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

使用电阻抗断层扫描 (EIT) 为危重患者的呼吸机优化提供模糊粒度决策支持

• 批准号: EP/C520807/1
• 负责人: Mahdi Mahfouf
• 金额: $ 28.57万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FC520807%2F1
• 关键词: Fuzzy; Granular; Decision; Support; Ventilator

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.

需要机械通气的危重病人的护理仍然受到危重疾病和肺机械通气的综合影响的困扰。由于不知道从机器上“断奶”的速度和时间，这种影响变得更加复杂。该项目旨在开发一个适应性决策支持系统，以帮助ICU工作人员优化通气和脱机过程。为了帮助实现这一目标，将引出一个自适应混合模型，该模型描述了在通气和脱机阶段患者与呼吸机的相互作用。除了通过血气和肺扩张数据收集的知识外，该项目还旨在利用谢菲尔德大学开发的一项革命性技术，称为电阻抗断层扫描（EIT），该技术包括以无创方式测量肺部扩张或收缩的程度以及通气策略对这些的影响。这个项目的两个重要方面涉及到EIT测量技术，以改善对患者呼吸需求的监测，以及使用颗粒计算来表示由神经模糊层表示的混合模型。该模型的得出将为设计和开发适应性决策支持系统奠定基础，该系统可提供有关呼吸机设置和脱机操作的最佳治疗建议。设想在一系列ICU试验中对该系统进行在线和离线验证。

项目成果

A closed-loop hybrid physiological model relating to subjects under physical stress.

与身体压力下的受试者相关的闭环混合生理模型。

• DOI: 10.1016/j.artmed.2006.07.009
• 发表时间: 2006
• 期刊: Artificial intelligence in medicine
• 影响因子: 7.5
• 作者: El-Samahy E

Initial Comparisons of Absolute Electrical Impedance Tomography (EIT) Lung Volume Estimates with Spirometry

绝对电阻抗断层扫描 (EIT) 肺容量估算值与肺活量测定法的初步比较

• 发表时间: 2007
• 期刊: British Journal of Anaesthesia
• 影响因子: 9.8
• 作者: G Panoutsos

Fuzzy modelling for controlled anaesthesia in hospital operating theatres

医院手术室控制麻醉的模糊建模

• DOI: 10.1016/j.conengprac.2004.08.004
• 发表时间: 2006
• 期刊: Control Engineering Practice
• 影响因子: 4.9
• 作者: Nunes C

A CONTINUOUSLY UPDATED HYBRID BLOOD GAS MODEL FOR VENTILATED PATIENTS

持续更新的通气患者混合血气模型

• DOI: 10.3182/20060920-3-fr-2912.00087
• 发表时间: 2006
• 期刊: IFAC Proceedings Volumes
• 作者: Wang A

Electrical Impedance Tomography:an Evaluation of its ability to detect changes in lung volume and expansion during single lung ventilation

电阻抗断层扫描：评估其检测单次肺通气期间肺容量和扩张变化的能力

• 发表时间: 2008
• 期刊: British Journal of Anaesthesia
• 影响因子: 9.8
• 作者: R Tunney