Physiology-based diagnosis algorithm for arteriovenous fistula stenosis detection
Journal
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Journal Volume
2014
Pages
4619-4622
ISBN
978-142447929-0
Date Issued
2014-11-02
Author(s)
Abstract
In this paper, a diagnosis algorithm for arteriovenous fistula (AVF) stenosis is developed based on auscultatory features, signal processing, and machine learning. The AVF sound signals are recorded by electronic stethoscopes at pre-defined positions before and after percutaneous transluminal angioplasty (PTA) treatment. Several new signal features of stenosis are identified and quantified, and the physiological explanations for these features are provided. Utilizing support vector machine method, an average of 90% two-fold cross-validation hit-rate can be obtained, with angiography as the gold standard. This offers a non-invasive easy-to-use diagnostic method for medical staff or even patients themselves for early detection of AVF stenosis. © 2014 IEEE.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Type
conference paper