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

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Wiley 2024-05-01
Series:Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Subjects:
ajmaline testing
artificial intelligence
Brugada syndrome
deep learning
Online Access:https://www.ahajournals.org/doi/10.1161/JAHA.123.033148

Internet

https://www.ahajournals.org/doi/10.1161/JAHA.123.033148

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