高密度阵列式表面肌电检测脑卒中后神经肌肉病变的新探索

The most common disability following stroke is a motor function dysfunction such as hemiparesis. Both neurogenic and myopathic alternations are likely to occur in paretic muscles post stroke. The mechanisms underlying the occurrence and recovery of these alternations remain unclear, which hinders development of improved therapeutic treatment for hemiparesis. It is of great importance to determine how a brain lesion affects motor unit survival and function for examination of possible neuromuscular alternations in paretic muscles. This proposal is designed to develop a series of noninvasive diagnostic approaches and markers for examining motor unit properties, with a specific application in revealing complex neuromuscular changes at the motor unit level and determining their degrees in paretic muscles after stroke, by taking advantage of both global and spatiotemporal information about muscle activities recorded from high-density surface electromyography (EMG). During the methodological development, several advanced signal processing techniques including EMG decomposition and EMG interference pattern analysis will be employed. Sensitivity analysis of the derived markers with respect to specific motor unit changes will also be performed based on a surface EMG simulation model. Machine learning for information fusion of multiple markers will be performed further to enhance the diagnostic power. In addition, longitudinal study will be conducted to demonstrate the feasibility of designing improved rehabilitation protocols based on the proposed approaches and markers. The research conducted in this proposal promotes the clinical application of high-density surface EMG examination, and helps to offer guidelines to the design of precise and individualized rehabilitation plans for patients with hemiparesis.

脑卒中后运动功能障碍的发生率很高，且偏瘫肌肉可能存在多种神经性或肌肉性的病变。这些病变的发生和恢复机制尚不明确，限制了偏瘫康复治疗水平的提高。判断偏瘫肌肉运动单位存活与功能状态对脑卒中后神经肌肉病变的检测非常关键。本课题旨在发挥高密度阵列式表面肌电可获取肌肉活动的全局信息和丰富的时空信息的优势，通过先进的信号处理技术包括肌电信号分解和肌电干扰相分析，提出一系列无创检测运动单位活动特性及其病变的方法和指标，并用于从运动单位层面揭示脑卒中后复杂的神经肌肉病变类型并检测其发生发展程度。在检测技术研究方面，课题拟结合肌电模型分析所提指标对各病因的敏感性，进一步采用机器学习融合多指标信息提高检测效力。此外，课题还将在临床开展纵向研究，验证所提指标用于指导偏瘫康复治疗的可行性。本课题的研究成果将促进阵列式表面肌电在脑卒中康复的临床应用，有助于为偏瘫患者制定高效精准的个性化治疗方案。

本课题采用高密度（High-density, HD）表面肌电（surface electromyogram, sEMG）电极阵列、sEMG干扰相时空频域分析、sEMG仿真、sEMG分解等先进的sEMG采集与处理技术，以脑卒中患者、健康成人为研究对象，对适用于临床的HD-sEMG柔性电极阵列设计和信号采集方案、从运动单位（motor unit, MU）层面实施HD-sEMG检测神经肌肉病变的方法、病变指标及其在脑卒中偏瘫康复的临床验证等问题开展了深入研究，取得了以下重要研究成果：（1）针对人体上肢骨骼肌大小不同形态各异的特性，研制了针对各个肌肉和肌群形状的特定形状、电极数量和排列方式的柔性电极阵列，并配置了能够同时采集肌电、二维力度信号及腕、五指关节角度等的一体化采集设备，实现对肌肉功能和特性的全面分析；（2）充分挖掘高密度表面肌电在MU层面神经肌肉病变的诊断效力，在多通道表面肌电空间滤波、干扰相分析、表面肌电分解、神经信息解码研究等方面提出了一系列适用于神经肌肉病变检测的方法，形成了多指标融合的诊断方法框架；（3）为探索临床脑卒中患者康复进程中神经肌肉病变的改善情况，设计针对脑卒中患者在康复治疗中的追踪监测并提供无创且客观有效的检测指标，揭示了卒中康复进程中相关病变改善过程的复杂性和多样性。本课题的研究成果能够反映脑卒中后神经肌肉病变的底层生理信息，揭示脑卒中运动功能障碍及康复机制；促进基于sEMG无创诊断技术的临床应用，帮助医师制定高效精准的个性化治疗方案，提高神经肌肉病变诊疗水平。