{"title":"Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller","authors":"M. E. Karar, M. El-Brawany","doi":"10.4137/BECB.S6495","DOIUrl":null,"url":null,"abstract":"This paper presents a fuzzy neural network (FNN) control system to automatically manage the hemodynamic variables of patients with hypertension and congestive heart failure (CHF) via simultaneous infusion of cardiac drugs such as vasodilators and inotropic agents. The developed system includes two FNN sub-controllers for regulating cardiac output (CO) and mean arterial pressure (MAP) by cardiac drugs, considering interactive pharmacological effects. The adaptive FNN controller was tested and evaluated on a cardiovascular model. Six short-term therapy conditions of hypertension and CHF are presented under different sensitivities of a vasodilator drug. The results of the automated system showed that root mean square errors were ≤ 5.56 mmHg and ≤ 0.22 L min-1 for regulating MAP and CO, respectively, providing short settling time responses of MAP (≤ 10.9 min) and CO (≤ 8.22 min) in all therapy conditions. The proposed FNN control scheme can significantly improve the performance of cardiac drug infusion System.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"3 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BECB.S6495","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4137/BECB.S6495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 23
Abstract
This paper presents a fuzzy neural network (FNN) control system to automatically manage the hemodynamic variables of patients with hypertension and congestive heart failure (CHF) via simultaneous infusion of cardiac drugs such as vasodilators and inotropic agents. The developed system includes two FNN sub-controllers for regulating cardiac output (CO) and mean arterial pressure (MAP) by cardiac drugs, considering interactive pharmacological effects. The adaptive FNN controller was tested and evaluated on a cardiovascular model. Six short-term therapy conditions of hypertension and CHF are presented under different sensitivities of a vasodilator drug. The results of the automated system showed that root mean square errors were ≤ 5.56 mmHg and ≤ 0.22 L min-1 for regulating MAP and CO, respectively, providing short settling time responses of MAP (≤ 10.9 min) and CO (≤ 8.22 min) in all therapy conditions. The proposed FNN control scheme can significantly improve the performance of cardiac drug infusion System.
本文提出了一种模糊神经网络(FNN)控制系统,通过同时输注血管扩张剂和肌力药物等心脏药物,自动管理高血压和充血性心力衰竭(CHF)患者的血流动力学变量。该系统包括两个FNN子控制器,用于通过心脏药物调节心输出量(CO)和平均动脉压(MAP),并考虑了相互作用的药理作用。在心血管模型上对自适应FNN控制器进行了测试和评价。在不同的血管扩张剂药物敏感性下,提出了高血压和CHF的六种短期治疗条件。自动化系统的结果表明,调节MAP和CO的均方根误差分别≤5.56 mmHg和≤0.22 L min-1,在所有治疗条件下MAP和CO的稳定时间响应均较短(≤10.9 min)。所提出的FNN控制方案可以显著提高心脏药物输注系统的性能。