Non-invasive characterisation of macroreentrant atrial tachycardia types from a vectorcardiographic approach with the slow conduction region as a cornerstone

Performance assessment of electrode configurations for the estimation of omnipolar electrograms from high density arrays

OBJECTIVE

The aim of this study is to propose a method to reduce the sensitivity of the estimated omnipolar electrogram (oEGM) with respect to the angle of the propagation wavefront.

METHODS

A novel configuration of cliques taking into account all four electrodes of a squared cell is proposed. To test this approach, simulations of HD grids of cardiac activations at different propagation angles, conduction velocities, interelectrode distance and electrogram waveforms are considered.

RESULTS

The proposed approach successfully provided narrower loops (essentially a straight line) of the electrical field described by the bipole pair with respect to the conventional approach. Estimation of the direction of propagation was improved. Additionally, estimated oEGMs presented larger amplitude, and estimations of the local activation times were more accurate.

CONCLUSIONS

A novel method to improve the estimation of oEGMs in HD grid of electrodes is proposed. This approach is superior to the existing methods and avoids pitfalls not yet resolved.

RELEVANCE

Robust tools for quantifying the cardiac substrate are crucial to determine with accuracy target ablation sites during an electrophysiological procedure.

Computer Modeling of the Heart for ECG Interpretation—A Review

Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.