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At least 1,225 records · Page 68Linked to original sources
PubMed2004

Neural network predictions of significant coronary artery stenosis in men.

OBJECTIVE: A neural network system was designed to predict whether coronary arteriography on a given patient would reveal any occurrence of significant coronary stenosis (>50%), a degree of stenosis which often leads to coronary intervention. METHODOLOGY: A dataset of 2004 records from male cardiology patients was derived from a national cardiac catheterization database. The catheterizations selected for analysis from the database were first-time and elective, and they were precipitated by chest pain. Eleven patient variables were used as inputs in an artificial neural network system. The network was trained on the earliest 902 records in the dataset. The next 902 records formed a cross-validation file, which was used to optimize the training. A third file composed of the next 100 records facilitated the choice of a cutoff number between 0 and 1. The cutoff number was applied to the last 100 records, which comprised a test file. RESULTS: When a cutoff of 0.25 was compared to the network outputs of all 100 records in the test file, 12 of 46 (specificity=26%) patients without significant stenosis had outputs 0.25 (sensitivity=100%). Therefore, the network identified a fraction of the patients in the test file who did not have significant coronary artery stenosis, while at the same time the network identified all of the patients in the test file who had significant stenosis capable of causing chest pain. CONCLUSION: Artificial neural networks may be helpful in reducing unnecessary cardiac catheterizations.

Cardiac Catheterization↗
PubMed2006

Discriminating signal from noise: recognition of a movement-based animal display by artificial neural networks.

In this study, we investigated the feasibility of applying neural networks to understanding movement-based visual signals. Networks based on three different models were constructed, varying in their input format and network architecture: a Static Input model, a Dynamic Input model and a Feedback model. The task for all networks was to distinguish a lizard (Amphibolurus muricatus) tail-flick from background plant movement. Networks based on all models were able to distinguish the two types of visual motion, and generalised successfully to unseen exemplars. We used curves defined by the receiver-operating characteristic (ROC) to select a single network from each model to be used in regression analyses of network response and several motion variables. Collectively, the models predicted that tail-flick efficacy would be enhanced by faster speeds, greater acceleration and longer durations.

Agonistic Behavior↗
PubMed2006

Learning in human neural networks on microelectrode arrays.

This paper describes experiments involving the growth of human neural networks of stem cells on a MEA (microelectrode array) support. The microelectrode arrays (MEAs) are constituted by a glass support in which a set of tungsten electrodes are inserted. The artificial neural network (ANN) paradigm was used by stimulating the neurons in parallel with digital patterns distributed on eight channels, then by analyzing a parallel multichannel output. In particular, the microelectrodes were connected following two different architectures, one inspired by the Kohonen's SOM, the other by the Hopfield network. The output signals have been analyzed in order to evaluate the possibility of organized reactions by the natural neurons.f The results show that the network of human neurons reacts selectively to the subministered digital signals, i.e., it produces similar output signals referred to identical or similar patterns, and clearly differentiates the outputs coming from different stimulations. Analyses performed with a special artificial neural network called ITSOM show the possibility to codify the neural responses to different patterns, thus to interpret the signals coming from the network of biological neurons, assigning a code to each output. It is straightforward to verify that identical codes are generated by the neural reactions to similar patterns. Further experiments are to be designed that improve the hybrid neural networks' capabilities and to test the possibility of utilizing the organized answers of the neurons in several ways.

Embryonic Stem Cells↗
PubMed2005

Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models.

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. This paper illustrates the use of combined neural network models to guide model selection for diagnosis of ophthalmic and internal carotid arterial disorders. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models.

Adult↗
PubMed2005

A mixture of experts network structure for modelling Doppler ultrasound blood flow signals.

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.

Adult↗
PubMed2005

The comparison of different feed forward neural network architectures for ECG signal diagnosis.

The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their ability to classify six different heart conditions. Two network architectures based on one stage and two stage feed forward neural networks are chosen for this investigation. The training and testing ECG signals are obtained from MIT-BIH database. The network inputs are comprised of 12 ECG features and 13 compressed components of each heart beat signal. The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.

Algorithms↗
PubMed2004

Neural networks for nonlinear and mixed complementarity problems and their applications.

This paper presents two feedback neural networks for solving a nonlinear and mixed complementarity problem. The first feedback neural network is designed to solve the strictly monotone problem. This one has no parameter and possesses a very simple structure for implementation in hardware. Based on a new idea, the second feedback neural network for solving the monotone problem is constructed by using the first one as a subnetwork. This feedback neural network has the least number of state variables. The stability of a solution of the problem is proved. When the problem is strictly monotone, the unique solution is uniformly and asymptotically stable in the large. When the problem has many solutions, it is guaranteed that, for any initial point, the trajectory of the network does converge to an exact solution of the problem. Feasibility and efficiency of the proposed neural networks are supported by simulation experiments. Moreover, the feedback neural network can also be applied to solve general nonlinear convex programming and nonlinear monotone variational inequalities problems with convex constraints.

