Vascular ageing and peripheral pulse: an improved model for assessing their relationship

• Published in: Physiological measurement 2021-12, Vol.42 (12), p.125002
• Main Authors: Aliani, Cosimo, Rossi, Eva, Francia, Piergiorgio, Bocchi, Leonardo
• Format: Article
• Language: English
• Subjects: Adult; ageing; Aging; Algorithms; Female; Heart Rate; Humans; laser Doppler flowmetry; Male; microcirculation; Normal Distribution; Pulse; pulse contour analysis; Pulse Wave Analysis; Vascular Stiffness
• ISSN: 0967-3334; 1361-6579
• DOI: 10.1088/1361-6579/ac3e87

Vascular ageing is associated with several alterations, including arterial stiffness and endothelial dysfunction. Such alterations represent an independent factor in the development of cardiovascular disease (CVD). In our previous works we demonstrated the alterations occurring in the vascular system are themselves reflected in the shape of the peripheral waveform; thus, a model that describes the waveform as a sum of Gaussian curves provides a set of parameters that successfully discriminate between (≤35 years old) and subjects (>35 years old). In the present work, we explored the feasibility of a new decomposition model, based on a sum of exponential pulses, applied to the same problem. The first processing step extracts each pulsation from the input signal and removes the long-term trend using a cubic spline with nodes between consecutive pulsations. After that, a Least Squares fitting algorithm determines the set of optimal model parameters that best approximates each single pulse. The vector of model parameters gives a compact representation of the pulse waveform that constitutes the basis for the classification step. Each subject is associated to his/her 'representative' pulse waveform, obtained by averaging the vector parameters corresponding to all pulses. Finally, a Bayesan classifier has been designed to discriminate the waveforms of under and over subjects, using the leave-one-subject-out validation method. Results indicate that the fitting procedure reaches a rate of 96% in under subjects and 95% in over subjects and that the Bayesan classifier is able to correctly classify 91% of the subjects with a specificity of 94% and a sensibility of 84%. This study shows a sensible vascular age estimation accuracy with a multi-exponential model, which may help to predict CVD.