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Characterisation of Arteriovenous Fistula’s sound recordings using principal component analysis

  • Marco Munguia Mena
  • Pablo Vasquez Obando
  • Bengt Mandersson
Publiceringsår: 2009
Språk: Engelska
Sidor: 5661-5664
Publikation/Tidskrift/Serie: [Host publication title missing]
Dokumenttyp: Konferensbidrag
Förlag: IEEE--Institute of Electrical and Electronics Engineers Inc.


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



  • Electrical Engineering, Electronic Engineering, Information Engineering
  • Principal Component Analysis
  • Signal Classification
  • Arteriovenous Fistula


Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
  • Signal Processing Group-lup-obsolete
  • Signal Processing-lup-obsolete
  • ISSN: 1557-170X

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