基于容错概念的模型预测控制在麻醉过程中的催眠调节

Bhavina J. Patel, H. Patel
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引用次数: 0

摘要

麻醉自动化首选BIS(来自脑电图的双谱指数)信号来调节术中催眠药异丙酚输注,避免不良反应,缩短术后恢复时间。但术中由于脑电图信号质量较差,可能导致BIS信号中断。因此,对BIS暂停的容错能力对于防止术中唤醒和异丙酚输注过量/不足等不良状态至关重要。本文提出了一种基于多变量容错概念的模型预测控制(MPC)来调节异丙酚的剂量。主要目的是设计具有容错能力的鲁棒系统,在正常和BIS传感器故障情况下,有效地保持BIS平滑,且异丙酚剂量变化较小。本文考虑了传感器故障和血流动力学手术干扰来评价控制系统的性能。在设计中使用了5个不同患者的真实药理数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Model Predictive Control with Fault Tolerance Concept to Regulate Hypnosis during Anesthesia
Anesthesia automation preferred BIS (bi-spectral index derived from electroencephalogram) signal to regulate hypnotic agent propofol infusion during surgery to avoid adverse reactions and to reduce post recovery time. But BIS signal may be suspend during surgery due to the poor quality of EEG signal. Thus, fault tolerance ability against BIS suspension is essential to prevent undesirable states such as intraoperative arousal and over/under infusion of propofol. This paper proposes a model predictive control (MPC) with multivariable fault tolerance concept based design to regulate propofol dose. The main aim is to design robust system with fault tolerance ability that works efficiently to maintain smooth BIS with less variations of propofol dose during normal and BIS sensor failure condition. In this paper sensor fault and hemodynamic surgical disturbance is considered to evaluate the performance of control system. Real pharmacological data of 5 different patients are used in proposed design.
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