Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study

Abstract

BackgroundLeft atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults.MethodsIn a sample of 2,206 male adults aged 17–43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window.ResultsThe greatest area under the receiver operating characteristic curve is present in machine learning of the SVM for ECG only (77.87%) and of the MLP for all biological and ECG features (81.01%), both of which are superior to the P wave duration (62.19%). If the sensitivity is fixed to 70–75%, the specificity of the SVM for ECG only is up to 72.4%, and that of the MLP for all biological and ECG features is increased to 81.1%, both of which are higher than 48.8% by the P wave duration.ConclusionsThis study suggests that machine learning is a reliable method for ECG and biological features to predict LAE in young adults. The proposed MLP, LR, and SVM methods provide early detection of LAE in young adults and are helpful to take preventive action on cardiovascular diseases.

Cardiorespiratory Fitness and Carotid Intima–Media Thickness in Physically Active Young Adults: CHIEF Atherosclerosis Study

“…The study included 1822 military men and women, aged 18-40 years, without any medications for hypertension or dyslipidemia from the Cardiorespiratory Fitness and Health in Eastern Armed Forces (CHIEF) study for atherosclerosis in Taiwan in 2018-2020 [14][15][16]. All participants received daily exercise training, e.g., a 3000 m run in the morning at the military base.…”

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

“…Hsu et al [ 44 ] applied three machine learning classifiers, including the multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) with a linear kernel, were used for 26 ECG features and with or without six biological training to identify the presence of Left atrial enlargement (LAE) from 2,206 male adults aged 17–43 years in Taiwan. The definition of LAE was based on an echocardiographic left atrial dimension >4 cm in the parasternal long-axis window.…”

“… 12-Lead ECG LASSO, MLR Patient-independent Hypertrophic Cardiomyopathy He et al [ 43 ] 2022 Chengdu (China) Journal article 100 patients from Department of Cardiovascular, West China Hospital of Sichuan University This study aimed to build statistical models and machine learning models based on P-wave parameters to predict Postoperative Atrial Fibrillation (POAF). 12-Lead ECG SVM Patient-independent Postoperative Atrial Fibrillation Hsu et al [ 44 ] 2022 Hualien (Taiwan) Journal article A population of 2,206 military males were obtained from the cardiorespiratory health in eastern armed forces study (CHIEF Heart Study) [ 45 , 46 ] This study proposed a machine learning method for electrocardiographic features to identify Left Atrial Enlargement in young adults. 12-Lead ECG MLP, SVM, LR Patient-independent Left Atrial Enlargement Zhao al [ 47 ].…”

Estimated power output for a distance run and maximal oxygen uptake in young adults

“…A history of moderate physical activity, such as a limited-time 3,000-m run in the morning per week in the past half year, was obtained from each participant. Each participant underwent the 2020 annual health examinations for physical examinations ( Hsu et al, 2022 ) in the Hualien Armed Forces General Hospital of Taiwan. Each participant also underwent the 2020 annual military exercise test for a 3,000-m run field test to assess endurance capacity.…”