Abstract
Atherosclerosis is known as the leading cause of heart attacks and brain strokes. One of the symptoms of this disease is the reduction of artery wall motion caused by age. This study presents a novel method to extract high frequency components of wall motion, wall vibrations, based on discrete wavelet transform. The fractal dimension, largest Lyapunov exponent, and spectral entropy are then analyzed to indicate the chaotic behavior in wall vibrations. Phase information from demodulated radiofrequency signals is extracted and the entropy of phase-difference is computed as a statistical measure for better characterization of the artery wall tissue. The results show that these features correlate with age (P $
Highlights
Physical stiffening of the carotid artery is the most important stroke and death risk factor in industrialized countries [1]
We aim to use Discrete wavelet transform (DWT) to extract the vibrations in the range of about a few millimeters that are superimposed on arterial wall motion
The Daubechies wavelets are a family of orthogonal wavelets defining a discrete wavelet transform and characterized
Summary
Physical stiffening of the carotid artery is the most important stroke and death risk factor in industrialized countries [1]. The stiffness of plaques can be estimated by stiffness meter to clarify the correlation between plaque stiffness and preoperative carotid ultrasonographic findings, and to predict the stiffness of plaques before surgery [2]. Diagnosis of this disease in the early stages can be very helpful [3]. Larsson et al [8] used ultrasound speckle tracking for carotid strain assessment in arterial stiffness and cardiovascular diseases They evaluated the contrast carotid strain assessment by speckle tracking applied on clinical and high-frequency ultrasound images in vitro.
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