Complexity and recurrence of body surface electrocardiograms correlate with estimated reentrant atrial activity using electrocardiographic imaging in atrial fibrillation patients

Background Complexity and signal recurrence metrics obtained from body surface potential mapping (BSPM) allow quantifying atrial fibrillation (AF) substrate complexity. This study aims to correlate electrocardiographic imaging (ECGI) detected reentrant patterns with BSPM-calculated signal complexity and recurrence metrics. Methods BSPM signals were recorded from 28 AF patients (17 male, 11 women, 62.69 +/- 8.09 y.o.), followed by ECGI calculation. Signal complexity and recurrence metrics were computed on BSPM and ECGI signals. Rotors per second and rotor duration were computed on ECGI signals for each atrium and the whole atrial surface. Correlation between BSPM metrics and ECGI reentrant patterns for the entire atrial surface and for left atrium (LA) and right atrium (RA) were analyzed. Results Atrial complexity and recurrence metrics strongly correlated when computed on BSPM and ECGI. Higher sample entropy and relative harmonic energy (RHE) correlated with rotors of short duration. The highest dominant frequency of the ECGI signals did not correlate with the reentrant activity of the ECGI. Higher short- and long-term recurrence of BSPM signals correlated with longer duration rotors, particularly for long-term recurrence (r(LA)=0.74 vs. r(RA)=0.42). Only ECGI-based reentrant parameters showed higher LA complexity compared to RA (p < 0.05). Conclusions BSPM metrics strongly correlate with metrics measured on ECGI signals. BSPM metrics indicate a more elevated atrial electro-structural remodeling aligned with more short-duration rotors from ECGI computations. Although BSPM delivers qualitative AF reentry data, ECGI remains essential for identifying regional substrate complexity.