Researchers have unveiled a groundbreaking AI based model capable of predicting irregular heartbeat, known as cardiac arrhythmia, approximately 30 minutes before its onset. This innovation marks a significant advancement in cardiac health monitoring and preventive care.
The study, published in the journal Patterns, highlights the efficacy of the AI model, dubbed WARN (Warning of Atrial fibRillatioN), in foreseeing the transition from a normal cardiac rhythm to atrial fibrillation, the most prevalent type of cardiac arrhythmia characterized by irregular atrial contractions.
Developed by a team of researchers, including experts from the University of Luxembourg, WARN exhibited an impressive 80% accuracy rate in predicting the onset of atrial fibrillation. Utilizing deep-learning algorithms, WARN analyses heart rate data to identify different cardiac phases and calculate the likelihood of an imminent arrhythmic episode.
“We used heart rate data to train a deep learning model that can recognise different phases — (normal) sinus rhythm, pre-atrial fibrillation and atrial fibrillation — and calculate a ‘probability of danger’ that the patient will have an imminent episode,” explained Jorge Goncalves, the study’s corresponding author from the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg.
This AI model represents a significant leap forward in cardiac health monitoring as it provides early warnings of impending atrial fibrillation, offering patients valuable time to take preventive measures and stabilize their cardiac rhythm. Importantly, WARN’s ability to predict arrhythmia onset far in advance distinguishes it as the first method of its kind to offer such advanced warning capabilities
Moreover, the researchers envision integrating this AI model into wearable technologies, such as smartphones and smartwatches, to enable real-time monitoring of cardiac health. With its low computational cost, WARN can seamlessly integrate into wearable devices, empowering individuals to monitor their heart health on a daily basis.
“These 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,” remarked Arthur Montanari, an LCSB researcher involved in the study.
By leveraging the power of artificial intelligence, the WARN model represents a promising tool in the fight against cardiac arrhythmias, offering new avenues for proactive cardiac health management and improved patient outcomes.
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