Showing papers in "IEEE Transactions on Biomedical Engineering in 1998"

Correction to "Two-Dimensional FDTD Analysis Of A Pulsed Microwave Confocal System For Breast Cancer Detection: Fixed-Focus And Antenna-Array Sensors"

Abstract: A novel focused active microwave system is investigated for detecting tumors in the breast. In contrast to X-ray and ultrasound modalities, the method reviewed here exploits the breast-tissue physical properties unique to the microwave spectrum, namely, the translucent nature of normal breast tissues and the high dielectric contrast between malignant tumors and surrounding lesion-free normal breast tissues. The system uses a pulsed confocal technique and time-gating to enhance the detection of tumors while suppressing the effects of tissue heterogeneity and absorption. Using published data for the dielectric properties of normal breast tissues and malignant tumors, the authors have conducted a two-dimensional (2-D) finite-difference time-domain (FDTD) computational electromagnetics analysis of the system. The FDTD simulations showed that tumors as small as 2 mm in diameter could be robustly detected in the presence of the background clutter generated by the heterogeneity of the surrounding normal tissue. Lateral spatial resolution of the tumor location was found to be about 0.5 cm.

Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks

Abstract: This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair. EEG signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of six-channel EEG were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loeve transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classification accuracy of 91.4% on novel, untrained, EEG signals.

A new interpretation of nonlinear energy operator and its efficacy in spike detection

Abstract: A nonlinear energy operator (NEO) gives an estimate of the energy content of a linear oscillator. This has been used to quantify the AM-FM modulating signals present in a sinusoid. Here, the authors give a new interpretation of NEO and extend its use in stochastic signals. They show that NEO accentuates the high-frequency content. This instantaneous nature of NEO and its very low computational burden make it an ideal tool for spike detection. The efficacy of the proposed method has been tested with simulated signals as well as with real electroencephalograms (EEGs).

Recursive MUSIC: A framework for EEG and MEG source localization

Abstract: The multiple signal classification (MUSIC) algorithm can be used to locate multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetocncephalography (MEG) data. The algorithm scans a single-dipole model through a three-dimensional (3-D) head volume and computes projections onto an estimated signal subspace. To locate the sources, the user must search the head volume for multiple local peaks in the projection metric. This task is time consuming and subjective. Here, the authors describe an extension of this approach which they refer to as recursive MUSIC (R-MUSIC). This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections. The new method is also able to locate synchronous sources through the use of a spatio-temporal independent topographies (IT) model. This model defines a source as one or more nonrotating dipoles with a single time course. Within this framework, the authors are able to locate fixed, rotating, and synchronous dipoles. The recursive subspace projection procedure that they introduce here uses the metric of canonical or subspace correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace, by recursively computing subspace correlations, the authors build up a model for the sources which account for a given set of data. They demonstrate here how R-MUSIC can easily extract multiple asynchronous dipolar sources that are difficult to find using the original MUSIC scan. The authors then demonstrate R-MUSIC applied to the more general IT model and show results for combinations of fixed, rotating, and synchronous dipoles.

A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait

Abstract: The aim of this work is to present an original double-threshold detector of muscle activation, specifically developed for gait analysis. This detector operates on the raw myoelectric signal and, hence, it does not require any envelope detection. Its performances are fixed by the values of 3 parameters, namely, false-alarm probability (P/sub fa/), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the P/sub fa/, they yield higher detection probability; furthermore, they allow the user to select the couple false alarm-detection probability with a higher degree of freedom, thus, adapting the performances of the detector to the characteristics of the myoelectric signal of interest and of the experimental situation. Here, first the authors derive the detection algorithm and describe different strategies for selecting its parameters, then they present the performances of the proposed procedure evaluated by means of computer simulations, and finally they report an example of application to myoelectric signals recorded during gait. The characterization of the proposed double-threshold detector demonstrates that, in most practical situations, the bias of the estimates of the on-off transitions is smaller than 10 ms, the standard deviation may be kept lower than 15 ms, and the percentage of erroneous patterns is below 5%. These results show that this detection approach is satisfactory in research applications as well as in the clinical practice.

Adaptive AR modeling of nonstationary time series by means of Kalman filtering

Abstract: An adaptive on-line procedure is presented for autoregressive (AR) modeling of nonstationary multivariate time series by means of Kalman filtering. The parameters of the estimated time-varying model can be used to calculate instantaneous measures of linear dependence. The usefulness of the procedures in the analysis of physiological signals is discussed in two examples: first, in the analysis of respiratory movement, heart rate fluctuation, and blood pressure, and second, in the analysis of multichannel electroencephalogram (EEG) signals. It was shown for the first time that in intact animals the transition from a normoxic to a hypoxic state requires tremendous short-term readjustment of the autonomic cardiac-respiratory control. An application with experimental EEG data supported observations that the development of coherences among cell assemblies of the brain is a basic element of associative learning or conditioning.

