作者
Shun Liao, Mahmoud Bokhari, Praloy Chakraborty, Adrian Suszko, Gavin Jones, Danna Spears, Michael Gollob, Zhaolei Zhang, Bo Wang, Vijay S Chauhan
发表日期
2022/8/1
期刊
Clinical Electrophysiology
卷号
8
期号
8
页码范围
1010-1020
出版商
American College of Cardiology Foundation
简介
Background
The diagnosis of Brugada syndrome by 12-lead electrocardiography (ECG) is challenging because the diagnostic type 1 pattern is often transient.
Objectives
This study sought to improve Brugada syndrome diagnosis by using deep learning (DL) to continuously monitor for Brugada type 1 in 24-hour ambulatory 12-lead ECGs (Holters).
Methods
A convolutional neural network was trained to classify Brugada type 1. The training cohort consisted of 10-second standard/high precordial leads from 12-lead ECGs (n = 1,190) and 12-lead Holters (n = 380) of patients with definite and suspected Brugada syndrome. The performance of the trained model was evaluated in 2 testing cohorts of 10-second standard/high precordial leads from 12-lead ECGs (n = 474) and 12-lead Holters (n = 716).
Results
DL achieved a receiver-operating characteristic area under the curve of 0.976 (95% CI: 0.973-0.979) in …
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S Liao, M Bokhari, P Chakraborty, A Suszko, G Jones… - Clinical Electrophysiology, 2022