Measured Body Surface Potentials Research Articles

Utility of noninvasive electrocardiographic imaging in the localization of nonpulmonary vein triggers of atrial fibrillation determined by pacing common trigger sites.

Identifying the origin of nonpulmonary vein atrial fibrillation (AF) triggers (NPVTs) after pulmonary vein isolation (PVI) can be challenging. We aimed to determine if noninvasive electrocardiographic imaging (ECGi) could localize pacing from common NPVT sites. ECGi combines measured body surface potentials with heart-torso geometry acquired from computed tomography (CT) to generate an activation map. In 12 patients with AF undergoing first time ablation, the ECGi vest was fitted for preprocedural CT scan and worn during the procedure. After PVI, we performed steady-state pacing from 15 typical anatomic NPVT sites at a cycle length of 700-800 ms. We co-registered the invasive anatomic map with the CT-based ECGi epicardial activation map to compare ECGi predicted to true pacing origin. In the study cohort (67% male, 58% persistent AF, and 67% with left atrial dilation), 148 (82%) pacing sites had both capture and adequate anatomy acquired from the three-dimensional mapping system to co-register with ECGi activation map. Median distance between true pacing sites and point of earliest epicardial activation derived from the ECGi maps for all sites was 17 mm (interquartile range, 10-22 mm). Assuming paced sites treated as regions with a radius of 2.5 cm, the earliest activation site on ECGi map falls within the region with 94% accuracy. ECGi can approximate the origin of paced beats from common NPVT sites to within a median distance of 17 mm. A rapidly identified region may then be the focus of more detailed catheter-based mapping techniques to facilitate successful localization and ablation of NPVTs.

A Patchwork Method to Improve the Performance of Current Methods for Solving the Inverse Problem of Electrocardiography.

Noninvasive electrocardiographic imaging (ECGI) reconstructs cardiac electrical activity from body surface potential measurements. However, current methods have demonstrated inaccuracies in reconstructing sinus rhythm, and in particular breakthrough sites. This study aims to combine existing inverse algorithms, making the most of their advantages while minimizing their limitations. The "patchwork method" (PM) combines two classical numerical methods for ECGI: the method of fundamental solutions (MFS) and the finite-element method (FEM). We assume that the method with the smallest residual in the predicted torso potentials, computed using the boundary element method (BEM), provides the most accurate solution. The PM selects for each heart node and time step the method whose estimated reconstruction error is smallest. The performance of the PM was evaluated using simulated ectopic and normal ventricular beats. Cardiac potentials and activation maps obtained with the PM (CC = 0.63 ± 0.01 and 0.61 ± 0.05 respectively) were more accurate than MFS (CC = 0.61 ± 0.01 and 0.48 ± 0.05 respectively), FEM (CC = 0.58 ± 0.01 and 0.51 ± 0.02 respectively) or BEM (CC = 0.57 ± 0.02 and 0.49 ± 0.02 respectively). The PM also located all epicardial breakthrough sites, whereas the traditional numerical methods usually missed one. Furthermore, the PM showed its robustness and stability in the presence of Gaussian noise added to the torso potentials. The PM overcomes some of the limitations of classical numerical methods, improving the accuracy of mapping important features of activation during sinus rhythm and paced beats. This novel method for optimizing ECGI solutions opens a new avenue for improving not only ECGI but also other inverse problems.

Abstract 14320: Regionalization of Non-Pulmonary Vein Triggers for Atrial Fibrillation Using Non-Invasive Electrocardiographic Imaging

Introduction Ablation of non-pulmonary vein triggers (NPVTs) after pulmonary vein isolation (PVI) reduces atrial fibrillation (AF) recurrence, but localizing NPVTs can be challenging. We evaluated the potential for noninvasive electrocardiographic imaging (ECGi) to regionalize NPVTs and expedite more focused catheter-based mapping. ECGi combines measured body surface potentials with heart-torso geometry acquired from computed tomography (CT), to generate an activation map from a single recorded beat. Methods In 12 patients with AF undergoing first time AF ablation, the ECGi vest was fitted for preprocedural CT scan and then worn during procedure. Following completion of PVI, we paced from 15 atrial sites where NPVTs commonly arise (Figure 1), and evaluated the epicardial activation map generated by ECGi during steady state pacing at cycle length of 700-800 ms allowing an unobscured P wave. Merging of invasive anatomic map with CT-based ECGi map allowed comparison of ECGi activation map with true pacing location (Figure 2). Results From 180 pacing sites in our patient cohort (67% male, 58% persistent AF, 67% left atrial dilation), median distance between earliest activation site on ECGi map and pacing location was 16 mm (interquartile range, 11-22 mm), with 95% of sites within 30 mm (Figure 2). Septal pacing sites were measured to the epicardial aspect of the interatrial septum. Conclusions ECGi is able to consistently approximate the origin of paced beats from common NPVT sites and suggests the potential for rapid regionalization of NPVTs. The identified region can then be the focus of more detailed catheter-based mapping techniques to facilitate successful ablation of NPVTs.

