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TECHNICAL PAPERS: Fluids/Heat/Transport

Wavelet Transforms in the Analysis of Mechanical Heart Valve Cavitation

[+] Author and Article Information
Luke H. Herbertson, Varun Reddy, Keefe B. Manning

Department of Bioengineering, The Pennsylvania State University, University Park, PA 16802

Joseph P. Welz

Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802

Arnold A. Fontaine

Department of Bioengineering and Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802

Steven Deutsch1

Department of Bioengineering and Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802

1

Corresponding author.

J Biomech Eng 128(2), 217-222 (Sep 21, 2005) (6 pages) doi:10.1115/1.2165694 History: Received May 12, 2005; Revised September 21, 2005

Cavitation is known to cause blood element damage and may introduce gaseous emboli into the cerebral circulation, increasing the patient’s risk of stroke. Discovering methods to reduce the intensity of cavitation induced by mechanical heart valves (MHVs) has long been an area of interest. A novel approach for analyzing MHV cavitation is presented. A wavelet denoising method is explored because currently used analytical techniques fail to suitably unmask the cavitation signal from other valve closing sounds and noise detected with a hydrophone. Wavelet functions are used to denoise the cavitation signal during MHV closure and rebound. The wavelet technique is applied to the signal produced by closure of a 29-mm Medtronic-Hall MHV in degassed water with a gas content of 5ppm. Valve closing dynamics are investigated under loading conditions of 500, 2500, and 4500mmHgs. The results display a marked improvement in the quantity and quality of information that can be extracted from acoustic cavitation signals using the wavelet technique compared to conventional analytical techniques. Time and frequency data indicate the likelihood and characteristics of cavitation formation under specified conditions. Using this wavelet technique we observe an improved signal-to-noise ratio, an enhanced time-dependent aspect, and the potential to minimize valve closing sounds, which disguise individual cavitation events. The overall goal of this work is to eventually link specific valves with characteristic waveforms or distinct types of cavitation, thus promoting improved valve designs.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

The Daubechies order 4 wavelet is implemented to denoise and isolate the cavitation signal. The Daubechies wavelet is scaled, sized, and positioned to deconstruct the raw signal into a number of wavelet coefficients.

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Figure 2

The hydrophone was positioned at the upper left-hand corner of the measured cavitation regions for this study

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Figure 3

The raw acoustic waveform consists of cavitation, electrical, mechanical, and vibration noises detected by the hydrophone over a 20ms window covering valve closure. This waveform represents a single closing event for a loading condition of 2500mmHg∕s.

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Figure 4

The decomposition cascade is shown for all 12 levels conducted. The original signal, as well as the 12th order approximation, are also depicted. The x-axis is a time scale in terms of the number of data points, and the y-axis is the pressure amplitude in millivolts.

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Figure 5

An original signal recorded at a dp∕dt of 2500mmHg∕s is denoised using wavelets to produce a new signal

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Figure 6

The continuous wavelet transform is shown for the raw signal. The initial valve closure and strongest rebound can be observed as the most intensely contrasted streaks.

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Figure 7

The continuous wavelet transform of the denoised signal exhibits less noise at all scales

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Figure 8

Cavitation events occurring at different times during throughout valve closure can be classified based on their duration and RMS values. The primary cavitation event consistently correlates to the initial closure, and the next largest event tends to occur during the primary rebound of the valve. Only events greater than 0.1ms were highlighted in this analysis.

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