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

Chu-Yu Hsu; Pang-Yen Liu; Shu-Hsin Liu; Younghoon Kwon; Carl J Lavie; Gen-Min Lin

doi:10.3389/fcvm.2022.840585

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

Front Cardiovasc Med. 2022 Mar 1:9:840585. doi: 10.3389/fcvm.2022.840585. eCollection 2022.

Authors

Chu-Yu Hsu^{1

2

3}, Pang-Yen Liu^{1

2}, Shu-Hsin Liu⁴, Younghoon Kwon⁵, Carl J Lavie⁶, Gen-Min Lin^{1

2}

Affiliations

¹ Department of Medicine, Hualien Armed Forces General Hospital, Hualien City, Taiwan.
² Department of Internal Medicine, Tri-Service General Hospital and National Defense Medical Center, Taipei City, Taiwan.
³ Department of Medicine, Taoyuan Armed Forces General Hospital, Taoyuan City, Taiwan.
⁴ Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Hualien City, Taiwan.
⁵ Department of Internal Medicine, University of Washington, Seattle, WA, United States.
⁶ John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine, New Orleans, LA, United States.

Abstract

Background: Left 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.

Methods: In 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.

Results: The 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.

Conclusions: This 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.

Keywords: echocardiography; electrocardiography; left atrial enlargement; machine learning; young adults.