Control strategies for arterial blood pressure regulation

An optimal interval type-2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure

BACKGROUND AND OBJECTIVE

The main aim of this work is to present an optimal and robust controller design in order to improve the drug infusion to the automatic control of mean arterial blood pressure in conditions like critically-ill or post-operative or anaesthesia administration. The physiological systems also have uncertainty issues such as parameter variations with time or external disturbances and noise. Therefore, a controlled drug administration is necessary to regulate the mean arterial blood pressure of a person during surgery/observation. Over the years, the proportional-integral-derivative (PID) controller is the most commonly used controller in industries due to its easy structure and simplicity. However, this controller does not meet the desired performance with the complex and uncertain plants. Therefore, a robust controller is required to regulate the physiological variables that are uncertain in nature and can affect the human life.

METHODS

In this work, a hybrid control scheme consisting of an interval type-2-fuzzy logic controller which acts as pre-compensator to the traditional PID controller is presented, to regulate the mean arterial blood pressure of a patient by administering the drug sodium nitroprusside in a controlled manner. An effective and well-established nature-inspired optimization technique namely cuckoo search algorithm is employed for obtaining the optimal parameters for the presented scheme.

RESULTS

Simulation results are presented to show the effectiveness and robustness of proposed interval type-2-fuzzy logic controller based PID controller scheme, for maintaining the mean arterial pressure to 100 mmHg within considerable limit through SNP infusion. The results are further compared with other two controllers namely type-1 fuzzy logic based PID and traditional PID controllers for the parameter variations and external noise.

CONCLUSION

In this study, the proposed interval type-2-fuzzy logic controller pre-compensator based PID controller provides an effective control than traditional type-1 fuzzy logic based control scheme and PID controller in terms of overshoot, settling-time and error which are the prime performance objectives of the closed-loop controlled drug delivery of human blood pressure. The presented study provides a firm base for initial design considerations for development of a low-cost closed-loop drug delivery system for blood pressure regulation.

Evolution of blood pressure control identification in lieu of post-surgery diabetic patients: a review

The blood pressure disparity is the major problem in post-operative surgery especially diabetic patients, because there is substantial interrelation between diabetic and hypertension and this abnormality creates complicated problems and needs to be controlled by continuous monitoring based on the severity. To overcome this problem, implementation of automatic drug infusion is required for critical patients, by which workload of the clinical staffs are reduced. Most commonly the sodium nitroprusside (SNP) is used to reduce the blood pressure in fast action based on the prescribed level. In this paper three different types of estimation techniques (PID, IMC and MPC) are uses to identify the valuation. The strength of the projected controller performance is evaluated under different types of patients such as sensitive, and normal along with insensitive patients. Therefore, this paper review the validation results based on the optimized SNP infusion rate for persistent Blood pressure control compare then the reviewed methods. The MATLAB simulation is used to evaluate the efficiency of the proposed work and obtain the results based on the projected values.

A NOVEL EFFICIENT LEARNING ALGORITHM FOR SELF-GENERATING FUZZY NEURAL NETWORK WITH APPLICATIONS

In this paper, a novel efficient learning algorithm towards self-generating fuzzy neural network (SGFNN) is proposed based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-Kang (TSK) fuzzy system. The proposed algorithm is simple and efficient and is able to generate a fuzzy neural network with high accuracy and compact structure. The structure learning algorithm of the proposed SGFNN combines criteria of fuzzy-rule generation with a pruning technology. The Kalman filter (KF) algorithm is used to adjust the consequent parameters of the SGFNN. The SGFNN is employed in a wide range of applications ranging from function approximation and nonlinear system identification to chaotic time-series prediction problem and real-world fuel consumption prediction problem. Simulation results and comparative studies with other algorithms demonstrate that a more compact architecture with high performance can be obtained by the proposed algorithm. In particular, this paper presents an adaptive modeling and control scheme for drug delivery system based on the proposed SGFNN. Simulation study demonstrates the ability of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

