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There is a growing emphasis being placed on the potential for cuffless blood pressure (BP) estimation through modelling of morphological features from the photoplethysmogram (PPG) and electrocardiogram (ECG). However, the appropriate features and models to use remain unclear. We investigated the best features available from the PPG and ECG for BP estimation using both linear and non-linear machine learning models. We conducted a clinical study in which changes in BP ([Formula: see text]BP) were induced by an infusion of phenylephrine in 30 healthy volunteers (53.8% female, 28.0 (9.0) years old). We extracted a large and diverse set of features from both the PPG and the ECG and assessed their individual importance for estimating [Formula: see text]BP through Shapley additive explanation values and a ranking coefficient. We trained, tuned, and evaluated linear (ordinary least squares, OLS) and non-linear (random forest, RF) machine learning models to estimate [Formula: see text]BP in a nested leave-one-subject-out cross-validation framework. We reported the results as correlation coefficient ([Formula: see text]), root mean squared error (RMSE), and mean absolute error (MAE). The non-linear RF model significantly ([Formula: see text]) outperformed the linear OLS model using both the PPG and the ECG signals across all performance metrics. Estimating [Formula: see text]SBP using the PPG alone ([Formula: see text] = 0.86 (0.23), RMSE = 5.66 (4.76) mmHg, MAE = 4.86 (4.29) mmHg) performed significantly better than using the ECG alone ([Formula: see text] = 0.69 (0.45), RMSE = 6.79 (4.76) mmHg, MAE = 5.28 (4.57) mmHg), all [Formula: see text]. The highest ranking features from the PPG largely modelled increasing reflected wave interference driven by changes in arterial stiffness. This finding was supported by changes observed in the PPG waveform in response to the phenylephrine infusion. However, a large number of features were required for accurate BP estimation, highlighting the high complexity of the problem. We conclude that the PPG alone may be further explored as a potential single source, cuffless, blood pressure estimator. The use of the ECG alone is not justified. Non-linear models may perform better as they are able to incorporate interactions between feature values and demographics. However, demographics may not adequately account for the unique and individualised relationship between the extracted features and BP.

Original publication




Journal article


Scientific reports

Publication Date





Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.


Humans, Blood Pressure Determination, Electrocardiography, Photoplethysmography, Blood Pressure, Child, Female, Male, Machine Learning