RT Journal Article
T1 Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation
A1 Ríos Muñoz, Gonzalo Ricardo
A1 Fernández Avilés, Francisco
A1 Arenal, Ángel
AB The maintaining and initiating mechanisms of atrial fibrillation (AF) remain controversial. Deep learning is emerging as a powerful tool to better understand AF and improve its treatment, which remains suboptimal. This paper aims to provide a solution to automatically identify rotational activity drivers in endocardial electrograms (EGMs) with convolutional recurrent neural networks (CRNNs). The CRNN model was compared with two other state-of-the-art methods (SimpleCNN and attention-based time-incremental convolutional neural network (ATI-CNN)) for different input signals (unipolar EGMs, bipolar EGMs, and unipolar local activation times), sampling frequencies, and signal lengths. The proposed CRNN obtained a detection score based on the Matthews correlation coefficient of 0.680, an ATI-CNN score of 0.401, and a SimpleCNN score of 0.118, with bipolar EGMs as input signals exhibiting better overall performance. In terms of signal length and sampling frequency, no significant differences were found. The proposed architecture opens the way for new ablation strategies and driver detection methods to better understand the AF problem and its treatment.
PB MPDI
SN 1422-0067
YR 2022
FD 2022-04-11
LK https://hdl.handle.net/20.500.14352/71739
UL https://hdl.handle.net/20.500.14352/71739
LA eng
NO Instituto de Salud Carlos III (ISCIII)
NO Ricors–Red de Investigación Cooperativa Orientada a Resultados en Salud–RICORS TERAV
DS Docta Complutense
RD 7 abr 2025