Decision Support for Clinical Neurophysiology

肌肉和神经紊乱会改变相关肌肉的结构和组织。临床肌电图（EMG）信号的特征，适当地检测到的受影响的肌肉反映了目前的程度和类型的障碍。目前，医生主观上和定性地评估肌电图信号特征，以支持特定疾病的诊断、治疗和管理。这种评估高度依赖于医生的技能和经验。此外，对受累程度的评估和追踪进行性变化的敏感性是有限的。定量肌电图（QEMG）涉及检测和分析肌电信号，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方案相关的纵向变化。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的关键步骤。

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.