Deep Learning Algorithms for Estimation of Demographic and Anthropometric Features from Electrocardiograms.
BMI
age
artificial intelligence
blood type
deep learning
demographics
electrocardiography
sex
Journal
Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588
Informations de publication
Date de publication:
12 Apr 2023
12 Apr 2023
Historique:
received:
12
03
2023
revised:
02
04
2023
accepted:
06
04
2023
medline:
28
4
2023
pubmed:
28
4
2023
entrez:
28
4
2023
Statut:
epublish
Résumé
The electrocardiogram (ECG) has been known to be affected by demographic and anthropometric factors. This study aimed to develop deep learning models to predict the subject's age, sex, ABO blood type, and body mass index (BMI) based on ECGs. This retrospective study included individuals aged 18 years or older who visited a tertiary referral center with ECGs acquired from October 2010 to February 2020. Using convolutional neural networks (CNNs) with three convolutional layers, five kernel sizes, and two pooling sizes, we developed both classification and regression models. We verified a classification model to be applicable for age (<40 years vs. ≥40 years), sex (male vs. female), BMI (<25 kg/m
Identifiants
pubmed: 37109165
pii: jcm12082828
doi: 10.3390/jcm12082828
pmc: PMC10146401
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : National Research Foundation of Korea
ID : NRF-2022R1A2C2091160
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