Reconstruction of cardiac position using body surface potentials

Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced.

Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods.

One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.

Electrocardiographic Imaging: A Comparison of Iterative Solvers.

Cardiac disease is a leading cause of morbidity and mortality in developed countries. Currently, non-invasive techniques that can identify patients at risk and provide accurate diagnosis and ablation guidance therapy are under development. One of these is electrocardiographic imaging (ECGI). In ECGI, the first step is to formulate a forward problem that relates the unknown potential sources on the cardiac surface to the measured body surface potentials. Then, the unknown potential sources on the cardiac surface are reconstructed through the solution of an inverse problem. Unfortunately, ECGI still lacks accuracy due to the underlying inverse problem being ill-posed, and this consequently imposes limitations on the understanding and treatment of many cardiac diseases. Therefore, it is necessary to improve the solution of the inverse problem. In this work, we transfer and adapt four inverse problem methods to the ECGI setting: algebraic reconstruction technique (ART), random ART, ART Split Bregman (ART-SB) and range restricted generalized minimal residual (RRGMRES) method. We test all these methods with data from the Experimental Data and Geometric Analysis Repository (EDGAR) and compare their solution with the recorded epicardial potentials provided by EDGAR and a generalized minimal residual (GMRES) iterative method computed solution. Activation maps are also computed and compared. The results show that ART achieved the most stable solutions and, for some datasets, returned the best reconstruction. Differences between the solutions derived from ART and random ART are almost negligible, and the accuracy of their solutions is followed by RRGMRES, ART-SB and finally the GMRES (which returned the worst reconstructions). The RRGMRES method provided the best reconstruction for some datasets but appeared to be less stable than ART when comparing different datasets. In conclusion, we show that the proposed methods (ART, random ART, and RRGMRES) improve the GMRES solution, which has been suggested as inverse problem solution for ECGI.

Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform

Myocardial ischemia is one of the most common cardiovascular pathologies and can indicate many severe and life threatening diseases. Despite these risks, current electrocardiographic detection techniques for ischemia are mediocre at best, with reported sensitivity and specificity ranging from 50%–70% and 70%–90%, respectively. Objective: To improve this performance, we set out to develop an experimental preparation to induce, detect, and analyze bioelectric sources of myocardial ischemia and determine how these sources reflect changes in body-surface potential measurements. Approach: We designed the experimental preparation with three important characteristics: (1) enable comprehensive and simultaneous high-resolution electrical recordings within the myocardial wall, on the heart surface, and on the torso surface; (2) develop techniques to visualize these recorded electrical signals in time and space; and (3) accurately and controllably simulate ischemic stress within the heart by modulating the supply of blood, the demand for perfusion, or a combination of both. Main results: To achieve these goals we designed comprehensive system that includes (1) custom electrode arrays (2) signal acquisition and multiplexing units, (3) a surgical technique to place electrical recording and myocardial ischemic control equipment, and (4) an image based modeling pipeline to acquire, process, and visualize the results. With this setup, we are uniquely able to capture simultaneously and continuously the electrical signatures of acute myocardial ischemia within the heart, on the heart surface, and on the body surface. Significance: This novel experimental preparation enables investigation of the complex and dynamic nature of acute myocardial ischemia that should lead to new, clinically translatable results.

Effects of Interpolation on the Inverse Problem of Electrocardiography.

