Arteriovenous fistula stenosis detection using wavelets and support vector machines.
The objective of this exploratory study was to develop signal processing methods for assisting in the diagnosis of arteriovenous fistula stenosis on patients suffering from endstage renal disease and undergoing haemodialysis treatments. The proposed method is based on the classification of vessels sounds utilizing parameter extraction from wavelets transform coefficients. The coefficients energy of selected scales (frequency bands) were fed to a support vector machine based system for classification. Results suggested that this technique can be useful for diagnosis purposes to physicians during the auscultation procedure.
Vesquez
Pablo O
PO
National University of Engineering, Managua, Nicaragua. pvo@eit.lth.se
Marco
Munguia M
MM
Mandersson
Bengt
B
D001164:Arteriovenous Fistula / Q000175:diagnosis*; D001185:Artificial Intelligence; D001314:Auscultation; D001698:Biomedical Engineering; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D012815:Signal Processing, Computer-Assisted