Researchers from the University of Luxembourg have developed a cutting-edge AI model that can predict irregular heartbeat, or cardiac arrhythmia, up to 30 minutes before it occurs. This innovative model, named WARN (Warning of Atrial fibRillatioN), has shown an impressive 80% accuracy rate in forecasting the transition from a normal cardiac rhythm to atrial fibrillation, the most common form of irregular heartbeat.
The AI-model was trained on data from 350 patients at Tongji Hospital in Wuhan, China, using deep-learning techniques to learn patterns from historical information. By analyzing heart rate data, WARN can recognize different phases – sinus rhythm, pre-atrial fibrillation, and atrial fibrillation – and calculate a ‘probability of danger’ indicating an imminent episode.
The research team found that WARN provided early warnings, typically 30 minutes before the onset of atrial fibrillation. This advanced predictive capability allows patients to take proactive measures to help stabilize their cardiac rhythm. Moreover, the AI-model is low in computational cost, making it ideal for integration into wearable technologies such as smartphones and smartwatches.
The integration of WARN into wearable devices opens up possibilities for real-time monitoring and early warnings for patients on a daily basis. This development represents a significant advancement in proactive cardiac care, empowering individuals to manage their cardiac health effectively. With its potential for seamless integration into wearable devices, the WARN AI-model could revolutionize how we approach cardiac arrhythmias and improve patient outcomes.
