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.

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters Bayesian Active Learning.

Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy.

Assessment of an ECG-Based System for Localizing Ventricular Arrhythmias in Patients With Structural Heart Disease.

Background We have previously developed an intraprocedural automatic arrhythmia-origin localization (AAOL) system to identify idiopathic ventricular arrhythmia origins in real time using a 3-lead ECG. The objective was to assess the localization accuracy of ventricular tachycardia (VT) exit and premature ventricular contraction (PVC) origin sites in patients with structural heart disease using the AAOL system. Methods and Results In retrospective and prospective case series studies, a total of 42 patients who underwent VT/PVC ablation in the setting of structural heart disease were recruited at 2 different centers.

Learning to Disentangle Inter-Subject Anatomical Variations in Electrocardiographic Data.

Objective: This work investigates the possibility of disentangled representation learning of inter-subject anatomical variations within electrocardiographic (ECG) data.

Methods: Since ground truth anatomical factors are generally not known in clinical ECG for assessing the disentangling ability of the models, the presented work first proposes the SimECG data set, a 12-lead ECG data set procedurally generated with a controlled set of anatomical generative factors. Second, to perform such disentanglement, the presented method evaluates and compares deep generative models with latent density modeled by nonparametric Indian Buffet Process to account for the complex generative process of ECG data.

Prospective Multicenter Assessment of a New Intraprocedural Automated System for Localizing Idiopathic Ventricular Arrhythmia Origins.

Objectives: The objective of this study was to present a new system, the Automatic Arrhythmia Origin Localization (AAOL) system, which used incomplete electroanatomic mapping (EAM) for localization of idiopathic ventricular arrhythmia (IVA) origin on the patient-specific geometry of left ventricular, right ventricular, and neighboring vessels. The study assessed the accuracy of the system in localizing IVA source sites on cardiac structures where pace mapping is challenging.

Background: An intraprocedural automated site of origin localization system was previously developed to identify the origin of early left ventricular activation by using 12-lead electrocardiograms (ECGs).

Feasibility study shows concordance between image-based virtual-heart ablation targets and predicted ECG-based arrhythmia exit-sites.

Introduction: We recently developed two noninvasive methodologies to help guide VT ablation: population-derived automated VT exit localization (PAVEL) and virtual-heart arrhythmia ablation targeting (VAAT). We hypothesized that while very different in their nature, limitations, and type of ablation targets (substrate-based vs. clinical VT), the image-based VAAT and the ECG-based PAVEL technologies would be spatially concordant in their predictions.

Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models.

The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh.

Automated intraprocedural localization of origin of ventricular activation using patient-specific computed tomographic imaging.

Background: To facilitate catheter ablation of ventricular tachycardia (VT), we previously developed an automated method to identify sources of left ventricular (LV) activation in real time using 12-lead electrocardiography (ECG), the accuracy of which depends on acquisition of a complete electroanatomic (EA) map.

Objective: The purpose of this study was to assess the feasibility of using a registered cardiac computed tomogram (CT) rather than an EA map to permit real-time localization and avoid errors introduced by incomplete maps.

Methods: Before LV VT ablation, 10 patients underwent CT imaging and 3-dimensional reconstruction of the cardiac surface to create a triangle mesh surface, which was registered to the EA map during the procedure and imported into custom localization software.

Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms.

Objective: This work presents a novel approach to handle the inter-subject variations existing in the population analysis of ECG, applied for localizing the origin of ventricular tachycardia (VT) from 12-lead electrocardiograms (ECGs).

Methods: The presented method involves a factor disentangling sequential autoencoder (f-SAE) - realized in both long short-term memory (LSTM) and gated recurrent unit (GRU) networks - to learn to disentangle the inter-subject variations from the factor relating to the location of origin of VT. To perform such disentanglement, a pair-wise contrastive loss is introduced.

Novel Metric Using Laplacian Eigenmaps to Evaluate Ischemic Stress on the Torso Surface.

The underlying pathophysiology of myocardial ischemia is incompletely understood, resulting in persistent difficulty of diagnosis. This limited understanding of underlying mechanisms encourages a data driven approach, which seeks to identify patterns in the ECG data that can be linked statistically to disease states. Laplacian Eigen-maps (LE) is a dimensionality reduction method popularized in machine learning that we have shown in large animal experiments to identify underlying ischemic stress both earlier in an ischemic episode, and more robustly, than typical clinical markers.

