Localization of Activation Origin on Patient-Specific Epicardial Surface by Empirical Bayesian Method

• Published in: IEEE transactions on biomedical engineering 2019-05, Vol.66 (5), p.1380-1389
• Main Authors: Zhou, Shijie, Sapp, John L., Dawoud, Fady, Horacek, B. Milan
• Format: Article
• Language: English
• Subjects: Ablation; Activation; Adult; Aged; Algorithms; Arrhythmia; Bayes methods; Bayes Theorem; Bayesian analysis; Body Surface Potential Mapping; Cardiac arrhythmia; Cardiac Imaging Techniques - methods; catheter ablation; Catheter Ablation - methods; Catheters; Computational modeling; Dipoles; EKG; Electric potential; Electrocardiographic imaging; Electrocardiography; Electrocardiography - methods; empirical Bayesian imaging; Humans; Image reconstruction; In vivo methods and tests; Inverse problems; Localization; Male; Mapping; Medical instruments; Methods; pace-mapping; Patient-Specific Modeling; Patients; Pericardium - diagnostic imaging; Pericardium - physiology; Regularization; Tachycardia; Tachycardia, Ventricular - diagnostic imaging; Tachycardia, Ventricular - physiopathology; Tachycardia, Ventricular - surgery; Torso; Ventricle; ventricular tachycardia
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2018.2872983

Objective: Ablation treatment of ventricular arrhythmias can be facilitated by pre-procedure planning aided by electrocardiographic inverse solution, which can help to localize the origin of arrhythmia. Our aim was to improve localization accuracy of the inverse solution by using a novel Bayesian approach. Methods: The inverse problem of electrocardiography was solved by reconstructing epicardial potentials from 120 body-surface electrocardiograms and from patient-specific geometry of the heart and torso for four patients suffering from scar-related ventricular tachycardia who underwent epicardial catheter mapping, which included pace-mapping. Simulations using dipole sources in patient-specific geometry were also performed. The proposed method, using dynamic spatio-temporal a priori constraints of the solution, was compared with classical Tikhonov methods based on fixed constraints. Results: The mean localization error of the proposed method for all available pacing sites (n = 78) was significantly smaller than that achieved by Tikhonov methods; specifically, the localization accuracy for pacing in the normal tissue (n = 17) was 8 ± 6 mm (mean ± SD) versus 13 ± 9 mm (P