Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller
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 F...
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Series: | Biomedical Engineering and Computational Biology |
Online Access: | https://doi.org/10.4137/BECB.S6495 |
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doaj-45cdf09dc91a420e8535bf70ec5696642020-11-25T03:40:13ZengSAGE PublishingBiomedical Engineering and Computational Biology1179-59722011-01-01310.4137/BECB.S6495Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks ControllerMohamed E. Karar0Mohamed A. El-Brawany1Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Semmelweisstrasse 14, D-04103 Leipzig, Germany.Present address: Department of Biomedical Engineering, College of Engineering, University of Dammam, Dammam, Kingdom of Saudi Arabia.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.https://doi.org/10.4137/BECB.S6495 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohamed E. Karar Mohamed A. El-Brawany |
spellingShingle |
Mohamed E. Karar Mohamed A. El-Brawany Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller Biomedical Engineering and Computational Biology |
author_facet |
Mohamed E. Karar Mohamed A. El-Brawany |
author_sort |
Mohamed E. Karar |
title |
Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller |
title_short |
Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller |
title_full |
Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller |
title_fullStr |
Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller |
title_full_unstemmed |
Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller |
title_sort |
automated cardiac drug infusion system using adaptive fuzzy neural networks controller |
publisher |
SAGE Publishing |
series |
Biomedical Engineering and Computational Biology |
issn |
1179-5972 |
publishDate |
2011-01-01 |
description |
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. |
url |
https://doi.org/10.4137/BECB.S6495 |
work_keys_str_mv |
AT mohamedekarar automatedcardiacdruginfusionsystemusingadaptivefuzzyneuralnetworkscontroller AT mohamedaelbrawany automatedcardiacdruginfusionsystemusingadaptivefuzzyneuralnetworkscontroller |
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