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Oscillometric Blood Pressure Estimation Based on Deep Learning

Title
Oscillometric Blood Pressure Estimation Based on Deep Learning
Author
장준혁
Keywords
Blood pressure (BP); bootstrap; deep neural networks (DNNs); machine learning; oscillometric blood pressure estimation
Issue Date
2017-04
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v. 13, no. 2, page. 461-472
Abstract
Oscillometric measurement is widely used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). In this paper, we propose a deep belief network (DBN)-deep neural network (DNN) to learn about the complex nonlinear relationship between the artificial feature vectors obtained from the oscillometric wave and the reference nurse blood pressures using the DBN-DNN-based-regression model. Our DBN-DNN is a powerful generative network for feature extraction and can address to stick in local minima through a special pretraining phase. Therefore, this model provides an alternative way for replacing a popular shallow model. Based on this, we apply the DBN-DNN-based regression model to estimate the SBP and DBP. However, there are a small amount of data samples, which is not enough to train the DBN-DNN without the overfitting problem. For this reason, we use the parametric bootstrap-based artificial features, which are used as training samples to efficiently learn the complex nonlinear functions between the feature vectors obtained and the reference nurse blood pressures. As far as we know, this is one of the first studies using the DBN-DNN-based regression model for BP estimation when a small training sample is available. Our DBN-DNN-based regression model provides a lower standard deviation of error, mean error, and mean absolute error for the SBP and DBP as compared with the conventional methods.
URI
https://ieeexplore.ieee.org/document/7576674https://repository.hanyang.ac.kr/handle/20.500.11754/113716
ISSN
1551-3203; 1941-0050
DOI
10.1109/TII.2016.2612640
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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