Identification of Responders to the FES Interventions in Stroke Population

确定中风人群中 FES 干预措施的反应者

基本信息

批准号：
8640223
负责人：
Rakesh Pilkar
金额：
$ 7.57万
依托单位：
KESSLER FOUNDATION, INC.
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2016-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8640223
关键词：
Accounting Algorithms Ankle Articular Range of Motion Bilateral Biomechanics Cerebral Palsy Characteristics Child Classification Clinical Data Development Devices Electric Stimulation Equation Equilibrium Foot-drop Frequencies Gait Gait abnormality Goals Hemiplegia Hemiplegic Gait Hip Joint Hip region structure Impairment Individual Injury Intervention Investigation Joints Knee Knee joint Knowledge Length Limb structure Measures Morphologic artifacts Movement Muscle National Institute of Neurological Disorders and Stroke Outcome Measure Paralysed Pattern Physiological Population Positioning Attribute Principal Component Analysis Process Property Recovery Rehabilitation therapy Reporting Research Personnel Risk Role Scientific Evaluation Signal Transduction Speed Stimulus Stroke Surface Survivors Techniques Technology Therapeutic Effect Time Training Translating Travel Visit Walking base cost data mining follow-up improved innovation kinematics nervous system disorder neuromuscular neuroprosthesis novel novel strategies post intervention prevent public health relevance response signal processing spatiotemporal stroke rehabilitation

项目摘要

DESCRIPTION (provided by applicant): Hemiplegia with associated foot drop occurs in 50% of the stroke survivors and frequently impairs an individual's ability to walk [1]-[3]. Functional Electrical Stimulation (FES) based neuroprosthetic devices have been developed to correct foot drop. The efficacy of these devices were initially examined by Liberson el al. who demonstrated that electrical stimulations could assist in restoring functional movements in paralyzed limbs [1]. In addition to assistance with foot drop, these devices have showed significant improvements in biomechanical variables such as walking speed, distance, stride length and physiological cost for individuals with stroke [2-6, 8]. In order to comprehensively understand the effect of electrical stimulations on gait recovery, it is critical to analyze the dynamic aspects of gait and measure gait variability during the functional electrical stimulation intervention. In the proposed investigation, we will determine the 'gait symmetry' of FES assisted walking using bilateral cyclograms of the ankle and knee over a period of 6 months. This novel approach will account for the dynamics and complexity of balance by measuring the deviations of joints from a line of symmetry at every instance of gait cycle and will provide bette measure of gait symmetry. Utilization of this outcome measure will allow us to understand the role of electrical stimulation at ankle and how this effect gets translated to the knee and hip joints during walking. The changes in the surface electromyograms (EMGs) of selective muscle groups will demonstrate how FES can contribute to muscle re-training after stroke. We will use advanced signal processing algorithms to remove FES artifact from the EMG signal in order to comprehensively analyze the carry-over effect of the FES intervention. Finally, we will employ Principal Component Analysis (PCA) - an advanced data mining technique to track and quantify the overall gait recovery process of individuals with stroke using pattern classification algorithms. The gait symmetry measure and the EMGs will be statistically classified to see their clear separation at baseline and 6 month intervals. This classification will allow us to identify he individuals who were most responsive to the intervention. This information is critical and will allow researchers and clinicians to re-strategize the rehabilitation process. Such scientific evaluation will provide the base for further development and implementation of FES devices or technologies, thus supporting the NINDS' fundamental goal of translating basic and clinical discoveries into better ways to treat neurological disorders.

描述（由申请人提供）：50% 的中风幸存者会出现伴有足下垂的偏瘫，并且经常损害个人的行走能力 [1]-[3]。功能性基于电刺激 (FES) 的神经假体装置已被开发用于矫正足下垂。 Liberson 等人最初检查了这些装置的功效。他证明电刺激可以帮助恢复瘫痪肢体的功能运动[1]。除了帮助足下垂之外，这些设备还显示出生物力学变量的显着改善，例如中风患者的步行速度、距离、步幅和生理成本 [2-6, 8]。为了全面了解电刺激对步态恢复的影响，在功能性电刺激干预期间分析步态的动态方面并测量步态变异性至关重要。在拟议的调查中，我们将使用 6 个月内踝关节和膝关节的双侧循环图来确定 FES 辅助行走的“步态对称性”。这种新颖的方法将通过测量步态周期的每个实例中关节与对称线的偏差来解释平衡的动态性和复杂性，并将提供更好的步态对称性测量。利用这种结果测量将使我们能够了解电刺激在脚踝处的作用以及这种作用在步行过程中如何转化为膝关节和髋关节。选择性肌肉群的表面肌电图 (EMG) 的变化将证明 FES 如何有助于中风后的肌肉再训练。我们将使用先进的信号处理算法从 EMG 信号中消除 FES 伪影，以便全面分析 FES 干预的残留效应。最后，我们将采用主成分分析（PCA）——一种先进的数据挖掘技术，使用模式分类算法来跟踪和量化中风患者的整体步态恢复过程。步态对称性测量和肌电图将进行统计分类，以查看它们在基线和 6 个月间隔时的明显分离。这种分类将使我们能够识别出对干预反应最敏感的人。这些信息至关重要，将使研究人员和临床医生能够重新制定康复过程策略。这种科学评估将为进一步开发和实施 FES 设备或技术奠定基础，从而支持 NINDS 将基础和临床发现转化为治疗神经系统疾病的更好方法的基本目标。