Deep learning unmasks the ECG signature of Brugada Syndrome

UBC Research Data

Deep learning unmasks the ECG signature of Brugada Syndrome Melo, Luke

Description

Abstract

One in ten cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a non-invasive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians everywhere to identify the presence of this potentially life-threatening heart disease more easily.

Item Metadata

Title	Deep learning unmasks the ECG signature of Brugada Syndrome
Creator	Melo, Luke
Date Issued	2023-05-25
Description	Abstract One in ten cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a non-invasive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians everywhere to identify the presence of this potentially life-threatening heart disease more easily.
Subject	Other
Type	Dataset
Notes	Dryad version number: 5 Version status: submitted Dryad curation status: Published Sharing link: https://datadryad.org/stash/share/dQOMBJKo3ovOitkAqmxz2rwRd-4EtX882JsEdljGRPw</p> Storage size: 469089781 Visibility: public
Date Available	2023-05-20
Provider	University of British Columbia Library
License	CC0 1.0
DOI	10.14288/1.0432637
URI	https://doi.org/10.5683/SP3/Y7VZ3L
Publisher DOI	https://doi.org/10.5683/SP3/Y7VZ3L; https://doi.org/10.5061/dryad.s1rn8pkd9
Aggregated Source Repository	Dataverse

Item Media

Item Citations and Data

License

CC0 1.0

Open Collections