Biomedical subjects

Robert S MacLeod

Publications and source records attributed to Robert S MacLeod.

8 recordsLinked to original sources

PubMed2006

Generalized training subset selection for statistical estimation of epicardial activation maps from intravenous catheter measurements.

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. This technique uses a linear estimation model that derives a relationship between venous catheter measurements and unmeasured epicardial sites from a set of previously recorded, high-resolution epicardial activation-time maps used as a training data set based on the spatial covariance of the measurement sites. We performed 14 dog experiments with various interventions to create an epicardial activation-time map database. This database included a total of 592 epicardial activation maps which were recorded using a sock array placed on the ventricles of dog hearts. We present five approaches, which examined sequential addition and removal of maps to select a generalized training set for the estimation technique. The selection consisted of choosing a subset of epicardial ectopic activation-time maps from the database of beats which resulted in estimation accuracy levels better than or at least similar to using all the maps in database. Our aim was to minimize the redundancy in the database and to be able to guide the eventual procedures required to obtain training data from open-chest surgery patients. The results from this study illustrated this redundancy and suggested that by including an optimal subset (around 100 maps) of the full database the estimation technique was able to perform as well as and even in some cases better than including all the maps in the database. The results also suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting and with fewer maps we can obtain similar reconstruction accuracy levels.

Animals↗

PubMed2006

Wavefront-based models for inverse electrocardiography.

We introduce two wavefront-based methods for the inverse problem of electrocardiography, which we term wavefront-based curve reconstruction (WBCR) and wavefront-based potential reconstruction (WBPR). In the WBCR approach, the epicardial activation wavefront is modeled as a curve evolving on the heart surface, with the evolution governed by factors derived phenomenologically from prior measured data. The body surface potential/wavefront relationship is modeled via an intermediate mapping of wavefront to epicardial potentials, again derived phenomenologically. In the WBPR approach, we iteratively construct an estimate of epicardial potentials from an estimated wavefront curve according to a simplified model and use it as an initial solution in a Tikhonov regularization scheme. Initial simulation results using measured canine epicardial data show considerable improvement in reconstructing activation wavefronts and epicardial potentials with respect to standard Tikhonov solutions. In particular the WBCR method accurately finds the anisotropic propagation early after epicardial pacing, and the WBPR method finds the wavefront (regions of sharp gradient of the potential) both accurately and with minimal smoothing.

Action Potentials↗

PubMed2006

Improved performance of bayesian solutions for inverse electrocardiography using multiple information sources.

The usual goal in inverse electrocardiography (ECG) is to reconstruct cardiac electrical sources from body surface potentials and a mathematical model that relates the sources to the measurements. Due to attenuation and smoothing that occurs in the thorax, the inverse ECG problem is ill-posed and imposition of a priori constraints is needed to combat this ill-posedness. When the problem is posed in terms of reconstructing heart surface potentials, solutions have not yet achieved clinical utility; limitations include the limited availability of good a priori information about the solution and the lack of a "good" error metric. We describe an approach that combines body surface measurements and standard forward models with two additional information sources: statistical prior information about epicardial potential distributions and sparse simultaneous measurements of epicardial potentials made with multielectrode coronary venous catheters. We employ a Bayesian methodology which offers a general way to incorporate these information sources and additionally provides statistical performance analysis tools. In a simulation study, we first compare solutions using one or more of these information sources. Then, we study the effects of varying the number of sparse epicardial potential measurements on reconstruction accuracy. To evaluate accuracy, we used the Bayesian error covariance as well as traditional error metrics such as relative error. Our results show that including even sparsely sampled information from coronary venous catheters can substantially improve the reconstruction of epicardial potential distributions and that a Bayesian framework provides a feasible approach to using this information. Moreover, computing the Bayesian error standard deviations offers a means to indicate confidence in the results even in the absence of validation data.

Action Potentials↗

PubMed2005

Mechanisms of ischemia-induced ST-segment changes.

