Biomedical Signal Processing and System Identification

我的研究的长期目标是开发和验证一个通用的工具集，用于生物医学信号和系统的定量分析。本文提出的工作将为两个特定领域构建工具：关节僵硬和心肺信号。 动态关节神经力学的系统辨识方法关节的神经力学行为来源于关节和肌肉的固有特性以及肌肉的反射激活。神经力学在姿势控制中很重要，在那里它决定了对干扰的反应，在那里它定义了必须控制的内部负荷。动态关节刚度将神经力学定量描述为关节位置和扭矩之间的关系。目前，有一个很好的了解关节僵硬的姿势条件下，位置和扭矩保持几乎恒定。然而，很少有人知道关节刚度在功能活动，当有大，快速变化的位置和扭矩。这项工作的目标是开发工具来测量和建模动态关节刚度在执行功能活动。这对于理解正常运动功能的机制以及伴随运动系统疾病的变化至关重要。使用这些工具将允许定量，客观的评价关节力学的诊断和监测的病理变化，评估治疗方式，并知情的设计和控制的假肢，矫形器和康复设备。心脏呼吸信号的信号处理和分类呼吸和心脏信号的分析对于包括睡眠呼吸暂停在内的许多疾病的诊断和监测非常重要。目前的“黄金标准”分析方法是手动对心肺信号进行评分，以识别呼吸暂停等重要事件。这种方法是非常劳动密集型的，并且受到大的评分者间和评分者内的可变性的影响。这项工作的目的是开发一个自动化的工具集，将呼吸信号分类为临床识别的模式。由此产生的工具将具有广泛的应用，包括：睡眠呼吸障碍的评估，包括早产儿呼吸暂停的儿科呼吸暂停的管理，婴儿机械通气的拔管，以及无创支持方式的评估。这项研究产生的算法将为研究人员提供解决神经肌肉控制和心肺功能中重要问题的方法。为了便于将这些工具转化为实践，我们将以全面、即用型工具箱的形式提供这些工具，并附带大量文档、示例数据集、教程和演示。我们还将举办研讨会，向研究人员、学生和行业从业者传授如何使用我们的工具箱来满足他们的研发需求

The long term objectives of my research are to develop and validate a general purpose tool-set for the quantitative analysis of biomedical signals and systems. The work proposed here will build tools for two specific areas: joint stiffness and cardio-respiratory signals. System Identification Methods for Dynamic Joint Neuromechanics A joint's neuromechanical behavior arises from the intrinsic joint and muscle properties combined with reflex activation of muscle. Neuromechanics is important in the control of posture, where it determines the response to disturbances, and movement, where it defines the internal load that must be controlled. Dynamic joint stiffness describes neuromechanics quantitatively as the relation between joint position and torque. Currently, there is a good understanding of joint stiffness under postural conditions, where position and torque remain nearly constant. However, little is known about joint stiffness during functional activities, when there are large, rapid changes in position and torque. The objective of this work is to develop tools to measure and model dynamic joint stiffness during the execution of functional activities. This is essential to understand the mechanisms underlying normal motor function and the changes accompanying motor system disease. Use of these tools will permit the quantitative, objective evaluation of joint mechanics for the diagnosis and monitoring of pathological changes, evaluation of treatment modalities, and the informed design and control of prosthetic, orthotic, and rehabilitation devices. Signal Processing and Classification of Cardio Respiratory Signals The analysis of respiratory and cardiac signals is important for the diagnosis and monitoring of many diseases including sleep apnea. The current "gold-standard" analysis approach is to score cardiorespiratory signals manually to identify significant events such as apneas. This approach is very labor intensive, and subject to large inter- and intra- scorer variability. The objective of this work is to develop an automated tool-set to classify respiratory signals into clinically recognized patterns. The resulting tools will have a wide range of applications including: the evaluation of sleep disordered breathing, the management of pediatric apneas including the apnea of prematurity, the extubation of infants from mechanical ventilation, and the evaluation of non-invasive support modalities. The algorithms resulting from this research will provide researchers with the means to address important questions in neuromuscular control and cardio-respiratory function. To facilitate the translation of these tools to practice, we will make them available as comprehensive, ready-to-use toolboxes, accompanied by extensive documentation, sample data sets, tutorials and demonstrations. We will also hold workshops to teach researchers, students, and industrial practitioners how to use our toolboxes to address their R&D needs