=Paper=
{{Paper
|id=Vol-3038/paper17
|storemode=property
|title=New Application of Blood Pressure Monitor with Software Environment Oranta-AO based on Arterial Oscillography Methods
|pdfUrl=https://ceur-ws.org/Vol-3038/paper11.pdf
|volume=Vol-3038
|authors=Dmytro Vakulenko,Hryhoriy Zaspa,Sergiy Lupenko
|dblpUrl=https://dblp.org/rec/conf/iddm/VakulenkoZL21
}}
==New Application of Blood Pressure Monitor with Software Environment Oranta-AO based on Arterial Oscillography Methods==
https://ceur-ws.org/Vol-3038/paper11.pdf
New application of blood pressure monitor with software
environment Oranta-AO based on Arterial Oscillography
methods
Dmytro Vakulenkoa,, Hryhoriy Zaspab and Sergiy Lupenkoc
a
I.Horbachevsky Ternopil National Medical University, 1 MaidanVoli, Ternopil, 46001, Ukraine
b
Cherkasy State Technological University, 460 Shevchenko Blvd., Cherkasy, 18006, Ukraine
c
Ternopil Ivan Puluj National Technical University, 56, Ruska Street, Ternopil, 46001, Ukraine
Abstract
The authors for the first time offered new application of blood pressure monitor. For this the
original methods of arterial oscillography are substantiated and developed. The methods were
implemented in developed Oranta-AO information system. The methods 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; to study the condition of blood vessels; to
identify the state of a pre-disease, the effectiveness of therapeutic, preventive and
rehabilitative measures. The authors also developed an expert system (based on machine
learning methods) for the differential diagnosis of risks of heart, lung, mental illnesses and
prognosis of some blood parameters.
Based on the methods and algorithms developed as a result of research, Oranta-AO
information system was developed allowing the user to take measurements with an electronic
blood pressure monitor, load them into the system, get calculated indicators, view them in a
convenient way and see analytical information on the basis of which one can assess the state
of the cardiovascular system and decide on further action. Information system is deployed on
AWS servers and is being tested now.
The software developed will significantly expand the scope of electronic pressure monitors.
The developed environment aims to be integrated into new models of electronic meters. The
next step will be to establish cooperation with manufacturers of electronic pressure monitors
and certification in Europe, America and other countries. The activities of Oranta-AO
information system provides the ability to integrate into patient monitoring systems and other
information systems as well.
Keywords 1
Arterial oscillography methods, blood pressure monitor, ANS, heart rate, health level,
vessels, pulsation, information system, machine learning
1. Introduction
The cardiovascular system is an indicator of the body’s adaptive capacity. Cardiovascular system
(CVS) disease is one of the most common human diseases (by WHO). Their "rejuvenation" dictates
the need to find new, improve existing methods of prevention, early diagnosis and treatment of these
dangerous diseases [1-4].
Blood pressure (BP) measurement is a mandatory procedure in the work of a doctor at all stages
of medical care [5, 6]. To evaluate the waveforms in arteries of the upper extremities, the authors first
IDDM-2021: 4th International Conference on Informatics & Data-Driven Medicine, November 19–21, 2021 Valencia, Spain
EMAIL: dmitro_v@ukr.net (A. 1); g.zaspa@chdtu.edu.ua (A. 2); lupenko.san@gmail.com (A. 3)
ORCID: 0000-0001-5062-9278 (A. 1); 0000-0001-8726-7100 (A. 2); 0000-0002-6559-0721 (A. 3)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
proposed a new method – Arterial Oscillography (AOG), which allows significantly expanding the
informativeness for blood pressure measuring [4, 7].
