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 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.
Munguía
Marco M
MM
UNI-Asdi/SAREC-FEC Group, Faculty of Electrical Engineering, National University of Engineering, Managua, Nicaragua. mmm@eit.lth.se
Vasquez
Pablo
P
Mandersson
Bengt
B
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*