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Complexity-based analysis for the detection of heart murmurs

J A Gómez-García1, J D Martínez-Vargas, G Castellanos-Dominguez

  • 1Grupo de Control y Procesamiento Digital de Señales, Universidad Nacional de Colombia, Magdalena. Manizales, Colombia. fjorgomezg@unal.edu.co

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed

Insights

This study introduces a new complexity-based method for detecting heart murmurs using regularity features. Gaussian Kernel Approximate Entropy and Fuzzy Entropy showed high accuracy in identifying abnormal heart sounds.

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Heart murmurs, abnormal heart sounds, can indicate serious cardiac disorders.
  • Accurate diagnosis of murmurs is crucial but challenging due to subjective auditory assessment.
  • Automatic detection systems can aid medical specialists in improving diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate a complexity-based analysis methodology for detecting heart murmurs.
  • To assess the effectiveness of regularity features in identifying abnormal heart sounds.

Main Methods:

  • Utilized complexity analysis of physiological signals, specifically focusing on regularity features.
  • Employed Approximate Entropy, Sample Entropy, Gaussian Kernel Approximate Entropy, and Fuzzy Entropy for murmur detection.
  • Applied the methodology to a dataset for evaluating diagnostic performance.

Main Results:

  • The proposed complexity-based method demonstrated significant potential for heart murmur detection.
  • Gaussian Kernel Approximate Entropy and Fuzzy Entropy exhibited high discriminative power, achieving up to 90% accuracy.
  • These entropy measures proved effective in distinguishing between normal and abnormal heart sounds.

Conclusions:

  • Complexity analysis, particularly using Gaussian Kernel Approximate Entropy and Fuzzy Entropy, offers a robust approach for automatic heart murmur detection.
  • This methodology can serve as a valuable tool for medical specialists, enhancing the accuracy and efficiency of cardiac diagnosis.
  • Further research can explore integrating these methods into clinical diagnostic workflows.

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