Body Surface Potential Research Articles

Prognostic value of synthetized right-side precordial leads in arrhythmogenic right ventricular cardiomyopathy: data from nordic, dutch, and canadian ARVC registers

Abstract Background T-wave inversion (TWI) in standard ECG leads is a proven risk marker for life-threatening ventricular arrhythmias (VA) in arrhythmogenic right ventricular cardiomyopathy (ARVC). Right-sided precordial leads (RSPL), which may be more sensitive for detection of the arrhythmogenic substrate, are rarely recorded and understudied in this context. Computerized ECG signal-processing allows synthesis of right-sided precordial ECG leads derived from standard 12-lead ECG. Objective We aimed to assess the value of TWI in RSPL, synthetized from the standard 12-lead ECG, for prediction of VA risk in ARVC patients with subtle ECG phenotypes. Methods Patients with definite ARVC by TFC2010 and no history of sustained VA prior to diagnosis from the Nordic, Canadian and Dutch ARVC registries were included. Patients with major repolarization criterion or complete right bundle branch block (RBBB) were excluded. RSPL (V3R-V6R) were derived from digitally recorded eight linearly independent leads (I, II and precordial V1 to V6) of the standard ECG using a linear ECG transformation matrix. The matrix was previously developed based on a library of body surface potential maps from a mixed group of healthy subjects and patients with cardiac diseases using multivariate linear regression and QRS-T data obtained from averaged beats of the right-sided-precordial leads and the basis leads. TWI exceeding 0.1 mV in any of the synthetized leads was tested for its ability to predict sustained VA, defined as either sustained ventricular tachycardia, appropriate ICD shock, aborted cardiac arrest, or sudden cardiac death. In a similar manner, the value of TWI in any of the inferior leads III, aVF or II was assessed. Hazard ratio was calculated using Cox regression analysis adjusted for age, sex, and proband status. Results In total, 144 patients without prior VA history were identified. After exclusion of patients with major repolarization criterion (n=37) and RBBB (n=11), 96 patients comprised the study group (mean age 40±16, 42% female, 41% probands, 48% ICD carriers). Fifty-nine patients (62%) had TWI in one or more synthetized RSPL and 33 patients (34%) had TWI in any of the inferior leads. During 10 years of follow-up, 11 patients (12%) developed VA. Kaplan-Meier curve analysis showed significant association between the presence of TWI in at least one of the RSPL and the risk of VA, which remained an independent predictor in the adjusted Cox regression analysis (HR 10.6, 95%CI 1.3-84.4, p=0.026). TWI in any of the inferior leads was not associated with the risk of VA in this cohort (HR 1.2 95%CI 0.26-5.42). Conclusions Synthetized right-sided precordial leads appear to contain information potentially useful for refining risk stratification of primary prevention ARVC patients with a subtle ECG phenotype, in whom traditional repolarization markers, such as major repolarization criterion or TWI in inferior leads, are not informative.

Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging.

State-space modeling (SSM) provides a general framework for many image reconstruction tasks. Error in a priori physiological knowledge of the imaging physics, can bring incorrectness to solutions. Modern deep-learning approaches show great promise but lack interpretability and rely on large amounts of labeled data. In this paper, we present a novel hybrid SSM framework for electrocardiographic imaging (ECGI) to leverage the advantage of state-space formulations in data-driven learning. We first leverage the physics-based forward operator to supervise the learning. We then introduce neural modeling of the transition function and the associated Bayesian filtering strategy. We applied the hybrid SSM framework to reconstruct electrical activity on the heart surface from body-surface potentials. In unsupervised settings of both in-silico and in-vivo data without cardiac electrical activity as the ground truth to supervise the learning, we demonstrated improved ECGI performances of the hybrid SSM framework trained from a small number of ECG observations in comparison to the fixed SSM. We further demonstrated that, when in-silico simulation data becomes available, mixed supervised and unsupervised training of the hybrid SSM achieved a further 40.6% and 45.6% improvements, respectively, in comparison to traditional ECGI baselines and supervised data-driven ECGI baselines for localizing the origin of ventricular activations in real data.

