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

Paul-Adrian Călburean Luigi Pannone Cinzia Monaco Domenico Giovanni Della Rocca Antonio Sorgente Alexandre Almorad Gezim Bala Filippo Aglietti Robbert RamakIngrid OvereinderErwin StrökerGudrun PappaertMarius Măru'teriMarius Harpa Mark La Meir Pedro BrugadaJuan SieiraAndrea SarkozyGian-Battista ChierchiaCarlos De Asmundis

Published in: Journal of the American Heart Association (2024)

BrS-Net, a deep convolutional neural network, can identify BrS type I pattern with high performance. BrS-Net can predict from baseline ECG the development of a BrS type I pattern after ajmaline with good performance in an unselected population.

Keyphrases

convolutional neural network
deep learning
drug induced
liver injury
heart rate variability
heart rate
artificial intelligence
machine learning
blood pressure
adverse drug