{"id":4570,"date":"2024-04-22T09:53:33","date_gmt":"2024-04-22T07:53:33","guid":{"rendered":"https:\/\/www.uni.lu\/lcsb-en\/?post_type=news&p=4570"},"modified":"2025-02-17T15:35:29","modified_gmt":"2025-02-17T14:35:29","slug":"predicting-arrhythmia-30-minutes-before-it-happens","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/lcsb-en\/news\/predicting-arrhythmia-30-minutes-before-it-happens\/","title":{"rendered":"Predicting arrhythmia 30 minutes before it happens"},"content":{"rendered":"\n
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Atrial fibrillation is the most common cardiac arrhythmia worldwide with around 59 million people concerned in 2019<\/a>. This irregular heartbeat is associated with increased risks of heart failure, dementia and stroke. It constitutes a significant burden to healthcare systems, making its early detection and treatment a major goal. Researchers from the Luxembourg Centre for Systems Biomedicine<\/a> (LCSB) of the University of Luxembourg have recently developed a deep-learning model capable of predicting the transition from a normal cardiac rhythm to atrial fibrillation. It gives early warnings on average 30 minutes before onset, with an accuracy of around 80%. These results, published in the scientific journal Patterns<\/em><\/a>, pave the way for integration into wearable technologies, allowing early interventions and better patient outcomes.<\/p>\n\n\n\n

During atrial fibrillation, the heart’s upper chambers beat irregularly and are out of sync with the ventricles. Reverting to a regular rhythm can require intensive interventions, from shocking the heart back to normal sinus rhythm to the removal of a specific area responsible for faulty signals. Being able to predict an episode of atrial fibrillation early enough would allow patients to take preventive measures to keep their cardiac rhythm stable. However, current methods based on the analysis of heart rate and electrocardiogram (ECG) data are only able to detect atrial fibrillation right before its onset and do not provide an early warning.<\/p>\n\n\n\n

\u201cIn contrast, our work departs from this approach to a more prospective prediction model,\u201d explains Prof. Jorge Goncalves<\/a>, head of the AI Modelling and Prediction group<\/a> at the LCSB. \u201cWe used heart rate data to train a deep learning model that can recognise different phases \u2013 sinus rhythm, pre-atrial fibrillation and atrial fibrillation \u2013 and calculate a \u201cprobability of danger\u201d that the patient will have an imminent episode.\u201d When approaching atrial fibrillation, the probability increases until it crosses a specific threshold, providing an early warning.<\/p>\n\n\n\n

This artificial intelligence model, called WARN (Warning of Atrial fibRillatioN), was trained and tested on 24h-recordings collected from 350 patients at Tongji Hospital (Wuhan, China) and gave early warnings, on average 30 minutes before the start of atrial fibrillation, with great accuracy. Compared to previous work on arrhythmia prediction, WARN is the first method to provide a warning far from onset.<\/p>\n\n\n

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\u201cAnother interesting aspect is that our model has a high performance using only R-to-R intervals, basically just heart rate data, that can be acquired from easy-to-wear and affordable pulse signal recorders such as smartwatches,\u201d highlights Dr Marino Gavidia, first author of the publication, who worked on this project during his PhD within the Systems Control group and the Doctoral Training Unit CriTiCS<\/a>. \u201cThese devices can be used by patients on a daily basis, so our results open possibilities for the development of real-time monitoring and early warnings from comfortable wearable devices,\u201d adds Dr Arthur Montanari, a LCSB researcher involved in the project.<\/p>\n\n\n\n