Improved Confidence Interval Estimation for Oscillometric Blood Pressure Measurement by Combining Bootstrap-After-Jackknife Function with Non-Gaussian Models

摘要

Confidence intervals (CIs) are generally not provided along with estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) measured using oscillometric blood pressure devices. No criteria exist to determine the CI from a small sample set of oscillometric blood pressure measurements. We provide an extended methodology to improve estimation of CIs of SBP and DBP based on a nonparametric bootstrap-after-jackknife function and a Bayesian approach. We use the nonparametric bootstrap-after-jackknife function to reduce maximum amplitude outliers. Improved pseudomaximum amplitudes (PMAs) and pseudoenvelopes (PEs) are derived from the pseudomeasurements. Moreover, the proposed algorithm uses an unfixed ratio obtained by employing non-Gaussian models based on the Bayesian technique to estimate the SBP and DBP ratios for individual subjects. The CIs obtained through our proposed approach are narrower than those obtained using the traditional Student t-distribution method. The mean difference (MD) and standard deviation (SD) of the SBP and DBP estimates using our proposed approach are better than the estimates obtained by conventional fixed ratios based on the PMA and PE (PMAE).