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PubMed2004

WeAidU-a decision support system for myocardial perfusion images using artificial neural networks.

This paper presents a computer-based decision support system for automated interpretation of diagnostic heart images (called WeAidU), which is made available via the Internet. The system is based on image processing techniques, artificial neural networks (ANNs) and large well-validated medical databases. We present results using artificial neural networks, and compare with two other classification methods, on a retrospective data set containing 1320 images from the clinical routine. The performance of the artificial neural networks detecting infarction and ischemia in different parts of the heart, measured as areas under the receiver operating characteristic curves, is in the range 0.83-0.96. These results indicate a high potential for the tool as a clinical decision support system.

Automation↗
PubMed2004

Foetal ECG recovery using dynamic neural networks.

Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both the methodology for selecting a model and the introduction of advanced neural models are the main contributions of this paper.

Electrocardiography↗
PubMed2000

Noninvasive feature-based detection of delayed gastric emptying in humans using neural networks.

Radioscintigraphy is currently the gold standard for gastric emptying test which involves radiation exposure and is considerably expensive. We present a feature-based detection approach using neural networks for the noninvasive diagnosis of delayed gastric emptying from the cutaneous electrogastrogram (EGG). Simultaneous recordings of the EGG and scintigraphic gastric emptying test were made in 152 patients with symptoms suggestive of delayed gastric emptying. Spectral analyses were performed to derive EGG parameters which were used as the input of the neural network. The result of scintigraphic gastric emptying was used as the gold standard for the training and testing of the neural network. A correct classification of 85% (a specificity of 89% and a sensitivity of 82%) was achieved using the proposed method.

Adolescent↗
PubMed2002

Categorization and analysis of pain and activity in patients with low back pain using a neural network technique.

Low back pain represents a significant medical problem, both in its prevalence and its cost to society. Most episodes of acute low back pain resolve without significant long-term functional impact. However, a minority of patients experience extended chronic pain and disability. In this paper, we have explored new techniques of patient assessment that may prospectively identify this minority ofpatients at risk of developing poor outcomes. We studied 15 patients with acute low back pain and 25 patients with chronic low back pain over 4 month's time. Patients monitored their pain and activity levels continuously over the first 3 weeks. Pain and functional status were assessed at baseline and at 3 weeks following enrollment. Follow-up assessment of functional status and progress were performed at 2 and 4 months. The pain and activity levels were categorized using a self-organizing-map neural network. A back-propagation neural network was trained with the categorization and outcome data. There was a good correlation between the true and predicted values for general health (r = 0.96, p < 0.01) and mental health (r = 0.80, p < 0.01). No significant correlation was found if activity and pain data were not entered into the analysis. Our results show that neural network techniques can be applied effectively to categorizing patients with acute and chronic low back pain. It is our hope that future research will allow these categorizations to be tied to prognostic and therapeutic decisions in patients who present with episodes of back pain.

Acute Disease↗
PubMed2001

Applications of neural networks for gene finding.

A basic description of artificial neural networks is given and applications of neural nets to problems in human gene mapping are discussed. Specifically, three data types are considered: (1) affected sibpair data for nonparametric linkage analysis, (2) case-control data for disequilibrium analysis based on genetic markers, and (3) family data with trait and marker phenotypes and possibly environmental effects.

Environmental Exposure↗
PubMed2005

A functional neural network computing some eigenvalues and eigenvectors of a special real matrix.

How to quickly compute eigenvalues and eigenvectors of a matrix, especially, a general real matrix, is significant in engineering. Since neural network runs in asynchronous and concurrent manner, and can achieve high rapidity, this paper designs a concise functional neural network (FNN) to extract some eigenvalues and eigenvectors of a special real matrix. After equivalent transforming the FNN into a complex differential equation and obtaining the analytic solution, the convergence properties of the FNN are analyzed. If the eigenvalue whose imaginary part is nonzero and the largest of all eigenvalues is unique, the FNN will converge to the eigenvector corresponding to this special eigenvalue with general nonzero initial vector. If all eigenvalues are real numbers or there are more than one eigenvalue whose imaginary part equals the largest, the FNN will converge to zero point or fall into a cycle procedure. Comparing with other neural networks designed for the same domain, the restriction to matrix is very slack. At last, three examples are employed to illustrate the performance of the FNN.

Algorithms↗
PubMed2006

Prediction of signal peptides using bio-basis function neural networks and decision trees.

Signal peptide identification is of immense importance in drug design. Accurate identification of signal peptides is the first critical step to be able to change the direction of the targeting proteins and use the designed drug to target a specific organelle to correct a defect. Because experimental identification is the most accurate method, but is expensive and time-consuming, an efficient and affordable automated system is of great interest. In this article, we propose using an adapted neural network, called a bio-basis function neural network, and decision trees for predicting signal peptides. The bio-basis function neural network model and decision trees achieved 97.16% and 97.63% accuracy respectively, demonstrating that the methods work well for the prediction of signal peptides. Moreover, decision trees revealed that position P(1'), which is important in forming signal peptides, most commonly comprises either leucine or alanine. This concurs with the (P(3)-P(1)-P(1')) coupling model.

