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GWAS Study

Deep learning on resting electrocardiogram to identify impaired heart rate recovery.

Diamant N, Di Achille P, Weng LC et al.

36046430 PubMed ID
GWAS Study Type
43722 Participants
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

DN
Diamant N
DA
Di Achille P
WL
Weng LC
LE
Lau ES
KS
Khurshid S
FS
Friedman S
RC
Reeder C
SP
Singh P
WX
Wang X
SG
Sarma G
GM
Ghadessi M
MJ
Mielke J
EE
Elci E
KI
Kryukov I
EH
Eilken HM
DA
Derix A
EP
Ellinor PT
AC
Anderson CD
PA
Philippakis AA
BP
Batra P
LS
Lubitz SA
HJ
Ho JE
Chapter II

Abstract

Summary of the research findings

Background and objective: Postexercise heart rate recovery (HRR) is an important indicator of cardiac autonomic function and abnormal HRR is associated with adverse outcomes. We hypothesized that deep learning on resting electrocardiogram (ECG) tracings may identify individuals with impaired HRR.

43,722 European ancestry individuals

Chapter III

Study Statistics

Key metrics and study information

43722
Total Participants
GWAS
Study Type
No
Replicated
European
Ancestry
U.K.
Recruitment Country
Chapter IV

Analysis

Comprehensive review of health and genetic findings

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