On Statistical Approach to Automated Normal Systolic Blood Pressure Detection in Continuously Monitored Blood Pressure Data

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