Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping. (October 2020)

• Record Type: Journal Article
• Authors: 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 A.; Fedorov, Maxim V.; Hummel, John D.; Dylov, Dmitry V.; Fedorov, Vadim V.
• Is Part Of: Circulation. Volume 13:Number 10(2020)
• Publication Date: 2020-10
• Subjects: ablation -- atrial fibrillation -- catheters -- electrodes -- electrophysiology -- machine learning -- optical mapping; Arrhythmia -- Periodicals; Heart -- Electric properties -- Periodicals; 616.128
• DOI: 10.1161/CIRCEP.119.008249
• Languages: English
• ISSNs: 1941-3149

Abstract

Abstract : Background: Atrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning 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; 0.3 mm 2 resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm 2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning 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 annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, 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 higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P <0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together). Conclusions: The machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients. Abstract : Supplemental Digital Content is available in the text.