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Biomedical subjects

J Vandewalle

Publications and source records attributed to J Vandewalle.

At least 19 recordsLinked to original sources
PubMed2001

Prognostic importance of degree of differentiation and cyst rupture in stage I invasive epithelial ovarian carcinoma.

BACKGROUND: Previous studies on prognostic factors in stage I invasive epithelial ovarian carcinoma have been too small for robust conclusions to be reached. We undertook a retrospective study in a large international database to identify the most important prognostic variables. METHODS: 1545 patients with invasive epithelial ovarian cancer (International Federation of Gynaecology and Obstetrics [FIGO] stage I) were included. The records of these patients were examined and data extracted for univariate and multivariate analysis of disease-free survival in relation to various clinical and pathological variables. FINDINGS: The multivariate analyses identified degree of differentiation as the most powerful prognostic indicator of disease-free survival (moderately vs well differentiated hazard ratio 3.13 [95% CI 1.68-5.85], poorly vs well differentiated 8.89 [4.96-15.9]), followed by rupture before surgery (2.65 [1.53-4.56]), rupture during surgery (1.64 [1.07-2.51]), FIGO 1973 stage Ib vs Ia 1.70 [1.01-2.85]) and age (per year 1.02 [1.00-1.03]). When the effects of these factors were accounted for, none of the following were of prognostic value: histological type, dense adhesions, extracapsular growth, ascites, FIGO stage 1988, and size of tumour. INTERPRETATION: Degree of differentiation, the most powerful prognostic indicator in stage I ovarian cancer, should be used in decisions on therapy in clinical practice and in the FIGO classification of stage I ovarian cancer. Rupture should be avoided during primary surgery of malignant ovarian tumours confined to the ovaries.

Cell Differentiation↗
PubMed2001

Optimal control by least squares support vector machines.

Support vector machines have been very successful in pattern recognition and function estimation problems. In this paper we introduce the use of least squares support vector machines (LS-SVM's) for the optimal control of nonlinear systems. Linear and neural full static state feedback controllers are considered. The problem is formulated in such a way that it incorporates the N-stage optimal control problem as well as a least squares support vector machine approach for mapping the state space into the action space. The solution is characterized by a set of nonlinear equations. An alternative formulation as a constrained nonlinear optimization problem in less unknowns is given, together with a method for imposing local stability in the LS-SVM control scheme. The results are discussed for support vector machines with radial basis function kernel. Advantages of LS-SVM control are that no number of hidden units has to be determined for the controller and that no centers have to be specified for the Gaussian kernels when applying Mercer's condition. The curse of dimensionality is avoided in comparison with defining a regular grid for the centers in classical radial basis function networks. This is at the expense of taking the trajectory of state variables as additional unknowns in the optimization problem, while classical neural network approaches typically lead to parametric optimization problems. In the SVM methodology the number of unknowns equals the number of training data, while in the primal space the number of unknowns can be infinite dimensional. The method is illustrated both on stabilization and tracking problems including examples on swinging up an inverted pendulum with local stabilization at the endpoint and a tracking problem for a ball and beam system.

Feedback↗
PubMed2001

Improved long-term temperature prediction by chaining of neural networks.

When an artificial neural network (ANN) is trained to predict signals p steps ahead, the quality of the prediction typically decreases for large values of p. In this paper, we compare two methods for prediction with ANNs: the classical recursion of one-step ahead predictors and a new kind of chain structure. When applying both techniques to the prediction of the temperature at the end of a blast furnace, we conclude that the chaining approach leads to an improved prediction of the temperature and avoidance of instabilities, since the chained networks gradually take the prediction of their predecessors in the chain as an extra input. It is observed that instabilities might occur in the iterative case, which does not happen with the chaining approach. To select relevant inputs and decrease the number of weights in this approach, Automatic Relevance Determination (ARD) for multilayer perceptrons is applied.

Bayes Theorem↗
PubMed2000

Fetal electrocardiogram extraction by blind source subspace separation.

