z-logo
open-access-imgOpen Access
Automated Cardiac Drug Infusion System Using Adaptive Fuzzy Neural Networks Controller
Author(s) -
Mohamed Esmail Karar,
Mohamed A. El-Brawany
Publication year - 2011
Publication title -
biomedical engineering and computational biology
Language(s) - English
Resource type - Journals
ISSN - 1179-5972
DOI - 10.4137/becb.s6495
Subject(s) - inotrope , cardiac output , medicine , heart failure , artificial neural network , settling time , controller (irrigation) , hemodynamics , vasodilation , blood pressure , mean arterial pressure , anesthesia , control theory (sociology) , cardiology , computer science , heart rate , artificial intelligence , control engineering , engineering , control (management) , agronomy , biology , step response
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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom