Acoustic Detection of ArterioVenous Access Stenosis Based on MUSIC Power Spectral Features

Abstract

Arterio Venous Vascular Access (AVA) stenosis is a common complication in hemodialysis patients. Clinically, AVA stenosis happens when the cross-sectional area is reduced to less than 50% of the normal area. In order to highlight the correlation between the power spectral features of AVA Phonoangiography signals (PCG) and degree of stenosis (DOS), In-vitro BioPhysical Simulation Model (BPSM) is used to control individual conditions. Previous studies have pointed out that the features of PCG can be used to detect stenosis, but there were differences in the specific frequency band range. In this study, a method for extracting the power spectral features of PCG based on MUltiple SIgnal Classification power spectrum estimation algorithm (MUSIC) is proposed. This method has a high resolution for the high-frequency low-energy sound caused by stenosis. Using the proposed method, a strong correlation is found between the frequency peak near 820 Hz (820 ±70 Hz) and the AVA stenosis. Based on the above feature extraction method, a support vector machine (SVM) classification model is trained on data obtained on the BPSM. Finally, using MUSIC features extraction model and SVM classification model, the correct classification rate on BPSM data is 96.4%, and the SVM model is validated on 19 clinical measured data, the accuracy is 84.2%.