Fuzzy Granular Decision Support for Ventilator Optimisation in Critically ill Patients using Electrical Impedance Tomography (EIT)

需要机械通风的危重病人的护理仍然受到危重疾病和肺部机械通风的综合影响的困扰。由于缺乏对机器“断奶”发生的速度和时间的了解，这种影响更加严重。该项目旨在开发一个适应性决策支持系统，以帮助ICU工作人员优化通风和脱机过程。为了帮助实现这一点，将引入一个自适应混合模型，该模型描述了患者-呼吸机在机械通气和撤机阶段的相互作用。除了通过与血气和肺扩张相关的数据收集知识外，该项目还旨在利用谢菲尔德开发的一种名为电阻抗断层扫描(EIT)的革命性技术，该技术包括以非侵入性方式测量肺部的扩张或收缩程度以及通风策略对这些程度的影响。该项目的两个重要方面涉及纳入EIT测量技术，以改进对患者呼吸需求的监测，以及对以神经-模糊层为代表的混合模型使用粒计算。这种模型的启发将成为设计和开发适应性决策支持系统的基础，以获得关于呼吸机设置和脱机操作的最佳治疗建议。设想在ICU的一系列试验中对该系统进行在线和离线验证。

The care of critically ill patients requiring mechanical ventilation remains beset by the combined effects of critical illness and of the mechanical ventilation of the lung. Such effects are compounded by the lack of knowledge of the rate and time at which 'weaning' from the machine should occur. This project aims at developing an adaptive decision support system to assist ICU staff in the optimisation of ventilation and weaning processes. To help achieve this, an adaptive hybrid model which describes the patient-ventilator interaction during ventilation as well as weaning phases will be elicited. In addition to knowledge gathered through data relating to blood gases and lung expansions, the project aims at exploiting a revolutionary technique developed at sheffield, called Electrical Impedance Tomography (EIT) which consists of measuring, in a non-invasive fashion, the degree of expansion or collapse of the lungs and the effect of the ventilation strategy upon these. Two important aspects of this project relate to the inclusion of the EIT measurement technique to improve the monitoring of the patient's respiratory demands and to the use of granular computing for the hybrid model represented by the neural-fuzzy layer. The elicitation of such a model will form the basis for the design and development of an adaptive decision support system for optimal therapeutic advice on ventilator settings and weaning operation. On-line and off-line validation of the system in a series of ICU trials are envisaged.