Automatic blood pressure monitor

Abstract

Blood pressure measuring device with a pneumatic cuff pneumatically connected to a pressure transducer, the output of which is connected to an analog-to-digital converter, the output of which is connected via a processor to a display, characterized in that it uses a processor with an algorithm: fao (x) ⋅ ft (x) ⋅ fsp (x) ⋅ fm (x) ⋅ fop (x) ⋅ ffr (x) = y (x), where f ao (x)f ao (x) - Values ​​of the electrical signal recorded by the sensor, including arterial oscillations; f t (x)f t (x) - Values ​​of indicators through the time-based analysis method; f sp (x)f sp (x) - Values ​​of the indicators by the method of spectral analysis; f m (x)f m (x) - Values ​​of the indicators by the morphological analysis method; f fr (x)f fr(x) - Values ​​of the indicators using the fractal analysis method; f op (x)f op (x) - Values ​​of indicators resulting from the ratio of interval and amplitude components; y(x)y(x) - an integral matrix containing the aforementioned values ​​and matrix functions. Following the formation of the state matrix... Exp(n) of the trough using the following algorithm: Z cl ( y ) ⋅ Z r ( y ) ⋅ Z cla ( y ) ⋅ Z sem ( y ) = E xp ( n ) where Z cl (y) - Values ​​and functions based on machine learning methods - a classification task; Z r (y) - Values ​​and functions based on regression modeling methods; Z cla (y)Z cl (y) - Values ​​and functions based on cluster analysis methods; Z sem(y) - Values ​​and functions based on methods of semantic analysis.

Description

[0001] The claimed utility model relates to the field of medicine, namely to oscillometric blood pressure monitors.

[0002] Among the prior art in the field under investigation, the closest to the claimed utility model in terms of the group of features is an automatic blood pressure monitor comprising a pneumatic cuff with valves pneumatically connected to a pressure sensor that converts the pneumatic signal into an electrical signal, the output of which is connected via an analog-to-digital converter to the input of a processor that processes the electrical signal, the output of which is connected to a display that displays the signal in the form of mathematical symbols that define the blood pressure parameters (https: / / technosova.ru / dlja-zdorovja / tonometr / podkachivaet-vozduhpovtorno / ).

[0003] The claimed utility model corresponds to the known tonometer in the following features, namely: It comprises a pneumatic cuff pneumatically connected to a pressure sensor, the output of which is connected to an analog-to-digital converter, the output of which is connected to a display via a processor.

[0004] However, the known tonometer does not provide the technical result of the utility model, which is allegedly due to the capabilities of the processor, which processes the electrical signal of the pressure sensor and allows determining the upper and lower arterial pressure and heart rate, that is, it has a low clinical and diagnostic property that does not allow assessing the adaptability of various parts of the body to compression of the shoulder (other parts of the body, human and animal) and with its help the activity of control systems (PA

[0005] The problem solved by the claimed utility model is to improve the known blood pressure monitor by replacing the processor that processes the electromagnetic signal reflecting the pressure change in the cuff, thereby expanding the functionality of the blood pressure monitor and making it possible to build an expert system for the differential diagnosis of diseases not only of the cardiovascular system, but also of the nervous and pulmonary systems.

[0006] The problem is solved in a tonometer containing a pneumatic cuff pneumatically connected to a pressure sensor, the output of which is connected to an analog-to-digital converter, the output of which is connected to a display via a processor, by using a processor with an algorithm according to the subject matter of the utility model fao(x)⋅ft(x)⋅fsp(x)⋅fm(x)⋅fop(x)⋅ffr(x)=y(x), with the following matrices f aa (x) - values ​​of the electrical signal recorded by the sensor, including arterial oscillations; f t (x) - values ​​of indicators by the time-based analysis method; f sp (x) - values ​​of indicators by the method of spectral analysis; f m (x) - values ​​of indicators by the morphological analysis method; f fr (x) - values ​​of indicators using the method of fractal analysis; f ap (x) - values ​​of indicators resulting from the ratio of interval and amplitude components; y(x) - an integral matrix containing the above values ​​and matrix functions.

[0007] The proposed utility model, within the framework of the specified characteristics, provides a technical result consisting in expanding the functionality of the tonometer and allowing the creation of an expert system for the differential diagnosis of diseases not only of the cardiovascular system, but also of the nervous and pulmonary systems, since the analysis of the values ​​of the electrical signal recorded by the sensor, including arterial oscillations during compression of the examination area by the cuff f ao (x) and the calculation of the matrix of indicators used in electrocardiography (heart rate variability (with adjustment)) by the temporal analysis method f t ( x ) and spectral f sp(x) - Calculation of spectral power (Fourier and Hilbert-Huang transform). For spectral analysis in the AO, depending on the degree of compression, three (from the recorded oscillations at minimum cuff pressure to diastolic pressure (DBP), between DBP and systolic pressure (SBP), from SBP to maximum compression) and five parts of the spectrum (from the recorded oscillations at minimum cuff pressure to DBP, from the occurrence of BPd to 70% of the pulsation amplitude, from 70% to 100% of the amplitude, from 100% to the occurrence of BPc, from the occurrence of BPc to maximum compression) are used. A fractal analysis method is used to analyze the arterial oscillogram in different phases of compression in general and its intervalograms f fr (x)to the arterial oscillogram and the ratio of interval and amplitude components - the optimal method for analyzing f op(x) arterial oscillogram. The objects of study in the morphological analysis are the general form of the oscillogram and the nature of individual pulsations in different phases of compression, which are formed into a matrix from a vector of morphological indicators f m(x). The total vector matrix y(x) is used for the biological interpretation of the indicators, and a number of complex indicators are calculated: the indicator of the activity of the regulatory systems of the arterial oscillogram (PARS-AO), the indicator of the activity of the regulatory systems of the arterial oscillogram by intervalogram analysis (PARS-AOI), the integrated functional vascular potential (IFVP), the autonomous cardiac hemodynamic factor index (ICF), the cardiac hemodynamic factor centralization index (ICCHF), the autonomous vascular hemodynamic factor index (IVCF), and the vascular hemodynamic factor centralization index (IVCVF). fao(x)⋅ft(x)⋅fsp(x)⋅fm(x)⋅fop(x)⋅ffr(x)=y(x)

