Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 13, Issue 2, Pages 461-472Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2016.2612640
Keywords
Blood pressure (BP); bootstrap; deep neural networks (DNNs); machine learning; oscillometric blood pressure estimation
Categories
Funding
- National Research Foundation of Korea [2014R1A2A1A10049735]
- National Research Foundation of Korea [2014R1A2A1A10049735] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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