In silico validation of electrocardiographic imaging to reconstruct the endocardial and epicardial repolarization pattern using the equivalent dipole layer source model

Impact of rigid cardiac motion on the accuracy of electrocardiographic imaging

Introduction

Electrocardiographic Imaging (ECGI) offers a non-invasive approach to reconstruct cardiac electrical activity. However, the inverse problem of ECGI is highly ill-conditioned, making it sensitive to errors. In practice, rigid displacements of the heart during beating introduce geometric errors into the ECGI problem. This study aims to investigate the impact of cardiac rigid motion on the accuracy of ECGI.

Methods

We employed the Boundary Element Method (BEM) to solve the forward problem and the Tikhonov method to address the inverse problem. We utilized a dataset from the CRVTI/SCI Institute, which involves Langendorff-perfused dog hearts suspended in a torso-shaped tank. Based on clinical experience, six different types of cardiac movement patterns, including translations and rotations, were designed to assess the impact of various displacements on the accuracy of the ECGI solution.

Results

Our study found that among the translational and rotational movements of the heart, rotational motion should be prioritized for attention, as it caused significantly stronger changes in ECGI correlation coefficient (CC) and relative error (RE) than translational motion. Among the translations along the coordinate axes, movement along the y-axis (anterior-posterior movement within the chest cavity) had the least impact. For rotational movements, rolling had the least impact, yaw had moderate impact, and pitch had the greatest impact.

Conclusion

The inverse solution of ECGI demonstrates a certain robustness to changes in heart position, with CC changes of less than 2% for 10 mm displacements and less than 5% for 10° rotations. This suggests that ECGI changes due to cardiac geometric motion can be disregarded within a certain range.

Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic imaging

This study aimed to analyze computational techniques in ECG imaging (ECGI) reconstruction, focusing on dataset identification, problem-solving, and feature extraction. We employed a PRISMA approach to review studies from Scopus and Web of Science, applying Cochrane principles to assess risk of bias. The selection was limited to English peer-reviewed papers published from 2010 to 2023, excluding studies that lacked computational technique descriptions. From 99 reviewed papers, trends show a preference for traditional methods like the boundary element and Tikhonov methods, alongside a rising use of advanced technologies including hybrid techniques and deep learning. These advancements have enhanced cardiac diagnosis and treatment precision. Our findings underscore the need for robust data utilization and innovative computational integration in ECGI, highlighting promising areas for future research and advances. This shift toward tailored cardiac care suggests significant progress in diagnostic and treatment methods.

Genesis of the T-wave through various modes of ventricular recovery patterns using the equivalent dipole layer model

BACKGROUND

The equivalent dipole layer (EDL) relates local endocardial and epicardial transmembrane potentials to body surface potentials and can therefore be used to gain insight into cardiac activation and recovery. To use the EDL-source model for the inverse problem of electrocardiography, initial estimates for local activation times (LAT) and recovery times (LRT) are required because of its non-linear relation with body surface potentials.

OBJECTIVE

To develop an AT-independent initial RT estimate in the EDL-source model.

METHODS

Body surface mapping (BSM) and cardiac imaging were performed in 15 subjects. LAT and LRT were estimated using the EDL-source model. Various ventricular recovery patterns were tested to investigate the relation between recovery patterns and normal T-waves, including LAT-dependent-recovery and RT differences along transmural, interventricular, anterior-posterior and apico-basal axes. A new algorithm was developed based on the backwards modeling of the T-wave (BackRep) to identify the latest area of recovery. Correlation coefficient (CC) and relative difference (RD) between the recorded and computed T-waves were reported.

RESULTS

BackRep (CC = 0.89 [IQR:0.83-0.90]; RD = 0.63 [IQR:0.49-0.69]), outperformed the anatomical axes based recovery patterns (CC = 0.29 [IQR:0.21-0.46] - 0.79 [IQR:0.78-0.83]; RD = 1.02 [IQR:0.98-1.18] - 0.61 [IQR:0.57-0.68]) and LAT-based recovery pattern (CC = 0.63 [IQR:0.60-0.73]; RD = 4.35 [IQR:2.74-9.05]). Of the RT differences along the anatomical axes, the apico-basal recovery pattern showed the best match between recorded and computed T-waves. A significant apex-to-base RT difference was also found in the BackRep recovery maps.

CONCLUSION

BackRep provides a reliable AT-independent initial RT estimate and supports the presence of an apex-to-base RT difference in normal T-wave morphology.

An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers

BACKGROUND AND OBJECTIVE

Electrocardiographic imaging (ECGI) has emerged as a non-invasive approach to identify atrial fibrillation (AF) driver sources. This paper aims to collect and review the current research literature on the ECGI inverse problem, summarize the research progress, and propose potential research directions for the future.

METHODS AND RESULTS

The effectiveness and feasibility of using ECGI to map AF driver sources may be influenced by several factors, such as inaccuracies in the atrial model due to heart movement or deformation, noise interference in high-density body surface potential (BSP), inconvenient and time-consuming BSP acquisition, errors in solving the inverse problem, and incomplete interpretation of the AF driving source information derived from the reconstructed epicardial potential. We review the current research progress on these factors and discuss possible improvement directions. Additionally, we highlight the limitations of ECGI itself, including the lack of a gold standard to validate the accuracy of ECGI technology in locating AF drivers and the challenges associated with guiding AF ablation based on post-processed epicardial potentials due to the intrinsic difference between epicardial and endocardial potentials.

