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

  • Marco Munguia Mena
  • Pablo Vasquez Obando (Mr.)
  • Bengt Mandersson
Publiceringsår: 2009
Språk: Engelska
Sidor: 5661-5664
Dokumenttyp: Konferensbidrag
Förlag: IEEE


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
Minneapolis, MN, USA
  • Sida/SAREC
  • Signal Processing Group
  • Signal Processing
  • ISSN: 1557170X

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