Many aspects of ischemia-induced changes in the electrocardiogram lack solid biophysical underpinnings although variations in ST segments form the predominant basis for diagnostic and monitoring of patients. This incomplete knowledge certainly plays a role in the poor performance of some forms of electrocardiogram-based detection and characterization of ischemia, especially when it is limited to the subendocardium. The focus of our recent studies has been to develop a comprehensive mechanistic model of the electrocardiographic effects of ischemia. The computational component of this model is based on highly realistic heart geometry with anisotropic fiber structure and allows us to assign ischemic action potentials to contiguous regions that can span a prescribed thickness of the ventricles. A separate, high-resolution model of myocardial tissue provides us with a means of setting electrical characteristics of the heart, including the status of gap junctional coupling between cells. The experimental counterpart of this model consists of dog hearts, either in situ or isolated and perfused with blood, in which we control coronary blood flow by means of a cannula and blood pump. By reducing blood flow through the cannula for various durations, we can replicate any phase of ischemia from hyper acute to early infarction. Based on the results of these models, there is emerging a mechanism of the electrocardiographic response to ischemia that depends strongly on the anisotropic conductivity of the myocardium. Ischemic injury currents flow across the boundary between healthy and ischemic tissue, but it is their interaction with local fiber orientation and the associated conductivity that generates secondary currents that determine epicardial ST-segment potentials. Results from experiments support qualitatively the findings of the simulations and underscore the role of myocardial anisotropy in electrocardiography.

Animals↗

PubMed2005

Bayesian solutions and performance analysis in bioelectric inverse problems.

In bioelectric inverse problems, one seeks to recover bioelectric sources from remote measurements using a mathematical model that relates the sources to the measurements. Due to attenuation and spatial smoothing in the medium between the sources and the measurements, bioelectric inverse problems are generally ill-posed. Bayesian methodology has received increasing attention recently to combat this ill-posedness, since it offers a general formulation of regularization constraints and additionally provides statistical performance analysis tools. These tools include the estimation error covariance and the marginal probability density of the measurements (known as the "evidence") that allow one to predictively quantify and compare experimental designs. These performance analysis tools have been previously applied in inverse electroencephalography and magnetoencephalography, but only in relatively simple scenarios. The main motivation here was to extend the utility of Bayesian estimation techniques and performance analysis tools in bioelectric inverse problems, with a particular focus on electrocardiography. In a simulation study we first investigated whether Bayesian error covariance, computed without knowledge of the true sources and based on instead statistical assumptions, accurately predicted the actual reconstruction error. Our study showed that error variance was a reasonably reliable qualitative and quantitative predictor of estimation performance even when there was error in the prior model. We also examined whether the evidence statistic accurately predicted relative estimation performance when distinct priors were used. In a simple scenario our results support the hypothesis that the prior model that maximizes the evidence is a good choice for inverse reconstructions.

Algorithms↗

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↗

PubMed2002

Multielectrode venous catheter mapping as a high quality constraint for electrocardiographic inverse solution.

A persistent challenge in solving inverse problems in electrocardiography is the application of suitable constraints to the calculation of cardiac sources. Whether one formulates the inverse problem in terms of epicardial potentials or activation wavefronts, the problem is physically ill-posed and hence results in numerically unstable computations. Suitable physiological constraints applied with appropriate weighting can recover useful inverse solutions. However, it is often difficult to determine the best possible constraints and their optimal weighting. We have recently begun to use multielectrode catheters as a means of mapping epicardial signals in animal models. To accommodate the sparse sampling of this venous catheter based approach, we have applied statistical signal processing methods to estimate complete epicardial maps of activation time and epicardial potentials. Such measurements--and the estimated maps from them--also have the potential to provide high quality constraints for electrocardiographic inverse problems because they provide direct--albeit sparse--access to the desired solution. In this presentation we describe several approaches we have applied to extract useful constraints from sparsely sampled epicardial signals as well as a training set of epicardial maps, and use them to improve the quality of computed inverse solutions. Results suggest that combining various information sources provides valuable constraint information. Such a multimodal approach to cardiac mapping is clinically and technically viable and offers a possible means to overcome a major remaining limitation of inverse electrocardiography.

Animals↗

PubMedDate not supplied

Spatial methods of epicardial activation time determination in normal hearts.

The purpose of this study was to demonstrate errors in activation time maps created using the time derivative method on fractionated unipolar electrograms, to characterize the epicardial distribution of those fractionated electrograms, and to investigate spatial methods of activation time determination. Electrograms (EGs) were recorded using uniform grids of electrodes (1 or 2 mm spacing) on the epicardial surface of six normal canine hearts. Activation times were estimated using the time of the minimum time derivative, maximum spatial gradient, and zero Laplacian and compared with the time of arrival of the activation wave front as assessed from a time series of potential maps as the standard. When comparing activation times from the time derivative for the case of epicardial pacing, spatial gradient and Laplacian methods with the standard for EGs without fractionation, correlations were high (R2 = 0.98, 0.98, 0.97, respectively). Similar comparisons using results from only fractionated EGs (R2 = 0.85,0.97,0.95) showed a lower correlation between times from the time derivative method and the standard. The results suggest an advantage of spatial methods over the time derivative method only for the case of epicardial pacing where large numbers of fractionated electrograms are found.

Action Potentials↗