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A multimodal approach for cardiac signals classification using deep learning with explainable AI methods.

Ali Mohammad Alqudah1, Ausilah Alfraihat2

  • 1Independent Researcher, Winnipeg, Canada.

Health Information Science and Systems
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel deep learning model integrating electrocardiogram (ECG) and phonocardiogram (PCG) signals for accurate cardiovascular disease diagnosis. The multimodal approach significantly improves diagnostic performance and interpretability.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiovascular diseases (CVDs) are a major global health concern requiring precise diagnostic tools.
  • Electrocardiogram (ECG) and phonocardiogram (PCG) signals offer complementary insights into cardiac electrical and mechanical functions.
  • Current diagnostic methods may benefit from advanced computational approaches for improved accuracy and efficiency.

Purpose of the Study:

  • To develop and validate a multimodal deep learning framework integrating ECG and PCG signals for enhanced cardiovascular disease diagnosis.
  • To assess the performance of the proposed framework against single-modality and existing multimodal approaches.
  • To utilize explainable AI techniques for interpreting the model's decision-making process and identifying clinically relevant features.
Keywords:
Cardiac signal classificationCross-modal attentionDeep learningElectrocardiogramExplainable AIMultimodal fusionPhonocardiogram

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Main Methods:

  • A dual-branch CNN-BiLSTM-SE architecture with cross-modal attention was employed to fuse ECG and PCG data.
  • A comprehensive preprocessing pipeline involving wavelet denoising, adaptive filtering, and normalization was implemented.
  • The model was rigorously evaluated on diverse public and custom datasets, including the MIT-BIH Arrhythmia, PTB Diagnostic ECG, and PhysioNet PCG datasets.

Main Results:

  • The multimodal deep learning model achieved a high overall accuracy of 97.0% and F1-scores between 94.3% and 98.1%.
  • Area Under the Curve (AUC) values exceeded 0.982 across all evaluated classes, demonstrating superior performance.
  • Explainable AI methods confirmed the model's focus on clinically significant indicators like irregular R-R intervals and systolic murmurs.

Conclusions:

  • The proposed multimodal deep learning framework provides a feasible, interpretable, and highly accurate decision-support system for cardiac diagnosis.
  • Integration of ECG and PCG signals via advanced deep learning significantly enhances diagnostic capabilities for cardiovascular diseases.
  • This approach holds promise for improving patient outcomes through more precise and timely cardiac condition identification.