Early warning of atrial fibrillation using deep learning

• Published in: Patterns (New York, N.Y.) Vol. 5; no. 6; p. 100970
• Main Authors: Gavidia, Marino, Zhu, Hongling, Montanari, Arthur N., Fuentes, Jesús, Cheng, Cheng, Dubner, Sergio, Chames, Martin, Maison-Blanche, Pierre, Rahman, Md Moklesur, Sassi, Roberto, Badilini, Fabio, Jiang, Yinuo, Zhang, Shengjun, Zhang, Hai-Tao, Du, Hao, Teng, Basi, Yuan, Ye, Wan, Guohua, Tang, Zhouping, He, Xin, Yang, Xiaoyun, Goncalves, Jorge
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 14.06.2024
• Subjects: artificial intelligence; atrial fibrillation; early warning signal; neural networks; prediction; prediction; atrial fibrillation; early warning signal; artificial intelligence; neural networks
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2024.100970

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. [Display omitted] •Predicts AF onset on average 30.8 min in advance on test data•Achieves 83% accuracy and 85% F1 score on test data•Uses R-to-R interval signals for monitoring, accessible via smartwatches•Has the potential to lower interventions and costs by early AF detection Atrial fibrillation (AF), the most prevalent heart rhythm disorder, affects millions globally, leading to significant increases in stroke risk, heart failure, and healthcare expenses. These challenges underscore the need for innovative monitoring solutions. Wearable technology, coupled with artificial intelligence, will eventually enable continuous, real-time tracking of heart health and warn users of imminent danger. This paper shows that such a future is not far. Our research introduces a model, WARN, that harnesses R-to-R intervals, the intervals between successive heartbeats, from readily available smartwatches to issue early warnings of AF onset. By leveraging extensive long-term data of individual patients, we expect that WARN can be personalized to significantly improve the prediction horizon, offering a future where many patients might manage AF proactively with as-needed medication rather than routine daily doses, thereby optimizing treatment regimens and improving quality of life. Unlocking the potential of wearable technology for cardiac health, this paper presents a deep learning model that can predict atrial fibrillation onset on average over 30 min in advance with high accuracy. Leveraging everyday wearable data, this work paves the way for a new era in proactive heart rhythm monitoring and reducing emergency interventions, offering a glimpse into the future of personalized and preemptive healthcare strategies.