Abstract
This paper considers a statistical approach for detecting normal systolic blood pressure pattern from a continuously acquired systolic blood pressure data. Blood pressure monitoring system able to detect subtle changes well in advance in physiological vital signs before clinical emergencies, requires knowledge of the normal blood pressure pattern. Nevertheless, normal data is not always available for pragmatic learning. Ability to learn the normal pattern of systolic blood pressure data is a significant element in the development of robust blood pressure monitoring system. This paper builds on Kernel density approach, based on statistics obtained from novelty scores of the density estimates. The methods are illustrated using simulations and a real data of a continuously acquired systolic blood pressure dataset from Biofourmis Singapore Pte., with detection accuracy of 98 %. Keywords: Systolic blood pressure, novelty score, probability density function, vital sign DOI: 10.7176/JNSR/10-4-05 Publication date: February 29 th 2020
Highlights
As the world is filled with changes so do physiological vital signs
Statistics obtained based on the probability density function of the data can provide a standard way for data exploration and development of attractive statistical methods for solving scientific problems
This is the direction we propose for addressing the problem of self-learning of normal systolic blood pressure measurements in the context of blood pressure monitoring
Summary
As the world is filled with changes so do physiological vital signs. Knowledge of such changes could be informative about the prevention of unforeseen emergencies and to harness important changeovers. Systolic blood pressure (SBP), one of the key components of blood pressure (BP) monitoring, exhibits subtle changes (Evans, Hodgkinson & Berry, 2001) These changes are a rich source of vital information such as a drift from normal physiology, if discovered and incorporated into model specification, will provide a basic source for building smart automated monitoring systems for the associated vital sign. Statistics obtained based on the probability density function of the data can provide a standard way for data exploration and development of attractive statistical methods for solving scientific problems This is the direction we propose for addressing the problem of self-learning of normal systolic blood pressure measurements in the context of blood pressure monitoring.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
