  Catheter ablation has limited therapeutic efficacy against non-paroxysmal
atrial fibrillation (AF), and electrophysiological studies using mapping
catheters have been applied to evaluate the AF substrate. However, many of
these approaches rely on detecting excitation timing from electrograms (ECGs),
potentially compromising their effectiveness in complex AF scenarios. Herein,
we introduce Deep-learning-based Electrode Action Potential Mapping (DEAP
Mapping), a deep learning model designed to reconstruct membrane potential
images from annotation-free unipolar ECG signals. We conducted ex vivo
experiments using porcine hearts (N = 6) to evaluate the accuracy of DEAP
Mapping by simultaneously performing fluorescence measurement of membrane
potentials and measurements of epicardial unipolar ECGs. Membrane potentials
estimated via DEAP Mapping were compared with those measured via optical
mapping. We assessed the clinical applicability of DEAP Mapping by comparing
the DEAP Mapping's estimations from clinically measured catheter electrode
signals with those from established electrode-mapping techniques. DEAP Mapping
accurately estimated conduction delays and blocks in ex vivo experiments. Phase
variance analysis, an AF substrate evaluation method, revealed that the
substrate identified from optical mapping closely resembled that identified
from DEAP Mapping estimations (structural similarity index of >0.8). In
clinical evaluations, DEAP Mapping estimation observed several conduction
delays and blocks that were not observed with existing methods, indicating that
DEAP Mapping can estimate excitation patterns with higher spatiotemporal
resolution. DEAP Mapping has a potential to derive detailed changes in membrane
potential from intra-operative catheter electrode signals, offering enhanced
visualisation of the AF substrate from the estimated membrane potentials.