Spatial filtering and neocortical dynamics: estimates of EEG coherence

Abstract: The spatial statistics of scalp electroencephalogram (EEG) are usually presented as coherence in individual frequency bands. These coherences result both from correlations among neocortical sources and volume conduction through the tissues of the head. The scalp EEG is spatially low-pass filtered by the poorly conducting skull, introducing artificial correlation between the electrodes. A four concentric spheres (brain, CSF, skull, and scalp) model of the head and stochastic field theory are used here to derive an analytic estimate of the coherence at scalp electrodes due to volume conduction of uncorrelated source activity, predicting that electrodes within 10-12 cm can appear correlated. The surface Laplacian estimate of cortical surface potentials spatially bandpass filters the scalp potentials reducing this artificial coherence due to volume conduction. Examination of EEG data confirms that the coherence estimates from raw scalp potentials and Laplacians are sensitive to different spatial bandwidths and should be used in parallel in studies of neocortical dynamic function.

Adaptive cancelling of physiological tremor for improved precision in microsurgery

Abstract: Physiological hand tremor impedes microsurgery. We present both a novel adaptive algorithm for tremor estimation and a new technique for active real-time cancelling of physiological tremor. Tremor is modeled online using the weighted-frequency Fourier linear combiner (WFLC). This adaptive algorithm models tremor as a modulating sinusoid, and tracks its frequency, amplitude and phase. Piezoelectric actuators move the surgical instrument tip in opposition to the motion of tremor, effectively subtracting the tremor from the total motion. We demonstrate the technique in 1D using a cantilever apparatus as a benchtop simulation of the surgical instrument. Actual hand motion, prerecorded during simulated surgery, is used as input. In 25 tests, WFLC tremor compensation reduces the RMS tip motion in the 6-16 Hz tremor band by 67%, and reduces the RMS error with respect to an a posteriori estimate of voluntary motion by 30%. The technique can be implemented in a hand-held microsurgical instrument.

An improved boundary element method for realistic volume-conductor modeling

Abstract: An improved boundary element method (BEM) with a virtual triangle refinement using the vertex normals, an optimized auto solid angle approximation, and a weighted isolated problem approach is presented. The performance of this new approach is compared to analytically solvable spherical shell models and highly refined reference BEM models for tangentially and radially oriented dipoles at different eccentricities. The lead fields of several electroencephalography (EEG) and magnetoencephalography (MEG) setups are analyzed by singular-value decompositions for realistically shaped volume-conductor models. Dipole mislocalizations due to simplified volume-conductor models are investigated for EEG and MEG examinations for points on a three dimensional (3-D) grid with 10-mm spacing inside the conductor and all principal dipole orientations. The applicability of the BEM in view of the computational effort is tested with a standard workstation. Finally, an application of the new method to epileptic spike data is studied and the results are compared to the spherical-shells approximation.

Stochastic complexity measures for physiological signal analysis

Abstract: Traditional feature extraction methods describe signals in terms of amplitude and frequency. This paper takes a paradigm shift and investigates four stochastic-complexity features. Their advantages are demonstrated on synthetic and physiological signals; the latter recorded during periods of Cheyne-Stokes respiration, anesthesia, sleep, and motor-cortex investigation.

An externally powered, multichannel, implantable stimulator-telemeter for control of paralyzed muscle

Abstract: An implantable integrated stimulator and telemetry system has been developed. The system is capable of fulfilling the stimulus and telemetry needs of advanced functional neuromuscular stimulation (FNS) applications requiring multiple channels of stimulation and multiple channels of sensor or biopotential sensing. This system provides a command control structure, an inductive radio frequency link providing power to the implant device as well as two-way transcutaneous communication, an ASIC for decoding the command and for providing functional control within the implant, and modular circuitry providing the application specific implant functions. Biocompatible hermetic packaging, lead systems, and in-line connectors suitable for long-term implantation, provide encapsulation for the circuitry and access to the electrodes and sensors used in the application. The first implant configuration realized from this modular system is targeted for clinical implementation in persons with tetraplegia at the C6 level for restoration of hand function, using wrist position as the command control source. The implant device realized has ten channels of stimulation and telemetry used to control and sense a joint angle transducer implanted in the radio-carpal joint of the wrist. A prototype device has been fabricated and is undergoing testing in an animal.

