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

Munguia Mena, Marco LU ; Vasquez Obando, Pablo LU and Mandersson, Bengt LU (2009) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009 In [Host publication title missing] p.5661-5664
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Principal Component Analysis, Signal Classification, Arteriovenous Fistula
in
[Host publication title missing]
pages
4 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
external identifiers
  • scopus:77951003137
ISSN
1557-170X
DOI
10.1109/IEMBS.2009.5333770
language
English
LU publication?
yes
id
66517f17-733a-476f-bae2-f2e5c6c099c7 (old id 1668963)
date added to LUP
2010-09-09 13:53:42
date last changed
2017-03-12 04:00:41
@inproceedings{66517f17-733a-476f-bae2-f2e5c6c099c7,
  abstract     = {In this study, a signal analysis framework based<br/><br>
on the Karhunen-Loève expansion and k-means clustering<br/><br>
algorithm is proposed for the characterisation of arteriovenous<br/><br>
(AV) fistula’s sound recordings. The Karhunen-Loève (KL) coefficients<br/><br>
corresponding to the directions of maximum variance<br/><br>
were used as classification features, which were clustered applying<br/><br>
k-means algorithm. The results showed that one natural<br/><br>
cluster was found for similar AV fistula’s state recordings. On<br/><br>
the other hand, when stenotic and non-stenotic AV fistula’s<br/><br>
recordings were processed together, the two most significant<br/><br>
KL coefficients contain important information that can be used<br/><br>
for classification or discrimination between these AV fistula’s<br/><br>
states.},
  author       = {Munguia Mena, Marco and Vasquez Obando, Pablo and Mandersson, Bengt},
  booktitle    = {[Host publication title missing]},
  issn         = {1557-170X},
  keyword      = {Principal Component Analysis,Signal Classification,Arteriovenous Fistula},
  language     = {eng},
  pages        = {5661--5664},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Characterisation of Arteriovenous Fistula’s sound recordings using principal component analysis},
  url          = {http://dx.doi.org/10.1109/IEMBS.2009.5333770},
  year         = {2009},
}