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Published on: May 23, 2021
Analysis of phonocardiogram signals using wavelet transform.
F Meziani1, S M Debbal, A Atbi
1Genie Biomedical Laboratory (GBM), Faculty of Technology, University A. B. Belkaid-Tlemcen BP 119, Tlemcen, Algeria.
Journal of Medical Engineering & Technology
|June 29, 2012
Summary
This study introduces a wavelet transform algorithm for analyzing phonocardiogram (PCG) signals. The method effectively detects and segments heart sounds (S1, S2) and murmurs for improved cardiac diagnosis.
Area of Science:
- Biomedical Engineering
- Cardiology
- Digital Signal Processing
Background:
- Phonocardiograms (PCG) record heart sounds and murmurs, crucial indicators of cardiac health.
- Traditional auscultation has limitations in quantitative analysis of PCG signals.
- Digital signal processing offers enhanced diagnostic capabilities for heart conditions.
Purpose of the Study:
- To analyze phonocardiogram (PCG) signals using wavelet transform.
- To develop an algorithm for detecting and segmenting heart sounds (S1, S2) and murmurs from PCG data.
- To improve the qualitative and quantitative analysis of cardiac acoustic signals.
Main Methods:
- Utilized Discrete Wavelet Transform (DWT) for denoising PCG signals.
- Developed a segmentation algorithm based on DWT to isolate heart sounds and murmurs.
- Applied wavelet transform analysis to extract statistical parameters from PCG signals.
Main Results:
- Successfully detected and segmented individual heart sounds (S1, S2) and murmurs.
- Demonstrated the effectiveness of DWT in denoising PCG recordings.
- Enabled the isolation of specific acoustic components within the PCG signal.
Conclusions:
- Wavelet transform analysis provides valuable insights into PCG signals.
- The developed algorithm enhances the detection and segmentation of cardiac acoustic events.
- This approach offers a promising tool for improved diagnosis of heart valve pathologies.
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