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

In English 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.