Improved Gaussian Mixture Regression Based on Pseudo Feature Generation Using Bootstrap in Blood Pressure Estimation

Support Vector Regression-Based Recursive Ensemble Methodology for Confidence Interval Estimation in Blood Pressure Measurements

“…Artificial Data Obtained Using Bootstrap Technique. To estimate the reference BP value, we removed outliers using a signal processing technique and the effective features of the oscillometric waveform (OMW) signals were extracted [15]. Because the five BP data for individual volunteers were small amounts as an input data for the training process, we used the bootstrap method [16] to increase the amount of blood pressure data for each volunteer, where this data was called as artificial data or features in this study.…”

“…The artificial input data were generated using the bootstrap technique [9,16] to improve estimation accuracy using limited data sets in difficult situations when enhancing accuracy with traditional approaches. More details regarding these features can be found in [15].…”

“…The first signal measured in mmHg when the pressure in the upper cuff was deflated, the blood flow generated a Korotko signal that was heard by a stethoscope. The first signal measured in mmHg in the upper left meter estimated the SBP and the fifth signal estimated the DBP [15]. The left upper arm and wrist BP could not be measured at the same time because of the difficulty of occlusion of the brachial artery through left upper-arm sphygmomanometers.…”

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

“…The algorithm of maximum amplitude (MAA) is predominantly utilized to estimate the BP mean by monitoring the cuff pressure in the maximum oscillation. However, the MAA using a fixed ratio is insufficient for estimating the BP since the proportion of these fixed characteristics significantly varies according to the rhythm of the heart, movement artifacts, and cuff size [1,2], which leads to uncertainties of the BP in practice in the oscillometric BP measurements [3][4][5]. Physiological characteristics have a significant impact on these ratios.…”

“…An NN with a feature-based technique employing a back-propagation algorithm was thus devised [9]. The Gaussian mixture regression (GMR) and improved GMR were further proposed by Lee et al to estimate BP [4,5]. In recent years, a deep learning technique has emerged as an outstanding trend in machine learning for signal processing areas.…”