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Related Concept Videos

Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

142
Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
142
Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
940

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Updated: Sep 20, 2025

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A Deep Learning-Based Multimodal Fusion Model for Recurrence Prediction in Persistent Atrial Fibrillation Patients.

Li Chen1, Xujian Feng1, Haonan Chen2

  • 1Department of Biomedical Engineering, Fudan University, Shanghai, China.

Journal of Cardiovascular Electrophysiology
|May 23, 2025
PubMed
Summary

Predicting atrial fibrillation (AF) ablation recurrence in persistent AF (PeAF) patients is improved by a new deep learning model. This model integrates electrocardiogram (ECG) signals with clinical data for better personalized treatment decisions.

Keywords:
12‐lead electrocardiogrammachine learningmultimodal fusionpersistent atrial fibrillationradiofrequency catheter ablation

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Long-term success of atrial fibrillation (AF) ablation is challenging, especially for persistent AF (PeAF) patients.
  • Predicting recurrence risk in PeAF is complex, with current clinical assessments limited by their inability to fully utilize electrocardiogram (ECG) data.
  • Integrating clinical features with ECG signals offers a promising approach to enhance prediction accuracy and personalize patient management.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting postablation recurrence in PeAF patients.
  • To investigate the efficacy of combining preprocedural AF rhythm 12-lead ECG signals with clinical data for improved risk prediction.
  • To enhance personalized clinical decision-making for PeAF patients undergoing radiofrequency catheter ablation.

Main Methods:

  • A retrospective analysis of 77 PeAF patients who underwent radiofrequency catheter ablation between 2016 and 2019.
  • Development of a multimodal fusion deep learning framework using a residual block network.
  • Integration of preprocedural AF rhythm 12-lead ECG, clinical scores, and patient baseline characteristics, with fivefold cross-validation for training and testing.

Main Results:

  • The fusion model achieved an average AUC of 0.74 (maximum 0.82) in predicting recurrence.
  • The model significantly outperformed traditional clinical scoring systems and single-modal ECG-based models.
  • Demonstrated robustness and stability, with a low variance of 0.08, even with small sample sizes.

Conclusions:

  • A novel deep learning model combining AF rhythm ECG signals and clinical characteristics effectively predicts recurrence risk in PeAF patients post-ablation.
  • This approach significantly improves prediction performance, supporting personalized clinical decision-making.
  • The model shows substantial potential for clinical application in managing PeAF patients.