The rapid development of digital technologies penetrates all spheres of life, it’s time to apply them
to the pulsations that occur when measuring BP. The oscilloscope was designed for the first time by
L. I. Uskov (1934). The essence of this method is to register with an oscilloscope the magnitude of the
pulse oscillations of the arterial wall at different pressures in the cuff, and the resulting curve reflects
the amplitude of stretching of the artery wall [4]. Now the analysis of arterial oscillograms is
performed in each electronic blood pressure monitor, but the whole analysis comes down to the
determination of systolic and diastolic pressure and heart rate, sometimes there is a violation of
pulsations rhythm [4, 8, 9, 10].
The authors developed Oranta-AO method (Arterial oscillography) to analyze pulsations occurring
in the cuff in response to compression of the shoulder (other part of the body) under the influence of
pulse vascular activity on compression or (and) decompression to assess the body’s adaptive
resources, the state of the autonomic nervous and cardiovascular systems, the state of blood vessels
and the risks of various diseases [11 - 13].
For registration of arterial pulsations the electronic tonometer VAT41-(1,2) and meters of other
manufacturers capable to register values of pressure in a cuff during the period of increase (and/or
decrease) of compression (to carry out calculations by methods of arterial oscillography (AOG)) and
to export the obtained values for further analysis in the user’s personal account in the web
environment.
The validity of studies is confirmed by registered and analyzed 4000 AOGs in people of different
ages and sexes, almost healthy and with abnormalities in health (14 nosological conditions)), at rest
and under the influence of various external factors. In addition, the correspondence of values of some
studied indicators of AOG to the indicators of heart rate variability (HRV) of the electrocardiographic
signal obtained from the literature was confirmed. Also the coincidence of some studied parameters
values obtained from simultaneously made by the authors electrocardiograms (ECG) and AOG
(correlation 0.75–0.98) in 354 almost healthy people and people with abnormalities in health was
registered. The same direction of dynamics of the studied parameters obtained by the authors before
and after the procedure of segmental reflex massage in patients with dorsopathy of the cervical spine,
registered by AOG (VAT41-1,2) and HRV (Omega-M) was observed, which was confirmed by the
dynamics of biochemical parameters (adrenaline, noradrenaline in the urine, acetylcholinesterase in
the blood) [14 -18].
In addition, in spring 2020, a comprehensive clinical study was conducted, where 172 people were
examined: 112 without health complaints, 60 with diseases of CVS (aged 18–65). The general,
biochemical analysis of blood, coagulogram, ELISA G, M (COVID-19), Endothelin were studied in
each patient. Spirogram, ECG (12 leads) was recorded. In addition, a rheogram (8-channel rheo-
complex REOCOM) was recorded in the following leads: central hemodynamics (by Kubicek), on
both forearms, on the shoulder, cuff pressure channel and ECG channel. And also – a test with a 5-
minute contraction of the shoulder with a cuff (to assess the endothelium condition). Vascular
stiffness (VAT-41-2) was determined. Some results were published.
Regarding the contingent of respondents the method is new. The examination of healthy people
made it possible to develop standards of AOG indicators, which highlight the participation of
cardiovascular and hemodynamic factors and levels of regulation of their activity in the compression
of the shoulder at rest and under the influence of various factors. The calculated indicators from the
registered measurement make it possible to use them for comparison with different groups of
indicators obtained earlier, which are characteristic for different diseases. Currently, the most studied
are AOG and ECG. Among them there are diseases of various stages and clinical manifestations:
coronary heart disease (303 people), hypertension (160), psychoneurological diseases (294), patients
diagnosed with tuberculosis (204), COVID-19 (74 people, 287 measurements) and others [19 -21].
The analysis of the results obtained gave the authors an opportunity to develop and substantiate the
new methods of arterial oscillography. The obtained results are presented in the thesis work of the
doctor of biological sciences, monograph, methodical recommendations (approved by the Ministry of
Health), 5 patents for utility model; in 6 articles indexed in Scopus, 35 articles in professional journals
[11 - 13, 19, 20, 24, 25, 26, 27].
