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PubMed2002

Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

Adult↗
PubMed2002

Neural networks with local receptive fields and superlinear VC dimension.

Local receptive field neurons comprise such well-known and widely used unit types as radial basis function (RBF) neurons and neurons with center-surround receptive field. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive field neurons, we show that the VC dimension of these networks is superlinear. In particular, we establish the bound Omega(W log k) for any reasonably sized network with W parameters and k hidden nodes. This bound is shown to hold for discrete center-surround receptive field neurons, which are physiologically relevant models of cells in the mammalian visual system, for neurons computing a difference of gaussians, which are popular in computational vision, and for standard RBF neurons, a major alternative to sigmoidal neurons in artificial neural networks. The result for RBF neural networks is of particular interest since it answers a question that has been open for several years. The results also give rise to lower bounds for networks with fixed input dimension. Regarding constants, all bounds are larger than those known thus far for similar architectures with sigmoidal neurons. The superlinear lower bounds contrast with linear upper bounds for single local receptive field neurons also derived here.

Algorithms↗
PubMed2005

Equivalence between RAM-based neural networks and probabilistic automata.

In this letter, the computational power of a class of random access memory (RAM)-based neural networks, called general single-layer sequential weightless neural networks (GSSWNNs), is analyzed. The theoretical results presented, besides helping the understanding of the temporal behavior of these networks, could also provide useful insights for the developing of new learning algorithms.

Algorithms↗
PubMed2002

Electroencephalogram processing using neural networks.

The electroencephalogram (EEG), a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. More than 100 current neural network applications dedicated to EEG processing are presented. Works are categorized according to their objective (sleep analysis, monitoring anesthesia depth, brain-computer interface, EEG artifact detection, EEG source-based localization, etc.). Each application involves a specific approach (long-term analysis or short-term EEG segment analysis, real-time or time delayed processing, single or multiple EEG-channel analysis, etc.), for which neural networks were generally successful. The promising performances observed are demonstrative of the efficiency and efficacy of systems developed. This review can aid researchers, clinicians and implementors to understand up-to-date interest in neural network tools for EEG processing. The extended bibliography provides a database to assist in possible new concepts and idea development.

Artifacts↗
PubMed1994

The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site.

A method for the rational design of locally encoded amino acid sequence features using artificial neural networks and a technique for simulating molecular evolution has been developed. De novo in machine design of Escherichia coli leader peptidase (SP1) cleavage sites serves as an example application. A modular neural network system that employs sequence descriptions in terms of physicochemical properties has been trained on the recognition of characteristic cleavage site features. It is used for sequence qualification in the design cycle, representing the sequence fitness function. Starting from a random sequence several cleavage site sequences were generated by a simulated molecular evolution technique. It is based on a simple genetic algorithm that takes the quality values calculated by the artificial neural network as a heuristic for inductive sequence optimization. Simulated in vivo mutation and selection allows the identification of predominant sequence positions in Escherichia coli signal peptide cleavage site regions (positions -2 and -6). Various amino acid distance maps are used to define metrics for the step size of mutations. Position-specific mutability values indicate sequence positions exposed to high or low selection pressure in the simulations. The use of several distance maps leads to different courses of optimization and to various idealized sequences. It is concluded that amino acid distances are context dependent. Furthermore, a method for identification of local optima during sequence optimization is presented.

Algorithms↗
PubMed1993

Automatic lung nodule detection using profile matching and back-propagation neural network techniques.

The potential advantages of using digital techniques instead of film-based radiography have been discussed extensively for the past 10 years. A major future application of digital techniques is computer-assisted diagnosis: the use of computer techniques to assist the radiologist in the diagnostic process. One aspect of this assistance is computer-assisted detection. The detection of small lung nodule has been recognized as a clinically difficult task for many years. Most of the literature has indicated that the rate for finding lung nodules (size range from 3 mm to 15 mm) is only approximately 65%, in those cases in which the undetected nodules could be found retrospectively. In recent published research, image processing techniques, such as thresholding and morphological analysis, have been used to enhance true-positive detection. However, these methods still produce many false-positive detections. We have been investigating the use of neural networks to distinguish true-positives nodule detections among those areas of interest that are generated from a signal enhanced image. The initial results show that the trained neural networks program can increase true-positive detections and moderately reduce the number of false-positive detections. The program reported here can perform three modes of lung nodule detection: thresholding, profile matching analysis, and neural network. This program is fully automatic and has been implemented in a DEC 5000/200 (Digital Equipment Corp, Maynard, MA) workstation. The total processing time for all three methods is less than 35 seconds. In this report, key image processing techniques and neural network for the lung nodule detection are described and the results of this initial study are reported.

