Biomedical subjects

D A Linkens

Publications and source records attributed to D A Linkens.

At least 19 recordsLinked to original sources

PubMed2006

Comparative study of intelligent models for the prediction of bladder cancer progression.

New techniques for the prediction of tumour behaviour are needed since statistical analysis has low accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide suitable methods. We have compared the predictive accuracies of neuro-fuzzy modelling (NFM), artificial neural networks (ANN) and traditional statistical methods for the prediction of bladder cancer. Experimental molecular biomarkers, including p53 expression and gene methylation, and conventional clinicopathological data were studied in a cohort of 117 patients with bladder cancer. For all 3 methods, models were produced to predict the presence and timing of tumour progression. Both methods of AI predicted progression with an accuracy ranging from 88-100%, which was superior to logistic regression, and NFM appeared to be better than ANN at predicting the timing of progression.

Biomarkers, Tumor↗

PubMed2004

Adaptive ventilator FiO2 advisor: use of non-invasive estimations of shunt.

A non-invasive and simple method of parameter estimation has been developed for the model-based decision support of the artificial ventilation in intensive care units. The parameter concerned was the respiratory shunt. Originally, the shunt had to be estimated using a numerical algorithm, which was slow and unreliable. The estimation process also required the knowledge of other parameters, whose values could only be obtained using invasive monitoring equipment. In this paper, the respiratory index is used for the shunt estimation. A linear regression model and a non-linear adaptive neuro-fuzzy inference system (ANFIS) model were used to describe the relationship between the respiratory index and the shunt. The shunts estimated using these models were then used to calculate the fractional inspired oxygen needed to attain the target arterial oxygen level of the model patient. The advisor also utilises population median values of the cardiac index and oxygen consumption index. This alleviates the need for invasive monitoring. In a simulation study, the mean squared error of the control using the ANFIS model was 0.75 kPa2 compared to 2.06 kPa2 using the linear regression model. Therefore, the performance of the FiO2 advisor was better when the shunt was estimated using the non-linear ANFIS model.

Algorithms↗

PubMed2004

Migrating from target-controlled infusion to closed-loop control in general anaesthesia.

The target-controlled infusion (TCI) technique has been successfully and commercially used in clinical general anaesthesia with the intravenous anaesthetic agent propofol. The technique is based on a population pharmacokinetic model and is an open-loop control system. Closed-loop control requires a reliable and consistent signal for feedback utilisation. With all anaesthetic agents the somatosensory evoked potentials (SEP) have been shown to give increased latency as anaesthetic depth is increased. Using infusion rate and SEP response data from rats anaesthetised with propofol a mathematical model was derived to describe the anaesthetic process. This model was used as a design reference to develop a proportional integral (PI) closed-loop control system using SEP as the feedback measure. A serials of 10 trials were conducted to investigate the difference between continuous bolus injection and infusion, all under closed-loop control. The trials showed that the use of SEPs in closed-loop control of anaesthesia is feasible.

Anesthesia, General↗

PubMed2003

Neuronal network modelling of the effects of anaesthetic agents on somatosensory pathways.

The whole question of consciousness, awareness and depth of anaesthesia is both timely, little understood and deeply challenging. Models of the underlying neural pathway mechanisms/dynamics are necessary for understanding the interactions involved and their structure and function. A neuronal network of the somatosensory pathways is proposed in this paper based on experimental information and physiological investigation into anaesthesia. Existing mathematical neuronal models from the literature have been modified and then employed to describe the dynamics of the proposed pathway network. Effects of anaesthetic agents on the cortex were simulated in the model which describes the evoked cortical responses. By comparison with responses from anaesthetised rats, the model's responses are able to describe the dynamics of typical responses. Thus, the proposed model promises to be valuable for investigating the mechanisms of anaesthesia on the cortex and the effects of brain lesions.

Anesthetics↗

PubMed2003

Rule-base derivation for intensive care ventilator control using ANFIS.

