Paper Info
Title
Classification Of Aortic Stiffness From Eigendecomposition Of The Digital Volume Pulse Waveform
Abstract
Aortic stiffness as measured by aortic pulse wave velocity (PWV) has been shown to be an independent predictor of Cardiovascular Disease (CVD), however, the measurement of PWV is time consuming. Recent studies have shown that pulse contour characteristics depend on arterial properties such as arterial stiffness. This paper presents a method for estimating PWV from the digital volume pulse (DVP), a waveform that can be rapidly and simply acquired by measuring transmission of infra-red light through the finger pulp. PWV and DVP were measured on 461 subjects attending a cardiovascular prevention clinic at St Thomas' Hospital, London. Using a non-linear Kernel based Support Vector Machine (SVM) classifier, it is possible to achieve results of up to 88% sensitivity and 82% specificity on unseen data. Further, we show that this approach outperforms traditional Artificial Neural Network (ANN) methods. This technique could be employed by health professionals to rapidly diagnose patients' cardiovascular fitness in general practice clinics.
Year
DOI
Venue
2006
10.1109/ICASSP.2006.1660556
2006 IEEE International Conference on Acoustics, Speech and Signal Processing, Vols 1-13
Keywords
Field
DocType
artificial neural networks,support vector machines,time measurement,artificial neural network,svm,support vector machine,cardiology,cardiovascular system,eigendecomposition,infra red
Pattern recognition,Cardiovascular fitness,Computer science,Support vector machine,Waveform,Arterial stiffness,Pulse (signal processing),Pulse wave velocity,Artificial intelligence,Eigendecomposition of a matrix,Artificial neural network
Conference
ISSN
Citations 
PageRank 
1520-6149
1
0.36
References 
Authors
1
4
Name
Order
Citations
PageRank
Natalia Angarita-Jaimes193.09
S. R. Alty2286.44
S. C. Millasseau371.36
Philip J. Chowienczyk410.70