Automatic Control Strategies of Mean Arterial Pressure and Cardiac Output. MIMO controllers, PID, internal model control, adaptive model reference, and neural nets are developed to regulate mean arterial pressure and cardiac output using the drugs sodium Nitroprusside and dopamine

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.Libyan Ministry of Higher Education scholarshi

Development and testing of a simulated closed loop drug delivery system for CHF patients under milrinone administration

The purpose of this thesis project is the development and testing of a simulated closed loop drug delivery (CLDD) system that consists of a pharmacokinetic, physiological, and feedback-controlling model. The focus of this study is on the control of milrinone inflision to maintain cardiac output at desired setpoint range for patients suffering from congestive heart failure (CHF). The simulated CLDD system is written in VisSim dynamic simulation language for an IBM-compatible PC. Milrinone pharmacokinetics are represented by a three compartment model. The physiological model consists of the cardiovascular system model linked to the pharmacodynamic submodel of milrinone. The feedback-controlling model consists of a cascade controlling mechanism incorporating a PID controller. Validation of system dynamics was performed by comparison of simulated results of the loop model (pharmacokinetic and physiological model) to available experimental data. Pharmacokinetic and hemodynamic responses showed that the behavior of the simulated open ioop model was similar to that of CHIF patients under milrinone administration. The addition of the feedback-controlling model to the open loop model resulted in the development of the CLDD system. Performance of the cascade controller was optimized with tuning of PIP controller. A two-hour control performance was monitored as the CLDD system underwent the following situations: (1) target CO was modified (transient response), (2) perturbation was incorporated as circulatory vessel resistances were changed, and (3) randomization of system parameter was achieved by varying the elimination rate constant. Onset delay, time taken for controller to bring CO within set boundaries, and percentage overshoot of cardiac output from target were the underlining results analyzed in understanding the performance of the controller. Aside from some minor refinements, the overall performance of the controller showed it to be robust in responding to the changes in the system by adjusting milrinone inftision so that cardiac output could track to the setpoint. The simulated CLDD system as a whole was observed to correctly represent clinical automated drug delivery. The results of the simulated controller also lead into the possibility of developing an automated control milrinone infusion system for maintaining cardiac output for CHF patients

Assessment of monthly rain fade in the equatorial region at C & KU-band using measat-3 satellite links

C & Ku-band satellite communication links are the most commonly used for equatorial satellite communication links. Severe rainfall rate in equatorial regions can cause a large rain attenuation in real compared to the prediction. ITU-R P. 618 standards are commonly used to predict satellite rain fade in designing satellite communication network. However, the prediction of ITU-R is still found to be inaccurate hence hinder a reliable operational satellite communication link in equatorial region. This paper aims to provide an accurate insight by assessment of the monthly C & Ku-band rain fade performance by collecting data from commercial earth stations using C band and Ku-band antenna with 11 m and 13 m diameter respectively. The antennas measure the C & Ku-band beacon signal from MEASAT-3 under equatorial rain conditions. The data is collected for one year in 2015. The monthly cumulative distribution function is developed based on the 1-year data. RMSE analysis is made by comparing the monthly measured data of C-band and Ku-band to the ITU-R predictions developed based on ITU-R’s P.618, P.837, P.838 and P.839 standards. The findings show that Ku-band produces an average of 25 RMSE value while the C-band rain attenuation produces an average of 2 RMSE value. Therefore, the ITU-R model still under predicts the rain attenuation in the equatorial region and this call for revisit of the fundamental quantity in determining the rain fade for rain attenuation to be re-evaluated

Data-driven resiliency assessment of medical cyber-physical systems

Advances in computing, networking, and sensing technologies have resulted in the ubiquitous deployment of medical cyber-physical systems in various clinical and personalized settings. The increasing complexity and connectivity of such systems, the tight coupling between their cyber and physical components, and the inevitable involvement of human operators in supervision and control have introduced major challenges in ensuring system reliability, safety, and security. This dissertation takes a data-driven approach to resiliency assessment of medical cyber-physical systems. Driven by large-scale studies of real safety incidents involving medical devices, we develop techniques and tools for (i) deeper understanding of incident causes and measurement of their impacts, (ii) validation of system safety mechanisms in the presence of realistic hazard scenarios, and (iii) preemptive real-time detection of safety hazards to mitigate adverse impacts on patients. We present a framework for automated analysis of structured and unstructured data from public FDA databases on medical device recalls and adverse events. This framework allows characterization of the safety issues originated from computer failures in terms of fault classes, failure modes, and recovery actions. We develop an approach for constructing ontology models that enable automated extraction of safety-related features from unstructured text. The proposed ontology model is defined based on device-specific human-in-the-loop control structures in order to facilitate the systems-theoretic causality analysis of adverse events. Our large-scale analysis of FDA data shows that medical devices are often recalled because of failure to identify all potential safety hazards, use of safety mechanisms that have not been rigorously validated, and limited capability in real-time detection and automated mitigation of hazards. To address those problems, we develop a safety hazard injection framework for experimental validation of safety mechanisms in the presence of accidental failures and malicious attacks. To reduce the test space for safety validation, this framework uses systems-theoretic accident causality models in order to identify the critical locations within the system to target software fault injection. For mitigation of safety hazards at run time, we present a model-based analysis framework that estimates the consequences of control commands sent from the software to the physical system through real-time computation of the system’s dynamics, and preemptively detects if a command is unsafe before its adverse consequences manifest in the physical system. The proposed techniques are evaluated on a real-world cyber-physical system for robot-assisted minimally invasive surgery and are shown to be more effective than existing methods in identifying system vulnerabilities and deficiencies in safety mechanisms as well as in preemptive detection of safety hazards caused by malicious attacks

A New Paradigm for the Personalized Delivery of Iodinated Contrast Material at Cardiothoracic, Computed Tomography Angiography

In North America more than 40 million doses of iodinated X-Ray contrast medium are delivered to patients undergoing CT imaging every year. This particular pharmaceutical is necessary to enable Computed Tomography of soft tissue, tumors, and vasculature. Very few of the contrast enhanced procedures are performed with the dose of the drug tailored to the individual patient or procedure and nearly every patient receives the same dose of contrast material. This dissertation presents a methodology to allow the routine administration of a personalized dose of contrast material to generate contrast enhancement sufficient for diagnosis during cardiothoracic CT Angiography imaging. Parameter estimation of a patient specific model is performed using Maximum Likelihood Estimation (MLE) with data generated from the scanner during a pre-diagnostic "test" injection of contrast agent. A non-parametric system identification technique, using the truncated Singular Value Decomposition, is also developed for deriving a patient specific prediction of contrast enhancement. The MLE technique produces contrast enhancement predictions with less error than the tSVD method. It is also shown that the MLE method is less sensitive to data length and has greater noise immunity. A novel, patient-specific contrast protocol generation algorithm is also presented. It is based upon a constrained minimization (Sequential Quadratic Programming) that enforces constraints on the input parameters while minimizing the volume of contrast sufficient to achieve a prospectively chosen enhancement target. A physiologically based pharmacokinetic (PBPK) numeric model is developed and used to validate the contrast prediction and protocol generation techniques. Finally, a novel, instrumented, flow phantom is developed and used to validate the identification and protocol generation techniques

Model-based development of a fuzzy logic advisor for artificially ventilated patients.

This thesis describes the model-based development and validation of an advisor for the maintenance of artificially ventilated patients in the intensive care unit (ICU). The advisor employs fuzzy logic to represent an anaesthetist's decision making process when adjusting ventilator settings to safely maintain a patient's blood-gases and airway pressures within desired limits. Fuzzy logic was chosen for its ability to process both quantitative and qualitative data. The advisor estimates the changes in inspired O2 fraction (FI02), peak inspiratory pressure (PEEP), respiratory rate (RR), tidal volume (VT) and inspiratory time (TIN), based upon observations of the patient state and the current ventilator settings. The advisor rules only considered the ventilation of patients on volume control (VC) and pressure regulated volume control (PRVC) modes. The fuzzy rules were handcrafted using known physiological relationships and from tacit knowledge elicited during dialogue with anaesthetists. The resulting rules were validated using a computer-based model of human respiration during artificial ventilation. This model was able to simulate a wide range of patho-physiology, and using data collected from ICU it was shown that it could be matched to real clinical data to predict the patient's response to ventilator changes. Using the model, five simulated patient scenarios were constructed via discussion with an anaesthetist. These were used to test the closed-loop performance of the prototype advisor and successfully highlighted divergent behaviour in the rules. By comparing the closed-loop responses against those produced by an anaesthetist (using the patient-model), rapid rule refinement was possible. The modified advisor demonstrated better decision matching than the prototype rules, when compared against the decisions made by the anaesthetist. The modified advisor was also tested using data collected from ICU. Direct comparisons were made between the decisions given by an anaesthetist and those produced by the advisor. Good decision matching was observed in patients with well behaved physiology but soon ran into difficulties if a patients state was changing rapidly or if the patient observations contained large measurement errors

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not