Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates
1
Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
2
Ingenium College, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
3
School of Electronic Engineering, Xidian Unversity, No. 2 South Taibai Road, Xi’an 710071, China
4
Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2137; https://doi.org/10.3390/s19092137
Received: 2 April 2019
/
Revised: 1 May 2019
/
Accepted: 4 May 2019
/
Published: 8 May 2019
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP’s normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, to mitigate uncertainties, we use a deep learning method to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed deep learning regression model decreases the standard deviation of error (SDE) of the mean error and the mean absolute error and reduces the uncertainties of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation which fits the standard normal distribution very well. We use a rank test in the deep learning technique to demonstrate the independence of the artificial systolic BP and diastolic BP estimations. We perform statistical tests to verify the normality of the BP measurements for individual subjects. The proposed methodology provides accurate BP estimations and reduces the uncertainties associated with the CLs and SDEs using the deep learning algorithm.
View Full-Text
Keywords:
blood pressure; oscillometric measurement; statistical analysis; normality; confidence limit; deep learning
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Lee, S.; Lee, G.; Jeon, G. Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates. Sensors 2019, 19, 2137. https://doi.org/10.3390/s19092137
AMA Style
Lee S, Lee G, Jeon G. Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates. Sensors. 2019; 19(9):2137. https://doi.org/10.3390/s19092137
Chicago/Turabian StyleLee, Soojeong, Gangseong Lee, and Gwanggil Jeon. 2019. "Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates" Sensors 19, no. 9: 2137. https://doi.org/10.3390/s19092137
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
