Estimated confidence interval from single blood pressure measurement based on algorithmic fusion

Dajani

Rajan

et al. 2020

Sensors

Self Cite

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.

Section: Bp Measurement and Protocolmentioning

confidence: 99%

Section: Lilliefors Test For Artificial Datamentioning

confidence: 99%

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

Dajani

Rajan

et al. 2020

Sensors

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“…Here, we assume that D * is a probability distribution for an artificial feature ðη * 1 , ⋯, η * R Þ, where R denotes the size of replication. We measure the homogeneity between the Gaussian distribution hypothesis and the distribution of artificial features [18]. If the Lilliefors test returns a decision value for the null hypothesis, then the artificial feature comes from a normal distribution family, against the alternative that it does not come from such a distribution utilizing a Lilliefors test function.…”

Section: The Goodness Of Fitmentioning

confidence: 99%

“…The BP data were measured from 85 people who do not have cardiovascular disease, from ages 12 to 80 years old with 48 men and 37 women. A wrist-mounted blood pressure device was used to measure 5 sets of oscillometric measurement BP from each subject on ANSI/AAMI protocol criteria [11,18]. The average value measured by the two experts was used as the reference value for SBP and DBP [9].…”

Section: Bp Measurements and Protocolmentioning

confidence: 99%

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

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.

“…Unlike DBN [13], each unit in the middle layer of the DBM acquires signal up and down to prevent uncertainty at the inference step [12]. More recently, the concepts of fusion and ensemble were employed in BP estimation to increase the performance of BP measurements [15][16][17]. DS fusion enables the evidence uncertainty to have upper and lower bounds that can be incorporated as a method for combined independent estimation of the observations, while also enabling the clear achievement and increased confidence in a given DBM estimate [16].…”

Section: Introductionmentioning

confidence: 99%

Dempster–Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation

Chang

2018

Applied Sciences

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We propose a technique using Dempster-Shafer fusion based on a deep Boltzmann machine to classify and estimate systolic blood pressure and diastolic blood pressure categories using oscillometric blood pressure measurements. The deep Boltzmann machine is a state-of-the-art technology in which multiple restricted Boltzmann machines are accumulated. Unlike deep belief networks, each unit in the middle layer of the deep Boltzmann machine obtain information up and down to prevent uncertainty at the inference step. Dempster-Shafer fusion can be incorporated to enable combined independent estimation of the observations, and a confidence increase for a given deep Boltzmann machine estimate can be clearly observed. Our work provides an accurate blood pressure estimate, a blood pressure category with upper and lower bounds, and a solution that can reduce estimation uncertainty. This study is one of the first to use deep Boltzmann machine-based Dempster-Shafer fusion to classify and estimate blood pressure.