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PubMed2002

Classifying coronary dysfunction using neural networks through cardiovascular auscultation.

The paper applies artificial neural networks (ANNs) to the analysis of heart sound abnormalities through auscultation. Audio auscultation samples of 16 different coronary abnormalities were collected. Data pre-processing included down-sampling of the auscultated data and use of the fast Fourier transform (FFT) and the Levinson-Durbin autoregression algorithms for feature extraction and efficient data encoding. These data were used in the training of a multi-layer perceptron (MLP) and radial basis function (RBF) neural network to develop a classification mechanism capable of distinguishing between different heart sound abnormalities. The MLP and RBF networks attained classification accuracies of 84% and 88%, respectively. The application of ANNs to the analysis of respiratory auscultation and consequently the development of a combined cardio-respiratory analysis system using auscultated data could lead to faster and more efficient treatment.

Adult↗
PubMed2005

A topographical method for the development of neural networks for artificial brain evolution.

Developmental neural networks, which are constructed according to developmental rules (i.e., genes), have the potential to be differentiated into heteromorphic neural structures capable of performing various kinds of activities. The fact that the biological neural architectures are found to be highly repetitive, layered, and topographically organized has important consequences for neural development methods. The purpose of this article is to propose a neural development method that can construct topographical neural connections, that is, a topographical development method, to facilitate fast and efficient development. This is achieved by arborizing neural connections on a developmental tree that rarely produces dead connections. Modular gene expression and corresponding modular networks have an important role in a gradual evolutionary process. Gene expression for modular networks is also proposed here as a way to reduce the probability of fatal mutants created through gene alteration. The corresponding evolutionary experiment shows that various neural structures--layered, repetitive, modular, and complex ones like those in the biological brain--can be constructed and easily observed. It also demonstrates that due to the efficiency of the proposed method, large neural networks can be easily managed, thereby making it suitable for long duration evolutionary experiments.

Animals↗
PubMed2000

On stability of nonlinear continuous-time neural networks with delays.

We utilize the Lyapunov function method to analyze stability of continuous nonlinear neural networks with delays and obtain some new sufficient conditions ensuring the globally asymptotic stability independent of delays. Three main conditions imposed on the weighting matrices are established. (i). The spectral radius rho(M(-1)(W0 + Wtau)K) < 1. (ii). The row norm M(-1)(W0 + Wtau)K + P(-1) ((W0 + Wtau)KM(-1))T P infinity < 2. (iii). mu2(W0) + Wtau2,F < (m/k). These three conditions are independent to each other. The delayed Hopfield network, Bidirectional associative memory network and cellular neural network are special cases of the network model considered in this paper. So we improve some previous works of other researchers.

Neural Networks, Computer↗
PubMed1992

Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull's-eye images.

We have developed a computerized system that can aid in the radiologist's diagnosis in the detection and classification of coronary artery diseases. The technique employs a neural network to analyze 201Tl myocardial SPECT bull's-eye images. This multi-layer feed-forward neural network with a backpropagation algorithm has 256 input units (pattern: compressed 16 x 16-matrix images), 5-140 units in a single hidden layer, and eight output units (diagnosis: one normal and seven different types of abnormalities). The neural network was taught using pairs of training (learning) input data (bull's-eye "EXTENT" image) and desired output data ("correct" diagnosis). The effects of the numbers of hidden units and learning iterations in the network on the recognition performance were examined. In our initial stage, the results show that the recognition performance of the neural network is better than that of the radiology resident but worse than that of the experienced radiologist. Our study also demonstrates that the result produced in the neural network depends on the variety of the training examples used. The preliminary study suggests that the neural network approach is useful for the computer-aided diagnosis of coronary artery diseases in myocardial SPECT bull's-eye images.

Coronary Disease↗
PubMed1997

Adaptive control of arterial blood pressure with a learning controller based on multilayer neural networks.

We discuss a two-model multilayer neural network controller for adaptive control of mean arterial blood pressure (MABP) using sodium nitroprusside. A model with an autoregressive moving average (ARMA), representing the dynamics of the system, and a modified back-propagation training algorithm are used to design the control system to meet specified objectives of design (settling time and undershoot/overshoot) and clinical constraints. The controller is associated with a weighting-determinant unit (WDU) to determine and update the output weighting factor of the parallel two-model neural network for adequate control action and a control-signal modification unit (CMU) to comply with clinical constraints and to suppress the effect of adverse noise and to improve the WDU performance. Extensive computer simulations indicate satisfactory performance and robustness of the proposed controller in the presence of much noise, over the full range of plant parameters, uncertainties, and large variations of parameters.

