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At least 847 records · Page 47Linked to original sources
PubMed2005

Venous catheter based mapping of ectopic epicardial activation: training data set selection for statistical estimation.

A source of error in most of the existing catheter cardiac mapping approaches is that they are not capable of acquiring epicardial potentials even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this subgroup of patients, mapping techniques that are limited to the endocardium result in localization errors and failure in subsequent ablation procedures. In addition, catheter-based electrophysiological studies of the epicardium are limited to regions near the coronary vessels or require transthoracic access. We have developed a statistical approach by which to estimate high-resolution maps of epicardial activation from very low-resolution multi-electrode venous catheter measurements. A training set of previously recorded maps is necessary for this technique so that composition of the database becomes an important determinant of accuracy. The specific hypothesis of the study was that estimation accuracy would be best when the training data set matches that of the test beat(s), whereby the matching was according to the site of initiation of the beats. This hypothesis suggests approaches to optimized selection of the training set, three of which we have developed and evaluated. One of these methods, the high-CC refinement method, was able to estimate the earliest activation site of left ventricularly paced maps within an average of 4.67 mm of the true site; in 89% of the cases (a total of 231 cases) the error was smaller than 10 mm. In another method, MHC-Spatial activation, right ventricularly paced maps (239 maps) were estimated with an error of 7.15 mm. The average correlation coefficient between the original and the estimated maps was also very high (0.97), which shows the ability of the training data set refinement methods to estimate the epicardial activation sequence. The results of these tests support the hypothesis and, moreover, suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting.

Algorithms↗
PubMed2006

Analysis of heartbeat dynamics by point process adaptive filtering.

Heartbeats are a point process yet, most of the current analysis methods do not model this important characteristic of these data. We describe human heartbeat time series as a history dependent inverse Gaussian model. We present a point process adaptive filter algorithm to estimate the model's time-varying parameters, and use it to compute new measures of heart rate variability. We apply our algorithm to analyze simulated heartbeat data and actual heartbeat data from a tilt table experiment and from healthy subjects and subjects with congestive heart failure during sleep. Our results suggest a new approach for characterizing heartbeat dynamics.

Algorithms↗
PubMed2006

Renyi entropy measures of heart rate Gaussianity.

Sample entropy and approximate entropy are measures that have been successfully utilized to study the deterministic dynamics of heart rate (HR). A complementary stochastic point of view and a heuristic argument using the Central Limit Theorem suggests that the Gaussianity of HR is a complementary measure of the physiological complexity of the underlying signal transduction processes. Renyi entropy (or q-entropy) is a widely used measure of Gaussianity in many applications. Particularly important members of this family are differential (or Shannon) entropy (q = 1) and quadratic entropy (q = 2). We introduce the concepts of differential and conditional Renyi entropy rate and, in conjunction with Burg's theorem, develop a measure of the Gaussianity of a linear random process. Robust algorithms for estimating these quantities are presented along with estimates of their standard errors.

Algorithms↗
PubMed2006

Multiscale probability density function analysis: non-Gaussian and scale-invariant fluctuations of healthy human heart rate.

For a detailed characterization of intermittency and non-Gaussianity of human heart rate, we introduce an analysis method to investigate the deformation process of the probability density function (PDF) of detrended increments when going from fine to coarse scales. To characterize the scale dependence of the multiscale PDF, we use two methods: 1) calculation of Kullback-Leibler relative entropy; 2) parameter estimation based on Castaing's equation (B. Castaing et al, 1990). We compare scale-dependence of the increment PDFs between actual heart rate fluctuations and artificially generated Gaussian and non-Gaussian noise, including a widely used autoregressive model and a recently proposed multifractal model based on a random cascade process. Our analysis highlights an essential difference between heart rate fluctuations and those generated by other models. The outstanding feature of human heart rate is the robust scale-invariance of the non-Gaussian PDF, which is preserved not only in a quiescent condition, but also in a dynamic state during waking hours, in which the mean level of heart rate is dramatically changing. Our results strongly suggest the need for revising existing models of heart rate variability to incorporate the scale-invariance in the PDF.

Adult↗
PubMed2006

Nonlinear analysis of cerebral hemodynamic and intracranial pressure signals for characterization of autoregulation.

