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Towards classifying non-segmented heart sound records using instantaneous frequency based features.

Ali Mohammad Alqudah1

  • 1Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, Jordan.

Journal of Medical Engineering & Technology
|November 27, 2019
PubMed
Summary

This study introduces a novel method for analyzing phonocardiograph (PCG) signals to classify heart conditions. The technique achieves over 95% accuracy in diagnosing normal versus abnormal heart sounds and multiple cardiac classes.

Keywords:
Heart soundsclassificationinstantaneous frequencynon-segmented PCGphonocardiogram (PCG)

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Phonocardiograph (PCG) signals are crucial biosignals for diagnosing cardiac diseases, often used alongside electrocardiograms (ECG).
  • Automated heart sound analysis systems are increasingly important for supporting medical professionals in clinical decision-making.
  • Current methods require robust feature extraction techniques for accurate classification of non-segmented PCG signals.

Purpose of the Study:

  • To propose a novel method for heart sound feature extraction from non-segmented phonocardiograph (PCG) signals.
  • To evaluate the effectiveness of the proposed method for both binary (Normal/Abnormal) and multi-class (Five Classes) cardiac condition classification.
  • To demonstrate the robustness and high performance of the extracted features in automated cardiac diagnosis.

Main Methods:

  • A two-phase approach was developed: 1) estimation of instantaneous frequency from the PCG signal, and 2) extraction of eleven features from the estimated instantaneous frequency.
  • The proposed feature extraction method was validated on two distinct datasets for binary and multi-class classification tasks.
  • Performance was evaluated using standard metrics including accuracy, sensitivity, specificity, and precision with Random Forest and K-Nearest Neighbors (KNN) classifiers.

Main Results:

  • The proposed feature extraction method demonstrated high efficacy in classifying cardiac conditions from non-segmented PCG signals.
  • Both binary and multi-class classification tasks achieved overall accuracy, sensitivity, specificity, and precision exceeding 95%.
  • The robustness of the extracted features was confirmed across different classification scenarios and using established machine learning algorithms.

Conclusions:

  • The novel instantaneous frequency-based feature extraction method provides a robust and highly accurate approach for automated heart sound analysis.
  • This technique significantly supports the development of advanced diagnostic tools for cardiac diseases using PCG signals.
  • The high performance metrics suggest strong potential for clinical application in augmenting cardiac diagnostics.