An artificial intelligence framework for improving predictive performance of left ventricular hypertrophy using electrocardiography

被引：0

作者：

Ryu, Jiseung ^{[1
]}

Lee, Yerin ^{[1
]}

Kang, Hyunyoung ^{[1
]}

Lee, Solam ^{[2
,3
]}

Park, Youngjun ^{[4
]}

Yang, Sejung ^{[1
]}

机构：

[1] Yonsei Univ, Dept Biomed Engn, Wonju, South Korea

[2] Yonsei Univ, Dept Dermatol, Wonju Coll Med, Wonju, South Korea

[3] Yonsei Univ, Inst Hair & Cosmet Med, Wonju Coll Med, Dept Prevent Med, Wonju, South Korea

[4] Yonsei Univ, Dept Internal Med, Div Cardiol, Wonju Severance Christian Hospita, Wonju, South Korea

来源：

2022 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC) | 2022年

关键词：

Left ventricular; Hypertrophy; Deep learning; Artificial intelligence; Electrocardiography; gender;

D O I：

10.1109/ICEIC54506.2022.9748483

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Left ventricular hypertrophy (LVH) is defined as an increase in left ventricular mass (LVM) associated with structural changes of myocardium. LVH is a risk factor in cardiovascular disease and clinical diagnosis is important. Electrocardiography (ECG) is the easiest, non-invasive, and economical diagnostic method. Diagnostic methods using ECG have been studied, but performance has been low. Recently, deep learning algorithms using big data and convolution layers have been in the spotlight. In this study, we developed a deep learning-based AI algorithms that can predict LVH in consideration of gender. Both algorithms had some linear relationship between the target value and the predicted value of the model, so the possibility of prediction could be confirmed.

引用

页数：3

共 7 条

[1] Left Ventricular Hypertrophy by the Surface ECG [J].