title: Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model.
abstract: Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients.
  
  
  
  This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities.
  
  
  
  The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE.
  
  
  
  The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates.
authors.affiliation: Department of Electronic Engineering, Hanyang University 222 Wangsimni-ro, Seongdong, Seoul 133-791, South Korea.
authors.forename: Soojeong
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada, K1S 5B6.
authors.forename: Sreeraman
authors.initials: S
authors.lastname: Rajan
authors.affiliation: Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 406-772, South Korea.
authors.forename: Gwanggil
authors.initials: G
authors.lastname: Jeon
authors.affiliation: Department of Electronic Engineering, Hanyang University 222 Wangsimni-ro, Seongdong, Seoul 133-791, South Korea. Electronic address: jchang@hanyang.ac.kr.
authors.forename: Joon-Hyuk
authors.initials: JH
authors.lastname: Chang
authors.affiliation: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario Canada, K1N 6N5.
authors.forename: Hilmi R
authors.initials: HR
authors.lastname: Dajani
authors.affiliation: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario Canada, K1N 6N5.
authors.forename: Voicu Z
authors.initials: VZ
authors.lastname: Groza
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001795:Blood Pressure Determination / Q000379:methods*; D002648:Child; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009991:Oscillometry / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: Blood pressure measurement; Bootstrap technique; Confidence interval; Gaussian mixture regression; Machine learning; Oscillometric method