In this paper, we propose the emerging technique of independent component analysis, also known as blind source separation, as an interesting tool for the extraction of the antepartum fetal electrocardiogram from multilead cutaneous potential recordings. The technique is illustrated by means of a real-life example.

Algorithms↗
PubMed1999

Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses.

OBJECTIVE: The aim of this study was to generate and evaluate artificial neural network (ANN) models from simple clinical and ultrasound-derived criteria to predict whether or not an adnexal mass will have histological evidence of malignancy. DESIGN: The data were collected prospectively from 173 consecutive patients who were scheduled to undergo surgical investigations at the University Hospitals, Leuven, between August 1994 and August 1996. The outcome measure was the histological classification of excised tissues as malignant (including borderline) or benign. METHODS: Age, menopausal status and serum CA 125 levels and sonographic features of the adnexal mass were encoded as variables. The ANNs were trained on a randomly selected set of 116 patient records and tested on the remainder (n = 57). The performance of each model was evaluated using receiver operating characteristic (ROC) curves and compared with corresponding data from an established risk of malignancy index (RMI) and a logistic regression model. RESULTS: There were 124 benign masses, five of borderline malignancy and 44 invasive cancers (of which 29% were metastatic); 37% of patients with a malignant or borderline tumor had stage I disease. The best ANN gave an area under the ROC curve of 0.979 for the whole dataset, a sensitivity of 95.9% and specificity of 93.5%. The corresponding values for the RMI were 0.882, 67.3% and 91.1%, and for the logistic regression model 0.956, 95.9% and 85.5%, respectively. CONCLUSION: An ANN can be trained to provide clinically accurate information, on whether or not an adnexal mass is malignant, from the patient's menopausal status, serum CA 125 levels, and some simple ultrasonographic criteria.

Adnexal Diseases↗
PubMed1997

Classification of normal and abnormal electrogastrograms using multilayer feedforward neural networks.

A neural network approach is proposed for the automated classification of the normal and abnormal EGG. Two learning algorithms, the quasi-Newton and the scaled conjugate gradient method for the multilayer feedforward neural networks (MFNN), are introduced and compared with the error backpropagation algorithm. The configurations of the MFNN are determined by experiment. The raw EGG data, its power spectral data, and its autoregressive moving average (ARMA) modelling parameters are used as the input to the MFNN and compared with each other. Three indexes (the percent correct, sum-squared error and complexity per iteration) are used to evaluate the performance of each learning algorithm. The results show that the scaled conjugate gradient algorithm performs best, in that it is robust and provides a super-linear convergence rate. The power spectral representation and the ARMA modelling parameters of the EGG are found to be better types of the input to the network for this specific application, both yielding a percent correctness of 95% on the test set. Although the results are focused on the classification of the EGG, this paper should provide useful information for the classification of other biomedical signals.

Algorithms↗
PubMed1996

Computer-supported analysis of continuous ambulatory manometric recordings in the human small bowel.

An algorithm has been developed for the offline analysis of prolonged manometric recordings in the upper small intestine of humans. Sample data are acquired in the human duodenum and jejunum six solid-state strain-gauge transducers mounted on a silicon catheter that is connected to a portable digital recording device. The data are sampled at 4 Hz and filtered. For accurate calculations, the filtered signals are converted to cubic B-spline functions of order four. Based on an exponential weighted moving average, a base-line is calculated from the signal. Contractions are recognised on the basis of thresholds for minimum amplitude and duration. The developed algorithm calculates properties of these contractions, such as amplitude, duration, area and a motility index. In addition, the program automatically recognises normal motor patterns of the fasted human small intestine, such as the migrating motor complex, and aids in the identification of the postprandial motor pattern. Motor patterns are defined in terms of properties such as contraction frequency and propagation. In a validation procedure using conventional manual analysis, the program correctly identifies the number of individual contractions with a 98% confidence interval and also correctly recognises 96% of phase 3 motor activity.

