Classification of Aortic Stiffness From Eigendecomposition of the Digital Volume Pulse Waveform

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