Noninvasive Reconstruction of Transmural Transmembrane Potential With Simultaneous Estimation of Prior Model Error.

To reconstruct electrical activity in the heart from body-surface electrocardiograms (ECGs) is an ill-posed inverse problem. Electrophysiological models have been found effective in regularizing these inverse problems by incorporating a priori knowledge about how the electrical potential in the heart propagates over time. However, these models suffer from model errors arising from, for example, parameters associated with tissue properties and the earliest sites of excitation.

Localization of Activation Origin on Patient-Specific Endocardial Surface by the Equivalent Double Layer (EDL) Source Model With Sparse Bayesian Learning.

Objective: Ablation treatment of ventricular arrhythmias can be facilitated by pre-procedure planning aided by electrocardiographic inverse solution, which can help to localize the origin of arrhythmia. Our aim was to improve localization accuracy of the inverse solution for activation originating on the left-ventricular endocardial surface, by using a sparse Bayesian learning (SBL).

Methods: The inverse problem of electrocardiography was solved by reconstructing endocardial potentials from time integrals of body-surface electrocardiograms and from patient-specific geometry of the heart and torso for three patients with structurally normal ventricular myocardium, who underwent endocardial catheter mapping that included pace mapping.

Localization of Ventricular Activation Origin from the 12-Lead ECG: A Comparison of Linear Regression with Non-Linear Methods of Machine Learning.

We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a generic left-ventricular (LV) endocardial surface in real time from the 12-lead ECG. The present study sought to investigate whether machine learning-namely, random-forest regression (RFR) and support-vector regression (SVR)-can improve the localization accuracy compared to MLR. For 38 patients the 12-lead ECG was acquired during LV endocardial pacing at 1012 sites with known coordinates exported from an electroanatomic mapping system; each pacing site was then registered to a generic LV endocardial surface subdivided into 16 segments tessellated into 238 triangles.

Localization of Activation Origin on Patient-Specific Epicardial Surface by Empirical Bayesian Method.

Objective: Ablation treatment of ventricular arrhythmias can be facilitated by pre-procedure planning aided by electrocardiographic inverse solution, which can help to localize the origin of arrhythmia. Our aim was to improve localization accuracy of the inverse solution by using a novel Bayesian approach.

Methods: The inverse problem of electrocardiography was solved by reconstructing epicardial potentials from 120 body-surface electrocardiograms and from patient-specific geometry of the heart and torso for four patients suffering from scar-related ventricular tachycardia who underwent epicardial catheter mapping, which included pace-mapping.

Criteria for ECG detection of acute myocardial ischemia: Sensitivity versus specificity.

Background: Criteria for electrocardiographic detection of acute myocardial ischemia recommended by the Consensus Document of ESC/ACCF/AHA/WHF consist of two parts: The ST elevation myocardial infarction (STEMI) criteria based on ST elevation (ST↑) in 10 pairs of contiguous leads and the other on ST depression (ST↓) in the same 10 contiguous pairs. Our aim was to assess sensitivity (SE) and specificity (SP) of these criteria-and to seek their possible improvements-in three databases of 12‑lead ECGs.

Methods: We used (1) STAFF III data of controlled ischemic episodes recorded from 99 patients (pts) during percutaneous coronary intervention (PCI) involving either left anterior descending (LAD) coronary artery, right coronary artery (RCA), or left circumflex (LCx) coronary artery.

Rapid 12-lead automated localization method: Comparison to electrocardiographic imaging (ECGI) in patient-specific geometry.

Background: Rapid accurate localization of the site of ventricular activation origin during catheter ablation for ventricular arrhythmias could facilitate the procedure. Electrocardiographic imaging (ECGI) using large lead sets can localize the origin of ventricular activation. We have developed an automated method to identify sites of early ventricular activation in real time using the 12-lead ECG.

Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.

Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption.

Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram.

Objectives: The aim of this study was to develop rapid computational methods for identifying the site of origin of ventricular activation from the 12-lead electrocardiogram.