Adaptation, Physiological↗
PubMed1996

Displacement field estimation and image segmentation using block matching enhanced by a neural network.

The block-matching method plays an important role in displacement field estimation due to its simplicity, achievement of long-range motion, and robustness to noise. In this paper, a single-layer feedback neural network model is proposed that enhances block matching, estimates the displacement field, and simultaneously performs image segmentation from consecutive images. In this paper, image segmentation is defined as partitioning each image into a set of moving objects and the background. For any two consecutive images, a neural network is created that learns the connection relationship of the pixels in an object from the displacement field and stores the relationship in the network. A modified block matching is used to compute a more accurate displacement field by utilizing the segmentation information embedded in the neural network. The displacement vector at the edge of an object or occluding boundary is hard to estimate, but the proposed model performs satisfactorily because it learns and uses the connection information. Furthermore, a flood-fill algorithm is used to compute the dense displacement field more efficiently and correctly than the exhaustive search does. The most important aspect of this paper is that image segmentation is performed simultaneously with the displacement-field estimation by the neural-network model. The novel idea of the work is to embed the segmentation information (connection relations) in the neural network and to perform the displacement-field estimation and image segmentation simultaneously. Two methods for retrieving segmentation information from the neural network with any two consecutive images are also presented.

Algorithms↗
PubMed1996

Possibilities of using neural networks for ECG classification.

Some characteristics of the neural network approach have been tested and validated for the particular problem of diagnostic classification in the field of computerized electrocardiography. Two different databases have been used for the evaluation process: CORDA, developed by the Medical Informatics Department of the University of Leuven, and ECG-UCL, developed by the Cliniques Universitaires Saint-Luc, Université Catholique de Louvain. Electrocardiographic signals classified on the basis of electrocardiographic independent clinical data, with a single diagnosis and no conduction abnormalities, have been considered. Seven diagnostic classes have been taken into account, including the different locations of ventricular hypertrophy and myocardial infarction. Two architectures of neural networks have been analyzed in detail considering three aspects: the normalization process, pruning techniques, and fuzzy preprocessing by the use of radial basis functions. The comparison of the results obtained with the two databases will be discussed in detail.

Algorithms↗
PubMed1999

A neural network approach to the biopsy diagnosis of early acute renal transplant rejection.

AIMS: To develop and test a neural network to assist in the histological diagnosis of early acute renal allograft rejection. METHODS AND RESULTS: We used three sets of biopsies to train and test the network: 100 'routine' biopsies from Leicester; 21 selected difficult biopsies which had already been evaluated by most of the renal transplant pathologists in the UK, in a study of the Banff classification of allograft pathology and 25 cases which had been classified as 'borderline' according to the Banff classification in a review of transplant biopsies from Oxford. The correct diagnosis for each biopsy was defined by careful retrospective clinical review. Biopsies where this review did not provide a clear diagnosis were excluded. Each biopsy was graded for 12 histological features and the data was entered into a simple single layer perception network, designed using the MATLAB neural network toolbox. Results were compared with logistic regression using the same data, and with 'conventional' histological diagnosis. If the network was trained only with the 100 'routine' cases, its performance with either of the other sets was poor. However, if either of the 'difficult' sets was added to the training group, testing with the other 'difficult' group improved dramatically; 19 of the 21 'Banff' study cases were diagnosed correctly. This was achieved using observations made by a trainee pathologist. The result is better than was achieved by any of the many experienced pathologists who had previously seen these biopsies (maximum 18/21 correct), and is considerably better than that achieved by using logistic regression with the same data. CONCLUSION: A neural network can provide a considerable improvement in the diagnosis of early acute allograft rejection, though further development work will be needed before this becomes a routine diagnostic tool. The selection of cases used to train the network is crucial to the quality of its performance. There is scope to improve the system further by incorporating clinical information. Other related areas where this approach is likely to be of value are discussed.

Acute Disease↗
PubMed1993

Analytical methods to differentiate similar electroencephalographic spectra: neural network and discriminant analysis.

