Version 1
: Received: 19 May 2018 / Approved: 21 May 2018 / Online: 21 May 2018 (12:54:26 CEST)
How to cite:
Lee, S.; Chang, J. Statistical approach Based on Deep Neural Networks for Oscillometric Blood Pressure Estimation. Preprints2018, 2018050276. https://doi.org/10.20944/preprints201805.0276.v1
Lee, S.; Chang, J. Statistical approach Based on Deep Neural Networks for Oscillometric Blood Pressure Estimation. Preprints 2018, 2018050276. https://doi.org/10.20944/preprints201805.0276.v1
Lee, S.; Chang, J. Statistical approach Based on Deep Neural Networks for Oscillometric Blood Pressure Estimation. Preprints2018, 2018050276. https://doi.org/10.20944/preprints201805.0276.v1
APA Style
Lee, S., & Chang, J. (2018). Statistical approach Based on Deep Neural Networks for Oscillometric Blood Pressure Estimation. Preprints. https://doi.org/10.20944/preprints201805.0276.v1
Chicago/Turabian Style
Lee, S. and Joon-Hyuk Chang. 2018 "Statistical approach Based on Deep Neural Networks for Oscillometric Blood Pressure Estimation" Preprints. https://doi.org/10.20944/preprints201805.0276.v1
Abstract
Oscillometric blood pressure (BP) devices currently estimate a single point but do not identify fluctuations in BP or distinguish them from variations in response to physiological properties. In this paper, to analyze BP 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 neural network (DNN) to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed DNN regression model decreases the standard deviation of error (SDE) of the mean error (ME) and the mean absolute error (MAE) and reduces the uncertainty of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation distribution which fits the Gaussian distribution very well. We use a rank test in the DNN regression model to demonstrate the independence of the artificial SBP and DBP estimations. First, 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 based on the DNN regression estimator.
Computer Science and Mathematics, Computer Science
Copyright:
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.
