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

Författare:
Publiceringsår: 2009
Språk: Engelska
Sidor: 5661-5664
Dokumenttyp: Konferensbidrag
Förlag: IEEE

Sammanfattning

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.

Disputation

Nyckelord

  • Technology and Engineering
  • Principal Component Analysis
  • Signal Classification
  • Arteriovenous Fistula

Övriga

Annual International Conference of the IEEE Engineering in Medicine and Biology Society
2009-09-02/2009-09-06
Minneapolis, MN, USA
  • Sida/SAREC
Published
Yes
  • Signal Processing Group
  • Signal Processing
  • ISSN: 1557170X

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