Prediction of Atrial Fibrillation Occurrence With Handheld Mobile Electrocardiogram: Deep Learning Model Development Using Real-World Data.

Minje Park¹, Hyun Jin Ahn¹, Yeongyeon Na¹, Sunghoon Joo¹, Young Ho Lee^2,3, Seongwoo Han⁴, Myung Soo Park⁴, Dae Young Cheon⁴, Jeen Hwa Lee⁴, Ki Hong Lee^3,5

¹VUNO Inc., Seoul, Republic of Korea.

²Department of Biomedical Sciences, Chonnam National University Graduate School, Gwangju, Republic of Korea.

³Department of Cardiovascular Medicine, Chonnam National University Hospital, The Heart Center of Chonnam National University Hospital, 42 Jaebongro, Dong-gu, Gwangju, 61469, Republic of Korea, 82 62-220-6242, 82 62-223-3105.

⁴Division of Cardiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Republic of Korea.

⁵Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea.

PubMed DOI

Abstract

BACKGROUND: Atrial fibrillation (AF) is a common arrhythmia associated with an increased risk of stroke and heart failure. To improve prevention, recent studies have used deep learning models to identify at-risk individuals early from normal sinus rhythm (NSR). However, studies using mobile electrocardiogram (mECG) in outpatient, real-world settings remain underexplored. OBJECTIVE: The study aime…

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