Electrocardiographic Imaging (ECGI) aims to reconstruct electrograms from the body surface potential measurements. Bad leads are usually excluded from the inverse problem solution. Alternatively, interpolation can be applied. This study explores how sensitive ECGI is to different bad-lead configurations and interpolation methods. Experimental data from a Langendorff-perfused pig heart suspended in a human-shaped torso-tank was used. Epicardial electrograms were acquired during 30 s (31 beats) of RV pacing using a 108-electrode array, simultaneously with torso potentials from 128 electrodes embedded in the tank surface. Six different bad lead cases were designed based on clinical experience. Inverse problem was solved by applying Tikhonov regularization i) using the complete data, ii) bad-leads-removed data, and iii) interpolated data, with 5 different methods. Our results showed that ECGI accuracy of an interpolation method highly depends on the location of the bad leads. If they are in the high-potential-gradient regions of the torso, a highly accurate interpolation method is needed to achieve an ECGI accuracy close to using complete data. If the BSP reconstruction of the interpolation method is poor in these regions, the reconstructed electrograms also have lower accuracy, suggesting that bad leads should be removed instead of interpolated. The inverse-forward method was found to be the best among all interpolation methods applied in this study in terms of both missing BSP lead reconstruction and ECGI accuracy, even for the bad leads located over the chest.

ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging

In electrocardiographic imaging (ECGI), one solves the inverse problem of electrocardiography (ECG) to reconstruct equivalent cardiac sources based on the body surface potential measurements and a mathematical model of the torso. Due to attenuation and spatial smoothing within the torso, this inverse problem is ill-posed. Among many regularization approaches used in the ECG literature to overcome this ill-posedness, statistical techniques have received great attention because of their flexibility to represent the data, and ability to provide performance evaluation tools for quantification of uncertainties and errors in the model. However, despite their potential to accurately reconstruct the equivalent cardiac sources, one major challenge in these methods is how to best utilize the prior information available in terms of training data. In this paper, we address the question of how to define the prior probability distributions (pdf) of the sources and the error terms so that we can obtain more accurate and robust inverse solutions. We employ two methods, maximum likelihood (ML) and maximum a posteriori (MAP), for estimating the model parameters such as the prior pdfs, error pdfs, and the state-transition matrix, based on the same training data. These model parameters are then used for the state-space representation and estimation of the epicardial potentials, which constitute the equivalent cardiac sources in this study. The performances of ML- and MAP-based model parameter estimation methods are evaluated qualitatively and quantitatively at various noise levels and geometric disturbances using two different simulated datasets. Bayesian MAP estimation, which is also a well-known statistical inversion technique, and Tikhonov regularization, which can be formulated as a special and simplified version of Bayesian MAP estimation, have been included here for comparison with the Kalman filtering method. Our results show that the state-space approach outperforms Bayesian MAP estimation in all cases; ML yields accurate results when the test and training beats come from the same physiological model, but MAP is superior to ML, especially if the test and training beats are from different physiological models. Graphical Abstract ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.

Optical Imaging of Ventricular Action Potentials in a Torso Tank: A New Platform for Non-Invasive Electrocardiographic Imaging Validation.

Background: Non-invasive electrocardiographic imaging (ECGI) is a promising tool to provide high-resolution panoramic imaging of cardiac electrical activity noninvasively from body surface potential measurements. Current experimental methods for ECGI validation are limited to comparison with unipolar electrograms and the relatively low spatial resolution of cardiac mapping arrays. We aim to develop a novel experimental set up combining a human shaped torso tank with high-resolution optical mapping allowing the validation of ECGI reconstructions.Methods: Langendorff-perfused pig hearts (n = 3) were suspended in a human torso-shaped tank, with the left anterior descending artery (LAD) cannulated on a separate perfusion. Electrical signals were recorded from an 108-electrode epicardial sock and 128 electrodes embedded in the tank surface. Simultaneously, optical mapping of the heart was performed through the anterior surface of the tank. Recordings were made in sinus rhythm and ventricular pacing (n = 55), with activation and repolarization heterogeneities induced by perfusion of hot and cold solutions as well as Sotalol through the LAD. Fluoroscopy provided 3D cardiac and electrode geometries in the tank that were transformed to the 2D optical mapping window using an optimization algorithm. Epicardial unipolar electrograms were reconstructed from torso potentials using ECGI and validated using optical activation and repolarization maps.Results: The transformation and alignment of the 3D geometries onto the 2D optical mapping window was good with an average correlation of 0.87 ± 0.10 and error of 7.7 ± 3.1 ms with activation derived from the sock. The difference in repolarization times were more substantial (error = 17.4 ± 3.7 ms) although the sock and optical repolarization patterns themselves were very similar (correlation = 0.83 ± 0.13). Validation of ECGI reconstructions revealed ECGI accurately captures the pattern of activation (correlation = 0.79 ± 0.11) and identified regions of late and/or early repolarization during different perfusions through LAD. ECGI also correctly demonstrated gradients in both activation and repolarization, although in some cases these were under or over-estimated or shifted slightly in space.Conclusion: A novel experimental setup has been developed, combining a human-shaped torso tank with optical mapping, which can be effectively used in the validation of ECGI techniques; including the reconstruction of activation and repolarization patterns and gradients.

