Arteriovenous fistula stenosis detection using wavelets and support vector machines
(2009) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009 p.1298-1301- Abstract
- 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 end-stage 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1670904
- author
- Vasquez Obando, Pablo LU ; Munguia Mena, Marco LU and Mandersson, Bengt LU
- organization
- publishing date
- 2009
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- [Host publication title missing]
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
- conference location
- Minneapolis, Minnesota, United States
- conference dates
- 2009-09-03 - 2009-09-06
- external identifiers
-
- scopus:77951009607
- pmid:19963492
- ISSN
- 1557-170X
- ISBN
- 978-1-4244-3296-7
- DOI
- 10.1109/IEMBS.2009.5332592
- language
- English
- LU publication?
- yes
- id
- 29889bd2-3ae5-419f-8ac6-58295ca24539 (old id 1670904)
- date added to LUP
- 2016-04-01 13:31:41
- date last changed
- 2022-04-21 22:09:25
@inproceedings{29889bd2-3ae5-419f-8ac6-58295ca24539,
abstract = {{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 end-stage 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.}},
author = {{Vasquez Obando, Pablo and Munguia Mena, Marco and Mandersson, Bengt}},
booktitle = {{[Host publication title missing]}},
isbn = {{978-1-4244-3296-7}},
issn = {{1557-170X}},
language = {{eng}},
pages = {{1298--1301}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Arteriovenous fistula stenosis detection using wavelets and support vector machines}},
url = {{http://dx.doi.org/10.1109/IEMBS.2009.5332592}},
doi = {{10.1109/IEMBS.2009.5332592}},
year = {{2009}},
}