In Circulation. Arrhythmia and electrophysiology
Background - Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multi-electrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of Machine Learning (ML) to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings. Methods - Temporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM) (0.3mm2 resolution) and 64-electrode MEM (Higher-Density (HD) or Lower-Density (LD) with 3mm2 and 9mm2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier Transform analysis into 28407 total Fourier spectra. Thirty-five features for ML were extracted from each Fourier spectrum. Results - Targeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated classifications for driver vs non-driver electrodes in MEM arrays. Compared to analysis of single electrogram frequency features, averaging the features for each surrounding 8 electrodes neighborhood, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation including driver periphery electrodes were added to driver center annotation. Notably, f1-score for the binary classification of HD catheter dataset were significantly higher than that of LD catheter (0.81 ± 0.02 vs 0.66 ± 0.04, p<0.05). The trained algorithm correctly highlighted 86% of driver regions with HD but only 80% with LD MEM arrays (81% for LD+HD arrays together). Conclusions - The ML model pre-trained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or non-driver compared to the NIOM gold-standard. Future application of NIOM-validated ML approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.
Zolotarev Alexander M, Hansen Brian J, Ivanova Ekaterina A, Helfrich Katelynn M, Li Ning, Janssen Paul M L, Mohler Peter J, Mokadam Nahush A, Whitson Bryan, Fedorov Maxim V, Hummel John D, Dylov Dmitry V, Fedorov Vadim V
machine learning, multi-electrode mapping, targeted ablation