Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms

• Published in: PloS one Vol. 16; no. 6; p. e0245026
• Main Authors: Jin, Weiwei, Chowienczyk, Philip, Alastruey, Jordi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.06.2021; Public Library of Science (PLoS)
• Subjects: Accuracy; Adult; Aged; Aging; Algorithms; Analysis; Biology and Life Sciences; Biomedical engineering; Blood Pressure; Cardiovascular diseases; Cardiovascular Diseases - diagnostic imaging; Cardiovascular Diseases - prevention & control; Carotid Arteries - diagnostic imaging; Carotid Arteries - physiology; Carotid-Femoral Pulse Wave Velocity - methods; Computer and Information Sciences; Diagnosis; Disease; Elastic waves; Engineering and Technology; Evaluation; Feature extraction; Female; Femoral Artery - diagnostic imaging; Femoral Artery - physiology; Gaussian process; Health risks; Heart Rate; Humans; Learning algorithms; Libraries; Machine Learning; Male; Medical tests; Medicine and Health Sciences; Middle Aged; Mortality; Neural networks; Physical Sciences; Pipelines; Population studies; Pressure; Principal components analysis; Pulse Wave Analysis - methods; Radial Artery - diagnostic imaging; Radial Artery - physiology; Random noise; Recurrent neural networks; Research and Analysis Methods; Risk analysis; Risk Factors; Vascular Stiffness - physiology; Velocity; Wave analysis; Wave velocity; Waveforms; United Kingdom; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0245026

One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave ( e.g . the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository ( https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal ).