Components of Oranta-AO software expert system for innovative application of blood pressure monitors

Abstract

The authors developed and substantiated the original methods of arterial oscillography, which were implemented in the developed Oranta-AO information system. The methods of application to the arterial oscillogram registered at measurement of arterial pressure gives the possibility to carry out the supplementary systematic assessment of health, functional state of cardiovascular system, its reserve possibilities etc. The authors also developed an Expert System (based on machine-learning methods) for the differential diagnosis of risks of heart, lung, mental illness and prognosis of some blood parameters. Oranta-AO software system was created based on research results due to methods and algorithms that were innovate. For the mathematical modeling of arterial oscillograms used cyclic random processes. Methods of arterial oscillograms processing based on its model in the form of a cyclic random process was developed. The method of evaluation of the rhythm function of arterial oscillograms and statistical methods for estimating the probabilistic characteristics of arterial oscillograms were developed. To solve the clustering problem, the Python k -means and k -means++ algorithm were used. Oranta-AO information system consists of three interrelated parts: mobile application, computing kernel and web system. Computing kernel and web system are deployed on AWS servers and have been tested already. The developed environment aims to be integrated into every new model of electronic meters in the world. Certification (EN 62304:2014, ISO 13485: 2018) in Ukraine is completed, PCT priority is completed. The next step will be to establish cooperation with manufacturers of electronic pressure monitors, patenting and certification in world.

Isomorphic Multidimensional Structures of the Cyclic Random Process in Problems of Modeling Cyclic Signals with Regular and Irregular Rhythms

“…The estimation T(t, 1) of the rhythm function T(t, 1) for these ECG signals is presented in Figure 7. The estimation of the rhythm function was carried out using the method of the piecewise linear interpolation of a discrete rhythm function [49]. The Python library NeuroKit2 was used for ECG segmentation and the ECG rhythm function evaluation.…”

“…interpolation of a discrete rhythm function [49]. The Python library NeuroKit2 was used for ECG segmentation and the ECG rhythm function evaluation.…”

“…Under the condition that 𝑘 = 1, Formula ( 60) is a calculation formula for the statistical estimation 𝑚 (𝑡) = 𝑚 (𝑡) of the mathematical expectation 𝑚 (𝑡) of the ECG (see Figures 8 and 9). interpolation of a discrete rhythm function [49]. The Python library NeuroKit2 was used for ECG segmentation and the ECG rhythm function evaluation.…”

“…In the dissertation of [47], a new approach to mathematical modeling, computer simulation and the processing of cyclic signals was created. This approach has been widely used for modeling cyclic signals of various natures [48][49][50][51][52]. As part of this approach, a conditional cyclic random process was developed, which enabled a consistent mathematical description of cyclic signals with double stochasticity, namely, with simultaneous stochasticity of their cyclic and rhythmic structures [53].…”

Smart technologies and its application for medical/healthcare services

“…Investigation [4] is dedicated to the problem of effective blood pressure monitors. The authors describe the structure and main functions of the Oranta-AO medical expert system developed by them.…”