Feedback↗
PubMed2005

An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.

Algorithms↗
PubMed1998

Recognition of daily life motor activity classes using an artificial neural network.

OBJECTIVE: To investigate a possible role of artificial neural networks for the automated recognition and classification of daily life activities (eg, sitting, lying, standing, walking, etc) in an attempt to reduce the cost of manual recognition and classification. METHODS: Data from sessions of about 10 hours of continuous recording of eight ambulatory patients were used to train and evaluate eight probabilistic neural networks, each of which is configured for one subject. To provide the reference data for building the training set, the instrumented subject follows a 15- to 30-minute protocol consisting of several daily life activities. To properly evaluate the networks, the remaining manually labeled data of each subject were compared with the output of each trained network. RESULTS: The average recognition rate of the trained neural networks was equal to 95% good classification of all presented cases of the daily life activity. Automatic misclassification of 5% resulted from certain activities being too short or the occurrence of activities that were not included in the training set. CONCLUSION: The preliminary results of the trained neural networks have indicated that the probabilistic neural network is a potentially useful tool for the recognition of daily life motor activities.

Activities of Daily Living↗
PubMed1994

Use of artificial neural networks within deterministic logic for the computer ECG diagnosis of inferior myocardial infarction.

An investigation into the use of software-based artificial neural networks for the electrocardiographic (ECG) detection of inferior myocardial infarction was made. A total of 592 clinically validated subjects, including 208 with inferior myocardial infarction, 300 normal subjects, and 84 left ventricular hypertrophy cases, were used in this study. A total of 200 ECGs (100 from patients with inferior myocardial infarction and 100 from normal subjects) were fed to 66 supervised feedforward neural networks for training using a back-propagation algorithm. QRS and ST-T wave measurements were used as the input parameters for the neural networks. The best performing network using QRS measurements only and the best using QRS and ST-T data were selected by assessing a test set of 292 ECGs (108 from patients with inferior myocardial infarction, 84 from patients with left ventricular hypertrophy, and 100 from normal subjects). These two networks were then implanted separately into the deterministic Glasgow program for further study. After the implementation, it was found necessary to include a small inferior Q criterion to improve the specificity of reporting inferior myocardial infarction, thereby producing a small loss of sensitivity as compared with use of the network alone. The use of an artificial neural network within the deterministic logic performed better than either alone in the diagnosis of inferior myocardial infarction, producing a 20% gain in sensitivity with 2% loss in overall specificity compared with the original deterministic logic.

Diagnosis, Computer-Assisted↗
PubMed2003

Network-based analysis of metabolic regulation in the human red blood cell.

Reconstruction of cell-scale metabolic networks is now possible. A description of allowable metabolic network functions can be obtained using extreme pathways, which are the convex basis vectors of the solution space containing all steady state flux distributions. However, only a portion of these allowable network functions are physiologically possible due to kinetic and regulatory constraints. Methods are now needed that enable us to take a defined metabolic network and deduce candidate regulatory structures that control the selection of these physiologically relevant states. One such approach is the singular value decomposition (SVD) of extreme pathway matrices (P), which allows for the characterization of steady state solution spaces. Eigenpathways, which are the left singular vectors from the SVD of P, can be described and categorized by their biochemical function. SVD of P for the human red blood cell showed that the first five eigenpathways, out of a total of 23, effectively characterize all the relevant physiological states of red blood cell metabolism calculated with a detailed kinetic model. Thus, with five degrees of freedom the magnitude and nature of the regulatory needs are defined. Additionally, the dominant features of these first five eigenpathways described key metabolic splits that are indeed regulated in the human red blood cell. The extreme pathway matrix is derived directly from network topology and only knowledge of Vmax values is needed to reach these conclusions. Thus, we have implemented a network-based analysis of regulation that complements the study of individual regulatory events. This topological approach may provide candidate regulatory structures for metabolic networks with known stoichiometry but poorly characterized regulation.

Erythrocytes↗
PubMedDate not supplied

Artificial neural networks estimate the contribution of taste neurons to coding.

Taste qualities are believed to be coded in the activity of populations of taste neurons. However, it is not clear whether all neurons are equally responsible for coding. To clarify the point the relative contribution of each taste neuron to coding was assessed by constructing simple three-layer neural networks with input neurons that represent cortical taste neurons of the rat. The networks were trained by the back-propagation learning algorithm to classify the neural response patterns to the basic taste stimuli (sucrose, HCl, quinine-hydrochloride, and NaCl). The networks had four output neurons representing the basic taste qualities, the values of which provide a measure for similarity of test stimuli to the basic taste stimuli. We estimated relative contributions of input neurons to the taste discrimination of the network by examining their significance S(j), which is defined as the sum of the absolute values of the connection weights from the jth input neuron to the hidden layer. When the input neurons with a smaller S(j) (e.g., 15 out of 39 input units) were "pruned" from the trained network, the ability of the network to discriminate the basic taste qualities was not greatly affected. On the other hand, the taste discrimination of the network progressively deteriorated much more rapidly with pruning of input neurons with a larger S(j). These results suggest that cortical taste neurons differentially contribute to the coding of taste qualities. Input neurons with a larger S(j) tended to be with a larger variation of neural discharge rates to the basic taste stimuli. The variation of neural discharges may be important in the coding of taste qualities.

