Support Vector Regression-Based Recursive Ensemble Methodology for Confidence Interval Estimation in Blood Pressure Measurements
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lee, Soojeong& Lee, Gangseong. 2020. Support Vector Regression-Based Recursive Ensemble Methodology for Confidence Interval Estimation in Blood Pressure Measurements. Journal of Sensors،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1190524
Modern Language Association (MLA)
Lee, Soojeong& Lee, Gangseong. Support Vector Regression-Based Recursive Ensemble Methodology for Confidence Interval Estimation in Blood Pressure Measurements. Journal of Sensors No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1190524
American Medical Association (AMA)
Lee, Soojeong& Lee, Gangseong. Support Vector Regression-Based Recursive Ensemble Methodology for Confidence Interval Estimation in Blood Pressure Measurements. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1190524
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190524