Blood Pressure Monitoring, Courtesy Of Cameras And AI

At the basic level, methods of blood pressure monitoring have slowly changed in the last few decades. While most types of sphygmomanometer still rely on a Velcro cuff placed around the arm, the methodology used in measurement varies. Analog mercury and aneroid types still abound, while digital blood pressure monitors using electrical sensors have become mainstream these days.

Researchers have now developed a new non-invasive method of measurement that does away with the arm cuff entirely. The method relies entirely on video capture with a camera and processing via AI.

Under Pressure

The technique was developed by researchers at the University of South Australia and Middle Technical University, Baghdad. It relies on using a DSLR camera to capture footage of the patient for approximately 10 seconds. The camera is placed a short distance from the patient and is aimed at the face. The video captured is then analyzed by AI to extract indicative signals of cardiac activity from two distinct forehead regions. The system is able to capture both systolic and diastolic blood pressure values as the heart pumps and fills back up.

The system relies on video captured at a resolution of 1920×1080 at 60 fps. Face detection is first used to find the part of the image containing the patient’s head, before zeroing in on the forehead and the precise regions of interest, one in the center of the forehead and a second section off to one side. The forehead region was chosen for analysis as it is least affected by movements from facial expressions, breathing, or blinking. The green channel is used for the analysis as prior research has shown it readily reveals stronger plethysmographic signals – i.e. those corresponding to blood-pressure related volume changes. Analysis of changes to the pixels in the selected analysis regions is fed into a model that estimates the patient’s blood pressure values.

The research builds on earlier work from the same universities, which in 2017 developed a method to determine a person’s heart rate from a video shot via drone. Other work has involved capturing data on blood oxygen levels and respiratory rates. Camera-based techniques have become increasingly common over the past decade, with SIGGRAPH featuring such research regularly over the years.

These methods of measurement have a simple medical benefit, in that they could theoretically reduce the spread of disease that could spread from close contact. The researchers involved in the project cite this as a primary driver of their work, much of which has taken place in the shadow of the coronavirus pandemic.

However, these non-contact methods may give up some accuracy compared to traditional medical practices. In the case of the blood pressure measurement system, testing showed it to be 90% accurate compared to a digital sphygmomanometer. That’s a solid result for a research project, but it means that one in ten patients would be subject to mismeasurement – an unacceptable performance level for medical-grade equipment. It also bears noting that digital sphygmomanometers are not considered the gold-class measurement option. Their measurements use certain base assumptions behind their calculations, and cannot always be relied upon for patients with certain complex conditions. Comparison to a lab-grade mercury sphygmomanometer could provide a truer baseline figure to work from.

The role of AI in medical assessements is a controversial issue, too. Outside of simple measurements, research has also explored the use of AI in performing complex diagnoses using techniques outside the abilities of human perception. The problem is that in many ways, an AI system can be a bit of a black box. Without a proper and comprehensive understanding of exactly what the AI is doing, it’s difficult to trust its measurements or conclusions. In the medical field, this is a major concern, where decisions on treatment regularly come down to life or death.

Technology for the non-contact measurement of vital signs has plenty of useful applications in the medical field. While it’s early days yet, these methods will continue to improve in refinement and reliability in coming years. Expect them to play a complementary role, due to their inherent limitations, rather than replacing traditional methods entirely.