Lanczos bidiagonalization-based inverse solution methods applied to electrical imaging of the heart by using reduced lead-sets: A simulation study

In inverse problem of electrocardiography (ECG), electrical activity of the heart is estimated from body surface potential measurements. This electrical activity provides useful information about t...

Forward Problem of Electrocardiography

The relationship between epicardial and body surface potentials defines the forward problem of electrocardiography. A robust formulation of the forward problem is instrumental to solving the inverse problem, in which epicardial potentials are computed from known body surface potentials. Here, the accuracy of different forward models has been evaluated experimentally. Body surface and epicardial potentials were recorded simultaneously in anesthetized closed-chest pigs (n=5) during sinus rhythm, and epicardial and endocardial ventricular pacing (65 records in total). Body surface potentials were simulated from epicardial recordings using experiment-specific volume conductor models constructed from magnetic resonance imaging. Results for homogeneous (isotropic electric properties) and inhomogeneous (incorporating lungs, anisotropic skeletal muscle, and subcutaneous fat) forward models were compared with measured body surface potentials. Correlation coefficients were 0.85±0.08 across all animals and activation sequences with no significant difference between homogeneous and inhomogeneous solutions (P=0.85). Despite this, there was considerable variance between simulated and measured body surface potential distributions. Differences between the body surface potential extrema predicted with homogeneous forward models were 55% to 78% greater than observed (P<0.05) and attenuation of potentials adjacent to extrema were 10% to 171% greater (P<0.03). The length and orientation of the vector between potential extrema were also significantly different. Inclusion of inhomogeneous electric properties in the forward model reduced, but did not eliminate these differences. These results demonstrate that homogeneous volume conductor models introduce substantial spatial inaccuracies in forward problem solutions. This probably affects the precision of inverse reconstructions of cardiac potentials, in which this assumption is made.

Using Transmural Regularization and Dynamic Modeling for Noninvasive Cardiac Potential Imaging of Endocardial Pacing With Imprecise Thoracic Geometry

Cardiac electrical imaging from body surface potential measurements is increasingly being seen as a technology with the potential for use in the clinic, for example for pre-procedure planning or during-treatment guidance for ventricular arrhythmia ablation procedures. However several important impediments to widespread adoption of this technology remain to be effectively overcome. Here we address two of these impediments: the difficulty of reconstructing electric potentials on the inner (endocardial) as well as outer (epicardial) surfaces of the ventricles, and the need for full anatomical imaging of the subject's thorax to build an accurate subject-specific geometry. We introduce two new features in our reconstruction algorithm: a nonlinear low-order dynamic parameterization derived from the measured body surface signals, and a technique to jointly regularize both surfaces. With these methodological innovations in combination, it is possible to reconstruct endocardial activation from clinically acquired measurements with an imprecise thorax geometry. In particular we test the method using body surface potentials acquired from three subjects during clinical procedures where the subjects' hearts were paced on their endocardia using a catheter device. Our geometric models were constructed using a set of CT scans limited in axial extent to the immediate region near the heart. The catheter system provides a reference location to which we compare our results. We compare our estimates of pacing site localization, in terms of both accuracy and stability, to those reported in a recent clinical publication , where a full set of CT scans were available and only epicardial potentials were reconstructed.

