Classification of blood pressure in critically ill patients using photoplethysmography and machine learning

•Estimation of blood pressure values using pulse rate variability features shows promise for the continuous, non-invasive measurement of systolic, diastolic, and mean arterial pressure.•Using photoplethysmography-based pulse rate variability features only, it is possible to classify hypertensive events in critically ill subjects with relatively good performance. However, the classification of hypertensive and normotensive events is still a challenge.•Using 5-min windows for the classification and estimation of blood pressure in critically ill subjects using solely pulse rate variability features gives a better performance than using 1-min windows. Objective: The aim of this study was to evaluate the capability of features extracted from photoplethysmography (PPG) based Pulse Rate Variability (PRV) to classify hypertensive, normotensive and hypotensive events, and to estimate mean arterial, systolic and diastolic blood pressure in critically ill patients. Methods: Time-domain, frequency-domain and non-linear indices from PRV were extracted from 5-min and 1-min segments obtained from PPG signals. These features were filtered using machine learning algorithms in order to obtain the optimal combination for the classification of hypertensive, hypotensive and normotensive events, and for the estimation of blood pressure. Results: 5-min segments allowed for an improved performance in both classification and estimation tasks. Classification of blood pressure states showed around 70% accuracy and around 75% specificity. The sensitivity, precision and F1 scores were around 50%. In estimating mean arterial, systolic, and diastolic blood pressure, mean absolute errors as low as 2.55 ± 0.78 mmHg, 4.74 ± 2.33 mmHg, and 1.78 ± 0.14 mmHg were obtained, respectively. Bland-Altman analysis and Wilcoxon rank sum tests showed good agreement between real and estimated values, especially for mean and diastolic arterial blood pressures. Conclusion: PRV-based features could be used for the classification of blood pressure states and the estimation of blood pressure values, although including additional features from the PPG waveform could improve the results. Significance: PRV contains information related to blood pressure, which may aid in the continuous, noninvasive, non-intrusive estimation of blood pressure and detection of hypertensive and hypotensive events in critically ill subjects.