Mimic Big Data and Low Power Infrastructure-based Small Blood Pressure Measurement for Internet of Things

Abstract

Although estimation of average arterial blood pressure is possible using oscillometric methods based on the internet of things (IOT), there are no established methods in the literature for obtaining confidence interval (CI) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates obtained from such measurements. This paper adopts bootstrap methodologies to build the CI with a small sample set of measurements. The proposed methodologies use multiple pseudo maximum amplitude (MPMA) and pseudo envelope (PE) using big measurement based on bootstrap principles with recursive approach to solve the bias of the pseudo maximum amplitude (PMA) in IOT. The SBP and DBP are derived using the new relationships between mean cuff pressure and PE and then the CIs for such estimates are obtained. Application of the proposed methodology on an experimental dataset of 85 patients with 5 sets of measurements for each patient yielded a tighter Cl than the conventional student t-method.