Cardiovascular disease prediction using support vector machines

被引：0

作者：

Alty, SR ^{[1
]}

Millasseau, SC ^{[1
]}

Chowienczyk, PJ ^{[1
]}

Jakobsson, A ^{[1
]}

机构：

[1] Univ London Kings Coll, Ctr Digital Signal Proc Res, London WC2R 2LS, England

来源：

Proceedings of the 46th IEEE International Midwest Symposium on Circuits & Systems, Vols 1-3 | 2003年

关键词：

cardiovascular disease; support vector machines; digital volume pulse; pulse wave velocity;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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 infra-red 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.

引用

页码：376 / 379

页数：4

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