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Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine

Ahmed Barnawi1, Mehrez Boulares1,2, Rim Somai3

  • 1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary

This study presents a simple method for detecting cardiovascular diseases (CVDs) using phonocardiogram (PCG) signals. By optimizing data selection and using a VGG19 model, accurate and accessible heart condition screening is achieved.

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CVD classificationconvolutional neural networkdata selectiondeep learningpretrained modeltransfer learning

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Cardiovascular diseases (CVDs) are a leading global cause of death, projected to increase significantly by 2030.
  • Unhealthy lifestyles and insufficient early diagnosis hinder effective CVD management.
  • Traditional cardiac auscultation is effective but often lacks accessibility and affordability.

Purpose of the Study:

  • To develop a simple, efficient, and accessible method for recognizing normal and abnormal phonocardiogram (PCG) signals for early CVD detection.
  • To overcome limitations of existing automated CVD screening methods that often involve complex preprocessing and segmentation.
  • To leverage machine learning, specifically Convolutional Neural Networks (CNNs), for improved PCG signal analysis.

Main Methods:

  • Utilized Physionet data for training and validation of machine learning models.
  • Employed data selection techniques including Kernel Density Estimation (KDE) for signal duration and Signal-to-Noise Ratio (SNR) assessment.
  • Applied Gaussian Mixture Model (GMM) clustering to enhance the performance of 17 pretrained Keras CNN models, focusing on fine-tuning the VGG19 architecture.

Main Results:

  • Achieved excellent classification performance in distinguishing normal from abnormal PCG signals.
  • Demonstrated the effectiveness of KDE for selecting optimal signal duration, leading to improved model accuracy.
  • Fine-tuning the VGG19 model, combined with optimized data selection, yielded an overall accuracy of 0.97, sensitivity of 0.946, precision of 0.944, and specificity of 0.946.

Conclusions:

  • The proposed approach offers a simplified yet highly effective method for automated CVD screening using PCG signals.
  • Integrating techniques like KDE for data selection significantly enhances the performance of CNN models in cardiac signal analysis.
  • This research paves the way for more accessible and user-friendly tools for routine cardiovascular health monitoring.