The objective of this study was to determine whether or not the underlying physiological systems that generates spontaneous arterial blood pressure (ABP), cerebral blood flow velocity (CBFV), and intracranial pressure signals could be adequately approximated as a linear stochastic process. Furthermore, a new measure (C) capable of capturing the degree of nonlinear dependency between two ABP and CBFV signals (including a time-varying situation) was proposed for quantifying the degree of cerebral blood flow autoregulation. A surrogate data test of fifteen ABP, CBFV, and intracranial pressure (ICP) segments was conducted for detecting whether there exists a statistically significant deviation from the null hypothesis of linear signals. The extension of the established block computation method of C measure to an adaptive one was achieved. This new algorithm was then applied to study the C evolution using brain injury patients data from a hyperventilation study and two propofol studies. Nonlinearity has not been detected for all the fifteen recordings, neither has nonlinear dependency between CBFV and ABP. However, their presences in some of the signal segments justified the adoption of a nonlinear measure of dependency capable of characterizing both linear and nonlinear correlations for inferring autoregulation status. C measure started to decrease with the introduction of hypocapnia state indicating that hyperventilation may reduce the dependency of CBFV on ABP fluctuations. On the other hand, complex patterns of C measure evolution were observed among 14 cases of propofol data indicating a nontrivial effect of propofol on the dependency of CBFV on ABP.

Adaptation, Physiological↗
PubMed2006

Using recurrence quantification analysis determinism for noise removal in cardiac optical mapping.

Selecting signal processing parameters in optical imaging by utilizing the change in Determinism, a measure introduced in Recurrence Quantification Analysis, provides a novel method using the change in residual noise Determinism for improving noise quantification and removal across signals exhibiting disparate underlying tissue pathologies. The method illustrates an improved process for selecting filtering parameters and how using measured signal-to-noise ratio alone can lead to improper parameter selection.

Algorithms↗
PubMed2006

Solving the cardiac bidomain equations for discontinuous conductivities.

Fast simulations of cardiac electrical phenomena demand fast matrix solvers for both the elliptic and parabolic parts of the bidomain equations. It is well known that fast matrix solvers for the elliptic part must address multiple physical scales in order to show robust behavior. Recent research on finding the proper solution method for the bidomain equations has addressed this issue whereby multigrid preconditioned conjugate gradients has been used as a solver. In this paper, a more robust multigrid method, called Black Box Multigrid, is presented as an alternative to conventional geometric multigrid, and the effect of discontinuities on solver performance for the elliptic and parabolic part is investigated. Test problems with discontinuities arising from inserted plunge electrodes and naturally occurring myocardial discontinuities are considered. For these problems, we explore the advantages to using a more advanced multigrid method like Black Box Multigrid over conventional geometric multigrid. Results will indicate that for certain discontinuous bidomain problems Black Box Multigrid provides 60% faster simulations than using conventional geometric multigrid. Also, for the problems examined, it will be shown that a direct usage of conventional multigrid leads to faster simulations than an indirect usage of conventional multigrid as a preconditioner unless there are sharp discontinuities.

Action Potentials↗
PubMed2006

The surface Laplacian operator of the potentials on a bounded volume conductor has a unique inverse.

In the discussion on the use of the surface Laplacian (SL) of the distribution of bioelectric potentials on the body surface, the question remained open whether a complete specification of the SL of the potential over the surface bounding a volume conductor would uniquely specify the potential on that surface up to a constant. This paper reports that this is indeed the case. In addition, it is shown that the integral of the SL over a closed surface is zero, a property that may serve as a check on the accuracy of any numerical approximation of the SL.

Algorithms↗
PubMed2006

ECG signal analysis through hidden Markov models.

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.

Artificial Intelligence↗
PubMed2004

Robust model-based vasculature detection in noisy biomedical images.

