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Advances in technology make the application of sophisticated approaches to assessing electrical condition of the heart practical. Estimates of cardiac electrical features inferred from body-surface electrocardiographic (ECG) maps are now routinely found in a clinical setting, but errors in those inverse solutions are especially sensitive to the accuracy of heart model geometry and placement within the torso. The use of a template heart model allows for accurate generation of individualized heart models and also permits effective comparison of inferred electrical features among multiple subjects. A collection of features mapped onto a common template forms a textbook of anatomically specific ECG variability. Our template warping process to individualize heart models based on a template heart uses ultrasonic images of the heart from a conventional, phased-array system. We chose ultrasound because it is nonionizing, less expensive, and more convenient than MR or CT imaging. To find the orientation and position in the torso model of each image, we calibrated the ultrasound probe by imaging a custom phantom consisting of multiple N-fiducials and computing a transformation between ultrasound coordinates and measurements of the torso surface. The template heart was warped using a mapping of corresponding landmarks identified on both the template and the ultrasonic images. Accuracy of the method is limited by patient movement, tracking error, and image analysis. We tested our approach on one normal control and one obese diabetic patient using the mixed-boundary-value inverse method and compared results from both on the template heart. We believe that our novel textbook approach using anatomically specific heart and torso models will facilitate the identification of electrophysiological biomarkers of cardiac dysfunction. Because the necessary data can be acquired and analyzed within about 30 min, this framework has the potential for becoming a routine clinical procedure.

The electrocardiographic (ECG) inverse problem offers more sophisticated approaches for assessing the electrical condition of the heart than the standard 12-lead ECG. Because of the ill-posted nature of the ECG inverse problem, regularization must be used to obtain clinically meaningful estimates of heart-surface potentials. We compared optimal inverse solutions using L1 regularization with those found using zero-order Tikhonov (ZOT) regularization in two heart-torso models with known heart-surface potentials, but different spatial detail. In the model based on the Visible Human Male with an epicardial-shell heart-model, there was no difference in relative error (RE) and correlation coefficient (CC) during activation and recovery of the ventricles (QT interval) between L1 and ZOT inferred heart-surface potentials. In the heart-torso model based on the ECGSIM package with a ventricular heart model there were significant differences in RE and CC throughout the QT interval. At the peak of the R wave, RE was reduced from 0.841 (ZOT) to 0.358 (L1). L1 regularization reduced RE compared to ZOT except during the downstroke of the R wave. These results suggest that improvement with L1 regularization is dependent on heart model complexity, not its sampling density. [ABSTRACT FROM AUTHOR]

Both diabetes and obesity cause cardiac dysfunction. To separate consequences of geometric changes due to obesity from electrophysiological ones, we investigated how changes in cardiac and torso geometry affected body-surface ECGs. For this study, we modified the realistic heart and torso models of the simulation package ECGSIM. ECGs were calculated from action potentials on the heart surface using our bidomain forward-problem solution. These ECGs were studied using spectral- and principal-component analyses and isopotential and energy maps. We found relative errors over the body-surface during the QT interval of 12%, 14%, and 68% for hypertrophy of the heart, extension of the abdomen, and heart displacement with obesity, respectively. The major change to the standard 12-lead set also occurred with heart displacement. The mean relative error over the QT interval in the precordial leads was 78% with heart displacement. These results demonstrate the limitations of using standard lead sets to characterize electrocardiographic changes in obese subjects and point to the need for more inclusive measures, such as body-surface mapping and inverse electrocardiography, to describe electrical remodeling in the presence of habitus changes due to obesity [ABSTRACT FROM AUTHOR]

Direct inference of heart-surface potentials from body-surface potentials has been the goal of most recent work on electrocardiographic inverse solutions. We developed and tested indirect methods for inferring heart-surface potentials based on estimation of regularized multipole sources. Regularization was done using Tikhonov, constrained-least-squares, and multipole-truncation techniques. These multipole-equivalent methods (MEMs) were compared to the conventional mixed boundary-value method (BVM) in a realistic torso model with up to 20% noise added to body-surface potentials and ±1 cm error in heart position and size. Optimal regularization was used for all inverse solutions. The relative error of inferred heart-surface potentials of the MEM was significantly less (p < 0.05) than that of the BVM using zeroth-order Tikhonov regularization in 10 of the 12 cases tested. These improvements occurred with a fourth-degree (24 coefficients) or smaller multipole moment. From these multipole coefficients, heart-surface potentials can be found at an unlimited number of heart-surface locations. Our indirect methods for estimating heart-surface potentials based on multipole inference appear to offer significant improvement over the conventional direct approach. [ABSTRACT FROM AUTHOR]