Novel Experimental Preparation to Assess Electrocardiographic Imaging Reconstruction Techniques

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.

A two-step inverse solution for a single dipole cardiac source

Introduction: The inverse problem of electrocardiography noninvasively localizes the origin of undesired cardiac activity, such as a premature ventricular contraction (PVC), from potential recordings from multiple torso electrodes. However, the optimal number and placement of electrodes for an accurate solution of the inverse problem remain undetermined. This study presents a two-step inverse solution for a single dipole cardiac source, which investigates the significance of the torso electrodes on a patient-specific level. Furthermore, the impact of the significant electrodes on the accuracy of the inverse solution is studied. Methods: Body surface potential recordings from 128 electrodes of 13 patients with PVCs and their corresponding homogeneous and inhomogeneous torso models were used. The inverse problem using a single dipole was solved in two steps: First, using information from all electrodes, and second, using a subset of electrodes sorted in descending order according to their significance estimated by a greedy algorithm. The significance of electrodes was computed for three criteria derived from the singular values of the transfer matrix that correspond to the inversely estimated origin of the PVC computed in the first step. The localization error (LE) was computed as the Euclidean distance between the ground truth and the inversely estimated origin of the PVC. The LE obtained using the 32 and 64 most significant electrodes was compared to the LE obtained when all 128 electrodes were used for the inverse solution. Results: The average LE calculated for both torso models and using all 128 electrodes was 28.8 ± 11.9 mm. For the three tested criteria, the average LEs were 32.6 ± 19.9 mm, 29.6 ± 14.7 mm, and 28.8 ± 14.5 mm when 32 electrodes were used. When 64 electrodes were used, the average LEs were 30.1 ± 16.8 mm, 29.4 ± 12.0 mm, and 29.5 ± 12.6 mm. Conclusion: The study found inter-patient variability in the significance of torso electrodes and demonstrated that an accurate localization by the inverse solution with a single dipole could be achieved using a carefully selected reduced number of electrodes.

Tipping the scales of understanding: An engineering approach to design and implement whole-body cardiac electrophysiology experimental models

The study of cardiac electrophysiology is built on experimental models that span all scales, from ion channels to whole-body preparations. Novel discoveries made at each scale have contributed to our fundamental understanding of human cardiac electrophysiology, which informs clinicians as they detect, diagnose, and treat complex cardiac pathologies. This expert review describes an engineering approach to developing experimental models that is applicable across scales. The review also outlines how we applied the approach to create a set of multiscale whole-body experimental models of cardiac electrophysiology, models that are driving new insights into the response of the myocardium to acute ischemia. Specifically, we propose that researchers must address three critical requirements to develop an effective experimental model: 1) how the experimental model replicates and maintains human physiological conditions, 2) how the interventions possible with the experimental model capture human pathophysiology, and 3) what signals need to be measured, at which levels of resolution and fidelity, and what are the resulting requirements of the measurement system and the access to the organs of interest. We will discuss these requirements in the context of two examples of whole-body experimental models, a closed chest in situ model of cardiac ischemia and an isolated-heart, torso-tank preparation, both of which we have developed over decades and used to gather valuable insights from hundreds of experiments.

Generalization and Regularization for Inverse Cardiac Estimators

Electrocardiographic Imaging (ECGI) aims to estimate the intracardiac potentials noninvasively, hence allowing the clinicians to better visualize and understand many arrhythmia mechanisms. Most of the estimators of epicardial potentials use a signal model based on an estimated spatial transfer matrix together with Tikhonov regularization techniques, which works well specially in simulations, but it can give limited accuracy in some real data. Based on the quasielectrostatic potential superposition principle, we propose a simple signal model that supports the implementation of principled out-of-sample algorithms for several of the most widely used regularization criteria in ECGI problems, hence improving the generalization capabilities of several of the current estimation methods. Experiments on simple cases (cylindrical and Gaussian shapes scrutinizing fast and slow changes, respectively) and on real data (examples of torso tank measurements available from Utah University, and an animal torso and epicardium measurements available from Maastricht University, both in the EDGAR public repository) show that the superposition-based out-of-sample tuning of regularization parameters promotes stabilized estimation errors of the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural in non-overfitted solutions. The superposition signal model can be used for designing adequate out-of-sample tuning of Tikhonov regularization techniques, and it can be taken into account when using other regularization techniques in current commercial systems and research toolboxes on ECGI.

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

Simultaneous Multi-Heartbeat ECGI Solution with a Time-Varying Forward Model: a Joint Inverse Formulation

Electrocardiographic imaging (ECGI) is an effective tool for noninvasive diagnosis of a range of cardiac dysfunctions. ECGI leverages a model of how cardiac bioelectric sources appear on the torso surface (the forward problem) and uses recorded body surface potential signals to reconstruct the bioelectric source (the inverse problem). Solutions to the inverse problem are sensitive to noise and variations in the body surface potential (BSP) recordings such as those caused by changes or errors in cardiac position. Techniques such as signal averaging seek to improve ECGI solutions by incorporating BSP signals from multiple heartbeats into an averaged BSP with a higher SNR to use when estimating the cardiac bioelectric source. However, signal averaging is limited when it comes to addressing sources of BSP variability such as beat to beat differences in the forward solution. We present a novel joint inverse formulation to solve for the cardiac source given multiple BSP recordings and known changes in the forward solution, here changes in the heart position. We report improved ECGI accuracy over signal averaging and averaged individual inverse solutions using this joint inverse formulation across multiple activation sequence types and regularization techniques with measured canine data and simulated heart motion. Our joint inverse formulation builds upon established techniques and consequently can easily be applied with many existing regularization techniques, source models, and forward problem formulations.