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Cardiovascular Disease Prediction using Support Vector Machines

Alty, Stephen R.; Millasseau, Sandrine; Chowienczyk, Philip and Jakobsson, Andreas LU (2003) Proceedings of the 46th International Midwest Symposium on Circuits and Systems In Proceedings of the 46th International Midwest Symposium on Circuits and Systems 1. p.376-379
Abstract
A method for rapidly assessing a patient's arterial stiffness and hence risk of developing cardiovascular disease (CVD) without resorting to laborious blood tests is presented. Simple measurement of a patient's volume pulse measured at the finger-tip (digital volume pulse) using an infrared light absorption detector placed on the index finger is sufficient to predict their CVD risk. Suitable features are extracted from the waveform and a support vector machine (SVM) classifier has been found to make accurate (>85%) prediction of high or low arterial stiffness as indicated by the aortal pulse wave velocity (PWV). This would otherwise require an extensive and time consuming procedure, and hence this new method is promising as a tool to... (More)
A method for rapidly assessing a patient's arterial stiffness and hence risk of developing cardiovascular disease (CVD) without resorting to laborious blood tests is presented. Simple measurement of a patient's volume pulse measured at the finger-tip (digital volume pulse) using an infrared light absorption detector placed on the index finger is sufficient to predict their CVD risk. Suitable features are extracted from the waveform and a support vector machine (SVM) classifier has been found to make accurate (>85%) prediction of high or low arterial stiffness as indicated by the aortal pulse wave velocity (PWV). This would otherwise require an extensive and time consuming procedure, and hence this new method is promising as a tool to help health professionals prevent cardiovascular diseases (Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
cardiovascular system, diseases, infrared detectors, patient diagnosis, support vector machines, cardiovascular disease prediction, arterial stiffness, digital volume pulse, infrared light absorption detector, feature extraction, support vector machine classifier, aortal pulse wave velocity
in
Proceedings of the 46th International Midwest Symposium on Circuits and Systems
volume
1
pages
376 - 379
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
Proceedings of the 46th International Midwest Symposium on Circuits and Systems
ISBN
0 7803 8294 3
language
English
LU publication?
no
id
f62d5f86-7744-4c41-97c1-8d0036bf0c5b (old id 1216812)
date added to LUP
2008-10-06 11:53:45
date last changed
2016-06-29 09:00:37
@misc{f62d5f86-7744-4c41-97c1-8d0036bf0c5b,
  abstract     = {A method for rapidly assessing a patient's arterial stiffness and hence risk of developing cardiovascular disease (CVD) without resorting to laborious blood tests is presented. Simple measurement of a patient's volume pulse measured at the finger-tip (digital volume pulse) using an infrared light absorption detector placed on the index finger is sufficient to predict their CVD risk. Suitable features are extracted from the waveform and a support vector machine (SVM) classifier has been found to make accurate (>85%) prediction of high or low arterial stiffness as indicated by the aortal pulse wave velocity (PWV). This would otherwise require an extensive and time consuming procedure, and hence this new method is promising as a tool to help health professionals prevent cardiovascular diseases},
  author       = {Alty, Stephen R. and Millasseau, Sandrine and Chowienczyk, Philip and Jakobsson, Andreas},
  isbn         = {0 7803 8294 3},
  keyword      = {cardiovascular system,diseases,infrared detectors,patient diagnosis,support vector machines,cardiovascular disease prediction,arterial stiffness,digital volume pulse,infrared light absorption detector,feature extraction,support vector machine classifier,aortal pulse wave velocity},
  language     = {eng},
  pages        = {376--379},
  publisher    = {ARRAY(0x9fc58e8)},
  series       = {Proceedings of the 46th International Midwest Symposium on Circuits and Systems},
  title        = {Cardiovascular Disease Prediction using Support Vector Machines},
  volume       = {1},
  year         = {2003},
}