Novel Noninvasive Approach for Detecting Arteriovenous Fistula Stenosis

Abstract

Hemodialysis is the most common treatment for patients with end-stage renal disease. For hemodialysis, consistently functional vascular access must be surgically created with an anastomosis of artery and vein, referred to as an arteriovenous fistula (AVF). However, AVF dysfunction may occur over time. Angiography and Doppler ultrasound are usually used to detect the flow or the diameter of the AVF. But they require well-trained operators and are expensive, and even angiography is invasive. In this study, a noninvasive approach based on stethoscope auscultation for monitoring AVF stenosis was proposed. Here, a wireless blood flow sound recorder was designed to record blood flow sounds wirelessly. In order to effectively extract the varying feature of blood flow sounds for AVF stenosis, the 2-D feature pattern built from S-transform was also proposed as the feature in the AVF stenosis detecting algorithm. Different from other frequency-related coefficients, the feature pattern can contain the information of blood flow sounds in time and frequency domains simultaneously. Preliminary findings showed that the proposed approach can provide high-quality estimation of AVF stenosis (positive predictive value = 87.84% and sensitivity = 89.24%).

Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor

“…Before having HD treatment, the patient must go through a surgical access known as anastomosis, which returns the purified blood through a thin fiber after filtering out the waste fluid. The anastomosis is done a few weeks or months before HD treatment and it is done on patient’s elbow or wrist using the non-dominant arm [ 3 , 4 , 5 ]. There are three types of vascular access which are well known in HD treatment, namely Arteriovenous Fistula (AVF), Arteriovenous Graft and Venous Catheter respectively.…”

A Novel Classification Technique of Arteriovenous Fistula Stenosis Evaluation Using Bilateral PPG Analysis

“…In 2009, Vasquez et al used wavelet and support vector machines to detect AVF stenosis by the sound of blood flow [ 4 ]. Wang et al in 2014 have improved the sound features of AVF blood flow stenosis by experiments exploiting the frequency and time domain analysis [ 9 ]. In 2015, Wu et al measured the difference of blood volume changes between the right and left side of the PPG signal by utilizing a self-synchronization error formulation (SSEF).…”

A Portable, Wireless Photoplethysomography Sensor for Assessing Health of Arteriovenous Fistula Using Class-Weighted Support Vector Machine

“…Moreover, the prior research of the authors [ 6 ] using a single PPG sensor with the neural network algorithms reveals an extremely high type II error, which may lead to delayed treatments in clinical practice. As for the researches presented in References [ 2 , 3 ], though acoustic sensors using stethoscope auscultation show promising results, the long tubes of the stethoscope sensors are inferior to a PPG sensor for device portability. However, it is possible that a sensor fusion system combining both optical PPG sensors and acoustic stethoscope sensors can be developed in the future to provide ESRD patients a more accurate and reliable solution to AVF assessments.…”

“…The experimental results reveal an accuracy of 84.3%. Similarly, Wang H. Y. et al [ 3 ] presented a small-sized stethoscope sensor for detecting the stenosis of AVF, which shows 87.84% accuracy with a very large database. However, the unavoidable environmental sound is a great issue for the sensitive stethoscope sensors [ 4 ].…”