This paper presents a set of algorithms for robust detection of vasculature in noisy retinal video images. Three methods are studied for effective handling of outliers. The first method is based on Huber's censored likelihood ratio test. The second is based on the use of a alpha-trimmed test statistic. The third is based on robust model selection algorithms. All of these algorithms rely on a mathematical model for the vasculature that accounts for the expected variations in intensity/texture profile, width, orientation, scale, and imaging noise. These unknown parameters are estimated implicitly within a robust detection and estimation framework. The proposed algorithms are also useful as nonlinear vessel enhancement filters. The proposed algorithms were evaluated over carefully constructed phantom images, where the ground truth is known a priori, as well as clinically recorded images for which the ground truth was manually compiled. A comparative evaluation of the proposed approaches is presented. Collectively, these methods outperformed prior approaches based on Chaudhuri et al. (1989) matched filtering, as well as the verification methods used by prior exploratory tracing algorithms, such as the work of Can et aL (1999). The Huber censored likelihood test yielded the best overall improvement, with a 145.7% improvement over the exploratory tracing algorithm, and a 43.7% improvement in detection rates over the matched filter.

Algorithms↗
PubMed2006

Shape statistics variational approach for the outer contour segmentation of left ventricle MR images.

Segmentation of left ventricles is one of the important research topics in cardiac magnetic resonance (MR) imaging. The segmentation precision influences the authenticity of ventricular motion reconstruction. In left ventricle MR images, the weak and broken boundary increases the difficulty of segmenting the outer contour precisely. In this paper, we present an improved shape statistics variational approach for the outer contour segmentation of left ventricle MR images. We use the Mumford-Shah model in an object feature space and incorporate the shape statistics and an edge image to the variational framework. The introduction of shape statistics can improve the segmentation with broken boundaries. The edge image can enhance the weak boundary and thus improve the segmentation precision. The generation of the object feature image, which has homogenous "intensities" in the left ventricle, facilitates the application of the Mumford-Shah model. A comparison of mean absolute distance analysis between different contours generated with our algorithm and that generated by hand demonstrated that our method can achieve a higher segmentation precision and a better stability than various approaches. It is a semiautomatic way for the segmentation of the outer contour of the left ventricle in clinical applications.

Algorithms↗
PubMed2002

Objective comparison of quantitative imaging modalities without the use of a gold standard.

Imaging is often used for the purpose of estimating the value of some parameter of interest. For example, a cardiologist may measure the ejection fraction (EF) of the heart in order to know how much blood is being pumped out of the heart on each stroke. In clinical practice, however, it is difficult to evaluate an estimation method because the gold standard is not known, e.g., a cardiologist does not know the true EF of a patient. Thus, researchers have often evaluated an estimation method by plotting its results against the results of another (more accepted) estimation method, which amounts to using one set of estimates as the pseudogold standard. In this paper, we present a maximum-likelihood approach for evaluating and comparing different estimation methods without the use of a gold standard with specific emphasis on the problem of evaluating EF estimation methods. Results of numerous simulation studies will be presented and indicate that the method can precisely and accurately estimate the parameters of a regression line without a gold standard, i.e., without the x axis.

Analysis of Variance↗
PubMed2003

Distributed versus compartment models for PET receptor studies.

Although distributed models are generally accepted as being more realistic than compartment models, use of simpler compartment models is pervasive in nuclear medicine applications, particularly in positron emission tomography (PET). Here, we report on comparisons made between distributed and compartment model outputs to address the question of whether differences between them are sufficient to justify distributed models for analysis of PET receptor experiments. For both two- and three-injection experiments, "data" sets were obtained by simulation using a distributed model and a wide range of parameter values. Optimal fits of the compartment model output to these "data" were achieved with three strategies in which values of different groups of parameter were estimated. Compartment model outputs yielded good fits to all the distributed model outputs and the values of the corresponding parameters were in close agreement. Given the temporal resolution typically available with PET, the use of a distributed model has no advantage over a compartment model for PET receptor quantification.

Computer Simulation↗
PubMed2003

Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment.

