Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Disturbances in Heart Rhythm

Disturbances in Heart Rhythm

Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Interpretable Disorder-Promoted Synchronization and Coherence in Coupled Laser Networks.

Physical review letters·2025

Same author

Optimal flock formation induced by agent heterogeneity.

Nature communications·2025

Same author

Multi-omic network inference from time-series data.

NPJ systems biology and applications·2025

Same author

Towards automatic home-based sleep apnea estimation using deep learning.

NPJ digital medicine·2024

Same author

Power-grid vulnerability and its relation with network structure.

Chaos (Woodbury, N.Y.)·2023

Same author

Buffering variability in cell regulation motifs close to criticality.

Physical review. E·2022

Search research articles

Related Experiment Video

Updated: Jun 21, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

Early warning of atrial fibrillation using deep learning.

Marino Gavidia¹, Hongling Zhu², Arthur N Montanari³

¹Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4367 Belvaux, Luxembourg.

Patterns (New York, N.Y.)

|July 15, 2024

Summary

A new deep-learning model, Warning of Atrial Fibrillation (WARN), can predict the onset of atrial fibrillation (AF) 30 minutes in advance using R-to-R intervals from wearable devices. This early detection aims to reduce hospitalizations and improve patient outcomes.

Keywords:

artificial intelligence atrial fibrillation early warning signal neural networks prediction

More Related Videos

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 21, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Cardiology
Artificial Intelligence
Biomedical Engineering

Background:

Atrial fibrillation (AF) is the most common heart rhythm disorder, leading to increased hospitalizations and health risks.
Transitioning from sinus rhythm (SR) to AF often necessitates intensive medical interventions.

Purpose of the Study:

To develop and validate a deep-learning model for early prediction of AF onset.
To assess the model's performance using R-to-R interval signals for potential integration into wearable technology.

Main Methods:

A deep convolutional neural network, named Warning of Atrial Fibrillation (WARN), was developed.
The model was trained and validated on 24-h Holter ECG data from 280 patients.
Performance was evaluated on 70 test patients and 33 patients from external centers.

Main Results:

The WARN model achieved 83% accuracy and an 85% F1 score in predicting AF onset.
Predictions were made an average of 30.8 minutes before the event.
The model utilizes R-to-R interval signals, compatible with wearable sensors.

Conclusions:

The WARN model demonstrates high accuracy in early AF detection using accessible R-to-R interval data.
Its low computational cost makes it suitable for continuous monitoring via wearable devices.
Early AF detection has the potential to decrease emergency interventions and enhance patient care.