In recent years, much research has been done on the use of fuzzy systems in medicine. The fuzzy rule-bases have usually been derived after extensive discussion with the clinical experts. This takes a lot of time from the clinical experts and the knowledge engineers. This paper presents the use of the adaptive neuro-fuzzy inference system (ANFIS) in rule-base derivation for ventilator control. The change of the inspired fraction of oxygen (FiO(2)) advised by eight clinical experts responding to 71 clinical scenarios was recorded. ANFIS and a multilayer perceptron (MLP) were then used to model the relationship between the inputs (the arterial oxygen tension (PaO(2)), FiO(2) and the positive end-expiratory pressure (PEEP) level) and the change in FiO(2) suggested. Compared to a previous fuzzy advisor (FAVeM), both the ANFIS and the MLP were found to correlate with the clinicians' decision better (correlation coefficient of 0.694 and 0.701, respectively compared to 0.630). A formerly developed model-based radial basis network advisor (RBN-MB) was used for comparison. Closed-loop simulations showed that the ANFIS, MLP and the RBN-MB's performance were comparable to the clinicians' performance (correlation coefficients of 0.852, 0.962 and 0.787, respectively). The FAVeM's performance differed from the clinicians' performance (correlation coefficient of 0.332) but the resulting PaO(2) was still within safety limits.

Computer Simulation↗

PubMed2002

Analysis of solvent central nervous system toxicity and ethanol interactions using a human population physiologically based kinetic and dynamic model.

The effect of acute ethanol-mediated inhibition of m-xylene metabolism on central nervous system (CNS) depression in the human worker population was investigated using physiologically based pharmacokinetic (PBPK) models and probabilistic random (Monte Carlo) sampling. PBPK models of inhaled m-xylene and orally ingested ethanol were developed and combined by a competitive enzyme (CYP2E1) inhibition model. Human interindividual variability was modeled by combining estimated statistical distributions of model parameters with the deterministic PBPK models and multiple random or Monte Carlo simulations. A simple threshold pharmacodynamic model was obtained by simulating m-xylene kinetics in human studies where CNS effects were observed and assigning the peak venous blood m-xylene concentration (C(V,max)) as the dose surrogate of toxicity. Probabilistic estimates of an individual experiencing CNS disturbances given exposure to the current UK occupational exposure standard (100 ppm time-weighted average over 8 h), with and without ethanol ingestion, were obtained. The probability of experiencing CNS effects given this scenario increases markedly and nonlinearly with ethanol dose. As CYP2E1-mediated metabolism of other occupationally relevant organic compounds may be inhibited by ethanol, simulation studies of this type should have an increasingly significant role in the chemical toxicity risk assessment.

Administration, Oral↗

PubMed2002

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.

Artificial Intelligence↗

PubMed2000

Intelligent control of a cryogenic cooling plant based on blackboard system architecture.

Intelligent system techniques have been rapidly assimilating into process control engineering, with many applications reported in the last decade. Intelligent control is bringing a new perspective as well as new challenges to process control. In this paper, a software architecture for a Blackboard for Integrated Intelligent Control Systems (BIICS) is described. The system is designed to simultaneously support multiple heterogeneous intelligent methodologies, such as neural networks. expert systems, fuzzy logic, neural networks and genetic algorithms. It will be shown how such methodologies can be readily assimilated into the software architecture. The BIICS system represents a multi-purpose platform for design and simulation of intelligent control paradigms for different kinds of processes. Currently the system utilizes intelligent control techniques (neuro-fuzzy and genetic optimization) for controlling a cryogenic plant used for superconductor testing at temperatures below 100 K.

Journal Article↗

PubMed2000

Somatosensory evoked potentials for closed-loop control of anaesthetic depth using propofol in the urethane-anaesthetized rat.