The use of morphological, temporal, spectral analysis of the arterial oscillogram registered at
measurement of arterial pressure gives the possibility to carry out in addition a complex assessment of
health, a functional condition of cardiovascular system, its reserve possibilities; get information from
4 levels of regulation of CVS activity; to study the condition of blood vessels; identify the state of the
disease, the effectiveness of therapeutic, preventive and rehabilitative measures. The authors also
developed an expert system (based on machine learning methods) for the differential diagnosis of
risks of heart, lung, mental diseases and prognosis of some blood parameters [11 -14].
Based on the methods and algorithms developed as a result of research, Oranta-AO information
system was designed and developed, which allows the user to take measurements with an electronic
blood pressure monitor, load them into the system, get calculated indicators, view them in a
convenient way and see analytical information on the basis of which one can assess the state of the
cardiovascular system and decide on further action [11-15, 21 - 24].
Application of AO (arterial oscillography) methods (D. Vakulenko, L. Vakulenko) is possible in
the electronic blood pressure monitors supporting work with Oranta-AO information system. The
scope of the AO method is possible in all accepted areas of application of blood pressure monitors.
The AO innovation provides extended and additional diagnostics, especially relevant for sports, space
and military medicine, daily monitoring, fitness, preventive and functional examinations, as well as
for household use. A patient, nurse, family doctor, cardiologist and others can be a user of the AO
[25- 27].
No approaches similar to the arterial oscillography was found. This conclusion is based on the
analysis of publications in the world, visits to medical exhibitions in Ukraine, Austria and Germany
(Dusseldorf, Medica 2016, 2018, 2020). Oranta-AO software is designed to supplement the
informativeness of the dynamic adaptive properties of blood vessels in Doppler, rheographic, pulse
examination and heart electrocardiographic examination.
2. Goal and tasks
The goal of the work is to develop concepts, substantiation, methods, and algorithms of the new
application of blood pressure monitor. For this task information technology for assessment and
prediction of human health based on arterial oscillography data obtained from a blood pressure
monitor is developed. To achieve this goal, two tasks were set:
1. Development of methods for the analysis of pulsations that occur in a pressure monitor cuff in
response to shoulder (or other part of the body) compression under the influence of pulse activity
of blood vessels on compression or (and) decompression. Selection and justification of the use of
mathematical apparatus to solve problems for the methods implementation. These methods should
provide the analysis of:
morphological component of the arterial oscillogram, individual pulsations and their
components in different phases of compression;
intervalogram at positive and negative intervals – for the use of methods of heart rate
variability;
the spectral component of the signal itself, both total and instantaneous power;
similarity of AOG components (fractal analysis);
additional evaluation of adaptive components involved in the reaction to cuff compression
(correlation analysis);
probabilities of similarity of the calculated AOG indicators to the studied diseases conditions
(machine learning methods – classification problem);
forecasting the values of blood parameters, the activity of central hemodynamics, blood
vessels, nervous system and other indicators (by regression analysis).
2. Development of software allowing the user to go through all the stages from direct
measurement to full analysis of the results. In more details, to receive data from the pressure
monitor, transfer data for calculations, make calculations on the basis of the developed methods,
save the results and present them to the user in the form convenient for the analysis.
3. Arterial oscillogram analysis methods
3.1. Arterial oscillogram analysis methods types
Significant advances in the analysis of electrocardiograms, rheograms and encephalograms and the
scientific substantiation of the results made it possible to adapt this knowledge to the analysis of
arterial oscillograms. Since the necessary terminology that would characterize the calculations
characteristics and results to describe the pressure oscillogram was not found, the terms and methods
used in related areas of cardiovascular research (rheography, electrocardiography) and nervous
(encephalography) systems were used. In Electrocardiography, methods of temporal and spectral
analysis (Fourier Transform), in Rheography - methods of morphological analysis, and in
Encephalography - methods of spectral analysis of the signal (Fourier Transform) are used. Since
during the pressure measurement the body builds an adaptive response to shoulder compression and
there are different types of reactions, the Hilbert-Huang transformation was additionally introduced to
assess the body instantaneous adaptive reactions [1 - 4, 11-14, 24-28].
