Vinthernedergaard8634

During pacing, RV and left ventricular activation dispersion in rToF patients were comparable to that of the non-rToF patients.

The results of the present study indicate that the delayed activation in the right ventricle of rToF patients is predominantly due to block(s) in the Purkinje system and that conduction in RV tissue is fairly normal.

The results of the present study indicate that the delayed activation in the right ventricle of rToF patients is predominantly due to block(s) in the Purkinje system and that conduction in RV tissue is fairly normal.

Ventricular activation patterns can aid clinical decision-making directly by providing spatial information on cardiac electrical activation or indirectly through derived clinical indices. The aim of this work was to derive an atlas of the major modes of variation of ventricular activation from model-predicted 3D bi-ventricular activation time distributions and to relate these modes to corresponding vectorcardiograms (VCGs). We investigated how the resulting dimensionality reduction can improve and accelerate the estimation of activation patterns from surface electrogram measurements.

Atlases of activation time (AT) and VCGs were derived using principal component analysis on a dataset of simulated electrophysiology simulations computed on eight patient-specific bi-ventricular geometries. The atlases provided significant dimensionality reduction, and the modes of variation in the two atlases described similar features. Utility of the atlases was assessed by resolving clinical waveforms against them and the VCG atlas was able to accurately reconstruct the patient VCGs with fewer than 10 modes. A sensitivity analysis between the two atlases was performed by calculating a compact Jacobian. Finally, VCGs generated by varying AT atlas modes were compared with clinical VCGs to estimate patient-specific activation maps, and the resulting errors between the clinical and atlas-based VCGs were less than those from more computationally expensive method.

Atlases of activation and VCGs represent a new method of identifying and relating the features of these high-dimensional signals that capture the major sources of variation between patients and may aid in identifying novel clinical indices of arrhythmia risk or therapeutic outcome.

Atlases of activation and VCGs represent a new method of identifying and relating the features of these high-dimensional signals that capture the major sources of variation between patients and may aid in identifying novel clinical indices of arrhythmia risk or therapeutic outcome.

Electric conduction in the atria is direction-dependent, being faster in fibre direction, and possibly heterogeneous due to structural remodelling. check details Intracardiac recordings of atrial activation may convey such information, but only with high-quality data. The aim of this study was to apply a patient-specific approach to enable such assessment even when data are scarce, noisy, and incomplete.

Contact intracardiac recordings in the left atrium from nine patients who underwent ablation therapy were collected before pulmonary veins isolation and retrospectively included in the study. The Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps (PIEMAP), previously developed, has been used to reconstruct the conductivity tensor from sparse recordings of the activation. Regional fibre direction and conduction velocity were estimated from the fitted conductivity tensor and extensively cross-validated by clustered and sparse data removal. Electrical conductivity was successfully reconstructed in allcardiac Electro-Anatomical Maps also enables personalization of cardiac electrophysiology models.

Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps offers a novel approach to extrapolate the activation in unmapped regions and to assess conduction properties of the atria. It could be seamlessly integrated into existing electro-anatomic mapping systems. Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps also enables personalization of cardiac electrophysiology models.

Ventricular conduction disorders can induce arrhythmias and impair cardiac function. Bundle branch blocks (BBBs) are diagnosed by 12-lead electrocardiogram (ECG), but discrimination between BBBs and normal tracings can be challenging. CineECG computes the temporo-spatial trajectory of activation waveforms in a 3D heart model from 12-lead ECGs. Recently, in Brugada patients, CineECG has localized the terminal components of ventricular depolarization to right ventricle outflow tract (RVOT), coincident with arrhythmogenic substrate localization detected by epicardial electro-anatomical maps. This abnormality was not found in normal or right BBB (RBBB) patients. This study aimed at exploring whether CineECG can improve the discrimination between left BBB (LBBB)/RBBB, and incomplete RBBB (iRBBB).

We utilized 500 12-lead ECGs from the online Physionet-XL-PTB-Diagnostic ECG Database with a certified ECG diagnosis. The mean temporo-spatial isochrone trajectory was calculated and projected into the anatomical 3D hcult discrimination between normal, iRBBB, and Brugada patients.

We aimed to examine whether routine pulmonary vein isolation (PVI) induces significant ventricular repolarization changes as suggested earlier.

Five-minute electrocardiograms were recorded at hospital's admission (T-1d), 1 day after the PVI-procedure (T+1d) and at 3 months post-procedure (T+3m) from a registry of consecutive atrial fibrillation (AF) patients scheduled for routine PVI with different PVI modalities (radiofrequency, cryo-ablation, and hybrid). Only patients who were in sinus rhythm at all three recordings (n = 117) were included. QT-intervals and QT-dispersion were evaluated with custom-made software and QTc was calculated using Bazett's, Fridericia's, Framingham's, and Hodges' formulas. Both QT- and RR-intervals were significantly shorter at T+1d (399 ± 37 and 870 ± 141 ms) and T+3m (407 ± 36 and 950 ± 140 ms) compared with baseline (417 ± 36 and 1025 ± 164 ms). There was no statistically significant within-subject difference in QTc Fridericia (T-1d 416 ± 28 ms, T+1d 419 ± 33 ms, and T+3m 414 ± 25 ms) and QT-dispersion (T-1d 18 ± 12 ms, T+1d 21 ± 19 ms, and T+3m 17 ± 12 ms) between the recordings.