Continuous non-invasive blood pressure estimation.

Lead Research Organisation: University of Oxford
Department Name: Engineering Science

Abstract

Blood pressure is one of the five vital signs and a major indicator of a patient's health. High blood pressure (hypertension) is one of the strongest risk factors for Cardio Vascular Disease (CVD), which is the leading cause of death worldwide. Current methods for measuring blood pressure use a sphygmomanometer, an inflatable cuff placed around the patients arm and inflated until the blood flow is occluded. This provides a single reading of a patient's systolic (highest blood pressure when the heart contracts) and diastolic (lowest blood pressure when the heart relaxes) blood pressure.

However, blood pressure is a signal that changes throughout the day and so a single reading cannot provide sufficient information for accurate diagnosis of CVD or other blood pressure related diseases. In addition, the presence of a "white coat" effect, in which a patient's blood pressure increases due to the clinician taking the measurement, has shown that many patients will have been misdiagnosed when having their blood pressure examined. This has proven the need for a continuous non-invasive blood pressure estimation.

Current research efforts in the field of continuous non-invasive blood pressure estimation are mostly focussed on Pulse Transit Time (PTT) which is defined as the time it takes for blood to travel from one arterial site to another. Changes in PTT are shown to correlate with changes in blood pressure after appropriate calibration. In order to record the onset of the blood flow at the two different locations most devices that use PTT to derive an estimate of blood pressure do so by using two or more devices placed on a patient. Examples of these include an electrogardiogram (ECG) and a photoplethysmography (PPG) sensor linked to the same system, or two PPG sensors. Due to the need for two sensors it is very unlikely that the device will be used at all times as patients will find them uncomfortable and will get in the way of their daily life; and therefore, it is difficult to call the devices truly continuous.

This DPhil project aims to approach a novel implementation of continuous blood pressure estimation. There are two methods that are currently being considered for this project (however, as the project is still young it is very likely that there will be more methods to be explored). The first is to use a single PPG sensor placed on the wrist of a patient to derive either a PTT-like signal or use machine learning techniques to estimate blood pressure from the PPG. The second method will study camera-based methods of deriving a blood pressure estimation. It has already been shown that a video camera stream of a patient is able to derive pathological signals of the patient, this work can be extended to consider the flow of blood in many locations on the patient and thus PTT analysis can be used to derive a blood pressure estimation from the time differences between these locations.

An ideal application for this project is in continuous monitoring of blood pressure during the night time. Recent research has shown a nocturnal rise in systolic BP for in-hospital patients above the age of 50 as opposed to the usual dipping patterns found in younger individuals. The research has also identified significant gender-specific differences, as well as variations according to the day of the week. These results suggest that tracking BP changes over time could not only help the assessment of patients with clinical conditions such as hypertension, but also provide new information about cardiovascular health. It is difficult to use a sphygmomanometer to repeatedly measure blood pressure changes during the night for risk of waking the patient which would result in a rise in blood pressure. Therefore, continuous non-invasive blood pressure estimation (or a derived surrogate of blood pressure that follows the same trends) will allow for a patient whose blood pressure does not dip to be easily identified for further treatment.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509711/1 01/10/2016 30/09/2021
2118168 Studentship EP/N509711/1 01/10/2018 31/03/2022 Eoin Finnegan