In world practice, there are 3 types of pulsations that can be analyzed: compression,
decompression and both. More than 500 sequentially recorded oscillograms of each type were
analyzed to understand better the characteristics of each type of pulsations.
Figure 1: Registered pressure changes in the cuff under the impact of compressor and pulse activity
of the vessels with subsequent separation of the vessels’ pulse activity
Morphological analysis provides the following information: vascular tone and patency, functional
capacity, cause of abnormalities (functional or organic), cardiac activity, blood pressure and neuro-
reflex effects on their condition.
The objects of research in morphological analysis are the general shape of the oscillogram and the
nature of individual pulsations in different phases of compression. In the general analysis of the
oscillogram the following features are used: rhythmicity of oscillations, nature of amplitude growth,
achievement of maximum and their decrease in the process of shoulder compression, number of
maximum amplitude oscillations, shape and symmetry of envelope placement created at maximum
and minimum extremes [4, 11-13].
The nature of individual oscillations in different phases of compression is evaluated by: duration
of ascending and descending part in one pulsation, forms of peaks of maximum and minimum
extremes, dynamics of change of ascending and descending oscillations areas, presence, localization,
magnitude of dichroic and additional waves on separate oscillations. In AO, depending on the degree
of compression, there are three (from the registered oscillations at the minimum pressure in the cuff to
reaching diastolic pressure (APd), between APd and systolic pressure (APs), from APs to maximum
compression) and five parts (from registered oscillations at the minimum pressure in the cuff to
achieving APd, from the appearance of APd to 70% of the pulsation amplitude, from 70% to 100% of
the amplitudes, from 100% to the appearance of APs, from the appearance of APs to maximum
compression).
Variability of the oscillations duration (using methods and indicators of temporal analysis and
variation pulsometry, adopted to assess electrocardiographic signals), correlation rhythm or
scatterogram, chaosgram are analyzed and evaluated.
To obtain additional information about the nature and the mechanisms involved in the adaptation
to various influences in the studies, the indicators are further subjected to correlation analysis for
further inclusion in the Expert System. The block diagram of the applied methods in Oranta-AO
software is given in Figure 2.
Figure 2: Block diagram of the methods used for the arterial oscillogram analysis
To solve the problems shown in Figure 2, the following tasks were solved: separation of the
compressor pulsations component from vessels pulsations; identification of positive and negative
extremes, artifacts, morphological features of each pulsation (amplitude, vertex radius, dichroic tooth,
phase of slow and rapid expulsion); Fourier and Hilbert-Huang transformation algorithms selection.
Morphological analysis (10 indicators), temporal (37 indicators) and spectral analysis (485
indicators) of oscillograms are used for the analysis of arterial oscillograms. The spectral analysis
calculates the spectral power of the intervalogram (45 indicators), the arterial oscillogram according
to the Fourier transform (340 indicators) and the instantaneous frequency and phase according to the
Hilbert-Huang transformation (90 indicators). The arterial oscillogram is divided into 5 parts: until
diastolic pressure is reached, from diastolic pressure to 70% of the amplitude of the oscillogram, from
70% to 100% of the amplitude of the oscillogram, from 100% to 70% of the amplitude and from 70%
to the end of the measurement. For each of these AO intervals, Fourier transform (68 indicators) and
the instantaneous frequency and phase of the Hilbert-Huang transformation (18 indicators) was used
[29 - 39].
3.2. Solving classification problem with decision tree induction method as
part of expert system
The goal is to apply the decision tree induction method for software implementation in the
decision-making system regarding classification and forecasting risks of diseases appearance based on
arterial oscillogram analysis.
