title: Characterisation of arteriovenous fistula's sound recordings using principal component analysis.
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 used for classification or discrimination between these AV fistula's states.
authors: UNI-Asdi/SAREC-FEC Group, Faculty of Electrical Engineering, National University of Engineering, Managua, Nicaragua. mmm@eit.lth.se
authors: Marco M
authors: MM
authors: Munguía
authors: Pablo
authors: P
authors: Vasquez
authors: Bengt
authors: B
authors: Mandersson
mesh_terms: D000465:Algorithms; D000714:Anastomosis, Surgical; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*; D014652:Vascular Diseases / Q000175:diagnosis* / Q000209:etiology*