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

Analytical Representation of Four-dimensional Hemodynamics for Drug Therapy Simulation in Acute Heart Failure Treatment

Acute heart failure imperils multiple organs, including the heart. Elucidating the impact of drug therapies across this multidimensional hemodynamic system remains a challenge. This paper proposes a simulator that analyzes the impact of drug therapies on four dimensions of hemodynamics: left atrial pressure, cardiac output, mean arterial pressure, and myocardial oxygen consumption. To mathematically formulate hemodynamics, the analytical solutions of four-dimensional hemodynamics and the direction of its change are derived as functions of cardiovascular parameters: systemic vascular resistance, cardiac contractility, heart rate, and stressed blood volume. Furthermore, a drug library which represents the multi-dependency effect of drug therapies on cardiovascular parameters was identified in animal experiments. In evaluating the accuracy of our derived hemodynamic direction, the average angular error of predicted versus observed direction was 18.85[deg] after four different drug infusions for acute heart failure in animal experiments. Finally, the impact of drug therapies on four-dimensional hemodynamics was analyzed in three different simulation settings. One result showed that, even when drug therapies were simulated with simple rules according to the Forrester classification, the predicted direction of hemodynamic change matched the expected direction in more than 80% in 963 different AHF patient scenarios. Our developed simulator visualizes the impact of drug therapies on four-dimensional hemodynamics so intuitively that it can support clinicians' decision-making to protect multiple organs.

System Design for Optimizing Drug Infusions Using Cardiovascular Space Mapping for Acute Heart Failure

Acute heart failure is caused by various factors and requires multiple drug therapies to remedy underlying causes. Due to the complexity of pharmacologic effects of cardiovascular agents, few studies have theoretically addressed the multidrug optimization problem. This paper proposes a drug infusion system for acute heart failure that controls cardiovascular performance metrics (cardiac output, left atrial pressure, and mean arterial pressure) within desired ranges as dictated by the cardiovascular parameters (systemic vascular resistance, cardiac contractility, heart rate, and stressed blood volume). The key to our system design is modeling and controlling cardiovascular parameters to yield the desired cardiovascular metrics. A 'tailored drug infusion' technique controls parameters by solving the optimization problem in order to conquer the complexity of multi-dependencies and the different dosage limits among multiple drugs. A 'cardiovascular space mapping' technique identifies the desired parameters from the desired metrics by deriving the analytical solutions of the metrics as functions of the parameters. To facilitate clinical discussions, parameters were set to realistic values in 5,600 simulated patients. Our results showed not only that the optimized drug combinations and dosages controlled the cardiovascular metrics to within the desired ranges, but also that they mostly corresponded to the recommended clinical use guidelines. An additional value of our system is that it proactively predicts the limitations of the tailored drug therapy, which supports the clinical decision of pivoting to alternative treatment strategies such as mechanical circulatory support.

Nonlinear System Identification Based on Convolutional Neural Networks for Multiple Drug Interactions

In heart failure patients, hemodynamics can be regulated by therapeutic drugs. Although the cardiovascular responses to these drugs usually include nonlinearity and drug interactions, it is difficult to identify the characteristics of the dynamics under such conditions. This study, therefore, was aimed at evaluating the technique used for nonlinear system identification based on convolutional neural networks (CNN). As an image i.e., pixel values corresponding to time-course data), CNN can be used to treat the complicated relation between previous inputs (i.e., drug infusions) and outputs i.e., hemodynamics). To compare the accuracy of CNN, traditional methods based on the standard neural networks (NN) and fast Fourier transformation FFT were applied to nonlinear system identification with drug interactions. The cardiac output and arterial blood pressure under heart failure were modulated by the drug infusions of an inotropic agent and a vasodilator. CNN accurately predicted the dynamic system responses regardless of the inclusion of nonlinearity and drug interactions. Based on the findings of this study, CNN to carry out nonlinear system identification could clarify complicated pharmacodynamics, and thus could be useful for in appropriate cardiac treatment with multiple therapeutic agents.

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 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.

Reinforcement learning: a novel method for optimal control of propofol-induced hypnosis

Reinforcement learning (RL) is an intelligent systems technique with a history of success in difficult robotic control problems. Similar machine learning techniques, such as artificial neural networks and fuzzy logic, have been successfully applied to clinical control problems. Although RL presents a mathematically robust method of achieving optimal control in systems challenged with noise, nonlinearity, time delay, and uncertainty, no application of RL in clinical anesthesia has been reported.