Differences in electroencephalographic (EEG) power spectra obtained under similar, but not identical, conditions may be difficult to discern using standard techniques. Statistical analysis may not be useful because of the large number of comparisons necessary. Visual recognition of differences also may be difficult. A new technique, neural network analysis, has been used successfully in other problems of pattern recognition and classification. We examined a number of methods of classifying similar EEG data: standard statistical analysis (analysis of variance), visual recognition, discriminant analysis, and neural network analysis. Twenty-nine volunteers received either thiopental (n = 9), midazolam (n = 10), or propofol (n = 10) in sedative doses in 3 different studies. These drugs produced very similar changes in the EEG power spectra. Except for beta 2 power during thiopental infusion, differences between drugs could not be detected using analysis of variance. Visual categorization was correct in 72% of the baseline EEGs, 70% of thiopental EEGs, 27% of propofol EEGs, and 46% of midazolam EEGs. A classification neural network (Learning Vector Quantization network) containing a Kohonen hidden layer was able to successfully classify 57 of 58 EEG samples (of 4 minutes' duration). Discriminant analysis had a similar rate of success. This level of performance was achieved by dividing the EEG power spectrum from 1 to 30 Hz into 15 2-Hz bandwidths. When the EEG power spectrum was divided into the "classical" frequency bandwidths (alpha, beta 1, beta 2, theta, delta), both neural network and discriminant analysis performance deteriorated. By training the network using only certain inputs we were able to identify drug-specific bandwidths that seemed to be important in correct classification. We conclude that propofol, thiopental, and midazolam produce different effects on the EEG and that both neural network and discriminant analysis are useful in identifying these differences. We also conclude that EEG spectra should be analyzed without using classical EEG bands (alpha, beta, etc.). Additionally, neural networks can be used to identify frequency bands that are "important" in specific drug effects on the EEG. Once a classification algorithm is obtained using either a neural network or discriminant analysis, it could be used as an on-line monitor to recognize drug-specific EEG patterns.

Adult↗
PubMed2004

Learning in neural networks by reinforcement of irregular spiking.

Artificial neural networks are often trained by using the back propagation algorithm to compute the gradient of an objective function with respect to the synaptic strengths. For a biological neural network, such a gradient computation would be difficult to implement, because of the complex dynamics of intrinsic and synaptic conductances in neurons. Here we show that irregular spiking similar to that observed in biological neurons could be used as the basis for a learning rule that calculates a stochastic approximation to the gradient. The learning rule is derived based on a special class of model networks in which neurons fire spike trains with Poisson statistics. The learning is compatible with forms of synaptic dynamics such as short-term facilitation and depression. By correlating the fluctuations in irregular spiking with a reward signal, the learning rule performs stochastic gradient ascent on the expected reward. It is applied to two examples, learning the XOR computation and learning direction selectivity using depressing synapses. We also show in simulation that the learning rule is applicable to a network of noisy integrate-and-fire neurons.

Action Potentials↗
PubMed1997

Adaptive classification of two-dimensional gel electrophoretic spot patterns by neural networks and cluster analysis.

The interpretation of two-dimensional gel electrophoresis spot profiles can be facilitated by statistical and machine learning programs. Two different approaches to classification of spot profiles - cluster analysis and neural networks - are discussed. Neural networks for two different model patterns were designed and an algorithm for training of the net for the classification was developed. It was shown that the performance of neural networks is higher compared to cluster and principal component analysis. The possibility of combining both approaches into one process can increase reliability and speed of classification. Artificially created training sets with added random noise can be used for network training. The analysis was applied on the Streptomyces coelicolor developmental two-dimensional (2-D) gel database.

Cluster Analysis↗
PubMed2005

Neural network based approach for anomaly detection in the lungs region by electrical impedance tomography.

In this paper, we have shown a simple procedure to detect anomalies in the lungs region by electrical impedance tomography. The main aim of the present study is to investigate the possibility of anomaly detection by using neural networks. Radial basis function neural networks are used as classifiers to classify the anomaly as belonging to the anterior or posterior region of the left lung or the right lung. The neural networks are trained and tested with the simulated data obtained by solving the mathematical model equation governing current flow through the simulated thoracic region. The equation solution and model simulation are done with FEMLAB. The effect of adding a higher number of neurons to the hidden layer can be clearly seen by the reduction in classification error. The study shows that there is interaction between the size (radius) and conductivity of anomalies and for some combination of these two factors the classification error of neural networks will be very small.

