Harnessing Motoneuron Activity: From Lab to Clinic

利用运动神经元活动：从实验室到诊所

• 批准号: 7637376
• 负责人: Carlo J De Luca
• 金额: $ 56.28万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7637376
• 关键词: Action Potentials; Address; Age; Aging; Algorithms; Artificial Intelligence; Biomedical Engineering; Boston; Characteristics; Clinic; Clinical; Clinical Markers; Computer Systems Development; Computer software; Computers; Development; Elderly; Electrodes; Engineering; Exercise; Human Resources; Individual; Laboratories; Lead; Leadership; Manufacturer Name; Medicine; Modeling; Modification; Motor; Motor Neurons; Neurology; Performance; Physical activity; Process; Rehabilitation therapy; Shapes; Signal Transduction; Source; Staging; Surface; System; Technology; Testing; Time; Universities; Wolves; Work; advanced system; age related; commercialization; computerized data processing; design; improved; markov model; mathematical model; medical schools; meetings; motor control; neuroadaptation; neuromuscular system; new technology; programs; relating to nervous system; software development

DESCRIPTION (provided by applicant): We propose to continue the development of an automatic system that will accurately and quickly decompose electromyographic (EMG) signals into their constituent action potentials and provide the timing of every firing of a set of concurrently active motor units. This information will enable a wide range of studies to investigate the workings of the healthy and diseased neuromuscular system. We will improve the performance of the decomposition algorithms by incorporating new Artificial Intelligence concepts, and a new multi-strategy Hidden Markov Model (HMM) processing stage, to address signal decomposition challenges that cannot be met by the present technology. We will improve the present accuracy from typically 85% for 8 concurrently active motor units to greater than 96% for up to 15 concurrently active motor units. We will also design and build the hardware and software for a stand-alone portable system that may be used in the laboratory or clinic. Then we will transfer the system to a manufacturer for commercialization. In so doing we will produce, for the first time, an advanced system for conveniently and accurately obtaining the firings of a large group of concurrently active motor units from an EMG signal. The new technology will be tested in two applied studies that will be carried out concurrently with the technical developments. One will investigate neural modifications in the firing characteristics of motor units as a function of aging and physical activity. The other will investigate the mitigating effects of resistive exercise on age-related neural adaptations, culminating in the development of a clinical marker to estimate the likelihood that an elderly individual will benefit from an exercise program. The proposed BRP will be lead by Drs. De Luca, Roy, and Adam, key personnel from Boston University (BU) with expertise in biomedical engineering and EMG system development. Signal processing/software development will be provided by the leadership from Dr. Nawab through BU's Department of Electrical and Computer Engineering. Clinical expertise on aging/motor control will be provided by Dr. Novak, from The Department of Neurology at BU School of Medicine, and through Dr. Wolf, from the Department of Rehabilitation Medicine at the Emory University School of Medicine in Atlanta

描述（由申请人提供）：我们建议继续开发自动系统，该系统将准确，快速地将肌电图（EMG）信号分解为其组成的动作电位，并提供每次射击一组同时活跃的运动单元的时机。这些信息将使广泛的研究能够研究健康和患病的神经肌肉系统的工作。我们将通过结合新的人工智能概念以及一个新的多策略隐藏式马尔可夫模型（HMM）处理阶段来提高分解算法的性能，以解决当前技术无法满足的信号分解挑战。对于8个同时活跃的电动机单位，我们将提高目前的准确性，从通常的85％，最多15个同时活跃的电动机单元。我们还将为实验室或诊所使用的独立便携式系统设计和构建硬件和软件。然后，我们将把系统转移到制造商的商业化中。这样一来，我们将首次生产出一种高级系统，用于方便，准确地从EMG信号中获取大量同时活跃的电动机单元的发射。这项新技术将在两项应用研究中进行测试，这些研究将与技术发展同时进行。人们将研究运动单位发射特征的神经修饰，这是衰老和体育锻炼的函数。另一个将调查抵抗运动对与年龄相关的神经适应的缓解作用，最终导致临床标记的发展，以估计老年人将从运动计划中受益的可能性。拟议的BRP将由博士领导。 De Luca，Roy和Adam是波士顿大学（BU）的主要人员，在生物医学工程和EMG系统开发方面具有专业知识。 Nawab博士的领导将通过BU的电气和计算机工程系提供信号处理/软件开发。 Novak博士将在BU医学院神经病学系和沃尔夫博士提供临床/运动控制的临床专业知识。