Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation

Author summaryAtrial fibrillation (AF) is the most common cardiac arrhythmia, with focal sources of excitation being an important mechanistic cause, especially in paroxysmal AF patients. The standard treatment for AF, pulmonary vein isolation, confines focal source activity within the veins, but may not eliminate the risk of AF since it leaves others untreated. We hypothesized that non-invasive electrical recordings, such as standard 12-lead electrocardiograms and 252-lead body surface potentials, could provide useful information for therapy planning. We generated a synthetic dataset of 2973 AF episodes, driven or initiated by focal and non-focal sources in patient-specific atrial geometries, to train three mechanism-inspired classifiers which determined the presence and location of focal sources, as well as the sustainability of AF in atria without a focal source present. We showed the mechanistic basis of using the pre-operative signals to predict AF susceptibility after focal ablation. Our classifiers were accurate, efficient and robust to inter-patient variability on the synthetic dataset. By using their pre-operative recordings to estimate AF mechanisms for 52 paroxysmal AF patients, our classifiers predicted a patient subgroup with improved two-to-three-year AF-free rates post-ablation, demonstrating the potential to guide mechanism-directed treatment based on non-invasive signals. Focal sources (FS) are believed to be important triggers and a perpetuation mechanism for paroxysmal atrial fibrillation (AF). Detecting FS and determining AF sustainability in atrial tissue can help guide ablation targeting. We hypothesized that sustained rotors during FS-driven episodes indicate an arrhythmogenic substrate for sustained AF, and that non-invasive electrical recordings, like electrocardiograms (ECGs) or body surface potential maps (BSPMs), could be used to detect FS and AF sustainability. Computer simulations were performed on five bi-atrial geometries. FS were induced by pacing at cycle lengths of 120-270 ms from 32 atrial sites and four pulmonary veins. Self-sustained reentrant activities were also initiated around the same 32 atrial sites with inexcitable cores of radii of 0, 0.5 and 1 cm. FS fired for two seconds and then AF inducibility was tested by whether activation was sustained for another second. ECGs and BSPMs were simulated. Equivalent atrial sources were extracted using second-order blind source separation, and their cycle length, periodicity and contribution, were used as features for random forest classifiers. Longer rotor duration during FS-driven episodes indicates higher AF inducibility (area under ROC curve = 0.83). Our method had accuracy of 90.6 +/- 1.0% and 90.6 +/- 0.6% in detecting FS presence, and 93.1 +/- 0.6% and 94.2 +/- 1.2% in identifying AF sustainability, and 80.0 +/- 6.6% and 61.0 +/- 5.2% in determining the atrium of the focal site, from BSPMs and ECGs of five atria. The detection of FS presence and AF sustainability were insensitive to vest placement (+/- 9.6%). On pre-operative BSPMs of 52 paroxysmal AF patients, patients classified with initiator-type FS on a single atrium resulted in improved two-to-three-year AF-free likelihoods (p-value < 0.01, logrank tests). Detection of FS and arrhythmogenic substrate can be performed from ECGs and BSPMs, enabling non-invasive mapping towards mechanism-targeted AF treatment, and malignant ectopic beat detection with likely AF progression.