A study of almost healthy patients and patients with abnormalities in health (14 nosological
conditions), at rest and under the influence of various external factors was conducted. Among them
are diseases of various stages and clinical manifestations: coronary heart disease (303 patients),
hypertension (160), psychoneurological diseases (294), patients diagnosed with tuberculosis (204),
COVID-19 (74 patients, 287 measurements) and others.
Let D is the set of N learning data tuples. Here any with value (A1,i A i2 ,..., A ip, , C i ) includes
attributes A1 ,, Ap . Can be accept both numerical and categorized values for attributes
A1 ,, Ap . . The attribute class’s C receives one of the K discrete values: C 1,, K . The goal is
to predict the value of class attribute C using decision tree on the basis of the values of the attributes
A1 ,, Ap . .
Decision tree generation. The Attribute Selection Algorithm at the recursion step j is based on the
following information indicator:
Gain(A i ) = Info(D j ) - Info Ai (D j ) (1)
K
Info(D j ) = p lj log 2 (p lj ) (2)
k =1
Information needed to classify a tuple ( A1 , A 2 ,, Ap . ) in D j
Info A i (D j ) =
K Info(D )
# D lj
# D
i (3)
k =1 j
Information needed to classify ( A1 , A 2 ,, Ap . ) in D j after splitting D j into subsets D lj with
respect to the values of attribute .
# C k D j
is estimated as p kj
# D j
In (2) the probability that any tuple from D j is from set
where C k D j is the set of tuples from D j for which class attribute C = k. Here #(•) is the number of
elements in the set.
is the estimate of probability that any tuple from
# D lj
belong to the set D lj , where
# D
From (3)
j
D l
j is the set of tuples from D j for which the attribute
. Attribute is A i a 1,i a i2 ,..., a i, i .
K
Gain(A i ) estimates decrease of information which is needed to classify any set of tuples in
when taking into account the known value of the attribute Ai . Therefore, from available attributes for
each node of the decision tree for splitting condition it is necessary to select the attribute A i with
the greatest value Gain(A i ) . As a result of such selection for completing classification process in
we need the least amount of information [14].
Using machine learning methods (problems classification and regression analysis methods),
training and analysis of the results is made. The results are integrated into the calculation kernel of
Oranta-AO information system to calculate the probabilistic risk assessment of diseases
(Cardiovascular (±4,165%), Pulmonary (±6,35%), Mental Illness (±3,67%), Covid-19 (±12,32%) and
others).
4. Oranta-AO information system
Oranta-AO information system performs the following tasks:
obtaining measurements from a pressure gauge;
transmission of measurement data to compute indicators that characterize the level of health,
risks for heart and vessel, lung and mental diseases and predicted some indicators of blood, central
hemodynamics and mental states;
saving measurement data and computed indicators;
displaying the measurement data and computed indicators in a user-friendly form, which
allows to qualitatively analyze the patient's state and make recommendations for further action to
maintain/improve the patient's health.
Oranta-AO information system consists of the following parts: mobile application, calculation
kernel and web system (Figure 3).
To obtain arterial oscillograms on compression, a blood pressure monitor VAT 41-2, Iks-Techno,
Ukraine was used. Pressure monitors Omron, Japan and Dr. Frei, Switzerland were selected to obtain
arterial oscillograms on compression and decompression. Since the pressure monitors of these
manufacturers do not provide the ability to obtain arterial oscillograms, the authors developed a
module for obtaining pulsations from the pressure sensor, storing them and sending them to the
Python core on a server via PC or mobile application with subsequent display in the web application.
Figure 3: Oranta-AO information system structure
The mobile application is designed to transfer measurement information from the blood pressure
gauge to the computational kernel. A program with this functionality is necessary because the blood
pressure gauge itself cannot do this. Before using the application, the user must log in with the name
and password that he receives when registering on the web system. The measurement data transmitted
from the mobile application to the calculation core is in a file with the txt extension, which records a
sequence of numbers that represent the oscillogram values.
The mobile application was developed on Flutter 2.2.3 [37], so it is multiplatform. Its testing was
performed on Android and iOS devices.