Noninvasive Mapping of Transmural Potentials During Activation in Swine Hearts From Body Surface Electrocardiograms

The three-dimensional cardiac electrical imaging (3DCEI) technique was previously developed to estimate the initiation site(s) of cardiac activation and activation sequence from the noninvasively measured body surface potential maps (BSPMs). The aim of this study was to develop and evaluate the capability of 3DCEI in mapping the transmural distribution of extracellular potentials and localizing initiation sites of ventricular activation in an in vivo animal model. A control swine model (n = 10) was employed in this study. The heart-torso volume conductor model and the excitable heart model were constructed based on each animal's preoperative MR images and a priori known physiological knowledge. Body surface potential mapping and intracavitary noncontact mapping (NCM) were simultaneously conducted during acute ventricular pacing. The 3DCEI analysis was then applied on the recorded BSPMs. The estimated initiation sites were compared to the precise pacing sites; as a subset of the mapped transmural potentials by 3DCEI, the electrograms on the left ventricular endocardium were compared to the corresponding output of the NCM system. Over the 16 LV and 48 RV pacing studies, the averaged localization error was 6.1±2.3 mm, and the averaged correlation coefficient between the estimated endocardial electrograms by 3DCEI and from the NCM system was 0.62±0.09. The results demonstrate that the 3DCEI approach can well localize the sites of initiation of ectopic beats and can obtain physiologically reasonable transmural potentials in an in vivo setting during focal ectopic beats. This study suggests the feasibility of tomographic mapping of 3D ventricular electrograms from the body surface recordings.

On epicardial potential reconstruction using regularization schemes with the L1-norm data term

The electrocardiographic (ECG) inverse problem is ill-posed and usually solved by regularization schemes. These regularization methods, such as the Tikhonov method, are often based on the L2-norm data and constraint terms. However, L2-norm-based methods inherently provide smoothed inverse solutions that are sensitive to measurement errors, and also lack the capability of localizing and distinguishing multiple proximal cardiac electrical sources. This paper presents alternative regularization schemes employing the L1-norm data term for the reconstruction of epicardial potentials (EPs) from measured body surface potentials (BSPs). During numerical implementation, the iteratively reweighted norm algorithm was applied to solve the L1-norm-related schemes, and measurement noises were considered in the BSP data. The proposed L1-norm data term-based regularization schemes (with L1 and L2 penalty terms of the normal derivative constraint (labelled as L1TV and L1L2)) were compared with the L2-norm data terms (Tikhonov with zero-order and normal derivative constraints, labelled as ZOT and FOT, and the total variation method labelled as L2TV). The studies demonstrated that, with averaged measurement noise, the inverse solutions provided by the L1L2 and FOT algorithms have less relative error values. However, when larger noise occurred in some electrodes (for example, signal lost during measurement), the L1TV and L1L2 methods can obtain more accurate EPs in a robust manner. Therefore the L1-norm data term-based solutions are generally less perturbed by measurement noises, suggesting that the new regularization scheme is promising for providing practical ECG inverse solutions.

The localization of focal heart activity via body surface potential measurements: tests in a heterogeneous torso phantom

The non-invasive localization of focal heart activity via body surface potential measurements (BSPM) could greatly benefit the understanding and treatment of arrhythmic heart diseases. However, the in vivo validation of source localization algorithms is rather difficult with currently available measurement techniques. In this study, we used a physical torso phantom composed of different conductive compartments and seven dipoles, which were placed in the anatomical position of the human heart in order to assess the performance of the Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) algorithm. Electric potentials were measured on the torso surface for single dipoles with and without further uncorrelated or correlated dipole activity. The localization error averaged 11 ± 5 mm over 22 dipoles, which shows the ability of RAP-MUSIC to distinguish an uncorrelated dipole from surrounding sources activity. For the first time, real computational modelling errors could be included within the validation procedure due to the physically modelled heterogeneities. In conclusion, the introduced heterogeneous torso phantom can be used to validate state-of-the-art algorithms under nearly realistic measurement conditions.

The transfer matrix for epicardial potential in a piece-wise homogeneous thorax model: the boundary element formulation

In epicardial potential imaging, the epicardial potential is reconstructed computationally from the measured body surface potential. The transfer function that relates the heart and body surface potentials is commonly constructed with some point-collocation-weighted boundary element technique, assuming an electrically homogeneous volume conductor. This assumption causes modeling errors. In this study, the system of surface integral equations that describes the relationship between the heart and body surface potentials is thoroughly derived in a piece-wise homogeneous volume conductor. The equations are discretized with the method of weighted residuals, enabling the use of Galerkin weighting in the numerical solution of the equations. The construction of the transfer matrix is described in detail for constant and linear collocation and Galerkin methods, and the resulting forward transfer matrices are validated via simple numerical simulations. The linear Galerkin method is found to generate the smallest errors. The presented method increases the accuracy of the forward-computed body surface potential and thus prepares the way for more accurate inverse reconstructions of epicardial potential.

