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Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning.

Ali Harimi1, Yahya Majd2, Abdorreza Alavi Gharahbagh3

  • 1Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood 43189-36199, Iran.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces chaogram, a novel image conversion technique for heart sound analysis. This method enables advanced machine learning models to classify cardiac pathologies from phonocardiogram (PCG) signals with high accuracy.

Keywords:
biomedical signaldeep learningphonocardiogramsignal to image transform

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Heart sounds offer critical diagnostic information for cardiac conditions.
  • Current heart sound classification methods are explored in telemedicine, digital signal processing, and machine learning for rapid cardiac pathology identification.

Purpose of the Study:

  • To introduce chaogram, a new transform for converting heart sound signals into color images.
  • To enable the application of deep convolutional neural networks and transfer learning to phonocardiogram (PCG) signal analysis.

Main Methods:

  • The chaogram transform projects the phase space representation of PCG signals onto three coordinate planes, creating color images.
  • The InceptionV3 model was utilized for classification on the PhysioNet dataset.

Main Results:

  • The chaogram method successfully converted heart sound signals into images suitable for deep learning models.
  • The InceptionV3 model achieved a classification score of 88.06% on the imbalanced PhysioNet dataset, using the average of sensitivity and specificity.

Conclusions:

  • Chaogram is a viable method for transforming PCG signals into image data for machine learning.
  • This approach facilitates the use of advanced deep learning techniques, like InceptionV3, for improved cardiac pathology detection.