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Related Experiment Videos

MAF-Net: Multimodal cross-attention-based fusion network for cardiovascular disease classification.

Chang Qu1, Xin Zhang1, Yansong Lu1

  • 1School of Artificial Intelligence, Changchun University of Science and Technology, Changchun, Jilin, China.

Plos One
|April 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces MAF-Net, a novel multimodal deep learning model for cardiovascular disease classification. By integrating clinical data with electrocardiogram (ECG) features, MAF-Net significantly improves diagnostic accuracy for various arrhythmias.

Related Experiment Videos

Area of Science:

  • Cardiology and Medical Informatics
  • Artificial Intelligence in Healthcare
  • Biomedical Signal Processing

Background:

  • Cardiovascular diseases (CVDs) are a leading global cause of mortality, necessitating accurate and rapid diagnostic tools.
  • Electrocardiograms (ECGs) are crucial for CVD detection but traditional single-modality analysis limits accuracy by ignoring clinical data interactions.
  • Integrating multimodal data, including clinical information and ECG signals, is essential for enhancing CVD diagnostic performance.

Purpose of the Study:

  • To develop and evaluate MAF-Net, a Multimodal Cross-Attention-based Fusion Network, for improved cardiovascular disease classification.
  • To fuse clinical data features with ECG signal features using a novel bidirectional cross-attention mechanism.
  • To enhance the accuracy of classifying five major arrhythmia super-categories: Normal, Myocardial Infarction, ST-T Segment Changes, Conduction Disturbance, and Hypertrophy.

Main Methods:

  • Proposed MAF-Net, a deep learning model with three components: X Branch for clinical data processing (second-order polynomial features, channel attention), Y Branch for multi-scale ECG feature extraction (convolutional modules, Bi-LSTM, multi-head attention), and a Bidirectional Modality Fusion Module.
  • The fusion module employs a bidirectional cross-attention mechanism, utilizing clinical features as Query and ECG features as Key/Value for deep data integration.
  • The model was evaluated on a dataset classifying five super-categories of arrhythmias.

Main Results:

  • MAF-Net achieved high performance across key metrics: 90.75% ± 0.32% accuracy, 84.58% ± 0.41% precision, and 87.12% ± 0.38% recall.
  • The model demonstrated a strong F1 score of 0.8069 ± 0.005 and a ROC-AUC value of 0.9407 ± 0.002.
  • Experimental results indicate that MAF-Net outperforms existing methods in cardiovascular disease classification.

Conclusions:

  • The proposed MAF-Net effectively integrates clinical and ECG data, significantly improving cardiovascular disease classification accuracy.
  • The bidirectional cross-attention fusion mechanism is key to capturing complex inter-modal interactions.
  • MAF-Net shows significant potential for clinical decision support systems in cardiology.