Estimated confidence interval from single blood pressure measurement based on algorithmic fusion.
BACKGROUND
Current oscillometric blood pressure measurement devices generally provide only single-point estimates for systolic and diastolic blood pressures and rarely provide confidence ranges for these estimates. A novel methodology to obtain confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates from a single oscillometric blood pressure measurement is presented.


METHODS
The proposed methodology utilizes the multiple regression technique to fuse optimally a set of SBP and DBP estimates obtained through different algorithms. However, the set of SBP and DBP estimates is a small number to determine the CI of each individual subject. To address this issue, the weighted bootstrap approach based on the multiple regression technique was used to generate a pseudo sample set for the SBP and the DBP. In this paper, the multiple regression technique can estimate the best-fitting surface of an efficient function that relates the input sample set as an independent vector to the auscultatory nurse measurement as a dependent vector to estimate regression coefficients. Consequently, the coefficients are assigned to an eight-sample set obtained from the fusion of different algorithms as optimally weighted parameters. CIs are also estimated using the conventional methods on the set of fused SBP and DBP estimates for comparison purposes.


RESULTS
The proposed method was applied to an experimental dataset of 85 patients. The results indicated that the proposed approach provides better blood pressure estimates than the existing algorithms and, in addition, is able to provide CIs for a single measurement.


CONCLUSIONS
The CIs derived from the proposed scheme are much smaller than those calculated by conventional methods except for the pseudo maximum amplitude-envelope algorithm for both the SBP and the DBP, probably because of the decrease in the standard deviation through the increase in the pseudo measurements using the weighted bootstrap method for each subject. The proposed methodology is likely the only one currently available that can provide CIs for single-sample blood pressure measurements.
School of Electronic Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong, Seoul 133-791, South Korea.
Soojeong
S
Lee
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada, K1N 6N5.
Sreeraman
S
Rajan
School of Electronic Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong, Seoul 133-791, South Korea.
Chee-Hyun
CH
Park
School of Electronic Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong, Seoul 133-791, South Korea. Electronic address: jchang@hanyang.ac.kr.
Joon-Hyuk
JH
Chang
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada, K1N 6N5.
Hilmi R
HR
Dajani
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada, K1N 6N5.
Voicu Z
VZ
Groza
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001794:Blood Pressure; D001795:Blood Pressure Determination / Q000295:instrumentation; D002648:Child; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests
Blood pressure; Confidence interval; Fusion; Multiple regression; Oscillometric blood pressure measurement; Parametric bootstrap