The benefits of using Guyton's model in a hypotensive control system

In order to improve the intraoperative applications, this paper presents the advantages of using Guyton's model in hypotensive control system development. In this system, the mean arterial pressure is decreased and maintained at a low level during anaesthesia by controlling sodium nitroprusside infusion rate. The key of the study is to develop a physiological model of cardiovascular dynamics to present the mean arterial pressure response to sodium nitroprusside, which was considered as a linear model in most of known blood pressure control systems. Being linear, the previous models cannot accurately mimic a physiological system of human circulation, especially at deep hypotensive control with strong reaction of the body. The enhanced model in this study was modified based on Guyton's model of human circulation. It is useful to design a PID controller, which allows studying and handling the wide range of the body sensitivities. This model is also helpful for studying the behaviors of patients under anaesthesia conditions, such as the perfusion of organs and the reaction of the body at hypotensive state. A fuzzy gain scheduler and a supervising algorithm were also developed for online tuning the controller to handle the behavior of the body. The control system was tested on 25 experiments on seven pigs in the animal laboratory. Simulation and experiment results proved the usefulness of Guyton's model in control system design which can present the dynamical response of blood pressure in the circulation under and after hypotensive control. The results also indicated the safety and stability of the controller.

Automatic regulation of hemodynamic variables in acute heart failure by a multiple adaptive predictive controller based on neural networks

Automated drug-delivery systems that can tolerate various responses to therapeutic agents have been required to control hemodynamic variables with heart failure. This study is intended to evaluate the control performance of a multiple adaptive predictive control based on neural networks (MAPC_NN) to regulate the unexpected responses to therapeutic agents of cardiac output (CO) and mean arterial pressure (MAP) in cases of heart failure. The NN components in the MAPC_NN learned nonlinear responses of CO and MAP determined by hemodynamics of dogs with heart failure. The MAPC_NN performed ideal control against unexpected (1) drug interactions, (2) acute disturbances, and (3) time-variant responses of hemodynamics [average errors between setpoints (+35 ml kg⁻¹ min⁻¹ in CO and ±0 mmHg in MAP) and observed responses; 6.4, 3.7, and 4.2 ml kg⁻¹ min⁻¹ in CO and 1.6, 1.4, and 2.7 mmHg (10.5, 20.8, and 15.3 mmHg without a vasodilator) in MAP] during 120-min closed-loop control. The MAPC_NN could also regulate the hemodynamics in actual heart failure of a dog. Robust regulation of hemodynamics by the MAPC_NN was attributable to the ability of on-line adaptation to adopt various responses and predictive control using the NN. Results demonstrate the feasibility of applying the MAPC_NN using a simple NN to clinical situations.

Hypothesis: the systemic circulation as a regulated free-market economy. A new approach for understanding the long-term control of blood pressure

1. The purpose of the present paper is to discuss the long-term regulation of arterial blood pressure. Current thinking on the topic favours the theory that tight regulation is achieved through the action of a central (or primary) controller, usually assumed to be in either the brain or kidneys. 2. Here, an alternative explanation is considered; namely, that the average long-term level of arterial pressure is an emergent property of a decentralized control system. The goal of the system is to deliver nutrient-rich blood to distinct vascular regions based on their energy demand. 3. Specifically, the circulation is conceptualized as a free-market economy where tissues 'compete' for a scarce resource (the energy contained in blood) supplied by the heart-lung unit; the 'price' of the resource (analogous to the reciprocal of arterial pressure) is determined primarily by the dynamic relationship between supply and demand, not by a central mechanism. 4. Based on this concept of the circulation as an energy market, economic analogies are used to suggest novel mechanisms by which the brain and kidney may affect the long-term control of blood pressure. 5. Market-based control, a process derived from quantitative theoretical analysis of the performance of economic markets, is proposed as a new, potentially useful strategy for mathematically modelling the behaviour of the circulation.

An Intelligent Adaptive Control Scheme for Postsurgical Blood Pressure Regulation

This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability. System approximation formulated by the G-FNN is employed in the adaptive controller design for drug infusion in intensive care environment. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through intravenous infusion of sodium nitroprusside (SNP), which is one attractive application in automation of drug delivery. Simulation studies demonstrate the capability of the proposed approach in estimating the drug's effect and regulating blood pressure at a prescribed level.

