Literature DB >> 22551623

Augmented blood pressure measurement through the noninvasive estimation of physiological arterial pressure variability.

Karen Soueidan1, Silu Chen, Hilmi R Dajani, Miodrag Bolic, Voicu Groza.   

Abstract

Current noninvasive blood pressure (BP) measurement methods, such as the oscillometric method, estimate the systolic and diastolic blood pressure (SBP and DBP) at two random instants in time and do not take into account the natural variability in BP. The standard for automated BP devices sets a maximum allowable system error of ±5 mmHg, even though natural BP variability often exceeds these limits. This paper proposes a new approach using simultaneous recordings of the oscillometric and continuous arterial pulse waveforms to augment the conventional noninvasive measurement by providing (1) the mean SBP and DBP over the measurement interval and the associated confidence intervals of the mean, (2) the standard deviation of SBP and DBP over the measurement interval, which indicates the degree of fluctuation in BP and (3) an indicator as to whether or not the oscillometric reading is an outlier. Recordings with healthy subjects demonstrate the potential utility of this approach to characterize BP, to detect outlier measurements, and that it does not suffer from bias relative to the conventional oscillometric method.

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Mesh:

Year:  2012        PMID: 22551623     DOI: 10.1088/0967-3334/33/6/881

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  7 in total

1.  Ensemble Methodology for Confidence Interval in Oscillometric Blood Pressure Measurements.

Authors:  Soojeong Lee; Gaseong Lee
Journal:  J Med Syst       Date:  2020-03-17       Impact factor: 4.460

2.  Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform.

Authors:  Pooi Khoon Lim; Siew-Cheok Ng; Wissam A Jassim; Stephen J Redmond; Mohammad Zilany; Alberto Avolio; Einly Lim; Maw Pin Tan; Nigel H Lovell
Journal:  Sensors (Basel)       Date:  2015-06-16       Impact factor: 3.576

3.  On using maximum a posteriori probability based on a Bayesian model for oscillometric blood pressure estimation.

Authors:  Soojeong Lee; Gwanggil Jeon; Gangseong Lee
Journal:  Sensors (Basel)       Date:  2013-10-10       Impact factor: 3.576

4.  Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates.

Authors:  Soojeong Lee; Gangseong Lee; Gwanggil Jeon
Journal:  Sensors (Basel)       Date:  2019-05-08       Impact factor: 3.576

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

Authors:  Soojeong Lee; Hilmi R Dajani; Sreeraman Rajan; Gangseong Lee; Voicu Z Groza
Journal:  Sensors (Basel)       Date:  2020-04-08       Impact factor: 3.576

6.  Oscillometric measurement of systolic and diastolic blood pressures validated in a physiologic mathematical model.

Authors:  Charles F Babbs
Journal:  Biomed Eng Online       Date:  2012-08-22       Impact factor: 2.819

7.  Two-Step Pseudomaximum Amplitude-Based Confidence Interval Estimation for Oscillometric Blood Pressure Measurements.

Authors:  Soojeong Lee; Gwanggil Jeon; Seokhoon Kang
Journal:  Biomed Res Int       Date:  2015-10-04       Impact factor: 3.411

  7 in total

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