Novel catheter guidance technique to the site of origin of the arrhythmia

The purpose of this study was to evaluate the accuracy of our catheter guidance system using Single Equivalent Moving Dipole (SEMD) method to guide the tip of the cardiac ablation catheter to the site of origin of the arrhythmia. 3 different patterns of 8 bipolar electrodes were sutured on the right ventricular myocardium of 3 swine through a standard thoracotomy. Electrode cables passed subcutaneously to a ventral interscapular position were exteriorized, fixed by stay sutures, and the chest closed in layers. The animals' heart rate, pulse oximetry, femoral artery blood pressure, and body temperature were continuously monitored. As the epicardial electrodes connected to a stimulator delivered pacing spikes simulating focal ventricular tachycardia at a rate of 120 bpm, the BioSemi ActiveTwo system at a frequency of 1Khz with a symmetrically patterned array of 64 electrodes placed on the chest measured body surface potentials. A paralytic drug was used to reduce error in cardiac mapping by minimizing muscular activity and allowing for full respirator control of breathing. Both simulation and real catheter's movements were well detected (R=0.95) and the final localization error of a simulation catheter to a pre‐defined target was less than 1 cm in imaginary space, which is equal to 0.4 cm in real space. These data support the validity of SEMD method for localizing the origin of arrhythmia in swine model.

Noninvasive three-dimensional electrocardiographic imaging of ventricular activation sequence

Imaging the myocardial activation sequence is critical for improved diagnosis and treatment of life-threatening cardiac arrhythmias. It is desirable to reveal the underlying cardiac electrical activity throughout the three-dimensional (3-D) myocardium (rather than just the endocardial or epicardial surface) from noninvasive body surface potential measurements. A new 3-D electrocardiographic imaging technique (3-DEIT) based on the boundary element method (BEM) and multiobjective nonlinear optimization has been applied to reconstruct the cardiac activation sequences from body surface potential maps. Ultrafast computerized tomography scanning was performed for subsequent construction of the torso and heart models. Experimental studies were then conducted, during left and right ventricular pacing, in which noninvasive assessment of ventricular activation sequence by means of 3-DEIT was performed simultaneously with 3-D intracardiac mapping (up to 200 intramural sites) using specially designed plunge-needle electrodes in closed-chest rabbits. Estimated activation sequences from 3-DEIT were in good agreement with those constructed from simultaneously recorded intracardiac electrograms in the same animals. Averaged over 100 paced beats (from a total of 10 pacing sites), total activation times were comparable (53.3 +/- 8.1 vs. 49.8 +/- 5.2 ms), the localization error of site of initiation of activation was 5.73 +/- 1.77 mm, and the relative error between the estimated and measured activation sequences was 0.32 +/- 0.06. The present experimental results demonstrate that the 3-D paced ventricular activation sequence can be reconstructed by using noninvasive multisite body surface electrocardiographic measurements and imaging of heart-torso geometry. This new 3-D electrocardiographic imaging modality has the potential to guide catheter-based ablative interventions for the treatment of life-threatening cardiac arrhythmias.

Noninvasive three-dimensional activation time imaging of ventricular excitation by means of a heart-excitation model

We propose a new method for imaging activation time within three-dimensional (3D) myocardium by means of a heart-excitation model. The activation time is estimated from body surface electrocardiograms by minimizing multiple objective functions of the measured body surface potential maps (BSPMs) and the heart-model-generated BSPMs. Computer simulation studies have been conducted to evaluate the proposed 3D myocardial activation time imaging approach. Single-site pacing at 24 sites throughout the ventricles, as well as dual-site pacing at 12 pairs of sites in the vicinity of atrio-ventricular ring, was performed. The present simulation results show that the average correlation coefficient (CC) and relative error (RE) for single-site pacing were 0.9992 ± 0.0008/0.9989 ± 0.0008 and 0.05 ± 0.02/0.07 ± 0.03, respectively, when 5 µV/10 µV Gaussian white noise (GWN) was added to the body surface potentials. The average CC and RE for dual-site pacing were 0.9975 ± 0.0037 and 0.08 ± 0.04, respectively, when 10 µV GWN was added to the body surface potentials. The present simulation results suggest the feasibility of noninvasive estimation of activation time throughout the ventricles from body surface potential measurement, and suggest that the proposed method may become an important alternative in imaging cardiac electrical activity noninvasively.