Adaptive and Neuro Adaptive Control for Intensive Care Unit Sedation and Intraoperative Anesthesia

重症监护病房镇静和术中麻醉的自适应和神经自适应控制

• 批准号: 0601311
• 负责人: Wassim Haddad
• 金额: $ 23.65万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0601311&HistoricalAwards=false
• 关键词: Adaptive; Neuro; Control; Intensive; Care

Abstract Haddad The objective of this research is to develop active control algorithms to significantly advance our understanding of the wide effects of pharmacological agents and anesthetics, as well as advance the state-of-the-art in active control of drug delivery systems for clinical pharmacology. In the practice of critical care medicine it is current clinical practice to administer potent drugs that profoundly influence levels of consciousness, respiratory, and cardiovascular function by manual control based on the clinician's experience and intuition. This project will investigate the use of closed-loop control based on appropriate dynamical system models as an alternative means of improving drug delivery in the operating room and the intensive care unit. The PI will develop adaptive control algorithms using nonnegative and compartmental system theory for the specific problem of closed-loop control of drug dosing. Specifically, he will develop adaptive control and neural network adaptive control algorithms for intensive care unit sedation and itraoperative anesthesia. Intellectual merit. Although the theories of adaptive and neuro adaptive control have been extensively addressed for linear and nonlinear systems in the literature, a unified linear and nonlinear control design framework for nonnegative and compartmental dynamical systems remains relatively undeveloped. This is due to the fact that nonnegative and compartmental systems have highly specialized structures and control (source) inputs are usually constrained to be nonnegative as are the states of the compartments. Broader benefits. This research will amalgamate control theory and pharmacology to provide efficient solutions to the challenging problem of automated anesthesia. Promising control algorithms will be implemented for the delivery of intraoperative anesthesia and intensive care unit sedation on site at the Northeast Georgia Medical Center. Payoffs would arise from improvements in medical care, health care, reliability of drug dosing equipment, and reduced cost for health care.

摘要Haddad本研究的目的是开发主动控制算法，以显着推进我们对药理学试剂和麻醉剂的广泛影响的理解，以及推进临床药理学药物输送系统的主动控制的最新技术。在重症监护医学的实践中，目前的临床实践是根据临床医生的经验和直觉通过手动控制来施用深刻影响意识水平、呼吸和心血管功能的强效药物。本项目将研究基于适当的动力系统模型的闭环控制的使用，作为改善手术室和重症监护室药物输送的替代手段。PI将开发自适应控制算法，使用非负和房室系统理论的闭环控制药物剂量的特定问题。具体来说，他将开发自适应控制和神经网络自适应控制算法，用于重症监护室镇静和术中麻醉。智力上的优点。虽然自适应和神经自适应控制的理论已经广泛地解决了线性和非线性系统在文献中，一个统一的线性和非线性控制设计框架的非负和房室动力系统仍然相对不发达。这是由于非负的和房室系统具有高度专业化的结构，并且控制（源）输入通常被限制为非负的，就像房室的状态一样。更广泛的利益。这项研究将融合控制理论和药理学，为自动麻醉的挑战性问题提供有效的解决方案。将在东北格鲁吉亚医疗中心实施有前途的控制算法，用于术中麻醉和重症监护室镇静。回报将来自医疗保健的改善，医疗保健，药物剂量设备的可靠性，以及医疗保健成本的降低。