Background: Catheter ablation of ventricular tachycardia in patients with structural heart disease frequently relies on a substrate-based approach, which may use pace mapping guided by body-surface electrocardiography to identify culprit exit sites.

Methods: Patients undergoing ablation of scar-related VT (n = 38) had 12-lead electrocardiograms recorded during pacing at left ventricular endocardial sites (n = 1,012) identified on 3-dimensional electroanatomic maps and registered to a generic left ventricular endocardial surface divided into 16 segments and tessellated into 238 triangles; electrocardiographic data were reduced for each lead to 1 variable, consisting of QRS time integral.

Localization of ventricular activation origin using patient-specific geometry: Preliminary results.

Background And Objectives: Catheter ablation of ventricular tachycardia (VT) may include induction of VT and localization of VT-exit site. Our aim was to assess localization performance of a novel statistical pace-mapping method and compare it with performance of an electrocardiographic inverse solution.

Methods: Seven patients undergoing ablation of VT (4 with epicardial, 3 with endocardial exit) aided by electroanatomic mapping underwent intraprocedural 120-lead body-surface potential mapping (BSPM).

Non-invasive epicardial and endocardial electrocardiographic imaging for scar-related ventricular tachycardia.

Aims: Contact mapping is currently used to guide catheter ablation of scar-related ventricular tachycardia (VT) but usually provides incomplete assessment of 3D re-entry circuits and their arrhythmogenic substrates. This study investigates the feasibility of non-invasive electrocardiographic imaging (ECGi) in mapping scar substrates and re-entry circuits throughout the epicardium and endocardium.

Methods And Results: Four patients undergoing endocardial and epicardial mapping and ablation of scar-related VT had computed tomography scans and a 120-lead electrocardiograms, which were used to compute patient-specific ventricular epicardial and endocardial unipolar electrograms (CEGMs).

Noninvasive epicardial and endocardial electrocardiographic imaging of scar-related ventricular tachycardia.

Background: The majority of life-threatening ventricular tachycardias (VTs) are sustained by heterogeneous scar substrates with narrow strands of surviving tissue. An effective treatment for scar-related VT is to modify the underlying scar substrate by catheter ablation. If activation sequence and entrainment mapping can be performed during sustained VT, the exit and isthmus of the circuit can often be identified.

Validation of the vessel-specific leads (VSLs) for detection of acute ischemia on a dataset with non-ischemic ST-segment deviation.

Background: Existing criteria recommended by ACC/ESC for identifying patients with ST elevation myocardial infarction (STEMI) from the 12-lead ECG perform with high specificity (SP) but low sensitivity (SE). In our previous studies, we found that the SE of acute ischemia detection can be markedly improved without any loss of SP by calculating, from the 12-lead ECG, ST deviation in 3 "optimal" vessel-specific leads (VSLs). To further validate the method, we evaluated the SP performance using a dataset with non-ischemic ST-segment changes.

Insights from Novel Noninvasive CT and ECG Imaging Modalities on Electromechanical Myocardial Activation in a Canine Model of Ischemic Dyssynchronous Heart Failure.

Introduction: The interplay between electrical activation and mechanical contraction patterns is hypothesized to be central to reduced effectiveness of cardiac resynchronization therapy (CRT). Furthermore, complex scar substrates render CRT less effective. We used novel cardiac computed tomography (CT) and noninvasive electrocardiographic imaging (ECGI) techniques in an ischemic dyssynchronous heart failure (DHF) animal model to evaluate electrical and mechanical coupling of cardiac function, tissue viability, and venous accessibility of target pacing regions.

Non-invasive electromechanical activation imaging as a tool to study left ventricular dyssynchronous patients: Implication for CRT therapy.

Aims: Electromechanical de-coupling is hypothesized to explain non-response of dyssynchrony patient to cardiac resynchronization therapy (CRT). In this pilot study, we investigated regional electromechanical uncoupling in 10 patients referred for CRT using two non-invasive electrical and mechanical imaging techniques (CMR tissue tracking and ECGI).

Methods And Results: Reconstructed regional electrical and mechanical activation captured delayed LBBB propagation direction from septal to anterior/inferior and finally to lateral walls as well as from LV apical to basal.