ArticleIEEE journal of translational engineering in health and medicine2020
A 12-Lead ECG-Based System With Physiological Parameters and Machine Learning to Identify Right Ventricular Hypertrophy in 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 22 papers.
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Who cites it
22 citing papers in PubMed, 36 citations in OpenAlex.
- 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
- Rosmarinic acid ameliorates the complications of monocrotaline-induced right ventricular hypertrophy on the left ventricle: Investigating the signaling pathway of Wnt/β-catenin in the heart.Iranian journal of basic medical sciences · 2024Article
- 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
- 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
- Electrocardiographic and cardiometabolic risk markers of left ventricular diastolic dysfunction in physically active adults: CHIEF heart study.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
- MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG.IEEE journal of translational engineering in health and medicine · 2021Article
- Athlete's Heart Assessed by Sit-Up Strength Exercises in Military Men and Women: The CHIEF Heart Study.Frontiers in cardiovascular medicine · 2021Article
- Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.Frontiers in cardiovascular medicine · 2021Article
- Psychological stress and long-term blood pressure variability of military young males: The cardiorespiratory fitness and hospitalization events in armed forces study.World journal of cardiology · 2020Article
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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
objectiveThe presence of right ventricular hypertrophy (RVH) accounts for approximately 5-10% in young adults. The sensitivity estimated by commonly used 12-lead electrocardiographic (ECG) criteria for identifying the presence of RVH is under 20% in the general population. The aim of this study is to develop a 12-lead ECG system with the related information of age, body height and body weight via machine learning to increase the sensitivity and the precision for detecting RVH.
methodIn a sample of 1,701 males, aged 17-45 years, support vector machine is used for the training of 31 parameters including age, body height and body weight in addition to 28 ECG data such as axes, intervals and wave voltages as the inputs to link the output RVH. The RVH is defined on the echocardiographic finding for young males as right ventricular anterior wall thickness > 5.5 mm.
resultsOn the system goal for increasing sensitivity, the specificity is controlled around 70-75% and all data tested in the proposed method show competent sensitivity up to 70.3%. The values of area under curve of receiver operating characteristic curve and precision-recall curve using the proposed method are 0.780 and 0.285, respectively, which are better than 0.518 and 0.112 using the Sokolow-Lyon voltage criterion, respectively, for detecting unspecific RVH.
conclusionWe present a method using simple physiological parameters with ECG data to effectively identify more than 70% of the RVH among young adults. Clinical Impact: This system provides a fast, precise and feasible diagnosis tool to screen RVH.
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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.