An automatic diagnostic system for CT liver image classification

Abstract: Computed tomography (CT) images have been widely used for liver disease diagnosis. Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, a CT liver image diagnostic classification system is presented which will automatically find, extract the CT liver boundary and further classify liver diseases. The system comprises a detect-before-extract (DBE) system which automatically finds the liver boundary and a neural network liver classifier which uses specially designed feature descriptors to distinguish normal liver, two types of liver tumors, hepatoma and hemageoma. The DBE system applies the concept of the normalized fractional Brownian motion model to find an initial liver boundary and then uses a deformable contour model to precisely delineate the liver boundary. The neural network is included to classify liver tumors into hepatoma and hemageoma. It is implemented by a modified probabilistic neural network (PNN) [MPNN] in conjunction with feature descriptors which are generated by fractal feature information and the gray-level co-occurrence matrix. The proposed system was evaluated by 30 liver cases and shown to be efficient and very effective.

Estimation of conduction velocity vector fields from epicardial mapping data

Abstract: An automated method to estimate vector fields of propagation velocity from observed epicardial extracellular potentials is introduced. The method relies on fitting polynomial surfaces T(x,y) to the space-time (x,y,t) coordinates of activity, Both speed and direction of propagation are computed from the gradient of the local polynomial surface. The components of velocity, which are total derivatives, are expressed in terms of the partial derivatives which comprise the gradient of T. The method was validated on two-dimensional (2-D) simulations of propagation and then applied to cardiac mapping data. Conduction velocity was estimated at multiple epicardial locations during sinus rhythm, pacing, and ventricular fibrillation (VF) in pigs. Data were obtained via a 528-channel mapping system from 23/spl times/22 and 24/spl times/21 arrays of unipolar electrodes sutured to the right ventricular epicardium. Velocity estimates are displayed as vector fields and are used to characterize propagation qualitatively and quantitatively during both simple and complex rhythms.

Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals

Abstract: This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. The authors have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals the authors have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.

A real-time experimental prototype for enhancement of vision rehabilitation using auditory substitution

Abstract: The rehabilitation of blindness, using noninvasive methods, requires sensory substitution. A theoretical frame for sensory substitution has been proposed (C. Veraart, 1989) which consists of a model of the deprived sensory system connected to an inverse model of the substitutive sensory system. This paper addresses the feasibility of this conceptual model in the case of auditory substitution, and its implementation as a rough model of the retina connected to an inverse linear model of the cochlea. The authors have developed an experimental prototype. It aims at allowing optimization of the sensory substitution process. This prototype is based on a personal computer which is connected to a miniature head-fixed video camera and to headphones. A visual scene is captured. Image processing achieves edge detection and graded resolution. Each picture element (pixel) of the processed image is assigned a sinusoidal tone; weighted summation of these sinewaves builds up a complex auditory signal which is transduced by the headphones. On-line selection of various parameters and real-time functioning of the device allow optimization of parameters during psychophysical experimentations. Assessment of this implementation has been initiated, and has so far demonstrated prototype usefulness for pattern recognition. An integrated circuit of this system is to be developed.

Phenomenological evolution equations for heat-induced shrinkage of a collagenous tissue

Abstract: Optimization of clinical heat treatments for various pathologies requires accurate numerical modeling of the heat transfer, evolution of thermal damage, and associated changes in the material properties of the tissues. This paper presents two phenomenological equations that quantify time-dependent thermal damage in a uniaxial collagenous tissue. Specifically, an empirical rule-of-mixtures model is shown to describe well heat-induced axial shrinkage (a measure of underlying denaturation) in chordae tendineae which results from a spectrum of thermomechanical loading histories. Likewise an exponential decay model is shown to describe well the partial recovery (e.g., renaturation) of chordae when it is returned to body temperature following heating. Together these models provide the first quantitative descriptors of the evolution of heat-induced damage and subsequent recovery in collagen. Such descriptors are fundamental to numerical analyses of many heat treatments because of the prevalence of collagen in many tissues and organs.

Global optimization in the localization of neuromagnetic sources

Abstract: The locations of active brain areas can be estimated from the magnetic field produced by the neural current sources. In many cases, the actual current distribution can be modeled with a set of stationary current dipoles with time-varying amplitudes. This work studies global optimization methods that find the minimum of the least-squares error function of the current dipole estimation problem. Three different global optimization methods were investigated: clustering method, simulated annealing, and genetic algorithms. In simulation studies, the genetic algorithm was the most effective method. The methods were also applied to analysis of actual measurement data.