Chronopharmaceutics: gimmick or clinically relevant approach to drug delivery?

Due to advances in chronobiology, chronopharmacology, and global market constraints, the traditional goal of pharmaceutics (e.g. design drug delivery systems with a constant drug release rate) is becoming obsolete. However, the major bottleneck in the development of drug delivery systems that match the circadian rhythm (chronopharmaceutical drug delivery systems: ChrDDS) may be the availability of appropriate technology. The last decade has witnessed the emergence of ChrDDS against several diseases. The increasing research interest surrounding ChrDDS may lead to the creation of a new sub-discipline in pharmaceutics known as chronopharmaceutics. This review introduces the concept of chronopharmaceutics, addresses theoretical/formal approaches to this sub-discipline, underscores potential disease-targets, revisits existing technologies and examples of ChrDDS. Future development in chronopharmaceutics may be made at the interface of other emerging disciplines such as system biology and nanomedicine. Such novel and more biological approaches to drug delivery may lead to safer and more efficient disease therapy in the future.

Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables

A model-based control methodology was developed for automated regulation of mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The control algorithm used a multiple-model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The controller was experimentally evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output. The controller performed better as compared to experiments on manual regulation of the hemodynamic variables. After the model bank was determined, mean arterial pressure was held within +/- 5 mm Hg 88.9% of the time with a standard deviation of 3.9 mm Hg. The cardiac output was held within +/- 1 l/min 96.1% of the time with a standard deviation of 0.5 l/min. The manual runs maintain mean arterial pressure only 82.3% of the time with a standard deviation of 5 mm Hg, and cardiac output 92.2% of the time with a standard deviation of 0.6 l/min.

Survey on the use of smart and adaptive engineering systems in medicine

In this paper, the current published knowledge about smart and adaptive engineering systems in medicine is reviewed. The achievements of frontier research in this particular field within medical engineering are described. A multi-disciplinary approach to the applications of adaptive systems is observed from the literature surveyed. The three modalities of diagnosis, imaging and therapy are considered to be an appropriate classification method for the analysis of smart systems being applied to specified medical sub-disciplines. It is expected that future research in biomedicine should identify subject areas where more advanced intelligent systems could be applied than is currently evident. The literature provides evidence of hybridisation of different types of adaptive and smart systems with applications in different areas of medical specifications.

Improving regulation of mean arterial blood pressure during anesthesia through estimates of surgery effects

In this paper, a scheme for improvement of the regulation of mean arterial blood pressure (MAP) during anesthesia based on model predictive control (MPC) and estimates of the effects of disturbances (surgical events) is proposed. A linear model for the combined effects of surgical stimulations and volatile anesthetics on MAP is derived from experimental data. Based on it the potential improvement in blood pressure regulation is evaluated via a simulation study. The simulation study shows that when information about the effect of the surgical events on MAP is utilized by the controller maximum MAP deviations can be reduced by as much as 50% even when this information is inaccurate. At worst, (highly inaccurate information) no improvement is obtained.

The first object oriented monitor for intravenous anesthesia

OBJECTIVE

To describe the design and implementation of "INFUSION TOOLBOX," a software tool to control and monitor multiple intravenous drug infusions simultaneously using pharmacokinetic and pharmacodynamic principles.

METHODS

INFUSION TOOLBOX has been designed to present a graphical interface. Object Oriented design was used and the software was implemented using Smalltalk, to run on a PC. Basic tools are available to manage patient, drugs, pumps and reports. These tools are the PatientPanel, the DrugPanel, the PumpPanel and the HistoryPanel. The screen is built dynamically. The panels may be collapsed or closed to avoid a crowded display. We also built control panels such as the Target ControlPanel which calculates the best infusion sequence to bring the drug concentration in the plasma compartment to a preset value. Before drug delivery, the user enters the patient's data, selects a drug, enters its dilution factor and chooses a pharmacokinetic model. The calculated plasma concentration is continually displayed and updated. The anesthetist may ask for the history of the delivery to obtain a graphic report or to add events to the logbook. A panel targeting the effect is used when a pharmacodynamic model is known. Data files for drugs, pumps and surgery are upgradable.

