Wearable sensors coupled with AI can generate blood pressure readings nearly as well as arterial lines used in intensive care units (ICUs) and operating rooms.
This is according to researchers at Johns Hopkins University.
Tests suggest the system could become an alternative to arterial catheters and allow hospitals to monitor arterial blood pressure beyond ICUs.
The sensors could also give people with hypertension a way to continuously monitor their health similar to the wearable glucose monitors used by people with diabetes.
Lead author Carl Harris, a PhD student in biomedical engineering, said: ‘Patients in the ICU need continuous blood pressure monitoring to catch problems early, but it means an arterial line, which comes with a risk of bleeding, clotting and infection. We wanted to find a better way.’
The team created a system called MOSAIC based on two sensors – one placed on a patient’s chest, and the other on a finger. Together, the sensors record the heart’s electrical activity and the flow of blood through the body and relay these signals to a deep learning model, which generates a waveform, a continuous readout of blood pressure over time.
Senior author Robert Stevens, chief of the Division of Informatics, Integration, and Innovation at Johns Hopkins Medicine, explained: ‘We reconstruct waveform data in a way that’s meaningful, accurate, reliable and, most importantly, non-invasive. It’s a possible solution for avoiding the current standard of care for measuring blood pressure, arterial lines, a very invasive procedure with a risk of many complications.’
In an initial study of 28 patients in the intensive care unit at Johns Hopkins Hospital, the system generated waveforms that closely matched those recorded by traditional arterial catheters.
The team is now validating the sensors and algorithm in a larger cohort of Johns Hopkins ICU patients.
Longer term, the researchers hope the system could reduce the need for invasive monitoring and become a way to continuously measure blood pressure in a range of settings, including on hospital wards and at home.
The team also hopes that these sensors could create a similar paradigm shift for the millions living with hypertension as glucose monitors have for people with diabetes.
The findings are published in Computers in Biology and Medicine.