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.

3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles

The proliferation of medical wearables necessitates the development of novel electrodes for cutaneous electrophysiology. In this work, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is combined with a deep eutectic solvent (DES) and polyethylene glycol diacrylate (PEGDA) to develop printable and biocompatible electrodes for long-term cutaneous electrophysiology recordings. The impact of printing parameters on the conducting properties, morphological characteristics, mechanical stability and biocompatibility of the material were investigated. The optimised eutectogel formulations were fabricated in four different patterns —flat, pyramidal, striped and wavy— to explore the influence of electrode geometry on skin conformability and mechanical contact. These electrodes were employed for impedance and forearm EMG measurements. Furthermore, arrays of twenty electrodes were embedded into a textile and used to generate body surface potential maps (BSPMs) of the forearm, where different finger movements were recorded and analysed. Finally, BSPMs for three different letters (B, I, O) in sign-language were recorded and used to train a logistic regressor classifier able to reliably identify each letter. This novel cutaneous electrode fabrication approach offers new opportunities for long-term electrophysiological recordings, online sign-language translation and brain-machine interfaces.

Non-invasive estimation of atrial fibrillation driver position using long-short term memory neural networks and body surface potentials

Background and ObjectiveAtrial Fibrillation (AF) is a supraventricular tachyarrhythmia that can lead to thromboembolism, hearlt failure, ischemic stroke, and a decreased quality of life. Characterizing the locations where the mechanisms of AF are initialized and maintained is key to accomplishing an effective ablation of the targets, hence restoring sinus rhythm. Many methods have been investigated to locate such targets in a non-invasive way, such as Electrocardiographic Imaging, which enables an on-invasive and panoramic characterization of cardiac electrical activity using recording Body Surface Potentials (BSP) and a torso model of the patient. Nonetheless, this technique entails some major issues stemming from solving the inverse problem, which is known to be severely ill-posed. In this context, many machine learning and deep learning approaches aim to tackle the characterization and classification of AF targets to improve AF diagnosis and treatment. MethodsIn this work, we propose a method to locate AF drivers as a supervised classification problem. We employed a hybrid form of the convolutional-recurrent network which enables feature extraction and sequential data modeling utilizing labeled realistic computerized AF models. Thus, we used 16 AF electrograms, 1 atrium, and 10 torso geometries to compute the forward problem. Previously, the AF models were labeled by assigning each sample of the signals a region from the atria from 0 (no driver) to 7, according to the spatial location of the AF driver. The resulting 160 BSP signals, which resemble a 64-lead vest recording, are preprocessed and then introduced into the network following a 4-fold cross-validation in batches of 50 samples. ResultsThe results show a mean accuracy of 74.75% among the 4 folds, with a better performance in detecting sinus rhythm, and drivers near the left superior pulmonary vein (R1), and right superior pulmonary vein (R3) whose mean sensitivity bounds around 84%-87%. Significantly good results are obtained in mean sensitivity (87%) and specificity (83%) in R1. ConclusionsGood results in R1 are highly convenient since AF drivers are commonly found in this area: the left atrial appendage, as suggested in some previous studies. These promising results indicate that using CNN-LSTM networks could lead to new strategies exploiting temporal correlations to address this challenge effectively.

Impact of Vernix Caseosa Distribution on Non-Invasive Fetal ECG Morphology: A Computational Study.

Accurate monitoring of fetal cardiac activity is paramount for the early detection of fetal pathologies during pregnancy. Non-invasive fetal ECG has shown promise, offering advantages over traditional fetal monitoring techniques such as cardiotocography. However, extracting fetal signals from maternal abdominal recordings poses challenges, particularly due to the presence of the vernix caseosa, a fatty layer surrounding the fetus. This study aims to investigate how vernix caseosa distribution influences ECG morphology in a novel computational framework. A multi-compartment volume conductor, integrating fetal and maternal hearts, fetal body, amniotic fluid, and vernix caseosa embedded in a maternal torso, is constructed. Vernix caseosa distribution is varied homogeneously and heterogeneously on the fetal body. Fetal cardiac activity is simulated using a pseudo-bidomain formulation. Resulting body surface potential and ECG is analysed in terms of RDM, lnMAG, QRS complex and T-wave morphology at six abdominal sensor placements. Results indicate vernix caseosa conductive properties and presence on the fetal head do not notably interfere with ECG readings, except in rare instances where the signal strength is extremely low. Signal strength is reduced more when covering back compared to front of the fetus. Nonetheless, both scenarios have a notable impact on ECG signal and T/QRS ratio, aligning with earlier findings suggesting caution in interpreting T/QRS ratio when vernix caseosa is present. The presence of vernix caseosa warrants careful consideration regarding ECG and especially T/QRS ratio interpretation. The study contributes to advancing the understanding of non-invasive fetal ECG.