Computer Simulation↗
PubMed2005

A bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets.

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.

Algorithms↗
PubMed2004

Direct neural network application for automated cell recognition.

BACKGROUND: Automated cell recognition from histologic images is a very complex task. Traditionally, the image is segmented by some methods chosen to suit the image type, the objects are measured, and then a classifier is used to determine cell type from the object's measurements. Different classifiers have been used with reasonable success, including neural networks working with data from morphometric analysis. METHODS: Image data of cells were input directly into neural networks to determine the feasibility of direct classification by using pixel intensity information. Several types of neural network and their ability to work with cells in a complex patterned background were assessed for a variety of images and cell types and for the accuracy of classification. RESULTS: Inflammatory cells from animal biomaterial implants in rabbit paravertebral muscle were imaged in histologic sections. Simple, three-layer, fully connected, back-propagation neural networks and four-layer networks with two layers of a shared-weights neural network were most successful at classifying the cells from the images, with 97% and 98% correct recognition rates, respectively. CONCLUSIONS: The high accuracy recognition rate shows the potential for direct classification of visual image pixel data by neural networks.

Animals↗
PubMed1997

Neural network modeling of the level of observation decision in an acute psychiatric ward.

Patients in an acute psychiatric ward need to be observed with varying levels of closeness. We report a series of experiments in which neural networks were trained to model this "level of observation" decision. One hundred eighty-seven such clinical decisions were used to train and test the networks which were evaluated by a multitrial v-fold cross-validation procedure. One neural network modeling approach was to break down the decision process into four subproblems, each of which was solved by a perceptron unit. This resulted in a hierarchical perceptron network having a structure that was equivalent to a sparsely connected two-layer perceptron. Neural network approaches were compared with nearest neighbor, linear regression, and naive Bayes classifiers. The hierarchical and sparse neural networks were the most accurate classifiers. This shows that the decision process is nonlinear, that neural nets can be more accurate than other statistical approaches, and that hierarchical decomposition is a useful methodology for neural network design.

Acute Disease↗
PubMed2000

Prediction of programmed-temperature retention values of naphthas by artificial neural networks.

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.

Chemical Industry↗
PubMed2004

Efficient chemical kinetic modeling through neural network maps.

An approach to modeling nonlinear chemical kinetics using neural networks is introduced. It is found that neural networks based on a simple multivariate polynomial architecture are useful in approximating a wide variety of chemical kinetic systems. The accuracy and efficiency of these ridge polynomial networks (RPNs) are demonstrated by modeling the kinetics of H(2) bromination, formaldehyde oxidation, and H(2)+O(2) combustion. RPN kinetic modeling has a broad range of applications, including kinetic parameter inversion, simulation of reactor dynamics, and atmospheric modeling.

Algorithms↗
PubMed2002

EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia.

BACKGROUND: The Bispectral Index (BIS) is a proprietary index of anaesthesia depth, which is correlated with the level of consciousness and probability of intraoperative recall. The present study investigates the use of a neural network technique to obtain a non-proprietary index of the depth of anaesthesia from the processed EEG data. METHODS: Two hundred patients, who underwent general abdominal surgery, were recruited for our trial. For anaesthesia we used a total i.v. technique, tracheal intubation, and artificial ventilation. Fourteen EEG variables, including the BIS, were extracted from the EEG, monitored with an EEG computerized monitor, and then stored on a computer. Data from 150 patients were used to train the neural network. All the variables, excluding the BIS, were used as input data in the neural network. The output targets of the network were provided by anaesthesia scores ranging from 10 to 100 assigned by the anaesthesiologist according to the observer's assessment of alertness and sedation (OAA/S) and other clinical means of assessing depth of anaesthesia. Data from the other 50 patients were used to test the model and for statistical analysis. RESULTS: The artificial neural network was successfully trained to predict an anaesthesia depth index, the NED (neural network evaluated depth), ranging from 0 to 100. The correlation coefficient between the NED and the BIS over the test set was 0.94 (P<0.0001). CONCLUSION: We have developed a neural network model, which evaluates 13 processed EEG parameters to produce an index of anaesthesia depth, which correlates very well with the BIS during total i.v. anaesthesia with propofol.

Abdomen↗