DISCUSSION

By creating a resizeable ControlPanel we enable the anesthetist to display the information he wishes, when he wishes it. The available panels are diverse enough to meet the anesthetist needs; they may be adapted to the drug used, pumps used and surgery. It is the anesthetist who builds dynamically its different control screens.

CONCLUSION

By adopting an evolutionary solution model we have achieved considerable success in building our drug delivery monitor. In addition we have gained valuable insight into the anesthesia information domain that will allow us to further enhance and expand the system.

Hemodynamic management of congestive heart failure by means of a multiple mode rule-based control system using fuzzy logic

A rule-based system was designed to control the mean arterial pressure (MAP) and the cardiac output (CO) of a patient with congestive heart failure (CHF), using two vasoactive drugs: sodium nitroprusside (SNP) and dopamine (DPM). The controller has three different modes, that engage according to the hemodynamic state. The critical conditions control mode (CCC) determines the initial infusion rates, and continues active if the MAP or the CO fall outside of the defined criticality thresholds: an upper and a lower boundary for the MAP and a lower boundary for the CO. Inside the boundaries the control is performed by noncritical conditions control modes (NCC's), which are fuzzy logic controllers. If the CO is within normal range and the MAP is close to the goal range, then the MAP is driven using only SNP, in a single-input-single-output mode (NCC-SISO). Otherwise the NCC multiple-input-multiple-output is active (NCC-MIMO). The goal values for the controlled variables are defined as a band of 5 mmHg for the MAP and 5 mL/kg/min for the CO, but there is little concern for this application if the CO is too high (i.e., in practical terms the CO only needs to achieve a necessary minimum rate). The NCC-MIMO includes a gain adaptation algorithm to cope with the wide variety in sensitivities to SNP. Supervisory capabilities to ensure adequate drug delivery complete the controller scheme. After extensive testing and tuning on a CHF-hemodynamics nonlinear model, the control system was applied in dog experiments, which led to further enhancements. The results show an adequate control, presenting a fast response to setpoint changes with an acceptable overshoot.

Automated infusion of vasoactive and inotropic drugs to control arterial and pulmonary pressures during cardiac surgery

OBJECTIVE

To evaluate the feasibility of a closed-loop system for simultaneous control of systemic arterial and pulmonary artery blood pressures during cardiac surgery.

DESIGN

Feasibility study.

SETTING

The cardiac surgery operating room.

PATIENTS

The performance of the multiple-drug closed-loop system was evaluated during cardiac surgery in 30 patients who required treatment with more than one vasoactive or inotropic drug.

INTERVENTIONS

A multiple-drug closed-loop system integrated five single-drug blood pressure controllers. Arterial hypertension was controlled using sodium nitroprusside or nitroglycerin, arterial hypotension was controlled using noradrenaline or dobutamine, and pulmonary hypertension was controlled using nitroglycerin. The anesthesiologist selected target pressures and single-drug blood pressure controllers. The multiple-drug closed-loop system had a set of priority rules that automatically activated from the selected single-drug controllers the optimum single-drug controller for each hemodynamic state. Drug infusion rates of the nonactive controllers were kept constant. The initial knowledge that was used to construct the priority rules was obtained from standard anesthetic protocols on perioperative management of cardiac surgical patients. A supervisory computer program defined the actions to be taken in cases of infusion pump problems, invalid pressure measurements, and during unexpected increases and decreases in systemic arterial pressure.

MEASUREMENTS AND MAIN RESULTS

The activation of single-drug controllers by the priority rules was accurate and fast. On average, a different single-drug controller was activated once every 7.2 mins. As a measure of variability, the average deviation of mean arterial pressure and mean pulmonary artery pressure from their target values was evaluated and was 8.6+/-4.0 and 4.4+/-4.0 mm Hg, respectively, before cardiopulmonary bypass and 8.0+/-3.6 and 2.4+/-0.9 mm Hg, respectively, after cardiopulmonary bypass. None of the single-drug controllers showed any signs of unstable response.

