Biomedical Signal Analysis and System Identification

这项研究旨在开发用于分析和识别生物医学信号和系统的算法和工具。所解决的问题是由生物学研究的要求激发和定义的，但研究的重点是开发和验证实用工具和算法，而不是调查具体的生物学问题。将解决两个生物医学领域的问题。(1)研究姿势和运动控制的识别方法。其目的是提供所需的工具，以表征关节在正常日常功能过程中的动态力学特性，并了解其潜在的机制。这对于了解正常和病理的运动功能是必不可少的，对于运动异常的诊断、治疗的量化评估以及假体和矫形器等先进辅助设备的设计将具有重要的实用价值。(2)儿科心肺功能监测的自动信号分析。众所周知，呼吸和心脏行为包含有助于管理呼吸问题的信息，如早产儿呼吸暂停和婴儿拔管可以安全终止。然而，由于需要进行人工数据分析，这些信息的应用受到了阻碍。这既昂贵，又耗时，而且不准确。我们将通过开发一个用于分析心肺信号的全自动系统来解决这个问题，并将这些系统与机器学习技术相结合，以预测婴儿遇到不良呼吸事件的可能性。

This research seeks to develop algorithms and tools for the analysis and identification of biomedical signals and systems. The problems addressed are motivated and defined by the requirements of biological research but the focus of the research is on the development and validation of practical tools and algorithms, rather than investigating specific biological questions. Problems from two biomedical areas will be addressed.(1) Identification methods to study the control of posture and movement. The objective is to provide the tools needed to characterize the dynamic, mechanical properties of joints during normal daily function and understand the underlying mechanisms. This is essential to understand both normal and pathological motor function and will have important practical results for the diagnosis of motor abnormalities, the quantitative assessment of treatment, and the design of advanced assistive devices such as prostheses and orthoses.(2) Automated signal analysis for pediatric cardio-respiratory function monitoring. It is known that respiratory and cardiac behavior contains information that could aid in the management of respiratory problems, such as apnea of prematurity and the extubation of infants from mechanical ventilation can safely terminated. However, application of this information is hampered by the need for manual data analysis. This is expensive, time consuming and inaccurate. We will address this issue by developing a fully automated system for the analysis of cardio-respiratory signals and combine these with machine learning techniques to predict the probability that an infant will encounter an adverse respiratory event.