Table 1.
Biological characteristics of the subjects from the Twins UK cohort (N = 3,082).
Fig 1.
Data pre-processing for pulse wave velocity estimation from the features extracted from the radial pressure wave.
(a) The fiducial points containing key features identified by the LASSO regression. (b) Identified outliers in the database using principal component analysis (PCA). Red, blue and green dots represent subject groups with pulse wave velocity (PWV) less than 7 m/s, 7–9 m/s, and greater than 9 m/s, respectively.
Fig 2.
Estimation of pulse wave velocity (PWV) on a hold-out test set containing 924 subjects using Gaussian process regression and recurrent neural network with long short-term memory.
Panels (a) and (b) show estimated PWV against measured PWV with the linear regression line in red, the coefficient of determination, r2, and the p-value. Panels (c) and (d) show the Bland-Altman plots comparing the estimated and measured PWV. Panels (e) and (f) show Pearson correlation coefficients (r) between the biological characteristics of the cohort and the “Difference” values shown on panels (c) and (d), respectively. BMI: body mass index; DBP: diastolic blood pressure; SBP: systolic blood pressure; MAP: mean arterial pressure.
Table 2.
Root mean square error (RMSE) and percentage error (ϵ) on the estimated pulse wave velocity (PWV) using different machine learning methods.
Fig 3.
Schematic illustration of the recurrent neural network structure used to estimate pulse wave velocity from the entire radial pressure wave.
Pt−1, Pt and Pt+1 are the radial pressure values at the discrete time points t − 1, t, and t+1, cfPWV is the carotid-femoral pulse wave velocity.
Fig 4.
An example of an original signal, and the same signal with added white noise, with signal to noise ratios (SNR) of 20, 10 and 5.
Fig 5.
Estimation of PWV on a hold-out test set containing 1312 virtual subjects using the recurrent neural network, with different levels of added white noise.
Estimated against measured PWV with the linear regression line in red, the coefficient of determination, r2, and the p-value (top). Corresponding Bland-Altman plots (bottom). SNR: signal to noise ratio.
Table 3.
Root mean square error (RMSE) and percentage error (ϵ) for the pulse wave velocity (PWV) estimation from the radial pressure wave by the recurrent neural network (RNN), with different intensities of added white noise.