Adaptive control and identification for on-line drug infusion in anaesthesia.

Anaesthesia is that part of the medical science profession which ensures that the patient’s body is insensitive to pain and possibly other stimuli during surgical operations. It includes muscle relaxation (paralysis) and unconsciousness, both conditions being crucial for the operating surgeon. Maintaining a steady level of muscle relaxation as well as an acceptable depth of anaesthesia (unconsciousness), while keeping the dosage of administered drugs which induce those effects at a minimum level, have successfully been achieved using automatic control. Fixed gain controllers such as P, PI, and PID strategies can perform well when used in clinical therapy and under certain conditions but on the other hand can lead to poor performances because of the large variability between subjects. This is the reason which led to the consideration of adaptive control techniques which seemed to overcome such problems. Two control strategies falling into the above scheme and including the two newly developed techniques, i.e Proportional-Integral-Plus (PIP) control algorithm, and Generalized Predictive Control algorithm (GPC), are considered under extensive simulation studies using the muscle relaxation process associated with two drugs known as Pancuronium-Bromide and Atracurium. Both models exhibit severe non-linearities as well as time-varying dynamics and delays. Only the strategy corresponding to the GPC algorithm is retained for implementation on a 380Z disk-based microcomputer system, while the muscle relaxation process corresponding to either drugs is simulated on a VIDAC 336 analogue computer. The sensitivity of the algorithm is investigated when patient-to-patient parameter variability is evoked. The study is seen to provide the necessary basis for future clinical implementation of the scheme. Following the satisfactory results obtained under such a real-time environment, the self-adaptive GPC algorithm has been successfully applied in theatre to control Atracurium infusion on humans during surgery. This success later motivated further research work in which simultaneous control of muscle relaxation and anaesthesia (unconsciousness) was achieved. A good multivariable model has been derived and controlled via the multivariable version of the SISO GPC algorithm. The results obtained are very encouraging

Knowledge discovery for friction stir welding via data driven approaches: Part 1 – correlation analyses of internal process variables and weld quality

For a comprehensive understanding towards Friction Stir Welding (FSW) which would lead to a unified approach that embodies materials other than aluminium, such as titanium and steel, it is crucial to identify the intricate correlations between the controllable process conditions, the observable internal process variables, and the characterisations of the post-weld materials. In Part I of this paper, multiple correlation analyses techniques have been developed to detect new and previously unknown correlations between the internal process variables and weld quality of aluminium alloy AA5083. Furthermore, a new exploitable weld quality indicator has, for the first time, been successfully extracted, which can provide an accurate and reliable indication of the ‘as-welded’ defects. All results relating to this work have been validated using real data obtained from a series of welding trials that utilised a new revolutionary sensory platform called ARTEMIS developed by TWI Ltd., the original inventors of the FSW process

A Physiological Model Describing Dobutamine Interation with Septic Patients: A Simulation Study

Mouloud Denai, Mahdi Mahfouf, Jonathan Ross, ‘A Physiological Model Describing Dobutamine Interation with Septic Patients: A Simulation Study’, paper presented at the 3rd European Medical and Biological Engineering Conference, Prague, Czech Republic, 20-25 November, 2005.Peer reviewe

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals

Physiological Modeling of Hemodynamic Effects of Simultaneous Administration of Noradrenaline and Dobutamine to Septic Shock Patients in CICU

Mahdi Mahfouf, Mouloud Denai, Jonathan Ross, ‘Physiological Modelling of Hemodynamic Effects of Simultaneous Administration of Noradrenaline and Dobutamine to Septic Shock Patients in CICU’, paper presented at the 4th IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, 15-17 February, 2006.Peer reviewe

Modeling and simulation of Electrical Impedance Tomography (EIT) on ventilated patients with ARDS lungs

Mouloud Denaï, Mahdi Mahfouf, and Gary H. Mills, 'Modeling and Simulation of Electrical Impedance Tomography (EIT) on Ventilated Patients with ARDS Lungs'. Paper presented at the 8th IEEE Conference on BioInformatics and Bioengineering, 8-10 October 2008, Athens, Greece.Bedside monitoring of lung recruitment is a key feature in the ventilatory management of patients with acute respiratory distress syndrome. Electrical Impedance Tomography is a relatively new imaging technique which reconstructs the electrical conductivity distribution in a lung cross section from electrical measurements made on electrodes placed on the surface of the thorax. The induced conductivity changes can be continuously monitored to assess the topographical distribution of air in the lung and optimise the ventilation strategy. The purpose of this paper is to illustrate these principles on a physiological model of the lung mechanics simulating the concept of recruitment in ventilated patients with acute respiratory distress syndrome.Peer reviewe

Interpretable fuzzy modeling using multi-objective immune-inspired optimization algorithms

Abstract—In this paper, an immune inspired multi-objective fuzzy modeling (IMOFM) mechanism is proposed specifically for high-dimensional regression problems. For such problems, high predictive accuracy is often the paramount requirement. With such a requirement in mind, however, one should also put considerable efforts in making the elicited model as interpretable as possible, which leads to a difficult optimization problem. The proposed modeling approach adopts a multi-stage modeling procedure and a variable length coding scheme to account for the enlarged search space due to the simultaneous optimization of the rule-base structure and its associated parameters. IMOFM can account for both Singleton and Mamdani Fuzzy Rule-Based Systems (FRBS) due to the carefully chosen output membership functions, the inference and the defuzzification methods. The proposed algorithm has been compared with other representatives using a simple benchmark problem, and has also been applied to a high-dimensional problem which models mechanical properties of hot rolled steels. Results confirm that IMOFM can elicit accurate and yet transparent FRBSs from quantitative data

An Artificial Immune Systems based Predictive Modelling Approach for the Multi-Objective Elicitation of Mamdani Fuzzy Rules A Special Application to Modelling Alloys

Abstract-In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability

A new adaptive Mamdani-type fuzzy modeling strategy for industrial gas turbines

The paper presents a new system identification methodology for industrial systems. Using the original Mamdani fuzzy rule based system (FRBS), an adaptive Mamdani fuzzy modeling (AMFM) is introduced in this paper. It differs from the original Mamdani FRBS in that it applies different membership functions and a defuzzification mechanism that is ‘differentiable’ with respect to the membership function parameters. The proposed system also includes a back error propagation (BEP) algorithm that is used to refine the fuzzy model. The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system

Physiological Modeling and Analysis of the Pulmonary Microcirculation in Septic Patients

Mouloud Denai, Mahdi Mahfouf, O.K. King, Jonathan Ross, ‘Physiological Modelling and Analysis of the Pulmonary Microcirculation in Septic Patients’, paper presented at the 6th IFAC Symposium on Modelling and Control in Biomedical Systems, Reims, France, 20-22 September, 2006.Peer reviewe