The Hemodynamic Parameters Values Prediction on the Non-Invasive Hydrocuff Technology Basis with a Neural Network Applying

Sensors (Basel). 2022 Jun 1;22(11):4229. doi: 10.3390/s22114229.

Abstract

The task to develop a mechanism for predicting the hemodynamic parameters values based on non-invasive hydrocuff technology of a pulse wave signal fixation is described in this study. The advantages and disadvantages of existing methods of recording the ripple curve are noted in the published materials. This study proposes a new hydrocuff method for hemodynamic parameters and blood pressure values measuring. A block diagram of the device being developed is presented. Algorithms for processing the pulse wave contour are presented. A neural network applying necessity for the multiparametric feature space formation is substantiated. The pulse wave contours obtained using hydrocuff technology of oscillation formation for various age groups are presented. According to preliminary estimates, by the moment of the dicrotic surge formation, it is possible to judge the ratio of the heart and blood vessels work, which makes it possible to form an expanded feature space of significant parameters based on neural network classifiers. This study presents the characteristics accounted for creating a database for training a neural network.

Keywords: blood pressure; feature selection algorithm; hemodynamic parameters; hydrocuff technology; machine learning; multiparameter feature space; pressure; pulse wave.

MeSH terms

  • Algorithms
  • Blood Pressure / physiology
  • Blood Pressure Determination* / methods
  • Hemodynamics
  • Neural Networks, Computer*
  • Technology

Grants and funding

This research received no external funding.