artificial intelligence; atrial fibrillation; early warning signal; neural networks; prediction; Cardiac rhythms; Early warning; F1 scores; Learning models; Neural-networks; Rhythm disorders; Sinus rhythm; Warning signals; Decision Sciences (all)
Abstract :
[en] Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
GAVIDIA, Marino ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > AI Modelling and Prediction > Team Jorge GONCALVES
Zhu, Hongling; Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
Montanari, Arthur N; Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
FUENTES, Jesús ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > AI Modelling and Prediction
Cheng, Cheng; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Dubner, Sergio; Clinica y Maternidad Suizo Argentina, Buenos Aires 1461, Argentina
Chames, Martin; Centro Integral Cardiovascular, Gualeguaychú, Entre Ríos, Argentina
Maison-Blanche, Pierre; Department of Cardiology, Hôpital Bichat, 75018 Paris, France
Rahman, Md Moklesur; Computer Science Department, University of Milan, 20133 Milan, Italy
Sassi, Roberto; Computer Science Department, University of Milan, 20133 Milan, Italy
Badilini, Fabio; Department of Physiologic Nursing, University of California, San Francisco, San Francisco, CA 94143, USA
Jiang, Yinuo; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Zhang, Shengjun; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Zhang, Hai-Tao; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Du, Hao; Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
Teng, Basi; Department of Plant Sciences, Cambridge University, CB2 3EA Cambridge, UK
Yuan, Ye; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Wan, Guohua; Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200052, China
Tang, Zhouping; Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
He, Xin; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yang, Xiaoyun; Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
GONCALVES, Jorge ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > AI Modelling and Prediction ; Department of Plant Sciences, Cambridge University, CB2 3EA Cambridge, UK
The authors are thankful for support from the Luxembourg National Research Fund (grant PRIDE15/10907093/CriTiCS ) and the National Natural Science Foundation of China (grants 92167201 and 82100531 ).
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