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Deep Belief Networks Ensemble for Blood Pressure Estimation

Title
Deep Belief Networks Ensemble for Blood Pressure Estimation
Author
장준혁
Keywords
Blood pressure measurement; oscillometry blood pressure estimation; deep neural networks; bootstrap-inspired technique; ensemble
Issue Date
2017-05
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE ACCESS, v. 5, page. 9962-9972
Abstract
In this paper, we propose a deep belief network (DBN) deep neural network (DNN) with mimic features based on the bootstrap inspired technique to learn the complex nonlinear relationship between the mimic feature vectors obtained from the oscillometry signals and the target blood pressures. Unfortunately, we have two problems in utilizing the DBN DNN technique to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP). First, our set of input feature vectors is very small, which is a fatal drawback to training based on the DBN DNN technique. Second, the special pre-training phase can also trigger an unstable estimation, because there are still a lot of random initialized assigns, such as the training data set, weights, and biases. For these reasons, we employ the bootstrap-inspired technique as a fusion ensemble estimator based on the DBN DNN-based regression model, which is used to create the mimic features to estimate the SBP and DBP. Our DBN DNN-based ensemble regression estimator provides a lower standard deviation of error, mean error, and mean absolute error for the SBP and DBP as compared with those of the conventional methods.
URI
https://ieeexplore.ieee.org/document/7921528https://repository.hanyang.ac.kr/handle/20.500.11754/114020
ISSN
2169-3536
DOI
10.1109/ACCESS.2017.2701800
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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