Related Experiment Video
Updated: Jun 18, 2026

Creating Radio-cephalic Arteriovenous Fistula in the Forearm with a Modified No-Touch Technique
Published on: April 1, 2022
Characterisation of arteriovenous fistula's sound recordings using principal component analysis
Marco M Munguía1, Pablo Vasquez, Bengt Mandersson
1UNI-Asdi/SAREC-FEC Group, Faculty of Electrical Engineering, National University of Engineering, Managua, Nicaragua. mmm@eit.lth.se
Abstract:
In this study, a signal analysis framework based on the Karhunen-Loève expansion and k-means clustering algorithm is proposed for the characterisation of arteriovenous (AV) fistula's sound recordings. The Karhunen-Loève (KL) coefficients corresponding to the directions of maximum variance were used as classification features, which were clustered applying k-means algorithm. The results showed that one natural cluster was found for similar AV fistula's state recordings. On the other hand, when stenotic and non-stenotic AV fistula's recordings were processed together, the two most significant KL coefficients contain important information that can be used for classification or discrimination between these AV fistula's states.
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