Algorithms↗
PubMed1994

A new model of neural associative memories.

In this paper, we present a new model of discrete neural associative memories and its design rule. The most important feature of this new model is that a static mapping instead of the dynamic convergent process is used to retrieve the stored messages. The new model features a two-layer structure, with feedforward connections only and uses two kinds of neurons which implement different output functions. Another important feature is that this new model employs an extremely simple weight setup rule and all the resulted weights can only assume two different values, -1 and +1, which facilitates the VLSI implementation. Compared to the famous discrete Hopfield model designed with the well-known Hebbian rule or any other rule, the new model can guarantee all the given patterns to be stored as fixed points. Moreover, each fixed point is surrounded by an attraction basin (which is a ball in the Hamming distance sense) with the maximal possible radius. The performances of the new model are compared through some illustrative examples with those of the Hopfield associative memory designed using different methods.

Association↗
PubMed1994

Predictive control of nonlinear systems based on identification by backpropagation networks.

Using the property of universal approximation of multilayer perceptron neural network, a class of discrete nonlinear dynamical systems are modeled by a perceptron with two hidden layers. A backpropagation algorithm is then used to train the model to identify the nonlinear systems to a desired level of accuracy. Based on the identified model, a one-step-ahead predictive control scheme is proposed in which the future control inputs are obtained through some nonlinear optimization process. Making use of the online learning properties of neural networks, the predictive control scheme is further developed into an adaptive one which is robust to the incompleteness of identification. Simulation results show that this neural control scheme works well even for some very complicated nonlinear systems.

Mathematics↗
PubMed1993

A rule-based neural controller for inverted pendulum system.

This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.

Algorithms↗
PubMed1992

Evaluation of two unstructured mathematical models for the penicillin G fed-batch fermentation.

The mathematical model for the penicillin G fed-batch fermentation proposed by Heijnen et al. (1979) is compared with the model of Bajpai & Reuss (1980). Although the general structure of these models is similar, the difference in metabolic assumptions and specific growth and production kinetics results in a completely different behaviour towards product optimization. A detailed analysis of both models reveals some physical and biochemical shortcomings. It is shown that it is impossible to make a reliable estimation of the model parameters, only using experimental data of simple constant glucose feed rate fermentations with low initial substrate amount. However, it is demonstrated that some model parameters might be key factors in concluding whether or not altering the substrate feeding strategy has an important influence on the final amount of product. It is illustrated that feeding strategy optimization studies can be a tool in designing experiments for parameter estimation purposes.

Fermentation↗
PubMed1992

Dynamic mathematical model to predict microbial growth and inactivation during food processing.

Many sigmoidal functions to describe a bacterial growth curve as an explicit function of time have been reported in the literature. Furthermore, several expressions have been proposed to model the influence of temperature on the main characteristics of this growth curve: maximum specific growth rate, lag time, and asymptotic level. However, as the predictive value of such explicit models is most often guaranteed only at a constant temperature within the temperature range of microbial growth, they are less appropriate in optimization studies of a whole production and distribution chain. In this paper a dynamic mathematical model--a first-order differential equation--has been derived, describing the bacterial population as a function of both time and temperature. Furthermore, the inactivation of the population at temperatures above the maximum temperature for growth has been incorporated. In the special case of a constant temperature, the solution coincides exactly with the corresponding Gompertz model, which has been validated in several recent reports. However, the main advantage of this dynamic model is its ability to deal with time-varying temperatures, over the whole temperature range of growth and inactivation. As such, it is an essential building block in (time-saving) simulation studies to design, e.g., optimal temperature-time profiles with respect to microbial safety of a production and distribution chain of chilled foods.

Bacteria↗
PubMed1990

Adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average modelling.