SIMULATION OF PREMATURE VENTRICULAR CONTRACTIONS IN PATIENT SPECIFIC BIDOMAIN VENTRICULAR MODEL

The goal of the study was to simulate electrical activation of the heart ventricles and corresponding body surface potentials (BSPs) during premature ventricular contractions (PVC) using the patient specific realistic homogeneous model of cardiac ventricles and the torso. Real position of the initial ectopic activation during PVC was determined by intracardial measurement in the upper part of the right ventricle near the His bundle and confirmed by successful catheter ablation of the PVC origin. Simulated electrical activation in the ventricular model was started at the position of the initial ectopic activation as well as at several other sites at various distances from this position. The propagation of electrical activation in the ventricular model was modeled using bidomain reaction-diffusion (RD) equations with the ionic transmembrane current density defined by the modified FitzHugh-Nagumo (FHN) equations. The torso was modeled as a homogeneous passive volume conductor. The RD equations were numerically solved in the Comsol Multiphysics environment. Simulated ECG signals and BSPs were compared with those measured during PVC in a real patient. The polarity and shape of simulated and measured ECG leads as well as the BSP distribution during the PVC were in best agreement when the stimulated region was less than 10 mm from the position of the initial ectopic activation.

Atrial Periodic Source Spectrum from Preoperative Body Surface Potentials Predicts Long-term Recurrence of Atrial Fibrillation.

About half of patients experience recurrence of atrial fibrillation (AF) within three to five years after a single catheter ablation procedure. The suboptimality of the long-term outcomes likely results from the inter-patient variability of AF mechanisms, which can be remedied by improved patient screening. We aim to improve the interpretation of body surface potentials (BSPs), such as 12-lead electrocardiograms and 252-lead BSP maps, to aid preoperative patient screening. We developed the Atrial Periodic Source Spectrum (APSS), a novel patient-specific representation based on atrial periodic content, computed on the f-wave segments of patient BSPs, using a second-order blind source separation and a Gaussian Process for regression. With follow-up data, Cox's proportional hazard model was used to select the most relevant feature from preoperative APSSs responsible for AF recurrence. Over 138 persistent AF patients, the presence of highly periodic content with cycle lengths between 220-230 ms or 350-400 ms indicates higher risks of 4-year post-ablation AF recurrence (log-rank test, p-value ). Preoperative BSPs demonstrate effective prediction in the long-term outcomes, highlighting their potential for patient screening in AF ablation therapy.

Localization of the ventricular pacing site from BSPM and standard 12-lead ECG: a comparison study

Inverse ECG imaging methods typically require 32–250 leads to create body surface potential maps (BSPM), limiting their routine clinical use. This study evaluated the accuracy of PaceView inverse ECG method to localize the left or right ventricular (LV and RV, respectively) pacing leads using either a 99-lead BSPM or the 12-lead ECG. A 99-lead BSPM was recorded in patients with cardiac resynchronization therapy (CRT) during sinus rhythm and sequential LV/RV pacing. The non-contrast CT was performed to localize precisely both ECG electrodes and CRT leads. From a BSPM, nine signals were selected to obtain the 12-lead ECG. Both BSPM and 12-lead ECG were used to localize the RV and LV lead, and the localization error was calculated. Consecutive patients with dilated cardiomyopathy, previously implanted with a CRT device, were enrolled (n = 19). The localization error for the RV/LV lead was 9.0 [IQR 4.8–13.6] / 7.7 [IQR 0.0–10.3] mm using the 12-lead ECG and 9.1 [IQR 5.4–15.7] / 9.8 [IQR 8.6–13.1] mm for the BSPM. Thus, the noninvasive lead localization using the 12-lead ECG was accurate enough and comparable to 99-lead BSPM, potentially increasing the capability of 12-lead ECG for the optimization of the LV/RV pacing sites during CRT implant or for the most favorable programming.