This paper deals with the estimation of the blood oxygen level-dependent response to a stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise estimation is essential for a better understanding of cerebral activations. The most recent works have used a nonparametric framework for this estimation, considering each brain region as a system characterized by its impulse response, the so-called hemodynamic response function (HRF). However, the use of these techniques has remained limited since they are not well-adapted to real fMRI data. Here, we develop a threefold extension to previous works. We consider asynchronous event-related paradigms, account for different trial types and integrate several fMRI sessions into the estimation. These generalizations are simultaneously addressed through a badly conditioned observation model. Bayesian formalism is used to model temporal prior information of the underlying physiological process of the brain hemodynamic response. By this way, the HRF estimate results from a tradeoff between information brought by the data and by our prior knowledge. This tradeoff is modeled with hyperparameters that are set to the maximum-likelihood estimate using an expectation conditional maximization algorithm. The proposed unsupervised approach is validated on both synthetic and real fMRI data, the latter originating from a speech perception experiment.

Algorithms↗
PubMed2004

Fully automatic luminal contour segmentation in intracoronary ultrasound imaging--a statistical approach.

In this paper, a fully automatic method for luminal contour segmentation in intracoronary ultrasound imaging is introduced. Its principle is based on a contour with a priori properties that evolves according to the statistics of the ultrasound texture brightness, which is generally Rayleigh distributed. The main interest of the technique is its fully automatic character. This is insured by an initial contour that is not set by the user, like in classical snake-based algorithms, but estimated and, thus, adapted to each image. Its estimation combines two pieces of information extracted from the a posteriori probability function of the contour position: the function maximum location (or maximum a posteriori estimator) and the first zero-crossing of its derivative. Then, starting from the initial contour, a region of interest is automatically selected and the process iterated until the contour evolution can be ignored. In vivo coronary images from 15 patients, acquired with the 20-MHz central frequency Jomed Invision ultrasound scanner, were segmented with the developed method. Automatic contours were compared to those manually drawn by two physicians in terms of mean absolute difference. The results demonstrate that the error between automatic contours and the average of manual ones is of small amplitude, and only very slightly higher (0.099 +/- 0.032 mm) than the interexpert error (0.097 +/- 0.027 mm).

Algorithms↗
PubMed2004

Three-dimensional myocardial strain reconstruction from tagged MRI using a cylindrical B-spline model.

In this paper, we present a new method for reconstructing three-dimensional (3-D) left ventricular myocardial strain from tagged magnetic resonance (MR) image data with a 3-D B-spline deformation model. The B-spline model is based on a cylindrical coordinate system that more closely fits the morphology of the myocardium than previously proposed Cartesian B-spline models and does not require explicit regularization. Our reconstruction method first fits a spatial coordinate B-spline displacement field to the tag line data. This displacement field maps each tag line point in the deformed myocardium back to its reference position (end-diastole). The spatial coordinate displacement field is then converted to material coordinates with another B-spline fit. Finally, strain is computed by analytically differentiating the material coordinate B-spline displacement field with respect to space. We tested our method with strains reconstructed from an analytically defined mathematical left ventricular deformation model and ten human imaging studies. Our results demonstrate that a quadratic cylindrical B-spline with a fixed number of control points can accurately fit a physiologically realistic range of deformations. The average 3-D reconstruction computation time is 20 seconds per time frame on a 450 MHz Sun Ultra80 workstation.

Algorithms↗
PubMed2004

Robust real-time myocardial border tracking for echocardiography: an information fusion approach.

Ultrasound is a main noninvasive modality for the assessment of the heart function. Wall tracking from ultrasound data is, however, inherently difficult due to weak echoes, clutter, poor signal-to-noise ratio, and signal dropouts. To cope with these artifacts, pretrained shape models can be applied to constrain the tracking. However, existing methods for incorporating subspace shape constraints in myocardial border tracking use only partial information from the model distribution, and do not exploit spatially varying uncertainties from feature tracking. In this paper, we propose a complete fusion formulation in the information space for robust shape tracking, optimally resolving uncertainties from the system dynamics, heteroscedastic measurement noise, and subspace shape model. We also exploit information from the ground truth initialization where this is available. The new framework is applied for tracking of myocardial borders in very noisy echocardiography sequences. Numerous myocardium tracking experiments validate the theory and show the potential of very accurate wall motion measurements. The proposed framework outperforms the traditional shape-space-constrained tracking algorithm by a significant margin. Due to the optimal fusion of different sources of uncertainties, robust performance is observed even for the most challenging cases.

Algorithms↗
PubMed2004

Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference.

A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how it can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data.

Adolescent↗