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

Summary, 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.

• Author: Marco Munguia Mena; Pablo Vasquez Obando; Bengt Mandersson
• Department/s: Department of Electrical and Information Technology
• Publishing year: 2009
• Language: English
• Pages: 5661-5664
• Publication/Series: [Host publication title missing]
• Links: http://dx.doi.org/10.1109/IEMBS.2009.5333770
• Document type: Conference paper
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Electrical Engineering, Electronic Engineering, Information Engineering
• Keywords: Principal Component Analysis; Signal Classification; Arteriovenous Fistula
• Conference name: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
• Conference date: 2009-09-03 - 2009-09-06
• Conference place: Minneapolis, Minnesota, United States
• Status: Published
• Research group: Signal Processing Group; Signal Processing
• ISBN/ISSN/Other: ISSN: 1557-170X