An AI model predicts atrial fibrillation (AF) 30 minutes in advanceDepositphotos AI trained on simple heart rate data can predict an episode of the most common heart rhythm disorder, atrial fibrillation, 30 minutes in advance, new research has shown. With plans for it to be incorporated into a smartphone so it can analyze data from a smartwatch, the model would act as an early warning system.
The most common heart rhythm disorder, atrial fibrillation (AF), significantly increases emergency department presentations and the risk of other diseases like stroke and dementia . The condition occurs when the heart’s upper chambers (atria) beat chaotically, out of sync with the lower chambers (ventricles), producing an irregular, often very rapid, heart rhythm.
Reverting a patient from AF back to regular sinus rhythm can require intensive interventions such as cardioversion, delivering a low-energy shock to ‘reset’ the heart’s conduction system. (Yes, it’s the same device used in medical programs, accompanied by a cry of “CLEAR!”) So, being able to detect an episode of AF before it happens would enable early interventions that might improve patient outcomes.
Researchers from the Luxembourg Center for Systems Biomedicine (LCSB) at the University of Luxembourg have published a study wherein they trained a deep-learning model to accurately predict, 30 minutes ahead of time, when a person will go into AF.
At present, electrocardiography (ECG) can only detect AF right before it occurs, so it can’t be considered an early warning system.
“In contrast, our work departs from this approach to a more prospective prediction […]
Smartwatch AI predicts atrial fibrillation 30 minutes before it arrives
