Confidence Interval Estimation for Oscillometric Blood Pressure Measurements Using Bootstrap Approaches

Lee, Soojeong; Bolić, Miodrag; Groza, Voicu; Dajani, Hilmi R.; Rajan, Sreeraman

doi:10.1109/tim.2011.2161926

Cited by 47 publications

(87 citation statements)

References 15 publications

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“…The Gaussian mixture regression technique was then used to obtain the optimum SBP and DBP coefficients for each cluster. In [80], a nonparametric bootstrap method was proposed to estimate the confidence interval for oscillometric BP readings obtained using the MAA. The proposed methodology used pseudo measurements based on the bootstrap principle to derive the pseudo maximum amplitudes and the pseudo envelopes.…”

Section: A Maximum Amplitude Algorithmmentioning

confidence: 99%

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Forouzanfar

Dajani

Groza

et al. 2015

IEEE Rev. Biomed. Eng.

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159

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The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.

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Section: A Maximum Amplitude Algorithmmentioning

confidence: 99%

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Forouzanfar

Dajani

Groza

et al. 2015

IEEE Rev. Biomed. Eng.

Self Cite

159

View full text Add to dashboard Cite

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“…The fundamental concept of the NPB technique is to offer a large number of independent bootstrap ratios by resampling the original five ratios r = ( r 1 , r 2 ,…, r m ) of m measurements at random from a unknown probability distribution F . A bootstrap resamples

{boldr}_{1}^{*} (= x_{1}^{*}, x_{2}^{*}, \dots, x_{m}^{*}), \dots {boldr}_{B}^{*} (= x_{1}^{*}, x_{2}^{*}, \dots, x_{m}^{*})

are acquired by sampling m time drawn randomly with replacement from the original sample r with elements occurring zero, once or multiple times, where m denotes an original ratio size (=5) and B denotes a number of resamples [4,16]. Based on Efron, et al [16], we use B = 1,000 [4].…”

Section: Methodsmentioning

confidence: 99%

On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation

Lee

Jeon

Lee

2013

Sensors

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The maximum amplitude algorithm (MAA) is generally utilized in the estimation of the pressure values, and it uses heuristically obtained ratios of systolic and diastolic oscillometric amplitude to the mean arterial pressure (known as systolic and diastolic ratios) in order to estimate the systolic and diastolic pressures. This paper proposes a Bayesian model to estimate the systolic and diastolic ratios. These ratios are an improvement over the single fixed systolic and diastolic ratios used in the algorithms that are available in the literature. The proposed method shows lower mean difference (MD) with standard deviation (SD) compared to the MAA for both SBP and DBP consistently in all the five measurements.

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“…Many BP measurements are required to estimate each patient's CI. However, it is difficult to measure each patient's BP several times using an oscillator BP device because it is costly and time-consuming since repeatable circumstances for a reproducible BP measurement cannot be guaranteed [9]. Hence, a bootstrap technique was proposed to obtain a CI estimate from BP measurements using a small sample size [9].…”

Section: Introductionmentioning

confidence: 99%

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

Lee

2020

Journal of Sensors

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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.

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Confidence Interval Estimation for Oscillometric Blood Pressure Measurements Using Bootstrap Approaches

Cited by 47 publications

References 15 publications

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Oscillometric Blood Pressure Estimation: Past, Present, and Future

On Using Maximum a Posteriori Probability Based on a Bayesian Model for Oscillometric Blood Pressure Estimation

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

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