Algorithms↗
PubMed1986

A neural network model for selective attention in visual pattern recognition.

A neural network model of the mechanism of selective attention in visual pattern recognition is proposed and simulated on a digital computer. When a complex figure consisting of two patterns or more is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the model is paying selective attention is affected by noise or defects, the model can recall the complete pattern from which the noise has been eliminated and the defects corrected. It is not necessary for perfect recall that the stimulus pattern should be identical in shape to the training pattern. Even though the pattern is distorted in shape or changed in size, it can be correctly recognized and the missing portions restored. The model consists of a hierarchical neural network which has efferent as well as afferent connections between cells. The afferent and the efferent signals interact with each other in the network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent ones, and, at the same time, the afferent signals gate efferent signal flow. When some feature in the stimulus is not extracted in the afferent paths, the threshold for detection of that feature is automatically lowered by decreasing the efficiency of inhibition, and the model tries to extract even vague traces of the undetected feature.

Afferent Pathways↗
PubMed2004

A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

Journal Article↗
PubMedDate not supplied

Reinforcement learning with internal expectation in the random neural networks for cascaded decisions.

The reinforcement learning scheme proposed in Halici (J. Biosystems 40 (1997) 83) for the random neural network (RNN) (Neural Computation 1 (1989) 502) is based on reward and performs well for stationary environments. However, when the environment is not stationary it suffers from getting stuck to the previously learned action and extinction is not possible. To overcome the problem, the reinforcement scheme is extended in Halici (Eur. J. Oper. Res., 126(2000) 288) by introducing a new weight update rule (E-rule) which takes into consideration the internal expectation of reinforcement. Although the E-rule is proposed for the RNN, it can be used for training learning automata or other intelligent systems based on reinforcement learning. This paper looks into the behavior of the learning scheme with internal expectation for the environments where the reinforcement is obtained after a sequence of cascaded decisions. The simulation results have shown that the RNN learns well and extinction is possible even for the cases with several decision steps and with hundreds of possible decision paths.

Computer Simulation↗
PubMed1998

Novel staging tool for localized prostate cancer: a pilot study using genetic adaptive neural networks.

PURPOSE: An estimated $1.5 billion is spent annually for direct medical expenses and an additional $2.5 billion for indirect costs for the management of prostate cancer. Today there are several procedures for staging prostate cancer, including lymph node dissection. Despite these procedures, the accuracy of predicting extracapsular disease remains low (range 37 to 63, mean 45%). Use of multiple staging procedures adds significantly to the costs of managing prostate cancer. Recently artificial intelligence based neural networks have become available for medical applications. Unlike traditional statistical methods, these networks do not assume linearity or homogeneity of variance and, thus, they are more accurate for clinical data. We applied this concept to staging localized prostate cancer and devised an algorithm that can be used for prostate cancer staging. MATERIALS AND METHODS: Our study comprised 1,200 men with clinically organ confined prostate cancer who underwent preoperative staging using serum prostate specific antigen, systematic biopsy and Gleason scoring before radical prostatectomy and lymphadenectomy. The performance of the neural network was validated for a subset of patients and network predictions were compared with actual pathological stage. Mean patient age was 62.9 years, mean serum prostate specific antigen 8.1 ng./ml. and mean biopsy Gleason 6. Of the patients 55% had organ confined disease, 27% positive margins, 8% seminal vesicle involvement and 7% lymph node disease. Of margin positive patients 30% also had seminal vesicle involvement, while of seminal vesicle positive patients 50% also had positive margins. RESULTS: The sensitivity of the network was 81 to 100%, and specificity was 72 to 75% for various predictions of margin, seminal vesicle and lymph node involvement. The negative predictive values tended to be relatively high for all 3 features (range 92 to 100%). The neural network missed only 8% of patients with margin positive disease, and 2% with lymph node and 0% with seminal vesicle involvement. CONCLUSIONS: Our study suggests that neural networks may be useful as an initial staging tool for detection of extracapsular extension in patients with clinically organ confined prostate cancer. These networks preclude unnecessary staging tests for 63% of patients with clinically organ confined prostate cancer.

Algorithms↗
PubMed2006

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism.

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.

Algorithms↗
PubMed1998

Application of Kohonen neural networks for the non-morphological distinction between glomerular and tubular renal disease.