CONCLUSIONS

Before performing ablation, ECGI can provide operators with predictive information about the underlying locations of AF driver by non-invasively and globally mapping the biatrial electrical activity. In the future, endocardial catheter mapping technology may benefit from the use of ECGI to enhance the diagnosis and ablation of AF.

Ex vivo Validation of Noninvasive Epicardial and Endocardial Repolarization Mapping

Background: The detection and localization of electrophysiological substrates currently involve invasive cardiac mapping. Electrocardiographic imaging (ECGI) using the equivalent dipole layer (EDL) method allows the noninvasive estimation of endocardial and epicardial activation and repolarization times (AT and RT), but the RT validation is limited to in silico studies. We aimed to assess the temporal and spatial accuracy of the EDL method in reconstructing the RTs from the surface ECG under physiological circumstances and situations with artificially induced increased repolarization heterogeneity.

Methods: In four Langendorff-perfused pig hearts, we simultaneously recorded unipolar electrograms from plunge needles and pseudo-ECGs from a volume-conducting container equipped with 61 electrodes. The RTs were computed from the ECGs during atrial and ventricular pacing and compared with those measured from the local unipolar electrograms. Regional RT prolongation (cooling) or shortening (pinacidil) was achieved by selective perfusion of the left anterior descending artery (LAD) region.

Results: The differences between the computed and measured RTs were 19.0 ± 17.8 and 18.6 ± 13.7 ms for atrial and ventricular paced beats, respectively. The region of artificially delayed or shortened repolarization was correctly identified, with minimum/maximum RT roughly in the center of the region in three hearts. In one heart, the reconstructed region was shifted by ~2.5 cm. The total absolute difference between the measured and calculated RTs for all analyzed patterns in selectively perfused hearts (n = 5) was 39.6 ± 27.1 ms.

Conclusion: The noninvasive ECG repolarization imaging using the EDL method of atrial and ventricular paced beats allows adequate quantitative reconstruction of regions of altered repolarization.

Electrocardiographic Imaging of Repolarization Abnormalities

Background

Dispersion and gradients in repolarization have been associated with life‐threatening arrhythmias, but are difficult to quantify precisely from surface electrocardiography. The objective of this study was to evaluate electrocardiographic imaging (ECGI) to noninvasively detect repolarization‐based abnormalities.

Methods and Results

Ex vivo data were obtained from Langendorff‐perfused pig hearts (n=8) and a human donor heart. Unipolar electrograms were recorded simultaneously during sinus rhythm from an epicardial sock and the torso‐shaped tank within which the heart was suspended. Regional repolarization heterogeneities were introduced through perfusion of dofetilide and pinacidil into separate perfusion beds. In vivo data included torso and epicardial potentials recorded simultaneously in anesthetized, closed‐chest pigs (n=5), during sinus rhythm, and ventricular pacing. For both data sets, ECGI accurately reconstructed T‐wave electrogram morphologies when compared with those recorded by the sock (ex vivo: correlation coefficient, 0.85 [0.52–0.96], in vivo: correlation coefficient, 0.86 [0.52–0.96]) and repolarization time maps (ex‐vivo: correlation coefficient, 0.73 [0.63–0.83], in vivo: correlation coefficient, 0.76 [0.67–0.82]). ECGI‐reconstructed repolarization time distributions were strongly correlated to those measured by the sock (both data sets, R² ≥0.92). Although the position of the gradient was slightly shifted by 8.3 (0–13.9) mm, the mean, max, and SD between ECGI and recorded gradient values were highly correlated (R²=0.87, 0.75, and 0.86 respectively). There was no significant difference in ECGI accuracy between ex vivo and in vivo data.

Conclusions

ECGI reliably and accurately maps potentially critical repolarization abnormalities. This noninvasive approach allows imaging and quantifying individual parameters of abnormal repolarization‐based substrates in patients with arrhythmogenesis, to improve diagnosis and risk stratification.

Atrial location optimization by electrical measures for Electrocardiographic Imaging

BACKGROUND

The Electrocardiographic Imaging (ECGI) technique, used to non-invasively reconstruct the epicardial electrical activity, requires an accurate model of the atria and torso anatomy. Here we evaluate a new automatic methodology able to locate the atrial anatomy within the torso based on an intrinsic electrical parameter of the ECGI solution.

METHODS

In 28 realistic simulations of the atrial electrical activity, we randomly displaced the atrial anatomy for ±2.5 cm and ±30° on each axis. An automatic optimization method based on the L-curve curvature was used to estimate the original position using exclusively non-invasive data.

RESULTS

The automatic optimization algorithm located the atrial anatomy with a deviation of 0.5 ± 0.5 cm in position and 16.0 ± 10.7° in orientation. With these approximate locations, the obtained electrophysiological maps reduced the average error in atrial rate measures from 1.1 ± 1.1 Hz to 0.5 ± 1.0 Hz and in the phase singularity position from 7.2 ± 4.0 cm to 1.6 ± 1.7 cm (p < 0.01).

CONCLUSIONS

This proposed automatic optimization may help to solve spatial inaccuracies provoked by cardiac motion or respiration, as well as to use ECGI on torso and atrial anatomies from different medical image systems.