High Density Body Surface Potential Mapping with Conducting Polymer-Eutectogel Electrode Arrays for ECG imaging.

Electrocardiography imaging (ECGi) is a non-invasive inverse reconstruction procedure which employs body surface potential maps (BSPM) obtained from surface electrode array measurements to improve the spatial resolution and interpretability of conventional electrocardiography (ECG) for the diagnosis of cardiac dysfunction. ECGi currently lacks precision, which has prevented its adoption in clinical setups. The introduction of high-density electrode arrays could increase ECGi reconstruction accuracy but is not attempted before due to manufacturing and processing limitations. Advances in multiple fields have now enabled the implementation of such arrays which poses questions on optimal array design parameters for ECGi. In this work, a novel conducting polymer electrode manufacturing process on flexible substrates is proposed to achieve high-density, mm-sized, conformable, long-term, and easily attachable electrode arrays for BSPM with parameters optimally selected for ECGi applications. Temporal, spectral, and correlation analysis are performed on a prototype array demonstrating the validity of the chosen parameters and the feasibility of high-density BSPM, paving the way for ECGi devices fit for clinical application.

Influence of the Tikhonov Regularization Parameter on the Accuracy of the Inverse Problem in Electrocardiography.

The electrocardiogram (ECG) is the standard method in clinical practice to non-invasively analyze the electrical activity of the heart, from electrodes placed on the body's surface. The ECG can provide a cardiologist with relevant information to assess the condition of the heart and the possible presence of cardiac pathology. Nonetheless, the global view of the heart's electrical activity given by the ECG cannot provide fully detailed and localized information about abnormal electrical propagation patterns and corresponding substrates on the surface of the heart. Electrocardiographic imaging, also known as the inverse problem in electrocardiography, tries to overcome these limitations by non-invasively reconstructing the heart surface potentials, starting from the corresponding body surface potentials, and the geometry of the torso and the heart. This problem is ill-posed, and regularization techniques are needed to achieve a stable and accurate solution. The standard approach is to use zero-order Tikhonov regularization and the L-curve approach to choose the optimal value for the regularization parameter. However, different methods have been proposed for computing the optimal value of the regularization parameter. Moreover, regardless of the estimation method used, this may still lead to over-regularization or under-regularization. In order to gain a better understanding of the effects of the choice of regularization parameter value, in this study, we first focused on the regularization parameter itself, and investigated its influence on the accuracy of the reconstruction of heart surface potentials, by assessing the reconstruction accuracy with high-precision simultaneous heart and torso recordings from four dogs. For this, we analyzed a sufficiently large range of parameter values. Secondly, we evaluated the performance of five different methods for the estimation of the regularization parameter, also in view of the results of the first analysis. Thirdly, we investigated the effect of using a fixed value of the regularization parameter across all reconstructed beats. Accuracy was measured in terms of the quality of reconstruction of the heart surface potentials and estimation of the activation and recovery times, when compared with ground truth recordings from the experimental dog data. Results show that values of the regularization parameter in the range (0.01-0.03) provide the best accuracy, and that the three best-performing estimation methods (L-Curve, Zero-Crossing, and CRESO) give values in this range. Moreover, a fixed value of the regularization parameter could achieve very similar performance to the beat-specific parameter values calculated by the different estimation methods. These findings are relevant as they suggest that regularization parameter estimation methods may provide the accurate reconstruction of heart surface potentials only for specific ranges of regularization parameter values, and that using a fixed value of the regularization parameter may represent a valid alternative, especially when computational efficiency or consistency across time is required.

Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.

Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imaging (ECGI) to learn efficiently with a relatively small dataset. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables over their respective geometrical domains. We then explicitly model the geometry-dependent physics in between the two domains via a bipartite graph over their graphical embeddings. We applied the resulting network to reconstruct electrical activity on the heart surface from body-surface potentials. In a series of generalization tasks with increasing difficulty, we demonstrated the improved ability of the network to generalize across geometrical changes underlying the data using less than 10% of training data and fewer variations of training geometry in comparison to its Euclidean alternatives. In both simulation and real-data experiments, we further demonstrated its ability to be quickly fine-tuned to new geometry using a modest amount of data.

Constructing Bodily Emotion Maps Based on High-Density Body Surface Potentials for Psychophysiological Computing.

Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.

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.

Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization.

Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fully-automated yet and is in consequence operator-dependent. We expect that the inter-operator variation in cardiac segmentation would influence the ECGI solution. This effect remains however non quantified. In the present work, we study the effect of segmentation variability on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Therefore, we designed two test cases: with and without shape model uncertainty. Moreover, we used four cases for ectopic ventricular excitation and compared the ECGI results in terms of reconstructed activation times and excitation origins. The preliminary results indicate that a small variation of the activation maps can be observed with a model uncertainty but no significant effect on the source localization is observed.

Incorporating structural abnormalities in equivalent dipole layer based ECG simulations.

Introduction: Electrical activity of the myocardium is recorded with the 12-lead ECG. ECG simulations can improve our understanding of the relation between abnormal ventricular activation in diseased myocardium and body surface potentials (BSP). However, in equivalent dipole layer (EDL)-based ECG simulations, the presence of diseased myocardium breaks the equivalence of the dipole layer. To simulate diseased myocardium, patches with altered electrophysiological characteristics were incorporated within the model. The relation between diseased myocardium and corresponding BSP was investigated in a simulation study. Methods: Activation sequences in normal and diseased myocardium were simulated and corresponding 64-lead BSP were computed in four models with distinct patch locations. QRS-complexes were compared using correlation coefficient (CC). The effect of different types of patch activation was assessed. Of one patient, simulated electrograms were compared to electrograms recorded during invasive electro-anatomical mapping. Results: Hundred-fifty-three abnormal activation sequences were simulated. Median QRS-CC of delayed versus dyssynchronous were significantly different (1.00 vs. 0.97, p < 0.001). Depending on the location of the patch, BSP leads were affected differently. Within diseased regions, fragmentation, low bipolar voltages and late potentials were observed in both recorded and simulated electrograms. Discussion: A novel method to simulate cardiomyopathy in EDL-based ECG simulations was established and evaluated. The new patch-based approach created a realistic relation between ECG waveforms and underlying activation sequences. Findings in the simulated cases were in agreement with clinical observations. With this method, our understanding of disease progression in cardiomyopathies may be further improved and used in advanced inverse ECG procedures.

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.

Advanced microstructural substrate detection in pre-hypertrophic HCM and its relationship to arrhythmogenesis; a hybrid CMR-ECG-Imaging study

