Researchers at the University of Luxembourg (LCSB) have developed an AI which is able to predict an episode of afib 30 minutes ahead of time with around 80% accuracy.
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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 recognise different phases -- sinus rhythm, pre-atrial fibrillation and atrial fibrillation -- and calculate a "probability of danger" that the patient will have an imminent episode." When approaching atrial fibrillation, the probability increases until it crosses a specific threshold, providing an early warning.
"Another 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. 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,"
The long-term objective is for patients to be able to continuously monitor their cardiac rhythm and receive early warnings that can provide sufficient time to take antiarrhythmic medication or use some targeted treatments to prevent the onset of atrial fibrillation.
Used on a smartwatch could this be a game changer? What do you think?