Estimation and Tracking of Blood Pressure Using Routinely Acquired Photoplethysmographic Signals and Deep Neural Networks

Continuous tracking of blood pressure in critically ill patients allows rapid identification of clinically important changes and helps guide treatment. Classically, such tracking requires invasive monitoring with its associated risks, discomfort, and low availability outside critical care units. We hypothesized that information contained in a prevalent noninvasively acquired signal (photoplethysmograph: a byproduct of pulse oximetry) combined with advanced machine learning will allow continuous estimation of the patient’s blood pressure. DESIGN: Retrospective cohort study with split sampling for model training and testing. SETTING: A single urban academic hospital. PATIENTS: Three-hundred twenty-nine adult patients admitted to a critical care unit. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One hundred thirty-six thousand four-hundred fifty-nine photoplethysmography waveforms of length 30 seconds were used for training (60%), validation (20%), and testing (20%) of the blood pressure estimation network. Each sample had an associated systolic, mean, and diastolic blood pressures extracted from concurrently recorded invasive arterial line waveforms. Blood pressure estimation using photoplethysmography waveforms is achieved using advanced machine learning methods (convolutional neural networks and a Siamese architectural configuration) calibrated for each patient on a single, first available photoplethysmography sample and associated blood pressure reading. The average estimation bias error was 0.52, 0.1, and –0.76 mm Hg for diastolic, mean, and systolic blood pressure, respectively, with associated mean absolute errors of 4.11, 5.51, and 7.98 mm Hg. If used to identify clinically important changes in blood pressure from the initial baseline, with a threshold of a 10 mm Hg increase or decrease in blood pressure, our algorithm shows an accuracy of 85%, 78%, and 74% for diastolic, mean, and systolic blood pressure, respectively. We also report the network’s performance in detecting systolic and diastolic hypo- or hypertension with accuracies ranging from 86% to 97%. CONCLUSIONS: Using advanced machine learning tools, we show that blood pressure estimation can be achieved using a common noninvasively recorded signal, the photoplethysmography. Such tools can allow for better monitoring of patients that do not have invasively recorded blood pressure, both in the critical care setting and on inpatient wards.