Abstract Background Hypertrophic cardiomyopathy is defined in three domains; clinically by unexplained hypertrophy, genetically by sarcomeric gene mutations and histologically by disarray, small vessel disease and fibrosis. Both ischaemia and myocyte disarray have been implicated in arrhythmogenesis and sudden cardiac death but whether disarray occurs before hypertrophy and its relationship to ischaemia is unknown. Diffusion-tensor CMR, perfusion mapping &amp; ECG Imaging (ECGI) can measure disarray, ischaemia and electrical aberrance respectively in vivo. We aimed to investigate these in genotype positive (G+) subjects without hypertrophy (LVH−) to identify further subclinical manifestations of gene expression and whether these relate to ventricular arrhythmia formation. Methods Diffusion-tensor CMR (3-Tesla) using a motion-compensated spin-echo sequence was acquired in 3 short-axis slices. Quantitative adenosine stress perfusion mapping was performed using standard clinical protocols. A novel ECGI vest, containing 256 unipolar electrodes acquired a 5-minute recording of body-surface potentials to quantify conduction and repolarisation dynamics intervals. Results ECGI/CMR was performed on 68 mutation carriers from 64 families and 24 age sex and ethnicity matched healthy controls. Of the mutation carriers, median age was 33 (24–41 years), 57% (39) were female, and 79% (54) were white. Mutations were 39 (57%) MYBPC3, 19 (28%) MYH7, 1 (1%) MYL2 and 9 (12%) were thin filament/non-sarcomeric mutations. There was no significant difference in ejection fraction or LV mass, however G+LVH− had a higher maximum wall thickness (9 (9–10) vs 8 (7–9) mm p=0.003). Compared to healthy volunteers, G+LVH− individuals had more perfusion defects (18/64 (30%) vs 0, p=0.004), lower Fractional Anisotropy (FA) (suggestive of more disarray) (0.32±0.02 vs 0.34±0.02, p&amp;lt;0.0001) and more prolonged Activation–Recovery Intervals (ARI, a surrogate for action potential duration (259±40 vs 240±31 ms, p=0.03). In G+LVH−, patients with perfusion defects had more prolonged ARI (263 (248 vs 292) vs 246 (225–283) ms, p=0.03) and lower FA suggestive of more disarray (0.32±0.2 vs 0.31±0.1, p=0.04). Conclusion Ischaemia, myocyte disarray and electrical abnormalities occur even in the absence of hypertrophy in HCM. These abnormalities associate to form a complex a clinical phenotype. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): British Heart FoundationBarts Charity

Body Surface Potential Mapping during Ventricular Depolarization in Rats after Acute Exhaustive Exercise.

Exhaustive physical exercise can cause substantial changes in the electrical properties of the myocardium. To evaluate, using body surface potential mapping, the electrical activity of the heart in rats during ventricular depolarization after acute exhaustive exercise. Twelve-week-old male rats were submitted to acute treadmill exercise at 36 m/min until exhaustion. Unipolar electrocardiograms (ECGs) from the torso surface were recorded in zoletil-anesthetized rats three to five days before (Pre-Ex), 5 and 10 minutes after exhaustive exercise (Post-Ex 5 and Post-Ex 10, respectively) simultaneously with ECGs in limb leads. The instantaneous body surface potential maps (BSPMs) were analyzed during ventricular depolarization. P values <0.05 were considered statistically significant. Compared with Pre-Ex, an early completion of the second inversion of potential distributions, an early completion of ventricular depolarization, as well as a decrease in the duration of the middle phase and the total duration of ventricular depolarization on BSPMs were revealed at Post-Ex 5. Also, compared with Pre-Ex, an increase in the amplitude of negative BSPM extremum at the R-wave peak on the ECG in lead II (RII-peak) and a decrease in the amplitude of negative BSPM extremum at 3 and 4 ms after RII-peak were showed at Post-Ex 5. At Post-Ex 10, parameters of BSPMs did not differ from those at Pre-Ex. In rats, acute exhaustive exercise causes reversible changes in the temporal and amplitude characteristics of BSPMs during ventricular depolarization, most likely related to alterations in the excitation of the main mass of the ventricular myocardium.

The Effect of Segmentation Variability in Forward ECG Simulation.

Segmentation of patient-specific anatomical models is one of the first steps in Electrocardiographic imaging (ECGI). However, the effect of segmentation variability on ECGI remains unexplored. In this study, we assess the effect of heart segmentation variability on ECG simulation. We generated a statistical shape model from segmentations of the same patient and generated 262 cardiac geometries to run in an ECG forward computation of body surface potentials (BSPs) using an equivalent dipole layer cardiac source model and 5 ventricular stimulation protocols. Variability between simulated BSPs for all models and protocols was assessed using Pearson's correlation coefficient (CC). Compared to the BSPs of the mean cardiac shape model, the lowest variability (average CC = 0.98 ± 0.03) was found for apical pacing whereas the highest variability (average CC = 0.90 ± 0.23) was found for right ventricular free wall pacing. Furthermore, low amplitude BSPs show a larger variation in QRS morphology compared to high amplitude signals. The results indicate that the uncertainty in cardiac shape has a significant impact on ECGI.