Deep-Learning Model Predicts Arrhythmia 30 Minutes before Onset

Now, researchers from the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg (Esch-sur-Alzette, Luxembourg) have achieved a breakthrough with the development of an advanced deep-learning model that can predict the onset of atrial fibrillation. Their model, named WARN (Warning of Atrial fibRillatioN), successfully provides early warnings about 30 minutes before atrial fibrillation begins, with approximately 80% accuracy.

This innovative model was trained and tested using 24-hour recordings from 350 patients, marking a significant improvement over previous prediction methods by offering a much earlier warning. The potential to integrate this technology into wearable devices could transform patient management, allowing for preemptive interventions that enhance outcomes. Notably, WARN stands out as the first method to offer a substantial lead time before the onset of atrial fibrillation, setting a new standard in arrhythmia prediction.

“Our work departs from this approach to a more prospective prediction model,” said Prof. Jorge Goncalves, head of the Systems Control group at the LCSB. “We used heart rate data to train a deep learning model that can recognize different phases – sinus rhythm, pre-atrial fibrillation and atrial fibrillation – and calculate a “probability of danger” that the patient will have an imminent episode.”

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