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Heart Sounds01:15

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
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Heart sound segmentation based on homomorphic filtering.

K Hassani1, K Bajelani2, M Navidbakhsh3

  • 1Department of Biomechanics, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran k.hasani@srbiau.ac.ir.

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PubMed
Summary

A new algorithm automatically segments phonocardiogram (heart sound) signals into four parts without an electrocardiogram (ECG). This innovation enhances heart sound analysis for diagnosing cardiac disorders more efficiently.

Keywords:
auscultationdiastolic periodfirst heart soundhomomorphic filteringmurmursnormalized average Shannon energyphonocardiographysecond heart soundsegmentationsystolic period

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Phonocardiography (digital heart sound recording) is a cost-effective tool for diagnosing heart disorders.
  • Electrocardiograms (ECGs) are often used with phonocardiograms to identify cardiac cycle components.
  • Accurate segmentation of heart sounds is crucial for reliable diagnosis.

Purpose of the Study:

  • To develop an algorithm for automatic segmentation of phonocardiogram signals.
  • To separate heart sound signals into distinct components (S1, systolic, S2, diastolic).
  • To achieve segmentation without the need for a simultaneous ECG recording.

Main Methods:

  • The study involved 100 patients with normal and abnormal heart sounds.
  • An algorithm utilizing homomorphic filtering was developed to create time-domain intensity envelopes.
  • Wavelet decomposition and reconstruction were employed to select optimal frequency bands for analysis.

Main Results:

  • The algorithm successfully segmented phonocardiogram signals into four overlapping parts: first heart sound, systolic period, second heart sound, and diastolic period.
  • Evaluation involved 14,000 cardiac periods from 100 digital phonocardiographic recordings.
  • The algorithm demonstrated over 93% accuracy in detecting the first and second heart sounds.

Conclusions:

  • The developed automatic segmentation algorithm effectively segments phonocardiogram signals into four key components.
  • The algorithm operates without requiring an ECG reference, simplifying the diagnostic process.
  • This method offers a promising approach for automated analysis of heart sounds in clinical settings.