Confidence interval estimation for blood pressure measurements with nonparametric bootstrap approach

2010 ◽  
Author(s):  
Soojeong Lee ◽  
Miodrag Bolic ◽  
Voicu Z Groza ◽  
Hilmi R Dajani ◽  
Sreeraman Rajan
Keyword(s):  
Blood Pressure ◽  
Confidence Interval ◽  
Interval Estimation ◽  
Pressure Measurements ◽  
Nonparametric Bootstrap ◽  
Bootstrap Approach ◽  
Confidence Interval Estimation

scholarly journals Two-Step Pseudomaximum Amplitude-Based Confidence Interval Estimation for Oscillometric Blood Pressure Measurements

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Soojeong Lee ◽  
Gwanggil Jeon ◽  
Seokhoon Kang
Keyword(s):  
Blood Pressure ◽  
Interval Estimation ◽  
Maximum Amplitude ◽  
Pressure Measurements ◽  
Arterial Blood ◽  
Novel Approach ◽  
Bootstrap Approach ◽  
Double Bootstrap ◽  
Weighted Median ◽  
Confidence Interval Estimation

Blood pressure (BP) is an important vital sign to determine the health of an individual. Although the estimation of average arterial blood pressure using oscillometric methods is possible, there are no established methods for obtaining confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we propose a two-step pseudomaximum amplitude (TSPMA) as a novel approach to obtain improved CIs of SBP and DBP using a double bootstrap approach. The weighted median (WM) filter is employed to reduce impulsive and Gaussian noises in the step of preprocessing. Application of the proposed method provides tighter CIs and smaller standard deviation of CIs than the pseudomaximum amplitude-envelope and maximum amplitude algorithms with Student’st-method.

scholarly journals Support Vector Regression-Based Recursive Ensemble Methodology for Confidence Interval Estimation in Blood Pressure Measurements

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Soojeong Lee ◽  
Gangseong Lee
Keyword(s):  
Blood Pressure ◽  
Support Vector Machine ◽  
Confidence Interval ◽  
Interval Estimation ◽  
Absolute Error ◽  
Support Vector ◽  
Measured Quantity ◽  
Pressure Measurements ◽  
Measure Blood Pressure ◽  
A New Technique

The monitors of oscillometry blood pressure measurements are generally utilized to measure blood pressure for many subjects at hospitals, homes, and office, and they are actively studied. These monitors usually provide a single blood pressure point, and they are not able to indicate the confidence interval of the measured quantity. In this paper, we propose a new technique using a recursive ensemble based on a support vector machine to estimate a confidence interval for oscillometry blood pressure measurements. The recursive ensemble is based on a support vector machine that is used to effectively estimate blood pressure and then measure the confidence interval for the systolic blood pressure and diastolic blood pressure. The recursive ensemble methodology provides a lower standard deviation of error, mean error, and mean absolute error for the blood pressure as compared to those of the conventional techniques.

scholarly journals Bootstrap Based Confidence Interval Estimation of \\[6pt]Quantiles for Current Status Data

2021 ◽  
Vol 10 (5) ◽  
pp. 38
Author(s):  
Wei Chen ◽  
Fengling Ren
Keyword(s):  
Confidence Interval ◽  
Interval Estimation ◽  
Real Data ◽  
Current Status Data ◽  
Current Status ◽  
Well Performance ◽  
Data Set ◽  
Bootstrap Approach ◽  
Confidence Interval Estimation ◽  
Status Data

In this paper, we proposed a bootstrap approach to construct the confidence interval of quantiles for current status data, which is computationally simple and efficient without estimating nuisance parameters. The reasonability of the proposed method is verified by the well performance presented in the extensive simulation study. We also analyzed a real data set as illustration.

Mortality rate and confidence interval estimation in humanitarian emergencies

Disasters ◽  
2009 ◽  
Vol 34 (1) ◽  
pp. 164-175 ◽  
Author(s):  
Kevin Sullivan ◽  
S.M. Moazzem Hossain ◽  
Bradley A. Woodruff
Keyword(s):  
Mortality Rate ◽  
Confidence Interval ◽  
Interval Estimation ◽  
Humanitarian Emergencies ◽  
Confidence Interval Estimation
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