Literature DB >> 16078614

On the closing sounds of a mechanical heart valve.

Changfu Wu1, Bruce A Herman, Stephen M Retta, Laurence W Grossman, Jia-Shing Liu, Ned H C Hwang.   

Abstract

In the 1994 Replacement Heart Valve Guidance of the U.S. Food and Drug Administration (FDA), in-vitro testing is required to evaluate the potential for cavitation damage of a mechanical heart valve (MHV). To fulfill this requirement, the stroboscopic high-speed imaging method is commonly used to visualize cavitation bubbles at the instant of valve closure. The procedure is expensive; it is also limited because not every cavitation event is detected, thus leaving the possibility of missing the whole cavitation process. As an alternative, some researchers have suggested an acoustic cavitation-detection method, based on the observation that cavitation noise has a broadband spectrum. In practice, however, it is difficult to differentiate between cavitation noise and the valve closing sound, which may also contain high-frequency components. In the present study, the frequency characteristics of the closing sound in air of a Björk-Shiley Convexo-Concave (BSCC) valve are investigated. The occluder closing speed is used as a control parameter, which is measured via a laser sweeping technique. It is found that for the BSCC valve tested, the distribution of the sound energy over its frequency domain changes at different valve closing speeds, but the cut-off frequency remains unchanged at 123.32 +/- 6.12 kHz. The resonant frequencies of the occluder are also identified from the valve closing sound.

Mesh:

Year:  2005        PMID: 16078614     DOI: 10.1007/s10439-005-3237-1

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  3 in total

1.  Characteristics of cavitation intensity in a mechanical heart valve using a pulsatile device: synchronized analysis between visual images and pressure signals.

Authors:  Hwansung Lee; Eiki Akagawa; Eisuke Tatsumi; Yoshiyuki Taenaka
Journal:  J Artif Organs       Date:  2008-07-06       Impact factor: 1.731

2.  An automatic approach for heart failure typing based on heart sounds and convolutional recurrent neural networks.

Authors:  Hui Wang; Xingming Guo; Yineng Zheng; Yang Yang
Journal:  Phys Eng Sci Med       Date:  2022-03-28

3.  Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis.

Authors:  Yang Yang; Xing-Ming Guo; Hui Wang; Yi-Neng Zheng
Journal:  Diagnostics (Basel)       Date:  2021-12-13
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.