The recording of the human gastric myoelectrical activity by means of cutaneous electrodes is called electrogastrography (EGG). It provides a noninvasive method of studying electrogastric behaviour. The normal frequency of the gastric signal is about 0.05 Hz. However, sudden changes of its frequency have been observed and are generally considered to be related to gastric motility disorders. Thus, spectral analysis, especially online spectral analysis, can serve as a valuable tool for practical purposes. The paper presents a new method of the adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average (ARMA) modelling. It is based on an adaptive ARMA filter and provides both time and frequency information of the signal. Its performance is investigated in comparison with the conventional FFT-based periodogram method. Its properties in tracking time-varying instantaneous frequencies are shown. Its applications to the running spectral analysis of cutaneous electrogastric signals are presented. The proposed adaptive ARMA spectral analysis method is easy to implement and is efficient in computations. The results presented in the paper show that this new method provides a better performance and is very useful for the online monitoring of cutaneous electrogastric signals.

Electromyography↗
PubMed1990

Comparison of SVD methods to extract the foetal electrocardiogram from cutaneous electrode signals.

The paper presents and compares three methods making use of the singular value decomposition (SVD) of a matrix to extract the foetal electrocardiogram (FECG) from cutaneously recorded electrode signals. The first method constructs a set of orthogonal foetal signals (the so-called principal foetal signals) from the recordings, but needs electrode positions far from the foetal heart, in addition to the abdominal electrodes that pick up a mixture of maternal and foetal electrocardiogram. An online adaptive algorithm has been developed such that a real-time implementation becomes feasible. The second method is a new online approach to a technique presented by van Oosterom. Although this method has some important drawbacks and is suboptimal as far as foetal signal-to-noise ratio is concerned, it is still very useful when only a foetal trigger is required, as the signal obtained is not a complete FECG. Finally, a third method is proposed, based on the generalised SVD and interpreted with the new concept of oriented signal-to-signal ratio. An online version is also presented for this method and some results are shown.

Electrocardiography↗
PubMed1990

Multichannel adaptive enhancement of the electrogastrogram.

The electrogastric signal can be measured cutaneously on the abdomen. This is attractive because it is harmless to patients or volunteers. However, the poor quality of the cutaneous measurements necessitates signal enhancements. Hence, in this paper, an adaptive multichannel signal enhancing system is proposed. The mu-vector least mean square (LMS) algorithm is applied to adjust the weights of the adaptive filters in the system. The detailed description and the performance analysis of the system is given in the paper. Applying the proposed system, the respiratory artifact, the electrode-skin noise, some of motion artifacts, and the electrocardiography (ECG) can be efficiently reduced while the characteristics of the relevant gastric signal is less affected.

Adaptation, Physiological↗
PubMed1989

Observation of the propagation direction of human electrogastric activity from cutaneous recordings.

Electrogastric signals have been successfully measured both intraluminally and cutaneously. Although it has been claimed by several researchers that the propagation direction of the electrogastric activities cannot be observed from cutaneous recordings, it is the aim of the paper to show that it is feasible. The reason why the propagation direction has never been observed from cutaneous recordings is that the reported methods for the abdominal measurements are not adequate. In the paper it is pointed out that the stomach should be localised before the measurement and the electrodes should be attached along the longitudinal axis of the stomach.

Diabetes Mellitus↗
PubMed1989

Algorithm for data reduction and automatic analysis of intestinal motility tracings and other smooth biological signals.

An algorithm is presented for the processing of smooth biological signals with an important implicit data reduction. It is a solid basis for an automatic analysis of these signals. The algorithm is based on the approximation of the signals with a linear combination of cubic B-splines. Depending on the parameters, a data reduction of between 8:1 and 12:1 can be achieved. The most important differences from conventional biological signal processing methods are the representation of the signal by the coefficients of the linear combination instead of a series of samples and the analysis by calculations of the time of the appearance of patterns instead of iterative searching. The approximation and analysis of long-lasting multichannel signals can be performed online with a modern microprocessor. Approximating the signal with a linear combination of cubic B-splines with equally spaced knots, according to the linear least-squares criterion gives the desired data reduction and an elegant way to perform an automatic analysis. A window calculation scheme makes it possible to handle very long signals online.

Algorithms↗