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Multi-branch convolutional network and LSTM-CNN for heart sound classification.

Seyed Amir Latifi1, Hassan Ghassemian2, Maryam Imani1

  • 1Image Processing and Information Analysis Lab, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

Physical and Engineering Sciences in Medicine
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Summary
This summary is machine-generated.

This study introduces two deep learning models for fast, cost-effective cardiac disease diagnosis using heart sounds. The Long Short-Term Memory-Convolutional Neural (LSCN) model achieved high accuracy, outperforming existing methods for early cardiovascular disease detection.

Keywords:
Cardiovascular diseases classificationFeature extractionHeart soundsLSTMMulti-branch-CNN

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Cardiovascular diseases are a major global health concern, demanding precise and timely diagnosis.
  • Current diagnostic methods for cardiac conditions face challenges related to complexity, cost, and accessibility.
  • Limited labeled medical datasets pose a significant hurdle in developing automated diagnostic tools.

Purpose of the Study:

  • To develop novel deep learning architectures for automated cardiac disease diagnosis from heart sound analysis.
  • To address the challenge of limited labeled datasets in medical AI applications.
  • To provide fast, accurate, and cost-effective diagnostic solutions for cardiovascular abnormalities.

Main Methods:

  • Proposed two deep learning models: a Multi-Branch Deep Convolutional Neural Network (MBDCN) and a Long Short-Term Memory-Convolutional Neural (LSCN) network.
  • MBDCN utilizes diverse filter sizes and power spectrum input for enhanced feature extraction, mimicking auditory processing.
  • LSCN integrates LSTM blocks with MBDCN to improve time-domain feature extraction, enhancing heart sound analysis.

Main Results:

  • The LSCN model achieved a multiclass classification accuracy of 89.65% and binary classification accuracy of 93.93%.
  • Both proposed models significantly outperformed traditional methods like Mel Frequency Cepstral Coefficients (MFCC) and wavelet transforms.
  • Fivefold cross-validation confirmed the robustness and reliability of the proposed deep learning approaches.

Conclusions:

  • The developed deep learning architectures, particularly LSCN, demonstrate high efficacy for automated heart sound analysis.
  • These models offer clinically viable and computationally efficient solutions for the early detection of cardiovascular diseases.
  • The study highlights the potential of AI in overcoming limitations of traditional diagnostic methods and improving patient outcomes globally.