A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem

Abstract: This paper explores the possibilities of using linear inverse solutions to reconstruct arbitrary current distributions within the human brain. The authors formally prove that due to the underdetermined character of the problem, the only class of measurable current distributions that can be totally retrieved are those of minimal norm. The reconstruction of smooth or averaged versions of the currents is also explored. A solution that explicitly attempts to reconstruct averages of the current is proposed and compared with the minimum norm and the minimum Laplacian solution. In contrast to the majority of previous analysis carried out in the field, in the comparisons, the authors avoid the use of measures designed for the case of dipolar sources. To allow for the evaluation of distributed solutions in the case of arbitrary current distributions the authors use the concept of resolution kernels. Two summarizing measures, source identifiability and source visibility, are proposed and applied to the comparison. From this study can be concluded: (1) linear inverse solutions are unable to produce adequate estimates of arbitrary current distributions at many brain sites and (2) averages or smooth solutions are better than the minimum norm solution estimating the position of single point sources. However, they systematically underestimate their amplitude or strength especially for the deeper brain areas. Based on these result, it appears unlikely that a three-dimensional (3-D) tomography of the brain electromagnetic activity can be based on linear reconstruction methods without the use of a significant amount of a priori information.

A Kalman filter approach to track fast impedance changes in electrical impedance tomography

Abstract: In electrical impedance tomography (EIT), an estimate for the cross-sectional impedance distribution is obtained from the body by using current and voltage measurements made from the boundary. All well-known reconstruction algorithms use a full set of independent current patterns for each reconstruction. In some applications, the impedance changes may be so fast that information on the time evolution of the impedance distribution is either lost or severely blurred. Here, the authors propose an algorithm for EIT reconstruction that is able to track fast changes in the impedance distribution. The method is based on the formulation of EIT as a state-estimation problem and the recursive estimation of the state with the aid of the Kalman filter. The performance of the proposed method is evaluated with a simulation of human thorax in a situation in which the impedances of the ventricles change rapidly. The authors show that with optimal current patterns and proper parameterization, the proposed approach yields significant enhancement of the temporal resolution over the conventional reconstruction strategy.

Hall effect imaging

Abstract: Presents a new imaging method based on the classical Hall effect (HE), which describes the origin of a detectable voltage from a conductive object moving in a magnetic field. HE images are formed using ultrasound imaging techniques in a magnetic field. These images reflect the electrical properties of the sample. To demonstrate the feasibility of this method, images of plastic and biological samples are collected. The contrast mechanism and signal-to-noise issues are discussed. Since electrical parameters vary widely among tissue types and pathological states, HE imaging may be a useful tool for biological research and medical diagnosis.

Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering

Abstract: Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. The authors present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined. The authors exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were inputted to the unsupervised optimal fuzzy clustering (UOFC) algorithm. The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of clusters overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters. The classification succeeded in identifying several, behavior-backed, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. Universality may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.

Neural predictive controller for insulin delivery using the subcutaneous route

Abstract: A neural predictive controller for closed-loop control of glucose using subcutaneous (s.c.) tissue glucose measurement and s.c. infusion of monomeric insulin analogs was developed and evaluated in a simulation study. The proposed control strategy is based on off-line system identification using neural networks (NNs) and nonlinear model predictive controller design. The system identification framework combines the concept of nonlinear autoregressive model with exogenous inputs (NARX) system representation, regularization approach for constructing radial basis function NNs, and validation methods for nonlinear systems. Numerical studies on system identification and closed-loop control of glucose were carried out using a comprehensive model of glucose regulation and a pharmacokinetic model for the absorption of monomeric insulin analogs from the s.c. depot. The system identification procedure enabled construction of a parsimonious network from the simulated data, and consequently, design of a controller using multiple-step-ahead predictions of the previously identified model. According to the simulation results, stable control is achievable in the presence of large noise levels, for unknown or variable time delays as well as for slow time variations of the controlled process. However, the control limitations due to the s.c. insulin administration makes additional action from the patient at meal time necessary.

Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle

Abstract: To restore functional use of paralyzed muscles by automatically controlled stimulation, an accurate quantitative model of the stimulated muscles is desirable. The most commonly used model for isometric muscle has had a Hammerstein structure, in which a linear dynamic block is preceded by a static nonlinear function. To investigate the accuracy of the Hammerstein model, the responses to a pseudo-random binary sequence (PRES) excitation of normal human plantarflexors, stimulated with surface electrodes, were used to identify a Hammerstein model but also four local models which describe the responses to small signals at different mean levels of activation. Comparison of the local models with the linearized Hammerstein model showed that the Hammerstein model concealed a fivefold variation in the speed of response. Also, the small-signal gain of the Hammerstein model was in error by factors up to three. We conclude that, despite the past widespread use of the Hammerstein model, it is not an accurate representation of isometric muscle. On the other hand, local models, which are more accurate predictors, can be identified from the responses to short PRES sequences. The utility of local models for controller design is discussed.

