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Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers

Abstract:

Aims: Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification. Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR) imaging.

Methods: High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3389/fphys.2018.00213

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
St Hugh's College
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
RDM; RDM Cardiovascular Medicine
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM; RDM Cardiovascular Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM; RDM Cardiovascular Medicine
Role:
Author
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Funding agency for:
Lyon, A
Grant:
RE/13/1/30181
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Funding agency for:
Ariga, R
Grant:
RE/13/1/30181
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Funding agency for:
Minchole, A
Grant:
G0601617
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Funding agency for:
Laguna, P
Grant:
TEC2013-44666-R
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Funding agency for:
Neubauer, S
Grant:
RE/13/1/30181
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Publisher:
Frontiers Media Publisher's website
Journal:
Frontiers in Physiology Journal website
Volume:
9
Issue:
213
Pages:
1-13
Publication date:
2018-03-13
Acceptance date:
2018-02-26
DOI:
ISSN:
1664-042X
Pubs id:
pubs:829315
UUID:
uuid:af357fbd-d6f1-4469-91be-a4b303b0ff3b
Local pid:
pubs:829315
Deposit date:
2018-03-13

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