Improvement of Artificial Auscultation on Hemodialysis Stenosis by the Estimate of Stenosis Site and the Hierarchical Categorization of Learning Data

Abstract

Many hemodialysis patients undergo plasitc surgery to form the arterio-venous fistula (AVF) in their forearm to improve the vascular access by shunting blood flows. The issue of AVF is the stenosis caused by the disturbance of blood flows; therefore the auscultation system to assist the stenosis diagnosis has been developed. Although the system is intended to be used as a steady monitoring for stenosis assessment, its efficiency was not always high because it cannot estimate where the stenosis locates. In this study, for extracting and estimating the stenosis signal, the shunt murmurs captured by many microphones were decomposed by the principal component analysis (PCA). Furthermore, applying the hierarchical categorization of the recursive subdivision self-organizing map (rs-SOM), the modelling of the stenosis signal was proposed to realise the effective stenosis assessment. The false-positive rate of the stenosis assessment was significantly reduced by using the improved auscultation system.