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Related Experiment Video

Updated: Sep 4, 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

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MGNN: A multiscale grouped convolutional neural network for efficient atrial fibrillation detection.

Sen Liu1, Aiguo Wang2, Xintao Deng2

  • 1Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China.

Computers in Biology and Medicine
|July 19, 2022
PubMed
Summary

This study introduces a novel deep learning method for accurate atrial fibrillation (AF) detection using RR intervals. The multiscale grouped neural network (MGNN) demonstrates high accuracy and efficiency for clinical use.

Keywords:
Atrial fibrillationDeep learningGrouped convolutional neural networkRR interval sequences

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Reliable detection of atrial fibrillation (AF) is crucial for disease management and personalized treatment.
  • Current methods require robust and efficient detection tools for widespread application.

Purpose of the Study:

  • To develop a novel and accurate deep learning-based method for atrial fibrillation detection.
  • To evaluate the performance and efficiency of the proposed model.

Main Methods:

  • Utilized RR interval sequences for AF detection.
  • Designed a multiscale grouped convolutional neural network (MGNN) incorporating self-attention for automatic feature extraction and classification.
  • Performed 5-fold cross-validation and tested on four independent datasets.

Main Results:

  • Achieved an average accuracy of 97.07% in 5-fold cross-validation.
  • Demonstrated strong generalization with accuracies of 92.23%, 96.86%, 94.23%, and 95.91% on unseen datasets.
  • The MGNN exhibited superior detection performance and lower computational complexity compared to other network structures.

Conclusions:

  • The proposed MGNN is an efficient and robust atrial fibrillation detector.
  • This model holds significant potential for clinical auxiliary diagnosis and long-term home monitoring using wearable devices.