Algorithms↗
PubMed1994

Artificial neural network comparison of expert and novice problem-solving strategies.

The successful strategies of second-year medical students were electronically captured from computer-based simulations in immunology and infectious disease and were used to train artificial neural networks for the rapid classification of subsequent students' and experts' strategies on these problems. Such networks could categorize problem solutions of other students as successful or nonsuccessful > 85% of the time. These neural networks, however, performed poorly (as low as 13%) when classifying experienced immunologists' or internists' successful performances, suggesting an ability to distinguish between novice and expert strategies. The neural networks also identified a group of students who framed the infectious disease problems correctly, but had difficulty discriminating between differential diagnoses.

Allergy and Immunology↗
PubMed2005

Image identification system based on an optical broadcast neural network processor.

We describe the implementation of a vision system based on a hardware neural processor. The architecture of the neural network processor has been designed to exploit the computational characteristics of electronics and the communication characteristics of optics in an optimal manner, thus it is based on an optical broadcast of input signals to a dense array of processing elements. The vision system has been built by use of a prototype implementation of a neural network processor with discrete optic and optoelectronic devices. It has been adapted to work as a Hamming classifier of the images taken with a 128 x 128 complementary metal-oxide semiconductor image sensor. Its results, performance characteristics of the image classification system, and an analysis of its scalability in size and speed, with the improvement of the optoelectronic neural processor, are presented.

Algorithms↗
PubMedDate not supplied

On a recurrent neural network producing oscillations.

A recurrent two-node neural network producing oscillations is analyzed. The network has no true inputs and the outputs from the network exhibit a circular phase portrait. The weight configuration of the network is investigated, resulting in analytical weight expressions, which are compared with numerical weight estimates obtained by training the network on the desired trajectories. The values predicted by the analytical expressions agree well with the findings from the numerical study, and can also explain the asymptotic properties of the networks studied.

Algorithms↗
PubMed1998

Hypothetical neural mechanism that may play a role in mental rotation: an attractor neural network model.

We propose an attractor neural network (ANN) model that performs rotation-invariant pattern recognition in such a way that it can account for a neural mechanism being involved in the image transformation accompanying the experience of mental rotation. We compared the performance of our ANN model with the results of the chronometric psychophysical experiments of Cooper and Shepard (Cooper L A and Shepard R N 1973 Visual Information Processing (New York: Academic) pp 204-7) on discrimination of alphanumeric characters presented in various angular departures from their canonical upright position. Comparing the times required for pattern retrieval in its canonical upright position with the reaction times of human subjects, we found agreement in that (i) retrieval times for clockwise and anticlockwise departures of the same angular magnitude (up to 180 degrees) were not different, (ii) retrieval times increased with departure from upright and (iii) increased more sharply as departure from upright approached 180 degrees. The rotation-invariant retrieval of the activity pattern has been accomplished by means of the modified algorithm of Dotsenko (Dotsenko V S 1988 J. Phys. A: Math. Gen. 21 L783-7) proposed for translation-, rotation- and size-invariant pattern recognition, which uses relaxation of neuronal firing thresholds to guide the evolution of the ANN in state space towards the desired memory attractor. The dynamics of neuronal relaxation has been modified for storage and retrieval of low-activity patterns and the original gradient optimization of threshold dynamics has been replaced with optimization by simulated annealing.

Algorithms↗
PubMed1999

Application of probabilistic neural network analysis to a disease with complex inheritance: the GAW11 simulated data.

A probabilistic neural network analysis was applied to the simulated GAW11 data. Six replicates drawn at random from one of the simulated populations were used to generate training and test vectors for pairs of siblings. The vectors incorporated two environmental indicators as well as identical-by-descent allele sharing scores from each of 300 genetic markers. The performance of a 'naïve' probabilistic neural network (PNN) was fair. However, by combining a traditional linkage analysis with a PNN which incorporated gene x environment interaction, the performance was considerably enhanced.

Alleles↗
PubMed1996

Performance of a computer simulated neural network trained to categorise normal, premalignant and malignant oral smears.

The accurate detection of malignant neoplasms whilst they are still small is recognised as one of the main factors increasing chances of survival. Neural networks have many biomedical applications and they have been applied to neoplasia but their use in oral pathology has only recently been documented. The objectives of this study were to train networks to discriminate between normal and dysplastic mucosa. Each network was trained by back propagation, internal cross validation and tested on additional data. The data were derived by analysing 348 intra-oral smears and included mean nuclear and mean cytoplasmic areas of the smears measured by image analysis. A neural network differentiated between normal/non-dysplastic mucosa and dysplastic/malignant mucosa (specificity 0.82, sensitivity 0.76). These early results suggest that integrating neural networks and image analysis, as well as investigating additional criteria, could enhance automation and accuracy of smear techniques in diagnosing oral malignancy.

