Using AI to predict arrhythmia 30 minutes before it happens

Atrial fibrillation (AF) is not just the most common cardiac arrhythmia affecting millions globally — it's a condition that significantly increases the risk of heart failure, dementia, and stroke.

Addressing this, researchers at the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg have unveiled a pioneering development in the detection of AF, making early intervention possible and potentially revolutionizing patient care.

How does it work?

The newly developed model, aptly named WARN (Warning of Atrial fibRillatioN), utilizes a deep learning algorithm trained on 24-hour heart rate data from 350 patients.

Unlike traditional methods that detect AF moments before its onset, WARN identifies the transition from normal cardiac rhythm to AF with an impressive average lead time of 30 minutes. This is achieved by analyzing R-to-R intervals (the time between heartbeats), which are then used to calculate the "probability of danger" of an impending AF episode. As this probability increases and crosses a certain threshold, an early warning is triggered.

Why does it matter?

This innovation stands to significantly impact how AF is managed, moving from reactive to proactive treatment. By integrating this technology into wearable devices like smartwatches, patients can receive timely warnings. This advance allows for earlier interventions, such as taking antiarrhythmic medication or other preventive measures, thus reducing the need for more severe emergency treatments like electrical cardioversion or surgical interventions.

Moreover, the potential for continuous monitoring through everyday wearable technology means that individuals can stay informed about their cardiac health in real time. This not only enhances the quality of life but also aids in the management of AF, which is vital given its association with severe health complications.

The context

The ability of WARN to operate with minimal computational resources further enhances its applicability in everyday devices. As noted by Dr. Marino Gavidia, "This model has high performance using only heart rate data that can be acquired from easy-to-wear and affordable devices." This accessibility could lead to widespread adoption, providing benefits on a global scale.

Moving forward, the LCSB team plans to refine this technology through personalized models. These models will adapt to an individual's unique cardiac rhythms, thanks to continuous data provided by smartwatches. Prof. Jorge Goncalves encapsulates the future vision: "Eventually, this approach could even lead to new clinical trials and innovative therapeutic interventions."

Ultimately, the promise of the WARN model represents a significant leap towards enhancing how cardiac health is monitored and managed, potentially setting a new standard in the treatment and prevention of atrial fibrillation.