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DTF-STCANet: A Dual Time-Frequency Swin Transformer and ConvNeXt Attention Network for Heart Sound Classification.

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Summary

This study introduces an AI model for early heart disease detection using phonocardiogram (PCG) signals, achieving 99.29% accuracy. The approach enhances cardiovascular disease diagnosis through advanced signal processing and machine learning.

Keywords:
cardiovascular diseasesconvnextdeep learningdual time–frequency fusionphonocardiogram (PCG)swin transformer

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Cardiovascular diseases represent a significant global health burden, necessitating early and accurate diagnostic methods.
  • Traditional stethoscope-based diagnosis of heart conditions requires specialized expertise, limiting accessibility.
  • Artificial intelligence (AI) is increasingly utilized for clinical decision support, offering potential for improved diagnostic capabilities.

Purpose of the Study:

  • To develop and evaluate an AI-driven system for the early detection of cardiovascular diseases.
  • To enhance the diagnostic accuracy of phonocardiogram (PCG) signals through advanced signal processing and machine learning techniques.
  • To investigate the efficacy of a novel Dual Time-Frequency Swin Transformer-ConvNeXt Attention Network (DTF-STCANet) for heart sound classification.

Main Methods:

  • Utilized the 2016 PhysioNet/CinC Challenge dataset comprising phonocardiogram (PCG) signals.
  • Generated time-frequency representations including spectrograms and continuous wavelet transform (CWT) images from PCG signals.
  • Implemented a Dual Time-Frequency Swin Transformer-ConvNeXt Attention Network (DTF-STCANet) model incorporating Weighted KNN for classification.

Main Results:

  • Achieved a high classification accuracy of 99.29% for detecting cardiovascular diseases.
  • Demonstrated superior performance compared to existing state-of-the-art models in heart sound analysis.
  • The integration of time and frequency domain features significantly improved diagnostic precision.

Conclusions:

  • The proposed AI-integrated approach significantly enhances the early diagnosis of heart disease.
  • The DTF-STCANet model shows promise for reliable and accurate cardiovascular disease screening.
  • This study underscores the potential of AI in revolutionizing cardiac diagnostics and improving patient outcomes.