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Combining Bootstrap Aggregation with Support Vector Regression for Small Blood Pressure Measurement

  • Soojeong Lee
  • Awais Ahmad
  • Gwanggil Jeon
Image & Signal Processing
Part of the following topical collections:
  1. Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics

Abstract

Blood pressure measurement based on oscillometry is one of the most popular techniques to check a health condition of individual subjects. This paper proposes a support vector using fusion estimator with a bootstrap technique for oscillometric blood pressure (BP) estimation. However, some inherent problems exist with this approach. First, it is not simple to identify the best support vector regression (SVR) estimator, and worthy information might be omitted when selecting one SVR estimator and discarding others. Additionally, our input feature data, acquired from only five BP measurements per subject, represent a very small sample size. This constitutes a critical limitation when utilizing the SVR technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To overcome these challenges, a fusion with an asymptotic approach (based on combining the bootstrap with the SVR technique) is utilized to generate the pseudo features needed to predict the BP values. This ensemble estimator using the SVR technique can learn to effectively mimic the non-linear relations between the input data acquired from the oscillometry and the nurse’s BPs.

Keywords

Blood pressure Oscillometric method Support vector regression Bootstrap 

Notes

Acknowledgments

This work was supported by the NRF Grant funded by the Korean Government 2016R1D1A1B03932925 and 2015R1D1A1A01058171.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Computer EngineeringHanyang UniversitySeongdong-guRepublic of Korea
  2. 2.Department of Information and Communication EngineeringYeungnam UniversityGyeongbukRepublic of Korea
  3. 3.Department of Embedded Systems Engineering, College of Information TechnologyIncheon National UniversityYeonsu-guKorea

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