Primary somatosensory cortical mass responses have been shown to exhibit dose-dependent changes in latency when general anaesthetics are administered. Here we describe a system in which the latency of evoked responses was measured automatically in real time in five animals. Latency changes were used to operate a closed-loop control of propofol delivery by intravenous infusion. The system attempted to induce and maintain a 1 ms increase in evoked response latency; this was reversed when infusion was discontinued. Allowing for the rapid and large biological fluctuations in the evoked response, this was achieved successfully. The system maintained a mean increase in latency of 1.27 (SD 0.42) ms. The mean statistical dispersion index of data obtained during the controlled period was 1.23 (0.3); in an ideal controllable system it approximates to 1. Such a system may provide a means for the automatic delivery of anaesthetics.

Anesthesia, Closed-Circuit↗

PubMed1999

Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery.

Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block of patients in the operating theater where the patient response exhibits high nonlinearity and individual patient dose requirements may vary fivefold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint of 10% of baseline in 10 patients undergoing general surgery, we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg/kg/h range in the mean atracurium infusion rate. This result compares favorably with more complex and computationally intensive model-based control strategies for atracurium infusion.

Algorithms↗

PubMed1999

Internal representation in neural networks used for classification of patient anaesthetic states and dosage.

In this study we aimed to explore the ability of artificial neural networks (ANN) to classify patient anaesthetic states and dosage. Surgical data obtained under different states of anaesthesia and dose levels were modelled via this approach. It is shown that inferential parameters can be used to determine the patient anaesthetic states and drug dosage. In addition to demonstrating the capability of ANN for classification we were interested in the internal representations that are developed automatically by networks while they are learning their processing task. An unsupervised learning procedure of clustering via which the classes are inferred from the data and a supervised learning technique of discrimination via which to construct a classification of the known categories were applied to analyse the performance of the ANN. Discriminant analysis (DA) was also utilised to optimise the network architecture.

Anesthesia↗

PubMed1999

Dynamic monitoring and control of patient anaesthetic and dose levels: time-delay, moving-average neural networks, and principal components analysis.

The goal of this study was to examine the capabilities of neural network models for dynamic monitoring and control of patient anaesthetic and dose levels. The network models that we considered are split into two basic groups: static networks and dynamic networks. Static networks are characterised by equations that are memoryless. On the other hand, dynamic networks are systems with memory. Additionally, principal components analysis was used to introduce a further improvement to network design by reducing the dimensionality of the encoded temporal information. Principal components analysis was applied as both pre-processing and post-processing techniques. In the first instance it was used to reduce the dimensionality of the data to more manageable intrinsic information. In the second instance it was employed to understand how the hidden layers separate the data, in order to optimise the network architecture.

Anesthesia↗

PubMed1997

Self-learning fuzzy control of atracurium-induced neuromuscular block during surgery.

Self-learning fuzzy logic control has the important property of accommodating uncertain, non-linear and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time, without the need for detailed process modelling. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block (NMB) of patients in the operating theatre. Here, the patient response exhibits high non-linearity, and individual patient dose requirements can vary five-fold during an operating procedure. A portable control system was developed to assess the clinical performance of a simplified self-learning fuzzy controller in this application. A Paragraph (Vital Signs) NMB device monitored T1, the height of the first twitch in a train-of-four nerve stimulation mode. Using a T1 setpoint = 10% of baseline in ten patients undergoing general surgery, a mean T1 error of 0.45% (SD = 0.44%) is found while a 0.13-0.70 mg k-1 h-1 range in the mean atracurium infusion rate is accommodated. The result compares favourably with more complex and computationally-intensive model-based control strategies for the infusion of atracurium.

Atracurium↗

PubMed1997

Recognition of patient anaesthetic levels: neural network systems, principal components analysis, and canonical discriminant variates.

The goal of this study was to examine the ability of Neural Networks to recognise the levels of anaesthetic state of a patient. Data obtained under different levels of anaesthesia have been modelled for the purpose. It is shown that inferential parameters can be used to recognise the levels of anaesthesia. In addition to demonstrating the ability of neural networks for classification we were interested in understanding the classification strategy discovered by the neural networks. Multivariate data analysis techniques, namely Principal Components Analysis and Canonical Discriminant Variates, were applied to analyse the resultant networks.

