Study of the effects of individual characteristics on the vessel wall changes using ultrasound RF time series

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

samrand sharifi ^{1 *}

1 - Department of Electrical Engineering, Boukan Branch, Islamic Azad University, Boukan, Iran

Received: 2023-09-07 Accepted : 2024-08-22 Published : 2025-03-15

Keywords: Ultrasound RF signal, Carotid vessel, fluctuation signal, Nakagami parameter, Scaling parameter,

Abstract :

This study investigates the changes caused by aging on the vessel wall vibrations and changes of acoustic and statistical parameters of the wall tissue using ultrasound radio frequency signals on healthy people of different ages. This study aims to find the vessel wall features discriminating the healthy people from the patients. In this method, the RF signal of the carotid vessel of 31 volunteers was first recorded using echocardiography, and then the raw RF signal was processed using matlab software. Phase tracking method was used to extract the radial movement of the vessel wall. The discrete wavelet transform was used to obtain the low amplitude and high-frequency vibrations. The peak-to-peak amplitude and the dominant frequency are obtained from the vibrations signal. Also, to investigate the changes of acoustic and statistical parameters of the carotid vessel wall tissue, the Nakagami distribution parameters of vessel wall are extracted. To analyze the data, the Pearson correlation test is used. Also, the data is applied to the SVM neural network using the Leave one out cross-validation (LOOCV) to be classified into two classes of young (<50 years) and old (>50 years). As age increases, the peak-to-peak amplitude and dominant frequency of the signal and Nakagami parameters are affected (P<0.05). The vibrations amplitude and dominant frequency of the vibrations signal decrease as the age increases. Also, the Nakagami parameters increase as age increases, and the number of scatterers and the back-scattered power increases. The results of the neural network show that the classification accuracy is 93.5%. Our results raise hopes that the proposed approach may be effective in diagnosing atherosclerosis.