We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Sekisui Diagnostics UK Ltd.

Download Mobile App

Deep-Learning Model Predicts Arrhythmia 30 Minutes before Onset

By HospiMedica International staff writers
Posted on 23 Apr 2024
Print article
Image: The deep-learning model can predict arrhythmia 30 minutes before it happens (Photo courtesy of 123RF)
Image: The deep-learning model can predict arrhythmia 30 minutes before it happens (Photo courtesy of 123RF)

Atrial fibrillation, the most common type of cardiac arrhythmia worldwide, affected approximately 59 million people in 2019. Characterized by an irregular and often rapid heart rate, atrial fibrillation occurs when the heart's upper chambers (atria) beat out of sync with the lower chambers (ventricles). Addressing arrhythmia can require aggressive interventions such as electrically shocking the heart back to a normal rhythm or surgically removing areas that generate faulty signals. Associated with increased risks of heart failure, dementia, and stroke, atrial fibrillation presents significant challenges to healthcare systems, emphasizing the importance of early detection and treatment. Traditional detection methods, relying on heart rate and electrocardiogram (ECG) data, typically identify atrial fibrillation just before its onset, offering no advanced warning.

Now, researchers from the Luxembourg Centre for Systems Biomedicine (LCSB) of the University of Luxembourg (Esch-sur-Alzette, Luxembourg) have achieved a breakthrough with the development of an advanced deep-learning model that can predict the onset of atrial fibrillation. Their model, named WARN (Warning of Atrial fibRillatioN), successfully provides early warnings about 30 minutes before atrial fibrillation begins, with approximately 80% accuracy.

This innovative model was trained and tested using 24-hour recordings from 350 patients, marking a significant improvement over previous prediction methods by offering a much earlier warning. The potential to integrate this technology into wearable devices could transform patient management, allowing for preemptive interventions that enhance outcomes. Notably, WARN stands out as the first method to offer a substantial lead time before the onset of atrial fibrillation, setting a new standard in arrhythmia prediction.

“Our work departs from this approach to a more prospective prediction model,” said Prof. Jorge Goncalves, head of the Systems Control group at the LCSB. “We used heart rate data to train 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.”

Related Links:
University of Luxembourg

Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Silver Member
Compact 14-Day Uninterrupted Holter ECG
Soft-Tissues Biopsy Needle

Print article


Surgical Techniques

view channel
Image: Ureteral electrothermal injury is visible via histology ex vivo (Photo courtesy of Long et al., doi 10.1117/1.BIOS.1.1.015001)

Minimally Invasive Imaging Technique to Revolutionize Ureteral Injury Detection

Electrothermal ureteral injuries are a frequent complication during pelvic surgery. The ureters, which are delicate tubes carrying urine from the kidneys to the bladder, are especially at risk due to their... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The Quantra Hemostasis System has received US FDA special 510(k) clearance for use with its Quantra QStat Cartridge (Photo courtesy of HemoSonics)

Critical Bleeding Management System to Help Hospitals Further Standardize Viscoelastic Testing

Surgical procedures are often accompanied by significant blood loss and the subsequent high likelihood of the need for allogeneic blood transfusions. These transfusions, while critical, are linked to various... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.