Determination of blood pressure using Bayesian approach

The maximum amplitude algorithm (MAA) is commonly used in the estimation of the pressure values, and it uses experimentally obtained ratios of systolic and diastolic oscillometric amplitude to the mean arterial pressure (known as systolic and diastolic ratios) for estimating the systolic and diastolic pressures. This paper provides a Bayesian approach to determine the systolic and diastolic ratios. The standard error of estimate (SEE) and correlation coefficient (CORR) of systolic blood pressure (SBP) and diastolic bloo

Oscillometric blood pressure estimation using principal component analysis and neural networks

Estimation of systolic and diastolic pressures from the oscillometric waveform is a challenging task in noninvasive electronic blood pressure (BP) monitoring devices. Since the conventional oscillometric algorithms cannot model and extract the complex and nonlinear relationship that may exist between BP and oscillometric waveform, artificial neural networks (NNs) have been proposed as a possible alternative. However, the research on this topic has been limited to some simple architectures that directly estimate the BP from raw oscillatio

Confidence interval estimation for blood pressure measurements with nonparametric bootstrap approach

Blood pressure is an important vital sign for determining the health of an individual. Although estimation of average arterial blood pressure is possible using oscillometric methods, there are no established methods in the literature for obtaining confidence interval (CI) for systolic pressure (SP) and diastolic pressure (DP) estimates obtained from such measurements. This paper presents a nonparametric bootstrap methodology to obtain CI with a small sample set of measurements. The proposed methodology uses pseudo measurements using bootstrap principle

Oscillometric blood pressure pulse morphology

This paper presents a new analysis of oscillometric blood pressure signals using pulse morphology under different pressure points. As the pulse morphology contains potentially critical clinical information, quantitative signal metrics are used to characterize the blood pressure pulse using pulse morphology. Signals metrics at three different pressure points namely systolic, mean arterial and diastolic pressure points are obtained and are compared across three different age groups (#60;30, 30-55, 55). It is observed that th

Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology

Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage

Confidence interval estimation for oscillometric blood pressure measurements using bootstrap approaches

Although estimation of average blood pressure is commonly done with oscillometric measurements, confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are not usually estimated. This paper adopts bootstrap methodologies to build CI from a small sample set of measurements, which is a situation commonly encountered in practice. Three bootstrap methodologies, namely, nonparametric percentile bootstrap, standard bootstrap, and bias-corrected and accelerated bootstrap are investigated. A two-step methodology is pro

Bayesian fusion algorithm for improved oscillometric blood pressure estimation

A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively

Model of human breathing reflected signal received by PN-UWB radar

Human detection is an integral component of civilian and military rescue operations, military surveillance and combat operations. Human detection can be achieved through monitoring of vit