Related Experiment Videos
Automatic detection of sounds and murmurs in patients with Ionescu-Shiley aortic bioprostheses
Insights
This study introduces an iterative algorithm for automatic cardiac sound and murmur detection from phonocardiograms (PCG). The method effectively identifies heart sounds and murmurs, showing promise for clinical applications.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Phonocardiogram (PCG) analysis for cardiac sound and murmur detection is challenging due to signal complexity, noise, and recording site variability.
- Deterministic events (valve operations) and random phenomena (blood flow turbulence) contribute to PCG signal complexity.
Purpose of the Study:
- To develop and evaluate an iterative automatic detection algorithm for cardiac acoustic events using a priori knowledge of their characteristics.
- To assess the algorithm's effectiveness in identifying first and second heart sounds, valve clicks, and murmurs.
Main Methods:
- An iterative automatic detection algorithm was developed, leveraging spectral and temporal characteristics of cardiac sounds and murmurs.
- The algorithm utilizes PCG envelope and noise level estimations for iterative identification of acoustic event positions and durations.
Main Results:
- The algorithm demonstrated particular effectiveness in detecting the second heart sound and its aortic component in patients with Ionescu-Shiley aortic valve bioprostheses.
- Potential was observed for detecting the first heart sound, systolic murmur, and diastolic murmur.
Conclusions:
- The developed iterative algorithm shows significant potential for automatic detection of cardiac acoustic events, especially the second heart sound.
- Further development could enhance its utility for detecting other cardiac sounds and murmurs, aiding in cardiovascular diagnostics.
Abstract:
The problems encountered in the automatic detection of cardiac sounds and murmurs are numerous. The phonocardiogram (PCG) is a complex signal produced by deterministic events such as the opening and closing of the heart valves, and by random phenomena such as blood-flow turbulence. In addition, background noise and the dependence of the PCG on the recording sites render automatic detection a difficult task. In the paper we present an iterative automatic detection algorithm based on the a priori knowledge of spectral and temporal characteristics of the first and second heart sounds, the valve opening clicks, and the systolic and diastolic murmurs. The algorithm uses estimates of the PCG envelope and noise level to identify iteratively the position and duration of the significant acoustic events contained in the PCG. The results indicate that it is particularly effective in detecting the second heart sound and the aortic component of the second heart sound in patients with Ionescu-Shiley aortic valve bioprostheses. It has also some potential for the detection of the first heart sound, the systolic murmur and the diastolic murmur.