Biomedical subjects

U R Abeyratne

Publications and source records attributed to U R Abeyratne.

7 recordsLinked to original sources

PubMed2006

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks.

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We define a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states "i" and "j" in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.

Brain Ischemia↗

PubMed2001

EEG source localization: comparative study of classical and neural network methods.

We address the problem of estimating biopotential sources within the brain, based on EEG signals observed on the scalp. This problem, known as the inverse problem of electrophysiology, has no closed-form solution, and requires iterative techniques such as the Levenberg-Marquardt (LM) algorithm. Considering the nonlinear nature of the inverse problem, and the low signal to noise ratio inherent in EEG signals, a backpropagation neural network (BPN) has been recently proposed as a solution. The technique has not been properly compared with classical techniques such as the LM method, or with more recent neural network techniques such as the Radial Basis Function (RBF) network. In this paper, we provide improved strategies based on BPN and consider RBF networks in solving the inverse problem. We compare the performances of BPN, RBF and a hybrid technique with that of the classical LM method.

Brain↗

PubMed2000

Wavelet transforms in estimating scatterer spacing from ultrasound echoes.

Ultrasound echoes from organs such as the liver display resolvable periodicity due to regular scattering centers within tissue. The spacing among such scattering centers has been proposed as a signature to characterize diffuse and focal diseases of the liver. Even though it is highly desirable to be able to estimate an inter-scatterer-spacing (ISS) distribution, current methods can estimate only the mean value of scatterer spacing (MSS) over a tissue length. In this paper, we propose a wavelet transform-based technique that is capable of estimating the location of each scattering center, making it possible to obtain the ISS distribution. We represent liver tissue with a point scatterer model, and show, via computer simulations, that the use of multi-scale information in the wavelet scale-space allows us to estimate the locations of regular scattering centers. We show that both the observation noise and random ultrasound returns from unresolvable tissue microstructure can be removed successfully in the wavelet domain via the properties of the modulus maxima sequence of observation across different scales.

Computer Simulation↗

PubMed2000

A novel scheme for the selective stimulation of nerve fibers using nonlinear properties of action potentials.

In this paper, we propose a novel technique for selective stimulation of nerve fibers. We show that a set of point electrodes arranged in the 3-D space around a nerve trunk can be used to systematically synthesize highly useful activation patterns within the nerve, by exploiting the spatial arrangement of the electrodes and the excitation currents. Using such activation patterns, we present a novel scheme to selectively stimulate nerve fibers, based on the nonlinear properties of action potential generation. We illustrate the developed techniques via computer simulations of a nerve trunk consisting of a large number of nerve fibers. The results indicate that the proposed technique has great potential to achieve position selective stimulation of nerve in FES.

Action Potentials↗

PubMed2000

Spectral information changes in obtaining heart rate variability from tachometer R-R interval signals.

The spontaneous changes in the heartbeat provide valuable information regarding serious cardiac pathologies such as Sudden Cardiac Death. Subtle anomalies in the heart rate can be discovered via an analysis of the Heart Rate Variability (HRV) signal, a sequence of numbers representing the instantaneous heart rate over time. The HRV signal is commonly modeled by an integral pulse frequency modulation (IPFM) scheme. Based on the model, several techniques have been proposed in the literature to capture features of the HRV signal. They however, suffer from severe spurious peaks, especially when the HRV signals contain multiple spectral lines. In this paper, we propose a new method to minimize spurious spectral peaks via the technique of phase interpolation.

Algorithms↗

PubMed2000

RBF networks for source localization in quantitative electrophysiology.

The backpropagation neural network methods have been proposed recently to solve the inverse problem in quantitative electrophysiology. A major advantage of the technique is that once a neural network is trained, it no longer requires iterations or access to sophisticated computations. We propose to use RBF networks for source localization in the brain, and systematically compare their performance to those of Levenberg-Marquardt (LM) algorithms. We show the use of two types of Radial Basis Function Networks (RBF) network: a classic network with fixed number of hidden layer neurons and an improved network, Minimal Resource Allocation Network (MRAN), recently proposed by one of the authors, capable for dynamically configuring its structure so as to obtain a compact topology to match the data presented to it.

Algorithms↗

PubMed1991

Artificial neural networks for source localization in the human brain.

Source localization in the brain remains an ill-posed problem unless further constraints about the type of sources and the head model are imposed. Human head is modeled in various ways depending critically on the computing power available and/or the required level of accuracy. Sophisticated and truly representative models may yield more accurate results in general, but at the cost of prohibitively long computer times and huge memory requirements. In conventional source localization techniques, solution source parameters are taken as those which minimize an index of performance, defined relative to the model-generated and clinically measured voltages. We propose the use of a neural network in the place of commonly employed minimization algorithms such as the Simplex Method and the Marquardt algorithm, which are iterative and time consuming. With the aid of the error-backpropagation technique, a neural network is trained to compute source parameters, starting from a voltage set measured on the scalp. Here we describe the methods of training the neural network and investigate its localization accuracy. Based on the results of extensive studies, we conclude that neural networks are highly feasible as source localizers. A trained neural network's independence of localization speed from the head model, and the rapid localization ability, makes it possible to employ the most complex head model with the ease of the simplest model. No initial parameters need to be guessed in order to start the calculation, implying a possible automation of the entire localization process. One may train the network on experimental data, if available, thereby possibly doing away with head models.

Brain↗