Automation↗
PubMed2002

Sufficient conditions for error backflow convergence in dynamical recurrent neural networks.

This article extends previous analysis of the gradient decay to a class of discrete-time fully recurrent networks, called dynamical recurrent neural networks, obtained by modeling synapses as finite impulse response (FIR) filters instead of multiplicative scalars. Using elementary matrix manipulations, we provide an upper bound on the norm of the weight matrix, ensuring that the gradient vector, when propagated in a reverse manner in time through the error-propagation network, decays exponentially to zero. This bound applies to all recurrent FIR architecture proposals, as well as fixed-point recurrent networks, regardless of delay and connectivity. In addition, we show that the computational overhead of the learning algorithm can be reduced drastically by taking advantage of the exponential decay of the gradient.

Journal Article↗
PubMed2005

Simultaneous determination of rifampicin and isoniazid by continuous-flow chemiluminescence with artificial neural network calibration.

In this paper a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous determination of rifampicin and isoniazid. This method is based on the different kinetic spectra of the analytes in their CL reaction with alkaline N-bromosuccinimide as oxidant. The CL intensity was measured and recorded every second from 1 to 300 s. The data obtained were processed chemometrically by use of an artificial neural network. The experimental calibration set was 20 sample solutions. The relative standard errors of prediction for both analytes were approximately 5%. The proposed method was successfully applied to the simultaneous determination of rifampicin and isoniazid in a combined pharmaceutical formulation.

Algorithms↗
PubMed1992

Neural network models of sound localization based on directional filtering by the pinna.

Three-layer neural-network functions were developed to transform spectral representations of pinna-filtered stimuli at the input to a space-mapped representation of sound-source direction at the output. The inputs are modeled after transfer functions of the external ear of the cat; the output is modeled on the spatial sensitivity of superior colliculus neurons. Network solutions are obtained by backpropagation and by a method that enforces uniform task distribution in the hidden layer of the model. Solutions are characterized using bandlimited inputs to study the relative strength of potential sound localization cues in various frequency regions. This analysis suggests that the frequency region containing the first spectral notch (5-18 kHz) provides the best localization cues. Response properties of model neurons were studied using input patterns modeled after auditory nerve response profiles to pure tones at various frequencies and sound levels. The response properties of hidden layer model neurons resemble cochlear nucleus types III and IV and their composites. Neurons in both hidden and output layers show the properties of spectral notch detectors. Although neural networks have limitations as models of real neural systems, the results illustrate how they can provide insight into the computation of complex transformations in the nervous system.

Auditory Perception↗
PubMedDate not supplied

The training and use of an artificial neural network to monitor use of medication in treatment of complex patients.

An artificial-neural-network-based drug interaction warning system was developed for use with a computerized real-time entry medical records system. The goal of the study was to provide physicians and nurses with timely warnings of potential drug interactions as therapies were prescribed. In a dialysis unit, physicians and clinical pharmacists defined rules of proper drug therapy, then trained a neural network with those rules. When the network was used to review the therapies of this patient population, a number of inconsistencies were discovered, and medication orders were changed on several patients. Real-time implementation of this monitoring system could provide messages to assure that drug therapy is consistent and proper, according to rules created by the providers of healthcare, thus preventing occasional mistakes in drug therapy.

Drug Interactions↗
PubMed1993

Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks).

The ability to supervise and control a highly non-linear and time variant bioprocess is of considerable importance to the biotechnological industries which are continually striving to obtain higher yields and improved uniformity of production. Two AI methodologies aimed at contributing to the overall intelligent monitoring and control of bioprocess operations are discussed. The development and application of a real-time knowledge-based system to provide supervisory control of fed-batch bioprocesses is reviewed. The system performs sensor validation, fault detection and diagnosis and incorporates relevant expertise and experience drawn from both bioprocess engineering and control engineering domains. A complementary approach, that of artificial neural networks is also addressed. The development of neural network modelling tools for use in bioprocess state estimation and inferential control are reviewed. An attractive characteristic of neural networks is that with the appropriate topology any non-linear functional relationship can be modelled, hence significantly reducing model-process mismatch. Results from industrial applications are presented.

Artificial Intelligence↗
PubMed1993

Assessment of gait patterns using neural networks.

A new approach for the assessment of gait patterns is presented. The use of neural network techniques for decision making in gait analysis is for some purposes more effective than biomechanical methods or conventional statistics. To demonstrate this, a neural network was trained to distinguish 'healthy' from 'pathological' gait. The algorithm presented here can be used for several purposes because it learns from examples of diagnosed gait patterns without having any built-in model of gait.

Algorithms↗