Abstract
This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities.
Highlights
Blood pressure is a vital sign that is widely used in the diagnosis and treatment of many medical conditions
To demonstrate the feasibility of the PyrAmes sensor, an early prototype was used to collect data from a male subject, >89 years old, who exhibited a wide variation of blood pressure on a daily basis
Brachial blood pressure measurements were taken while the subject was seated with both feet flat on the ground with a Series 10 Model BP786 oscillatory cuff (OMRON Healthcare, Kyoto, Japan) or Model ABPM50 ambulatory blood pressure monitor (Contec, Hebei, China) at varying intervals over a period of 7 months
Summary
Blood pressure is a vital sign that is widely used in the diagnosis and treatment of many medical conditions. Blood pressure monitoring and management is an essential part of medical care, in the treatment of chronic hypertension, critical care monitoring and trauma care. Blood pressure monitoring will be a vital part of care for virtually every one of the five million plus patients admitted annually to Intensive Care Units (ICUs) in the US [2,3]. Blood pressure monitoring will be a required part of care for virtually every one of the 130 million plus patients admitted annually to emergency departments (EDs) across the US [4,5].
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