Deep Belief Networks Ensemble for Blood Pressure Estimation

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.