Activation recovery interval imaging of premature ventricular contraction

A novel method to correct repolarization time estimation from unipolar electrograms distorted by standard filtering

Reliable patient-specific ventricular repolarization times (RTs) can identify regions of functional block or afterdepolarizations, indicating arrhythmogenic cardiac tissue and the risk of sudden cardiac death. Unipolar electrograms (UEs) record electric potentials, and the Wyatt method has been shown to be accurate for estimating RT from a UE. High-pass filtering is an important step in processing UEs, however, it is known to distort the T-wave phase of the UE, which may compromise the accuracy of the Wyatt method. The aim of this study was to examine the effects of high-pass filtering, and improve RT estimates derived from filtered UEs. We first generated a comprehensive set of UEs, corresponding to early and late activation and repolarization, that were then high-pass filtered with settings that mimicked the CARTO filter. We trained a deep neural network (DNN) to output a probabilistic estimation of RT and a measure of confidence, using the filtered synthetic UEs and their true RTs. Unfiltered ex-vivo human UEs were also filtered and the trained DNN used to estimate RT. Even a modest 2 Hz high-pass filter imposes a significant error on RT estimation using the Wyatt method. The DNN outperformed the Wyatt method in 62.75% of cases, and produced a significantly lower absolute error (p=8.99E-13), with a median of 16.91 ms, on 102 ex-vivo UEs. We also applied the DNN to patient UEs from CARTO, from which an RT map was computed. In conclusion, DNNs trained on synthetic UEs improve the RT estimation from filtered UEs, which leads to more reliable repolarization maps that help to identify patient-specific repolarization abnormalities.

Non-invasive electrocardiographic imaging in patients with idiopathic premature ventricular contractions from the right ventricular outflow tract: New insights into arrhythmia substrate

AIMS

The aim of this study was to use non-invasive electrocardiographic imaging (ECGI) to study the electrophysiological properties of right ventricular outflow tract (RVOT) in patients with frequent premature ventricular contractions (PVCs) from the RVOT and in controls.

METHODS

ECGI is a combined application of body surface electrocardiograms and computed tomography or magnetic resonance imaging data. Unipolar electrograms are reconstructed on the epicardial and endocardial surfaces. Activation time (AT) was defined as the time of maximal negative slope of the electrogram (EGM) during QRS, recovery time (RT) as the time of maximal positive slope of the EGM during T wave, Activation recovery interval (ARI) was defined as the difference between RT and AT. ARI dispersion (Δ ARI) and RT dispersion (Δ RT) were calculated as the difference between maximal and minimal ARI and RT respectively. We evaluated those parameters in patients with frequent PVCs from the RVOT, defined as >10.000 per 24 h, and in a control group.

RESULTS

We studied 7 patients with frequent RVOT PVCs and 17 controls. Patients with PVCs from the RVOT had shorter median RT than controls, in the endocardium and in the epicardium, respectively 380 (239-397) vs 414 (372-448) ms, p = 0.047 and 275 (236-301) vs 330 (263-418) ms, p = 0.047. The dispersion of ARI and of RT in the epicardium was higher than in controls, Δ ARI of 145 (68-216) vs 17 (3-48) ms, p = 0.001 and Δ RT of 201 (160-235) vs 115 (65-177), p = 0.019.

CONCLUSION

In this group of patients we found a shorter median RT in the endocardium and in the epicardium of the RVOT and a higher dispersion of the ARI and RT across the epicardium in patients with PVCs from the RVOT when comparing to controls.