ArticleIEEE journal of translational engineering in health and medicine2020
An Electrocardiographic System With Anthropometrics via Machine Learning to Screen Left Ventricular Hypertrophy among Young Adults.
Article in IEEE journal of translational engineering in health and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Classification of "Athlete's Heart" using machine learning of conventional 12-lead ECG: male elite 3,000-m runner data in the CHIEF study.Frontiers in sports and active living · 2026Article
- Article
- Nocturnal sentry duty and cardiometabolic characteristics in armed forces personnel.World journal of cardiology · 2024Article
- Deep residual 2D convolutional neural network for cardiovascular disease classification.Scientific reports · 2024Article
- Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Asian Female Athlete's Heart: The CHIEF Heart Study.Acta Cardiologica Sinica · 2023Article
- Electrocardiographic and echocardiographic predictors of greater carotid intima-media thickness in tactical athletes: The CHIEF atherosclerosis study.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.Journal of healthcare engineering · 2023Article
- Editorial: Insights in cardiovascular epidemiology and prevention: 2022.Frontiers in cardiovascular medicine · 2023Article
- Muscular Strength and Carotid Intima-Media Thickness in Physically Fit Young Adults: The CHIEF Atherosclerosis Study.Journal of clinical medicine · 2022Article
- Sex-specific cardiometabolic risk markers of left ventricular mass in physically active young adults: the CHIEF heart study.Scientific reports · 2022Article
- Cardiorespiratory Fitness and Carotid Intima-Media Thickness in Physically Active Young Adults: CHIEF Atherosclerosis Study.Journal of clinical medicine · 2022Article
- Electrocardiographic and cardiometabolic risk markers of left ventricular diastolic dysfunction in physically active adults: CHIEF heart study.Frontiers in cardiovascular medicine · 2022Article
- Deep learning assessment of left ventricular hypertrophy based on electrocardiogram.Frontiers in cardiovascular medicine · 2022Article
- Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study.Frontiers in cardiovascular medicine · 2022Article
- Editorial: Physical Fitness and Cardiovascular Health in Specific Populations.Frontiers in cardiovascular medicine · 2022Article
- Metabolically healthy obesity and physical fitness in military males in the CHIEF study.Scientific reports · 2021Article
- Prevalence and characteristics of mitral valve prolapse in military young adults in Taiwan of the CHIEF Heart Study.Scientific reports · 2021Article
- Athlete's Heart in Asian Military Males: The CHIEF Heart Study.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prevalence of physiological and pathological left ventricular hypertrophy (LVH) among young adults is about 5%. A use of electrocardiographic (ECG) voltage criteria and machine learning for the ECG parameters to identify the presence of LVH is estimated only 20-30% in the general population. The aim of this study is to develop an ECG system with anthropometric data using machine learning to increase the accuracy and sensitivity for a screen of LVH. In a large sample of 2,196 males, aged 17-45 years, the support vector machine (SVM) classifier is used as the machine learning method for 31 characteristics including age, body height and body weight in addition to 28 ECG parameters such as axes, intervals and voltages to link the output of LVH. The diagnosis of LVH is based on the echocardiographic criteria for young males to be 116 gram/meter
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.