BACKGROUND: A Kohonen topological map is an artificial intelligence system of the connectionist school (neural networks). The map learns the typical features of the subclasses in the learning set by means of a shortest Euclidean distance algorithm, after which self-adaptation of the neurons occurs. By its ability of self-organization and generalization, a Kohonen map is useful for pattern recognition, and its application in the medical field as an aid for decision making seems promising. This study describes the use of a Kohonen topological mapping system in the classification of renal diseases as being glomerular or tubular on basis of clinical characteristics and laboratory results. METHODS: Forty-one parameters from 75 patients were retrospectively retrieved and used to train four different Kohonen maps of 10 x 10 neurons. For reference diagnostic classification, we referred to the results of the light-microscopic examination. The classification of the patients by the four different Kohonen networks was compared to the classification by a rule-based system and by three nephrologists. We also developed a 'hybrid' decision system that makes a classification on basis of the opinion of the four networks and that of the rule-based system. RESULTS: The results show that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a higher sensitivity and predictive value than the nephrologists and the rule-based system, and that the best classification was performed by the hybrid system: sensitivity and predictive value for the diagnosis 'glomerular' respectively 100 and 88% for the network with the most adequate results, 90 and 83% for the nephrologists, 90 and 95% for the rule-based system, and 95 and 96% for the hybrid system; sensitivity and predictive value for the diagnosis 'tubular' respectively 50 and 100% for the neural networks, 31 and 45% for the nephrologists, 81 and 68% for the rule-based system, and 87 and 82% for the hybrid system). CONCLUSION: We conclude that a Kohonen map is capable of classifying the patients as having glomerular or tubular disease with a high sensitivity and predictive value. The rule-based system performs worse than the neural networks. The most adequate results were obtained with the hybrid system.

Adult↗
PubMed2004

Responses of central auditory neurons modeled with finite impulse response (FIR) neural networks.

To simulate central auditory responses to complex sounds, a computational model was implemented. It consists of a multi-scale classification process, and an artificial neural network composed of two modules of finite impulse response (FIR) neural networks connected to a maximum network. Electrical activities of single auditory neurons were recorded at the rat midbrain in response to a repetitive pseudo-random frequency modulated (FM) sound. The multi-scale classification process divides the training dataset into either strong or weak response using a multiple-scale Gaussian filter that based on response probability. Two modules of FIR neural network are then independently trained to model the two types of responses. This caters for the possible differences in neuronal circuitry and transmission delay. Their outputs are connected to a maximum network to generate the final output. After training, we use a different set of FM responses collected from the same neuron to test the performance of the model. Two criteria are adopted for assessment. One measures the matching of the modeled output to the actual output on a point-to-point basis. Another measures the matching of bulk responses between the two. Results show that the proposed model predicts the responses of central auditory neurons satisfactorily.

Action Potentials↗
PubMed1994

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines.

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

Animals↗
PubMed1996

A comparison of neural network and Bayes recognition approaches in the evaluation of the brainstem trigeminal evoked potentials in multiple sclerosis.

This article describes the application of Multi-Layer Perceptron (MLP), Probabilistic Neural Network and Kohonen's Learning Vector Quantization to the problem of diagnosing Multiple Sclerosis. The classification information is obtained from brainstem trigeminal evoked potential. The performance of the neural networks based classifiers is compared with that of the human experts and the Bayes classifier. The ability of the MLP classifier to generalize is far better than that of the Bayes classifier. The efficiency of the neural network based classifiers in conjunction with several types of well-known evoked potential features, such as Fourier transform space, latency and temporal wave, is examined. Although a large clinical data base would be necessary, before this approach can be fully validated, the initial results are promising.

Bayes Theorem↗
PubMed2001

A hierarchical unsupervised growing neural network for clustering gene expression patterns.

MOTIVATION: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226-233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. RESULTS: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used. AVAILABILITY: A server running the program can be found at: http://bioinfo.cnio.es/sotarray.

Algorithms↗
PubMedDate not supplied

Estimating UV erythemal irradiance by means of neural networks.

In recent years, there has been a substantial increase in attempts to model the flux of ultraviolet radiation (UV). UV irradiance at surface level is a result of the combined effects of solar zenith angle, surface elevation, cloud cover, aerosol load and optical properties, surface albedo and the vertical profile of ozone. In this study, we present the development of an artificial neural network (ANN) model that can be used to estimate solar UV irradiance on the basis of optical air mass, ozone columnar content, latitude, horizontal visibility data and cloud information such as type, coverage and height. ANN are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and, once trained, can perform prediction and generalization at high speed. In this study, a multilayer perceptron network (MLP) consisting of an input layer, an output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regulation back propagation algorithm. The study was developed using data from three stations on the Iberian Peninsula: Madrid and Murcia during the period 2000-2001 and Zaragoza in 2001. To train and validate the MPL neural networks, independent subsets of data were extracted from the complete database at each station. The results suggest that a MLP neural network using optical air mass, ozone columnar content, latitude and total cloud coverage provides the best estimates, with mean bias deviation and root mean square deviation of -0.1% and 18.0%, 1.6% and 19.6%, 0.1% and 14.6% at Madrid, Murcia and Zaragoza, respectively. Despite the dependence of the cloud radiative effect on cloud type, the use of additional information such as cloud type or cloud elevation did not improve these results. The performance of the developed ANN has been checked regarding its ability to estimate the UV index (UVI); results indicate that in more than 95% of the cases, the difference between estimated and measured values does not exceed one unit of UVI.

Journal Article↗