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Matrix decomposition based feature extraction for murmur classification.

Yuerong Chen1, Shengyong Wang, Chia-Hsuan Shen

  • 1Department of Mechanical Engineering, The University of Akron, Akron, OH 44325-3903, USA.

Medical Engineering & Physics
|October 18, 2011
PubMed
Summary

This study introduces novel features to differentiate innocent heart murmurs from organic ones using advanced signal processing. The goal is to develop an intelligent, home-use diagnostic system for heart murmurs.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Heart murmurs can indicate valvular disorders, but some, like musical murmurs in children, are innocent.
  • Mastering auscultation is challenging, and healthcare costs are rising.
  • Distinguishing innocent from organic murmurs is crucial for accurate diagnosis and treatment.

Purpose of the Study:

  • To identify new features for differentiating innocent from organic heart murmurs.
  • To develop an intelligent diagnostic system for home use.
  • To improve the accuracy and accessibility of heart murmur diagnosis.

Main Methods:

  • Continuous wavelet transform applied to phonocardiographic signals.
  • Singular value decomposition and QR decomposition for feature extraction.
  • Sequential forward floating selection (SFFS) and classification and regression trees (CART) for feature selection and classification.

Main Results:

  • Achieved an average sensitivity of 94%.
  • Achieved an average specificity of 83%.
  • Achieved an overall classification accuracy of 90%.

Conclusions:

  • The proposed feature extraction methods based on matrix decomposition are effective.
  • The study demonstrates potential for an intelligent diagnostic system for heart murmurs.
  • This approach offers a promising avenue for accessible cardiac diagnostics.