Why the study?
Does an automated cardiac drug infusion system using an adaptive fuzzy neural networks controller improve the regulation of mean arterial pressure and cardiac output in a cardiovascular model of hypertension and congestive heart failure?
Does an automated cardiac drug infusion system using an adaptive fuzzy neural networks controller improve the regulation of mean arterial pressure and cardiac output in a cardiovascular model of hypertension and congestive heart failure?
An adaptive fuzzy neural network controller can effectively automate the simultaneous infusion of vasodilators and inotropic agents to regulate hemodynamics in a simulated cardiovascular model.
Automated control feasible with low error in models; leaves open clinical translation for hypertension and heart failure.
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.
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Karar et al. (2011) studied this question.
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