Quantitative Electrophysiological Neuromuscular Characterization

定量电生理神经肌肉表征

• 批准号: RGPIN-2017-04377
• 负责人: Stashuk, Daniel
• 金额: $ 1.75万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665841
• 关键词: Quantitative; Electrophysiological; Neuromuscular; Characterization

The structures and actions of nerves and muscles are studied by analyzing the electrophysiological signals they generate. Electromyographic (EMG) signals are used to study processes affecting skeletal muscle morphology and physiology. Skeletal muscles are organized into motor units (MUs); groups of fibres activated by single motor neurons. Each MU generates motor unit potentials (MUPs), which are summations of muscle fibre potentials (MFPs) generated by active MU fibres. During muscle contraction, MUs are repetitively active. Each active MU generates a motor unit potential train (MUPT), and an EMG signal is the summation of the MUPTs of active MUs. Neuromuscular disorders and aging affect the morphology and physiology of MUs, and the MFPs, MUPs and MUPTs they generate. Currently, the diagnosis of many neuromuscular disorders relies on subjective and qualitative analysis of the MUPs and MUPTs of needle-detected EMG signals. Patient outcomes are therefore highly dependent on individual physician expertise and training, and it is very difficult to assess disease progression and provide reliable prognoses. Current studies of the neuromuscular effects of aging use statistics of composite EMG signals, which cannot provide detailed individual MU information. As such, these methods are unable to detect important neuromuscular changes.***The proposed research objectives are to develop novel methods to create a quantitative electrophysiological neuromuscular characterization. These include methods to: 1) extract relevant MUP and MUPT data from EMG signals detected using standard methods; 2) suitably represent extracted MUP and MUPT data using sets of feature values which relate to specific aspects of MU morphology and physiology and; 3) apply sets of suitably represented MUPTs, relating to a representative set of MUs sampled from a test muscle to a classifier and interpretation module to provide a quantitative neuromuscular characterization. Novel signal processing and pattern recognition methods will be developed to extract MUP and MUPT data. Novel machine learning methods will be developed to interpret the data to provide decision support. The provided characterizations will be based on novel measures of MU fibre spatial arrangements, MFP temporal dispersion and MUP stability. This will allow them to be used to support and improve clinical decisions related to the diagnosis, treatment and management of neuromuscular disorders and provide better patient outcomes. The newly available detailed individual MU information will also facilitate the study of the neuromuscular effects of aging. Students working on these objectives will obtain valuable experience in both the natural sciences (neuromuscular physiology, electrophysiology) and engineering (signal processing, machine learning) and will be well equipped for employment related to the development of biomedical devices.

通过分析神经和肌肉产生的电生理信号来研究神经和肌肉的结构和活动。肌电图（EMG）信号用于研究影响骨骼肌形态和生理的过程。骨骼肌被组织成运动单位（MU）;由单个运动神经元激活的纤维群。每个MU产生运动单位电位（MUP），其是由活动MU纤维产生的肌纤维电位（MFP）的总和。在肌肉收缩期间，MU是重复活跃的。每一个活动的MU产生一个运动单位电位序列（MUPT），而EMG信号是活动MU的运动单位电位序列的总和。神经肌肉疾病和衰老影响MU的形态和生理，以及它们产生的MFP、MUP和MUPT。目前，许多神经肌肉疾病的诊断依赖于对针检测到的EMG信号的MUP和MUP T的主观和定性分析。因此，患者结局高度依赖于个体医生的专业知识和培训，并且很难评估疾病进展并提供可靠的诊断。目前的研究的神经肌肉老化的影响使用的复合肌电信号的统计，不能提供详细的个人MU信息。因此，这些方法无法检测重要的神经肌肉变化。拟议的研究目标是开发新的方法来创建一个定量的电生理神经肌肉表征。这些措施包括：2）使用与MU形态学和生理学的特定方面相关的特征值集合适当地表示所提取的MUP和MUPT数据; 3）应用适当表示的MUPT的集合，将从测试肌肉采样的代表性MU集合与分类器和解释模块相关联，以提供定量神经肌肉特征化将开发新的信号处理和模式识别方法来提取MUP和MUPT数据。将开发新的机器学习方法来解释数据，以提供决策支持。所提供的表征将基于MU纤维空间排列，MFP时间色散和MUP稳定性的新措施。这将使它们能够用于支持和改善与神经肌肉疾病的诊断、治疗和管理相关的临床决策，并提供更好的患者结局。新获得的详细的个体MU信息也将促进衰老对神经肌肉影响的研究。致力于这些目标的学生将获得自然科学（神经肌肉生理学，电生理学）和工程（信号处理，机器学习）方面的宝贵经验，并将为与生物医学设备开发相关的就业做好准备。