An algorithm for imaging isochrones of ventricular activation on patient-specific epicardial surface
Shijie Zhou, John L. Sapp, Amir M. AbdelWahab, B. Milan Horáček
Abstract
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Shijie Zhou, John L. Sapp, Amir M. AbdelWahab, B. Milan Horáček
Abstract
Open-access reader
Electrocardiographic imaging has been shown to provide useful information for pre-procedure planning of catheter-ablation procedures.The methodology involves reconstruction of unipolar electrograms (EGMs) and isochronal maps on the epicardial surface from noninvasively acquired body-surface potentials.We have developed an algorithm for evaluating global myocardial activation times.First, the cross-correlation method determines the delay in local activation times among pairs of neighboring nodes.Next, a sparse linear system is constructed from known activation delays of neighboring nodes.To solve this system, we use a sparse Bayesian learning method to calculate the global myocardial activation times.The aim of this study was to assess the proposed method in both structurally normal and scarred ventricular myocardium.Isochronal maps of calculated activation times were compared with local activation times (LATs) derived from directly-measured epicardial EGMs obtained by electroanatomic contact mapping, for pacing delivered by an implantable cardioverter defibrillator (ICD) at the endocardial right-ventricular (RV) apex, and for catheter pacing at RV epicardial site.We found that even in the presence of infarct scar, isochronal maps calculated by the proposed method correlated closely with known LATs exported from an electroanatomic mapping system.
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Electrocardiographic imaging has been shown to provide useful information for pre-procedure planning of catheter-ablation procedures.The methodology involves reconstruction of unipolar electrograms (EGMs) and isochronal maps on the epicardial surface from noninvasively acquired body-surface potentials.We have developed an algorithm for evaluating global myocardial activation times.First, the cross-correlation method determines the delay in local activation times among pairs of neighboring nodes.Next, a sparse linear system is constructed from known activation delays of neighboring nodes.To solve this system, we use a sparse Bayesian learning method to calculate the global myocardial activation times.The aim of this study was to assess the proposed method in both structurally normal and scarred ventricular myocardium.Isochronal maps of calculated activation times were compared with local activation times (LATs) derived from directly-measured epicardial EGMs obtained by electroanatomic contact mapping, for pacing delivered by an implantable cardioverter defibrillator (ICD) at the endocardial right-ventricular (RV) apex, and for catheter pacing at RV epicardial site.We found that even in the presence of infarct scar, isochronal maps calculated by the proposed method correlated closely with known LATs exported from an electroanatomic mapping system.
Key concepts: Computer science, Algorithm