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