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. Author manuscript; available in PMC: 2021 Oct 1.
Published in final edited form as: Circ Arrhythm Electrophysiol. 2020 Sep 13;13(10):e008249. doi: 10.1161/CIRCEP.119.008249

Figure 1.

Figure 1.

Analytical Pipeline for Driver Prediction. (A) - Example of sustained AF episode maintained by reentrant driver, which was recorded simultaneously by clinical MEM (Higher-Density (HD) and Lower-Density (LD) catheters) and high-resolution NIOM (Cameras 1–2). (B) - Ground-truth labels for electrodes (red – center of driver, blue – non-driver) were acquired by NIOM, driver periphery labels (red/blue) were counted as driver in center plus periphery annotation or as non-driver in driver center annotation. (C) - Fourier spectra of annotated unipolar HD electrograms and co-located NIOM OAPs were used for generation of frequency features for Machine Learning approach. LAA/RAA = Left/Right Atria Appendage, I/SVC = Inferior/Superior Vena Cava, MEM = Multi-Electrode Mapping, NIOM = Near-Infrared Optical Mapping, OAP = Optical Action Potential, A.U. = Arbitrary Units.