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Heart murmur recognition and segmentation by complexity signatures

D Kumar1, P Carvalho, M Antunes

  • 1Centre for Informatics and Systems, University of Coimbra, Portugal. dinesh@dei.uc.pt

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed

Insights

This study presents a novel algorithm for identifying heart murmurs by analyzing the chaotic behavior of heart sounds using nonlinear dynamics. The method accurately detects murmurs, aiding in early heart disorder assessment.

Area of Science:

  • Cardiology
  • Biomedical Signal Processing
  • Nonlinear Dynamics

Background:

  • Heart sound analysis offers early detection of cardiac disorders by reflecting mechanical heart activity.
  • Heart murmurs are a common indicator of various heart conditions, often appearing before symptoms manifest.
  • Assessing heart sounds is crucial for diagnosing and monitoring cardiovascular health.

Purpose of the Study:

  • To introduce an innovative algorithm for accurate heart murmur identification.
  • To leverage nonlinear dynamics and phase space reconstruction for analyzing heart sound signals.
  • To improve the early detection and characterization of heart disorders.

Main Methods:

  • The proposed algorithm analyzes the chaotic behavior of heart sounds using nonlinear dynamics.
  • Heart sound signals are transformed into a phase space and reconstructed using an embedded matrix for segmentation.
  • Signal complexity and strength are computed in the phase space to locate sound component boundaries.

Main Results:

  • The algorithm demonstrated high performance in identifying heart murmurs.
  • Achieved a sensitivity of 91.09% and a specificity of 95.25% on a diverse heart sound database.
  • Successfully segmented murmurs from other heart sound components.

Conclusions:

  • The developed algorithm effectively identifies heart murmurs based on nonlinear dynamics.
  • This method provides a robust tool for heart sound analysis and cardiac disorder assessment.
  • The high accuracy suggests potential for clinical application in pre-symptomatic diagnosis.

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