This new AI model will predict cardiac irregularity 30 minutes before its onset

Researchers have made a new AI model that predicts irregular heartbeats like atrial fibrillation, a common type of heart rhythm problem. It's about 80 per centaccurate and can give warnings 30 minutes before the irregular heartbeat starts.

This model could be put on smartphones to read data from smartwatches, helping people take action to keep their heart rhythms stable. The study has been publishedin the journal Patterns.

To develop the model, the team used24-hour recordings collected from 350 patients at Tongji Hospital in Wuhan, China.

Add WION as a Preferred Source

Named WARN (Warning of Atrial fibRillatioN), the model relies on deep-learning, a form of machine-learning AI algorithms that discern patterns from historical data to forecast outcomes. Deep-learning is distinguished by its multi-layered decision-making process.

Understanding this technology

The researchers observed that WARN issued advance alerts, typically 30 minutes ahead of atrial fibrillation onset, marking it as the initial method to offer warnings significantly prior to the event, they remarked.

"We used heart rate data to train a deep learning model that can recognise different phases -- (normal) sinus rhythm, pre-atrial fibrillation and atrial fibrillation -- and calculate a 'probability of danger' that the patient will have an imminent episode," Jorge Goncalves, from the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, and the study's corresponding author, said.

Goncalves mentioned that as atrial fibrillation approaches, the likelihood steadily rises until it surpasses a predefined threshold, triggering an early warning.

The researchers noted that due to its low computational requirements, the AI model is "well-suited for incorporation into wearable devices."

Also watch |Artificial Intelligence vs Hollywood actors: AI emerges as big concern in entertainment industry

"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," study author Arthur Montanari, an LCSB researcher, said.

(With inputs from agencies)