AI-Based Model Predicts Irregular Heartbeat 30 Minutes Prior to Onset
Researchers have developed a groundbreaking AI-based model, named WARN, that can forecast cardiac arrhythmia, commonly known as irregular heartbeat, approximately 30 minutes before its occurrence. The model exhibits an impressive 80% accuracy in predicting the transition from a normal cardiac rhythm to atrial fibrillation, the most prevalent form of cardiac arrhythmia.
By leveraging deep-learning techniques, a subset of machine-learning AI algorithms, WARN learns patterns from historical data to forecast cardiac arrhythmias, enabling patients to take preemptive measures to maintain stable cardiac rhythm.
The development of the model involved training it on 24-hour recordings obtained from 350 patients at Tongji Hospital in Wuhan, China. 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.
Being of low computational cost, the AI-model is “ideal for integration into wearable technologies.” 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.”
The study was published in the journal Patterns and is a significant stride in proactive cardiac care, empowering patients with timely alerts and enabling them to take preemptive measures to maintain stable cardiac rhythm.
