Stenosis detection algorithm for screening of arteriovenous fistulae

Abstract

The aim of the study was to develop an algorithm that can detect stenosis formation in arteriovenous fistulae based on audio recordings. 34 patients with a mature arteriovenous fistula were examined with use of an electronic stethoscope and subsequently by ultrasound. 27 patients had a patent fistula, while the other group consisted of 5 patients with stenosis and 2 with artificial narrowing of the fistula. Feature extraction was carried out using wavelet packet decomposition at depth 4. For each recording the scale energies SEi and the percentage of scale energy versus total energy SEp i, were calculated. The two most discriminative features with low correlation were found to be SE8 and SEp8. These features were evaluated using leave-one-out cross-validation with a quadratic discriminant function. Cross-validation using SE8 and SEp8 yielded a sensitivity of 100% and a specificity of 94%. The algorithm developed using the features obtained by wavelet analysis is reliable for detecting stenosis in a vein segment of an arteriovenous fistula. Based on these results, the prospects of developing an accurate, low-cost screening method for patients undergoing hemodialysis, are promising. © 2011 Springer-Verlag.