In 1992, Pedro and Josep Brugada described a typical electrocardiographic (ECG) pattern
including right bundle branch block (RBBB) and persistent ST-segment elevation associated
with sudden cardiac disease, without underlying electrolyte disturbances, ischemia,
or structural heart disease. The syndrome, which later became widely known as the
Brugada syndrome (BrS), first came to their attention in 1986 when a 3-year-old Polish
boy named Lech and his father Andreas were referred to Professor Wellens in Maastricht,
The Netherlands, owing to multiple episodes of syncope. On the ECG they observed the
typical ECG pattern that later came to be associated with the syndrome. Despite the
language barrier, they discovered that the boy had a sister who died at the age of
2 years, probably owing to cardiac arrest.
1
,2
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Canadian Journal of CardiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Right bundle branch block, persistent ST segment elevation and sudden cardiac death: a distinct clinical and electrocardiographic syndrome. A multicenter report.J Am Coll Cardiol. 1992; 20: 1391-1396
- Brugada syndrome 1992-2012: 20 years of scientific excitement, and more.Eur Heart J. 2013; 34: 3610-3615
- Brugada syndrome and reduced right ventricular outflow tract conduction reserve: a final common pathway?.Eur Heart J. 2021; 42: 1073-1081
- About Brugada syndrome and its prevalence.Europace. 2012; 14: 925-928
- Brugada phenocopy: new terminology and proposed classification.Ann Noninvasive Electrocardiol. 2012; 17: 299-314
- Expert cardiologists cannot distinguish between Brugada phenocopy and Brugada syndrome electrocardiogram patterns.Europace. 2016; 18: 1095-1100
- Deep learning for cardiovascularmedicine: a practical primer.Eur Heart J. 2019; 40: 2058-2073
- Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.Nat Med. 2019; 25 (Erratum in: Nat Med 2019;25:53): 65-69
- A deep learning–enabled electrocardiogram model for the identification of a rare inherited arrhythmia: Brugada syndrome.Can J Cardiol. 2022; 38: 152-159
- Use of artificial intelligence and deep neural networks in evaluation of patients with electrocardiographically concealed long QT syndrome from the surface 12-lead electrocardiogram.JAMA Cardiol. 2021; 6: 532-538
- Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram.J Am Coll Cardiol. 2020; 75: 722-733
- Computer versus cardiologist: is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?.Heart Rhythm. 2021; 18: 79-87
- Improving electrocardiogram-based detection of rare genetic heart disease using transfer learning: an application to phospholamban p.Arg14del mutation carriers.Comput Biol Med. 2021; 131: 104262
- Discovering and visualizing disease-specific electrocardiogram features using deep learning: proof-of-concept in phospholamban gene mutation carriers.Circ Arrhythmia Electrophysiol. 2021; 14e009056
- J-Wave syndromes expert consensus conference report: Emerging concepts and gaps in knowledge.Heart Rhythm. 2016; 13: e295-e324
- Yield and pitfalls of ajmaline testing in the evaluation of unexplained cardiac arrest and sudden unexplained death: single-center experience with 482 families.JACC Clin Electrophysiol. 2017; 3: 1400-1408
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nat Mach Intell. 2019; 1: 206-215
Article info
Publication history
Published online: September 23, 2021
Accepted:
September 16,
2021
Received:
September 8,
2021
Footnotes
See article by Liu et al., pages 152–159 of this issue.
See page 150 for disclosure information.
Identification
Copyright
© 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.
ScienceDirect
Access this article on ScienceDirectLinked Article
- A Deep Learning–Enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada SyndromeCanadian Journal of CardiologyVol. 38Issue 2
- PreviewBrugada syndrome is a major cause of sudden cardiac death in young people and has distinctive electrocardiographic (ECG) features. We aimed to develop a deep learning–enabled ECG model for automatic screening for Brugada syndrome to identify these patients at an early point in time, thus allowing for life-saving therapy.
- Full-Text
- Preview