Computational kernel is the central part of the information system, based on the methods
developed in this research. It calculates indicators that can be used to analyze and predict the activity
of the cardiovascular system and other systems of the human body. It is developed in Python 3.7 [38].
A set of libraries Numpy 1.19.5, Pandas 1.2.4, Scipy 1.6.3, Hurst 0.0.5, Emd 0.3.2 was used to
implement algorithms for temporal, fractal, spectral, morphological and other types of signal analysis,
as well as a set of libraries Scikit-learn 0.24.2, Lightgbm 3.1.1 for the implementation of algorithms
for predicting the indicators of the human body cardiovascular system and the classification of human
health states.
Computational kernel can work in two directions of data transmission:
1. Receive measurements from the mobile application, perform computations and send a request
with the result to the web system for storing and displaying;
2. Receive measurements from the web system, perform calculations and transfer the result back to
the web system for storing and displaying.
The computational core is able to receive input data in four formats:
raw measurements data (txt);
previously calculated data with specification (json);
multiple measurements with some additional data specified (ini);
MATLAB files (mat).
The results are returned in json format, which can contain results for one or more measurements.
The web system is designed to store the results of calculations performed by the computational
kernel, and display these results in a form convenient for analysis by the patient or doctor. The web
system consists of backend and frontend parts.
The backend part provides the REST API for the frontend part. It is developed using nodejs 12.16
[39] and express framework 4.16 [40]. Also mongodb 4.2 [41] database server was used. The frontend
part is developed on the basis of TypeScript language and Angular 11 [42] framework. The structure
of the web system functions is shown in Figure 4.
Figure 4: Oranta-AO web system structure
Web system can be used with patients and experts or consultants (doctors). A patient can upload
his or her personal measurements data and view the calculated indicators. They also can select
consultants to help at interpreting the data. A consultant can upload the measurements data of his
patients and view the calculated indicators for them.
Both computational kernel and web system are deployed on AWS servers and are available in test
mode now.
5. Conclusion
The authors for the first time offered new application of blood pressure monitor. For this original
concepts, substantiation, methods, and algorithms of the new application of blood pressure monitor
were substantiated and developed. The methods were implemented in developed Oranta-AO
information system.
The application of the methods to the arterial oscillogram registered at arterial pressure
measurement gives the possibility to make the supplementary systematic assessment of human health
in general, the functional state of cardiovascular system, its reserve possibilities; get information from
4 levels of regulation of CVS activity (peripheral-autonomous, autonomic, hypothalamic-pituitary,
central nervous system); to study the condition of blood vessels: their tone, elasticity, quality of
adaptation to different levels of compression when measuring blood pressure; identify the state of a
disease, the effectiveness of therapeutic, preventive and rehabilitative measures. The authors also
developed an Expert System (based on machine learning methods) for the differential diagnosis of
risks of heart (±4,165%), lung (±6,35%), mental illness (±3,67%) and prognosis of some blood
parameters.
Based on the methods and algorithms developed as a result of research, Oranta-AO information
system was designed and developed allowing the user to take measurements with an electronic blood
pressure monitor, load them into the system, get calculated indicators, view them in a convenient way
and see analytical information which is the basis for one to assess the state of the cardiovascular
system and decide on further action.
Oranta-AO information system consists of three interrelated parts: mobile application,
computational kernel and web system. Computational kernel and web system are deployed on AWS
servers and are being tested now. Version in Ukrainian is available; English version is being
developed.
The software developed will significantly expand the scope of electronic pressure monitors. The
developed environment aims to be integrated into every new model of electronic meters in the world.
Certification (ISO 14155: 2015) in Ukraine is currently being completed, PCT priority has been
obtained. The next step will be to establish cooperation with manufacturers of electronic pressure
monitors and certification in Europe, America and other countries. The activities of Oranta-AO
information system provides the ability to integrate into patient monitoring systems and other
information systems as well.
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