Imperial College London
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Dr Kiran Haresh Kumar Patel

Faculty of Medicine, National Heart & Lung Institute

Honorary Clinical Lecturer
 
 
 
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Contact

 

kiran.patel

 
 
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Location

 

ICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Patel:2021:10.1016/j.cvdhj.2021.10.003,
author = {Patel, K and Li, X and Sun, L and Peters, N and Ng, FS},
doi = {10.1016/j.cvdhj.2021.10.003},
journal = {Cardiovascular Digital Health Journal},
pages = {S1--S10},
title = {Neural networks applied to 12-lead electrocardiograms predict body mass index, visceral adiposity and concurrent cardiometabolic ill-health},
url = {http://dx.doi.org/10.1016/j.cvdhj.2021.10.003},
volume = {2},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundObesity is associated with electrophysiological remodeling, which manifests as detectable changes on the surface electrocardiogram (ECG).ObjectiveTo develop neural networks (NN) to predict body mass index (BMI) from ECGs and test the hypothesis that discrepancies between NN-predicted BMI and measured BMI are indicative of underlying adiposity and/or concurrent cardiometabolic ill-health.MethodsNN models were developed using 36,856 12-lead resting ECGs from the UK Biobank. Two architectures were developed for continuous and categorical BMI estimation (normal weight [BMI <25 kg/m2] vs overweight/obese [BMI ≥25 kg/m2]). Models for male and female participants were trained and tested separately. For each sex, data were randomly divided into 4 folds, and models were evaluated in a leave-1-fold-out manner.ResultsECGs were available for 17,807 male and 19,049 female participants (mean ages: 61 ± 7 and 63 ± 8 years; mean BMI 26 ± 5 kg/m2 and 27 ± 4 kg/m2, respectively). NN models detected overweight/obese individuals with average accuracies of 75% and 73% for male and female subjects, respectively. The magnitudes of difference between NN-predicted BMI and actual BMI were significantly correlated with visceral adipose tissue volumes. Concurrent hypertension, diabetes, dyslipidemia, and/or coronary heart disease explained false-positive classifications (ie, calculated BMI <25 kg/m2 misclassified as ≥25 kg/m2 by NN model, P < .001).ConclusionNN models applied to 12-lead ECGs predict BMI with a reasonable degree of accuracy. Discrepancies between NN-predicted and calculated BMI may be indicative of underlying visceral adiposity and concomitant cardiometabolic perturbation, which could be used to identify individuals at risk of cardiometabolic disease.
AU  - Patel,K
AU  - Li,X
AU  - Sun,L
AU  - Peters,N
AU  - Ng,FS
DO  - 10.1016/j.cvdhj.2021.10.003
EP  - 10
PY  - 2021///
SN  - 2666-6936
SP  - 1
TI  - Neural networks applied to 12-lead electrocardiograms predict body mass index, visceral adiposity and concurrent cardiometabolic ill-health
T2  - Cardiovascular Digital Health Journal
UR  - http://dx.doi.org/10.1016/j.cvdhj.2021.10.003
UR  - https://www.sciencedirect.com/science/article/pii/S2666693621001195?via%3Dihub
UR  - http://hdl.handle.net/10044/1/92590
VL  - 2
ER  -
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