Strengthening of synchronized activity by tetanic stimulation in cortical cultures: application of planar electrode arrays

Abstract: Rat cortical neurons were cultured on planar electrode arrays with 64 embedded electrodes. Whole-cell recording from single neurons and multisite extracellular recording were carried out simultaneously in the cultured cortical networks, and the effects of focal tetanic stimulation of the culture were studied. Both the number of action potentials and the propagation velocity of stimulated bursts were increased after tetanic stimulation. These changes were associated with a marked increase in the number of late components in the synaptic current, but with little or no increase in the early peak synaptic current. The effects of tetanic stimulation were consistent with a widespread increase in the reliability of monosynaptic transmission.

Numerical simulation of SAR and B/sub 1/-field inhomogeneity of shielded RF coils loaded with the human head

Abstract: The finite-difference time-domain (FDTD) method is combined with the method of moments (MoM) to compute the electromagnetic fields of shielded radio-frequency (RF) coils loaded with an anatomically accurate model of a human head for high-frequency magnetic resonance imaging (MRI) applications. The combined method can predict both the specific energy absorption rate (SAR) and the magnetic field (known as the B/sub 1/ field) excited by any RF coils. Results for SAR and B/sub 1/ field distribution, excited by shielded and end-capped birdcage coils, are calculated at 64, 128, 171, and 256 MHz. The results show that the value of SAR increases when the frequency of the B/sub 1/ field increases and the B/sub 1/ field exhibits a strong inhomogeneity at high frequencies.

The NavBelt-a computerized travel aid for the blind based on mobile robotics technology

Abstract: This paper presents a new concept for a travel aid for the blind. A prototype device, called the NavBelt, was developed to test this concept. The device can be used as a primary or secondary aid, and consists of a portable computer, ultrasonic sensors, and stereophonic headphone. The computer applies navigation and obstacle avoidance technologies that were developed originally for mobile robots. The computer then uses a stereophonic imaging technique to process the signals from the ultrasonic sensors and relays their information to the user via stereophonic headphones. The user can interpret the information as an acoustic "picture" of the surroundings, or, depending on the operational mode, as the recommended travel direction. The acoustic signals are transmitted as discrete beeps or continuous sounds. Experimental results with the NavBelt simulator and a portable prototype show that users can travel safely in an unfamiliar and cluttered environment at speeds of up to 0.8 m/s.

Fetal ECG extraction with nonlinear state-space projections

Abstract: Describes a method to suppress maternal and noise contaminations in single-lead fetal ECG recordings. A nonlinear state space projection technique originally developed for noise reduction in deterministically chaotic signals is used. The method is successfully applied to recordings with fetal components and noise of comparable amplitude.

A high-resolution technique for multidimensional NMR spectroscopy

Abstract: A scheme for estimating frequencies and damping factors of multidimensional nuclear magnetic resonance (NMR) data is presented-multidimensional NMR data can be modeled as the sum of several multidimensional damped sinusoids. The estimated frequencies and damping factors of multidimensional NMR data play important roles in determining protein structures. The authors present a high-resolution subspace method for estimating the parameters of NMR data, Unlike other methods, this algorithm makes full use of the rank-deficiency and Hankel properties of the prediction matrix composed of NMR data. Hence, it can estimate the signal parameters under low signal-to-noise ratio (SNR) by using a few data points. The effectiveness of the new algorithm is confirmed by computer simulations and it is tested by experimental data.

An improved method for estimating epicardial potentials from the body surface

Abstract: The authors present a new method for regularizing the ill-posed problem of computing epicardial potentials from body surface potentials. The method simultaneously regularizes the equations associated with all time points, and relies on a new theorem which states that a solution based on optimal regularization of each integral equation associated with each principal component of the data will be more accurate than a solution based on optimal regularization of each integral equation associated with each time point. The theorem is illustrated with simulations mimicking the complexity of the inverse electrocardiography problem. As must be expected from a method which imposes no additional a priori constraints, the new approach addresses uncorrelated noise only, and in the presence of dominating correlated noise it is only successful in producing a "cleaner" version of a necessarily compromised solution. Nevertheless, in principle, the new method is always preferred to the standard approach, since it (without penalty) eliminates pure noise that would otherwise be present in the solution estimate.

A wavelet transform-based ECG compression method guaranteeing desired signal quality

Abstract: This paper presents a new electrocardiogram (ECG) compression method based on orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity.