Spectral analysis of closing sounds produced by lonescu-Shiley bioprosthetic aortic heart valves

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.

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Automatic detection of sounds and murmurs in patients with lonescu-Shiley aortic bioprostheses

Baranek

Lee

Cloutier

et al. 1989

Med. Biol. Eng. Comput.

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Time-frequency analysis of the first heart sound. Part 2: An appropriate time-frequency representation technique

Chen

Durand

Guo

et al. 1997

Med. Biol. Eng. Comput.

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A simulated first heart sound (S1) signal is used to determine the best technique for analysing physiological S1 from the following five time-frequency representations (TFR): the spectrogram, time-varying autoregressive modelling, binomial reduced interference distribution, Bessel distribution and cone-kernel distribution (CKD). To provide information on the time and frequency resolutions of each TFR technique, the instantaneous frequency and the -3 dB bandwidth as functions of time were computed for each simulated component of the S1. The performance index for selecting the best technique was based on the relative error and the correlation coefficient of the instantaneous frequency function between the theoretical distribution and the computed TFR. This index served to select the best technique. The sensitivity of each technique to noise and to small variations of the signal parameters was also evaluated. The results of the comparative study show that, although important limitations were found for all five TFRs tested, the CKD appears to be the best technique for the time-frequency analysis of multicomponent signals such as the simulated S1.

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Acoustic analysis of the closing sounds of bileaflet prosthetic valves in a sheep model

Donnerstein¹,

Scott²,

Vasu³

et al. 1991

The Journal of Thoracic and Cardiovascular Surgery

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Spectral analysis of closing sounds produced by lonescu-Shiley bioprosthetic aortic heart valves

Cited by 7 publications

References 14 publications

Automatic detection of sounds and murmurs in patients with lonescu-Shiley aortic bioprostheses

Automatic detection of sounds and murmurs in patients with lonescu-Shiley aortic bioprostheses

Time-frequency analysis of the first heart sound. Part 2: An appropriate time-frequency representation technique

Acoustic analysis of the closing sounds of bileaflet prosthetic valves in a sheep model

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