be developed and evaluated. The characterizations will be presented in linguistic terms. Their rationale will be easy to understand and their statistical basis will be able to be examined. In addition, a valid measure of the statistical confidence of a neuromuscular characterization will be provided. The developed system by facilitating the interpretation of QEMG data will greatly increase its usefulness and ultimately its clinical use.