CONCLUSION

Closed-loop control of both arterial and pulmonary pressures using multiple drugs is feasible during cardiac surgery.

Adaptive control of arterial blood pressure with a learning controller based on multilayer neural networks

We discuss a two-model multilayer neural network controller for adaptive control of mean arterial blood pressure (MABP) using sodium nitroprusside. A model with an autoregressive moving average (ARMA), representing the dynamics of the system, and a modified back-propagation training algorithm are used to design the control system to meet specified objectives of design (settling time and undershoot/overshoot) and clinical constraints. The controller is associated with a weighting-determinant unit (WDU) to determine and update the output weighting factor of the parallel two-model neural network for adequate control action and a control-signal modification unit (CMU) to comply with clinical constraints and to suppress the effect of adverse noise and to improve the WDU performance. Extensive computer simulations indicate satisfactory performance and robustness of the proposed controller in the presence of much noise, over the full range of plant parameters, uncertainties, and large variations of parameters.

Automated infusion of nitroglycerin to control arterial hypertension during cardiac surgery

OBJECTIVE

To evaluate the feasibility of closed-loop blood pressure control during cardiac surgery.

DESIGN

A closed-loop system regulated peroperative hypertension by controlling the infusion rate of the vasodilator nitroglycerin (NTG). The controller consisted of a regulator which was monitored by a supervisory computer program. Mean arterial pressure (MAP) was calculated every 5 s from measurements of the radial artery pressure signal. The regulator calculated an NTG infusion rate with each new MAP measurement. The supervisory computer program monitored the regulator's actions and adapted or overruled the regulator when required.

SETTING

The cardiac surgery operating room.

PATIENTS

46 patients who were scheduled for cardiac surgery and who developed peroperative hypertension.

INTERVENTIONS

Patients were scheduled for either bypass or valve replacement surgery. The closed-loop system was used to control hypertension before and after cardiopulmonary bypass. The use of the closed-loop system did not require deviation from the protocol normally used during cardiac surgery. All patients received standard continuous anaesthesia with opioids.

MEASUREMENTS AND RESULTS

Initial automatic control was achieved in 9.4 (4.1 SD) min. The percentage of time that MAP remained in a range around the target MAP of +/- 10 and +/- 20 mmHg was 74 and 94%, respectively. The mean NTG infusion rate while MAP was within 5 mmHg of target MAP was 1.14 (0.84 SD) micrograms kg-1 min-1. Target MAP was set between 65 and 90 mmHg. There was a small group of patients (6 out of 46) who did not respond to NTG and required alternative drug therapy.

CONCLUSIONS

The controller provided fast and stable control in all patients. The expert knowledge implemented through the supervisory computer program enabled the controller to respond adequately to the rapid changes in arterial pressures commonly associated with cardiac surgery. We conclude that closed-loop control of arterial pressure is feasible not only in the cardiac surgical care unit but also during cardiac surgery.

Blood pressure control during surgical operations

In order to reduce intraoperative blood loss and spare blood transfusion, we developed a blood pressure control system using a state-predictive controller. Using adult mongrel dogs, the mean arterial pressure (MAP) was recorded from a femoral artery while trimethaphan camsilate was infused at constant rates. A pure delay plus a first-order delay model was then derived from the dose-response curves and the values of plant parameters (gain, time-constant, dead-time, and so on) were estimated based on the experimental data. For this model, a state-predictive servo system was designed to cope with the pure delay existing in the model, and simulated. In order to evaluate the accuracy and reliability of this system, we experimented on dogs. With a reference MAP set at 60 mmHg, the MAP reached the reference level in 5.8 to 26.5 min. The duration of error from the reference MAP (+/- 10%) was 2.3 +/- 3.9 min/h (n = 7). These results indicated the safety and stability of our system.