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  1. Home
  3. Classification Of Heart Sounds Using Chaogram Transform And Deep Convolutional Neural Network Transfer Learning.
  1. Home
  3. Classification Of Heart Sounds Using Chaogram Transform And Deep Convolutional Neural Network Transfer Learning.

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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.