Deep Boltzmann Regression With Mimic Features for Oscillometric Blood Pressure Estimation

Dempster–Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation

“…This critical limitation leads to various problems, such as overfitting, since the DBN comprises the complex nonlinear model, including many layers, parameters, and weights. This problem was addressed by increasing the size of the input data by utilizing the parameter bootstrap scheme [12]. Furthermore, a deep Boltzmann machine (DBM) regression model [13] was recently proposed by Lee et al [12] to resolve the uncertainty.…”

“…This problem was addressed by increasing the size of the input data by utilizing the parameter bootstrap scheme [12]. Furthermore, a deep Boltzmann machine (DBM) regression model [13] was recently proposed by Lee et al [12] to resolve the uncertainty. However, the DBN and DBM models can also cause estimate uncertainties in cases in which many random initialized parameters and functions remain.…”

“…Motivated by these issues, a DBM-based [12,13] Dempster-Shafer (DS) fusion [12,14] technique is herein presented to classify and estimate the SBP and DBP categories without characteristic ratios. DBM is an advanced technology of the accumulation of restricted Boltzmann machines (RBMs) [13].…”

“…DBM is an advanced technology of the accumulation of restricted Boltzmann machines (RBMs) [13]. Unlike DBN [13], each unit in the middle layer of the DBM acquires signal up and down to prevent uncertainty at the inference step [12]. More recently, the concepts of fusion and ensemble were employed in BP estimation to increase the performance of BP measurements [15][16][17].…”

“…Consequently, we provide an accurate BP category and a methodology that can reduce the estimation uncertainty. Specifically, this research provides the following contributions and improvements over that presented in [10,12]:…”