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Open AccessArticle

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

1
Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
2
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N6N5, Canada
3
Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada
4
Ingenium College, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2108; https://doi.org/10.3390/s20072108
Received: 18 February 2020 / Revised: 21 March 2020 / Accepted: 27 March 2020 / Published: 8 April 2020
(This article belongs to the Special Issue Artificial Intelligence in Medical Sensors)
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. View Full-Text
Keywords: uncertainty; confidence interval; oscillometry blood pressure measurement; deep neural network; ensemble method uncertainty; confidence interval; oscillometry blood pressure measurement; deep neural network; ensemble method
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Lee, S.; Dajani, H.R.; Rajan, S.; Lee, G.; Groza, V.Z. Uncertainty in Blood Pressure Measurement Estimated Using Ensemble-Based Recursive Methodology. Sensors 2020, 20, 2108.

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