Algorithms↗
PubMed2001

Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals.

Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.

Acceleration↗
PubMed2002

Ethnic identities, social capital and health inequalities: factors shaping African-Caribbean participation in local community networks in the UK.

This paper examines the impact of ethnic identity on the likelihood of peoples' participation in local community networks, in the context of recent policy emphasis on the participation of marginalised communities in such networks as a means of reducing health inequalities. Conceptually, the paper is located against the background of debates about possible links between health and social capital--defined in terms of grassroots participation in local community networks--and an interest in the way in which social exclusion impacts on social capital. The paper draws on lengthy semi-structured, open-ended interviews with 25 African-Caribbean residents of a deprived multi-ethnic area of a south England town. While African-Caribbean identity played a central role in peoples' participation in inter-personal networks, this inter-personal solidarity did not serve to unite people at the local community level beyond particular face-to-face networks. Levels of participation in voluntary organisations and community activist networks were low. Informants regarded this lack of African-Caribbean unity within the local community as a problem, saying that it placed African-Caribbean people at a distinct disadvantage--furthering their social exclusion through limiting their access to various local community resources. The paper examines the way in which the construction of ethnic identities--within a context of institutionalised racism at both the material and symbolic levels--makes it unlikely that people will view local community organisations or networks as representative of their interests or needs, or be motivated to participate in them. Our findings highlight the limitations of policies which simply call for increased community participation by socially excluded groups, in the absence of specific measures to address the obstacles that stand in the way of such participation.

Adolescent↗
PubMed2003

Controlling chaos in a chaotic neural network.

The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern.

Computer Simulation↗
PubMed2003

Polynomial harmonic GMDH learning networks for time series modeling.

This paper presents a constructive approach to neural network modeling of polynomial harmonic functions. This is an approach to growing higher-order networks like these build by the multilayer GMDH algorithm using activation polynomials. Two contributions for enhancement of the neural network learning are offered: (1) extending the expressive power of the network representation with another compositional scheme for combining polynomial terms and harmonics obtained analytically from the data; (2) space improving the higher-order network performance with a backpropagation algorithm for further gradient descent learning of the weights, initialized by least squares fitting during the growing phase. Empirical results show that the polynomial harmonic version phGMDH outperforms the previous GMDH, a Neurofuzzy GMDH and traditional MLP neural networks on time series modeling tasks. Applying next backpropagation training helps to achieve superior polynomial network performances.

Algorithms↗
PubMed2004

Dynamics of periodic delayed neural networks.

This paper formulates and studies a model of periodic delayed neural networks. This model can well describe many practical architectures of delayed neural networks, which is generalization of some additive delayed neural networks such as delayed Hopfield neural networks and delayed cellular neural networks, under a time-varying environment, particularly when the network parameters and input stimuli are varied periodically with time. Without assuming the smoothness, monotonicity and boundedness of the activation functions, the two functional issues on neuronal dynamics of this periodic networks, i.e. the existence and global exponential stability of its periodic solutions, are investigated. Some explicit and conclusive results are established, which are natural extension and generalization of the corresponding results existing in the literature. Furthermore, some examples and simulations are presented to illustrate the practical nature of the new results.

Algorithms↗
PubMed1998

The prediction of common bile duct stones using a neural network.

BACKGROUND: The role of preoperative ERCP and endoscopic sphincterotomy (ES) in the diagnosis and treatment of suspected common bile duct stones (CBDS) in the laparoscopic age is controversial. The preoperative diagnosis of CBDS by ERCP and the removal of CBDS by ES are advantageous because of technical difficulties in performing laparoscopic exploration of the common bile duct. Approximately 50% of preoperative ERCP examinations are normal, however. The noninvasive diagnosis of CBDS has assumed new importance, but it has proved to be an elusive goal. Neural networks are a form of artificial computer intelligence that have been used successfully to interpret ECGs and to diagnose myocardial infarcts. The purpose of this study was to determine whether a neural network could be trained to predict CBDS accurately in patients at high risk of having duct stones. STUDY DESIGN: We trained a back-propagation neural network to predict the presence of CBDS. Retrospective data from patients who had a cholecystectomy and either a preoperative ERCP or intraoperative cholangiogram were used to build the network, and it was tested using unseen data. RESULTS: One hundred forty patients were used to train the network, and 16 patients were used to test it. The trained network was able to predict CBDS in 100% of the patients in both the training and test sets. CONCLUSIONS: Screening of high-risk patients for CBDS by neural network analysis is highly accurate. This promising new, noninvasive, and inexpensive technique can potentially decrease the need for preoperative ERCP by 50%, but additional prospective evaluation is indicated.

Adult↗