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Updated: May 7, 2026
12:10
Semi-automated Optical Heartbeat Analysis of Small Hearts
Published on: September 16, 2009
11.7K
Automatic heart sounds detection and systolic murmur characterization using wavelet transform and AR modeling.
Summary
This study presents a novel signal processing method to detect and characterize systolic heart murmurs. The procedure accurately identifies heart sounds, isolates systole, and analyzes murmur characteristics like timing and pitch.
Area of Science:
- Biomedical Engineering
- Cardiovascular Signal Processing
- Medical Diagnostics
Background:
- Accurate detection and characterization of heart murmurs are crucial for diagnosing cardiac conditions.
- Traditional methods may lack precision in identifying subtle murmur characteristics.
Purpose of the Study:
- To develop and validate a signal processing procedure for identifying and characterizing systolic heart murmurs.
- To improve the accuracy of automated cardiac auscultation analysis.
Main Methods:
- Utilized discrete wavelet transform (DWT) with Coiflet wavelet for heart sound identification (S1 and S2).
- Employed signal activity indicators and AR modeling for systole extraction and murmur characterization (timing, duration, pitch, shape).
Main Results:
- Successfully identified first (S1) and second (S2) heart sounds and isolated systole.
- Enabled detection and characterization of systolic murmurs, classifying them by shape (crescendo, decrescendo, etc.).
- Validated the procedure using clinically recorded murmur episodes.
Conclusions:
- The proposed signal processing procedure offers a robust method for automated systolic murmur analysis.
- This technique has potential applications in clinical settings for improved cardiac diagnostics.
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