期刊
IEEE ACCESS
卷 5, 期 -, 页码 9962-9972出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2701800
关键词
Blood pressure measurement; oscillometry blood pressure estimation; deep neural networks; bootstrap-inspired technique; ensemble
资金
- National Research Foundation of Korea [2016R1D1A1B03932925, 2014R1A2A1A10049735]
- National Research Foundation of Korea [2016R1D1A1B03932925, 2014R1A2A1A10049735] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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