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 $