LUX Researchers’ New AI Model Claims 80% Accuracy in Predicting Irregular Heartbeat 30 Mns Advance

The AI tool WARN offers early warnings and can be seamlessly integrated into smartphones for data processing from smartwatches.

Researchers from the University of Luxembourg have developed an AI-based model that can predict irregular heartbeat, known as cardiac arrhythmia, approximately 30 minutes before its onset.

The AI tool named WARN (Warning of Atrial fibRillatioN) offers early warnings and can be seamlessly integrated into smartphones for data processing from smartwatches.

Reportedly, the model 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 characterized by irregular beating in the heart's upper chambers.

Key Functionalities

The WARN is a deep-learning model that can recognize different phases – sinus rhythm, pre-atrial fibrillation, and atrial fibrillation – and calculate a ‘probability of danger’ that the patient will have an imminent episode.

WARN gives early warnings, on average 30 minutes before the start of atrial fibrillation, and is the first method to provide a warning far from the onset, stated the researchers.

The development of the model involved training it on 24-hour recordings obtained from 350 patients at Tongji Hospital in Wuhan, China.

Further, the model is trained using heart rate data, specifically R-to-R intervals, which can be acquired from easy-to-wear and affordable pulse signal recorders such as smartwatches.

Per the researchers, the AI tool has multiple layers in its decision-making process. This allows for the development of real-time monitoring and early warnings from wearable devices.

On the development, Jorge Goncalves, 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. Being of low computational cost, the AI-model is "ideal for integration into wearable technologies."

With this move, the Luxembourg researchers aim to develop personalized models that continuously refine and retrain based on an individual’s heart dynamics, leading to enhanced performance and even earlier warnings.

This approach could potentially lead to new clinical trials and innovative therapeutic interventions.

“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.

The study was published in the journal Patterns. This journal features innovative research in various fields of science and technology.

AI Advancing Healthcare Delivery

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