New AI model predicts irregular heartbeat 30 minutes in advance

Researchers have developed a groundbreaking AI-based model capable of forecasting irregular heartbeat, known as cardiac arrhythmia, approximately 30 minutes before its onset.

The model, developed by a team including researchers from the University of Luxembourg, has demonstrated an impressive 80% accuracy in predicting the transition from a normal cardiac rhythm to atrial fibrillation, the most prevalent form of cardiac arrhythmia characterised by irregular beating in the heart’s upper chambers.

Named WARN (Warning of Atrial fibRillatioN), the AI-model offers early warnings and can be seamlessly integrated into smartphones for data processing from smartwatches. This innovation empowers patients with timely alerts, enabling them to take preemptive measures to maintain stable cardiac rhythm, as detailed in the study published in the journal Patterns.

The development of the model involved training it on 24-hour recordings obtained from 350 patients at Tongji Hospital in Wuhan, China. Leveraging deep-learning techniques, a subset of machine-learning AI algorithms, WARN learns patterns from historical data to forecast cardiac arrhythmias, heralding a significant stride in proactive cardiac care.

Deep-learning is more specialised as it has multiple layers in its decision-making process.

The researchers found that WARN gave early warnings, on average 30 minutes before the start of atrial fibrillation, and is the first method to provide a warning far from onset, they said.

“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.

When approaching atrial fibrillation, the probability increases until it crosses a specific threshold, providing an early warning, Goncalves said.

Being of low computational cost, the AI-model is “ideal for integration into wearable technologies,” the researchers said.

“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 PTI