Anesthesia↗

PubMed1997

Self-learning fuzzy logic control of neuromuscular block.

We have assessed the performance of a "self-learning" fuzzy logic controller to administer atracurium to a required depth of neuromuscular block. We studied 20 ASA I and II patients undergoing surgery anticipated to last longer than 90 min. A Datex Relaxograph was used to measure the degree of neuromuscular block, and control to a T1 twitch height set point of 10% of baseline neuromuscular function was selected. The controller commenced with a blank rule-base and instructed a Graseby 3400 infusion pump to administer an atracurium infusion to maintain this level of block. The system achieved stable control of neuromuscular block with a mean T1 error of -0.52% (SD 0.55%) accommodating a range in mean atracurium infusion rate of 0.25-0.44 mg kg-1 h-1. These results compare favourably with the more computationally intensive and unwieldy adaptive control strategies for atracurium infusion used previously. There was less variation in infusion rates than in our previously studied fixed rules fuzzy controller.

Adult↗

PubMed1996

Synthesis of an anaesthetic agent administration system using fuzzy inductive reasoning.

Control of the depth of anaesthesia is a difficult undertaking. Progress has been made during recent years by use of different methodologies and monitoring systems that suggest the safe amount of an anaesthetic drug, considering the condition of an individual patient. Despite these improvements, anaesthetists still rely heavily on personal experience when suggesting the anaesthetic dosage during surgical operations. The purposes of this paper are twofold. One is a description of the design of an anaesthetic agent control system using a qualitative modelling and simulation methodology called Fuzzy Inductive Reasoning (FIR). A comparison with a system developed for the same application using a neural network approach is also presented. The second purpose is a discussion of the problem of separating system-generic from patient-specific behaviour in the context of inductive modeling using the FIR methodology. In order to be useful, the model generated by FIR should reflect upon system-generic behavioural characteristics exclusively, while suppressing patient-specific behavioural patterns. A technique based on combining knowledge obtained from different patients is designed that makes it possible to derive a single model characterizing a specific class of similar patients undergoing similar operations, preserving the common characteristics of all these patients while filtering out the specific behavioural patterns of any one of the individual patients from whom the data were obtained.

Anesthetics↗

PubMed1996

Development of a portable closed-loop atracurium infusion system: systems methodology and safety issues.

Safety of closed-loop drug infusion systems is an issue often raised as a matter of concern. As a result, many closed-loop control systems are reported in the literature merely as computer simulation studies and few ever reach the stage of physical realisation and formal clinical evaluation. We address the safety issues involved with such systems by describing the development of a portable closed-loop control system for atracurium-induced muscle relaxation. This is a safety-critical system particularly when applied to brain and eye surgery where movement could have serious deleterious effects. The benefits of closed-loop muscle relaxation in providing stable surgical operating conditions over a wide range of patient sensitivities while infusing the minimum amount of drug makes this a worthwhile aim and serves to demonstrate safety issues which are generally applicable to other closed-loop drug infusion systems. It is hoped that the described methodology will facilitate and encourage the clinical application of closed-loop drug infusion systems so that clinical staff and patients may receive the benefits of closed-loop drug therapy.

Atracurium↗

PubMed1996

Development of a pharmacokinetic model-based infusion system for ketamine analgesia.

Model-driven infusion systems in anaesthesia overcome the difficulties in obtaining on-line measurements of controlled variables. A linear pharmacokinetic model for ketamine was used to achieve target blood concentrations and was implemented using a palmtop PC. Although the use of ketamine for analgesia in total intravenous anaesthesia with propofol has been reported, this is the first such application to spontaneously breathing patients. Preliminary results show this to be a useful system, which may easily be applied to other intravenous anaesthetic agents.

Anesthetics, Dissociative↗