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Related Experiment Video

Updated: Dec 18, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network.

Palani Thanaraj Krishnan1, Parvathavarthini Balasubramanian2, Snekhalatha Umapathy3

  • 1Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Anna University, Chennai, Tamil Nadu, India.

Physical and Engineering Sciences in Medicine
|June 12, 2020
PubMed
Summary

Automated heart sound classification using deep neural networks (DNNs) offers a solution for real-time screening of phonocardiogram (PCG) signals, especially in underserved regions. A Feed-forward Neural Network achieved 85.65% accuracy, simplifying analysis by eliminating manual feature engineering.

Keywords:
Convolutional neural networkDeep learningFeature extractionFeedforward neural networkHeart soundPhonocardiogramTime series classification

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Cardiology

Background:

  • Low patient-to-doctor ratios in low- and middle-income countries necessitate automated diagnostic tools.
  • Phonocardiogram (PCG) analysis for cardiovascular disease detection is crucial but often requires expert interpretation.
  • Automating PCG analysis can improve accessibility and efficiency of cardiac screening.

Purpose of the Study:

  • To propose and evaluate deep neural network (DNN) architectures for automated classification of unsegmented PCG signals.
  • To eliminate the need for manual feature engineering and segmentation in PCG analysis.
  • To develop a real-time screening system for heart sound classification.

Main Methods:

  • Down-sampling PCG signals to 500 Hz and segmenting into 6-second epochs.
  • Applying a Savitzky-Golay filter for noise reduction and data smoothing.
  • Utilizing one-dimensional Convolutional Neural Networks (1D-CNN) and Feed-forward Neural Networks (F-NN) for classification.
  • Training and validating DNN models on 1081 PCG records.

Main Results:

  • The Feed-forward Neural Network (F-NN) model achieved an overall accuracy of 85.65%.
  • The F-NN model demonstrated a sensitivity of 86.73% and specificity of 84.75%.
  • Balanced accuracy was 85.74%, with an Area Under the Curve (AUC) of 0.857 from ROC analysis.
  • DNN models showed comparable performance to existing methods without feature engineering.

Conclusions:

  • Deep neural networks, particularly F-NN, are effective for automated, real-time heart sound classification from PCG signals.
  • The proposed DNN approach simplifies PCG analysis by removing the need for manual feature extraction and segmentation.
  • This technology holds potential for improving cardiovascular screening accessibility, especially in resource-limited settings.