Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Dec 24, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

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

Published on: July 20, 2022

2.0K

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.

Chaur-Heh Hsieh1, Yan-Shuo Li2, Bor-Jiunn Hwang2

  • 1College of Artificial Intelligence, Yango University, Fuzhou 350015, China.

Sensors (Basel, Switzerland)
|April 16, 2020
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Prenatal Imaging of Micrognathia, Micromelia, and Fetal Hydrops Leading to the Diagnosis of Achondrogenesis Type II with a <i>COL2A1</i> Missense Mutation.

International journal of molecular sciences·2025
Same author

Identification of vascular hotspots and analysis of micro-vessel flow velocity waveforms in high-grade squamous intraepithelial lesions of the cervix.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics·2025
Same author

Fetal Aortic Blood Flow Velocity and Power Doppler Profiles in the First Trimester: A Comprehensive Study Using High-Definition Flow Imaging.

Bioengineering (Basel, Switzerland)·2024
Same author

A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm.

Diagnostics (Basel, Switzerland)·2024
Same author

Osteopoikilosis: benign bone islands in periarticular bones.

The Medical journal of Australia·2022
Same author

Cost-Effective Fitting Model for Indoor Positioning Systems Based on Bluetooth Low Energy.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

This study introduces a 1D convolutional neural network (CNN) for accurate atrial fibrillation (AF) detection from electrocardiogram (ECG) signals. The novel method reduces complexity and improves detection accuracy compared to existing deep learning approaches.

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Atrial fibrillation (AF) detection is vital due to its link with embolic stroke risk.
  • Current AF detection methods often use complex 2D spectrograms, leading to high computational costs.
  • There is a need for more efficient and accurate AF detection systems.

Purpose of the Study:

  • To develop a simplified and accurate AF detection method using a 1D convolutional neural network (CNN).
  • To reduce the computational complexity and implementation cost of AF detection systems.
  • To improve the detection accuracy of atrial fibrillation from ECG signals.

Main Methods:

  • An end-to-end 1D CNN architecture was designed for AF detection.
  • The impact of convolutional block components on accuracy was investigated.
Keywords:
atrial fibrillation (AF)convolutional neural network (CNN)deep learningelectrocardiogram (ECG)

More Related Videos

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
09:17

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

Published on: July 29, 2011

15.1K

Related Experiment Videos

Last Updated: Dec 24, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

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

Published on: July 20, 2022

2.0K
High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
09:17

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

Published on: July 29, 2011

15.1K
  • Grid search was employed for optimal CNN hyperparameter tuning.
  • A length normalization algorithm was developed for variable-length ECG records.

    • Main Results:

    • The proposed 1D CNN achieved an average F1 score of 78.2%.
    • The method demonstrated improved detection accuracy compared to existing deep learning techniques.
    • The 1D CNN exhibited lower network complexity.

    Conclusions:

    • The 1D CNN approach offers a simple yet effective method for AF detection.
    • This method provides a balance between high detection accuracy and reduced computational complexity.
    • The findings suggest a promising direction for efficient AF screening and management.