Key result
A supervisory-fuzzy rule-based adaptive control system successfully maintained mean arterial pressure and cardiac output within prescribed limits in a simulated dog model of congestive heart failure.
Why the study?
Does a supervisory-fuzzy rule-based adaptive control system adequately control mean arterial pressure and cardiac output using SNP and DPM in a simulated dog model of congestive heart failure?
Does a supervisory-fuzzy rule-based adaptive control system adequately control mean arterial pressure and cardiac output using SNP and DPM in a simulated dog model of congestive heart failure?
A novel fuzzy logic-based adaptive control system successfully managed simulated hemodynamic variables using SNP and dopamine in a computational model of canine heart failure.
Answers are grounded in this paper and cite their sources.
Caution against clinical use; leaves open validation in live models and trials.
A control device that uses an expert system approach for a two input-two output system has been developed and evaluated using a mathematical model of the hemodynamic response of a dog. The two inputs are the infusion rates of two drugs: sodium nitroprusside (SNP) and dopamine (DPM). The two controlled variables are the mean arterial pressure and the cardiac output. The control structure is dual mode, i.e., it has two levels: a critical conditions (coarse) control mode and a noncritical conditions (fine) control mode. The system switches from one to the other when threshold conditions are met. Different "controller parameters sets"-including the values for the threshold conditions-can be given to the system which will lead to different controller outputs. Both control modes are rule-based, and supervisory capabilities are added to ensure adequate drug delivery. The noncritical control mode is a fuzzy logic controller. The system includes heuristic features typically considered by anesthesiologists, like waiting periods and the observance of a "forbidden dosage range" for DPM infusion when used as an inotrope. An adaptation algorithm copes with the wide range of sensitivities to SNP found among different individuals, as well as the time varying sensitivity frequently observed in a single patient. The control device is eventually tested on a nonlinear model, designed to mimic the conditions of congestive heart failure in a dog. The test runs show a highest overshoot of 3 mmHg with nominal SNP sensitivity. When tested with different simulated SNP sensitivities, the controller adaptation produces a faster response to lower sensitivities, and reduced oscillations to higher sensitivities. The simulations seem to show that the system is able to drive and adequately keep the two hemodynamic variables within prescribed limits.
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Held et al. (1995) studied Congestive heart failure (simulated dog model). Supervisory-fuzzy rule-based adaptive control system was evaluated on Mean arterial pressure and cardiac output. A supervisory-fuzzy rule-based adaptive control system successfully maintained mean arterial pressure and cardiac output within prescribed limits in a simulated dog model of congestive heart failure.
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