Predicting and Recognizing Drug‐Induced Type I Brugada Pattern Using ECG‐Based Deep Learning

Background Brugada syndrome (BrS) has been associated with sudden cardiac death in otherwise healthy subjects, and drug‐induced BrS accounts for 55% to 70% of all patients with BrS. This study aims to develop a deep convolutional neural network and evaluate its performance in recognizing and predict...

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Bibliografiska uppgifter

Huvudupphov:	Paul‐Adrian Călburean, Luigi Pannone, Cinzia Monaco, Domenico Della Rocca, Antonio Sorgente, Alexandre Almorad, Gezim Bala, Filippo Aglietti, Robbert Ramak, Ingrid Overeinder, Erwin Ströker, Gudrun Pappaert, Marius Măru’teri, Marius Harpa, Mark La Meir, Pedro Brugada, Juan Sieira, Andrea Sarkozy, Gian‐Battista Chierchia, Carlo de Asmundis
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	Wiley 2024-05-01
Serie:	Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Ämnen:	ajmaline testing artificial intelligence Brugada syndrome deep learning
Länkar:	https://www.ahajournals.org/doi/10.1161/JAHA.123.033148
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