Decision Support for Clinical Neurophysiology

肌肉和神经疾病改变了相关肌肉的结构和组织。 从受影响的肌肉适当地检测到的临床肌电图（EMG）信号的特征反映了存在的障碍的程度和类型。 目前，医生主观和定性地评估EMG信号特征，以支持特定疾病的诊断、治疗和管理。 该评估高度依赖于医生的技能和经验。 此外，评估参与的严重性和敏感性，可以跟踪渐进的变化是有限的。 定量肌电图（QEMG）涉及EMG信号的检测和分析，以计算神经肌肉表征的统计数据。 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方案相关的纵向变化。 然而，目前需要一种用于完成解释QEMG数据以产生神经肌肉表征的关键步骤的方法。 我们正在进行的研究计划的当前主要目标如下： 1.开发和评估有助于解释QEMG结果的方法。 2.引入新的定量统计，更有效地反映神经肌肉结构和生理。 基于临床上可行的EMG信号分解系统，提供QEMG数据，其中包含有关研究中的肌肉的结构，组织和操作状态的信息，将开发和评估用于提取潜在信息并通过创建神经肌肉表征来促进QEMG结果解释的新的基于解剖学的模式发现技术。 这些特征将以语言学术语呈现。 其理由将易于理解，其统计基础将能够得到审查。 此外，还将提供神经肌肉表征的统计置信度的有效测量。 通过促进QEMG数据的解释，开发的系统将大大增加其实用性，并最终提高其临床应用。

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.