[0008] Based on the results of applying these methods f t (x) · f sp (x) · f m (x) · f op (x) · f fr (x) Analysis on the signal fao (x) we obtain the matrix y(x), which is represented by indicators, semantic values, and functions. The resulting vector y(x) is applied to the resulting vector using machine learning methods - classification task Z. cl (y)Methods of regression modeling Z r (y)Cluster analysis Z cla (y) and semantic analysis. Z sem (y). Based on the results of the obtained matrix of indicators and assessments Z cl (y) · Z r (y) · Z cla (y) · Z sem (y) a matrix of states Exp(n) of the user's states is formed depending on his field of expertise. Zcl(y)⋅Zr(y)⋅Zcla(y)⋅Zsem(y)=Exp(n)

[0009] The proposed automatic tonometer is explained with the help of the following drawings: Fig. - Schematic representation of an automatic tonometer Fig.- Scheme of the processor of the automatic tonometer.

[0010] The proposed automatic tonometer contains a pneumatic cuff 1 with valves (not shown in the figure, not marked), pneumatically connected by a tube 2 to a pressure sensor 3, which converts the pressure change in the pneumatic cuff 1 into an electrical signal, the output of which is connected through an analog-to-digital converter 4 to the input of the processor 5, which processes the electrical signal, the output of which is connected to the display 6, which displays the signal in the form of mathematical symbols, graphs, diagrams that determine the parameters of the blood pressure of the object (human, animal).

[0011] The proposed automatic tonometer is used in the following way.

[0012] A part of the human body (or an animal), depending on the research, for example, the shoulder, is wrapped and fixed with a pneumatic cuff 1, after which air is pumped into the cuff. In response to the compression of the shoulder by the cuff, pulsations occur in the cuff, which are caused by the activity of the vessel wall during the movement of the blood flow. This pulsation activity causes a change in pressure in the cuff (the recording can be during compression or The cuff is pneumatically connected to a pressure sensor, which converts the pneumatic signal into an electrical signal, the output of which is made via an analog-to-digital converter with a processor input, which processes the electrical signal, from which the arterial oscillogram fao(x) is extracted, to which the methods of arterial oscillography are applied and an array of vector matrices through time ft(x) = [pNN50_pos=8,1, SDSD_pos=0,18, Mpos=0,8, AMpos=36, BP_pos=0,72, IVR_pos=44,42, VPR_pos=1,07, IN_pos=28, HVR_index_pos=48,66, NN50_neg=9, SDSD_neg=0,27, Mneg=0,8, AMneg=34.68, BP_neg=1, IVR_neg=30.56, VPR_neg=1.31, IN_neg=20.43, HVR_index_neg=46.69,.....] morphologisch M1=5.06, M2=3.05, M3=1.40, M4=1.00, M5=4.35, M6=1.90, M7=3.08, M8=4.10, M9=3.13, Msr=3.12,..... spektral fsp(x)=[ULF_per=2.96, VLF_per=11.71, LF_per=21.44, HF_per=59.59, ULF_per_int_p 6.68, VLF_per_int_p=11.9, LF_per_int_p=30.05, HF_per_int_p=59,59, ULF_per_int_n=6,68, VLF_per_int_n=11,9, LF_per_int_n=30,05, HF_per_int_n=49,67, Delta_per=98,21, Teta_per=1.51, Alpha_per=0.16, Beta_per=0.09, ULF_per=2.96, ULF_per_20=0.88, ULF_per_20-70=2.86,ULF_per_70-100=6.06, , ULF_per_100-70=5.06, ULF_per_70-end=3,0, VLF_per=11,71, LF_per_20=26,25, VLF_per_20-70=16,47, VLF_per_70-100=23,51, VLF_per_100-7015,64, VLF_per_70-end=14,06, LF_per=21.44, LF_per_20=26.25, LF_per_20-70=29.66, LF_per_70-100=31.06, LF_per_100-70=21.39, LF_per_70-end=24.75, HF_per=59.59, HF_per_20=65.47, HF_per_20-70=47.92, HF_per_70-100=36.55, HF_per_100-70=47.21, HF_per_70-end=54.07, ULF_per_int_p=6.68, LF_per_20_int_p=0.1, ULF_per_20-70_int_p=0.12, ULF_per_70-100_int_p=0.06, ULF_per_100-70_int_p=0.02, ULF_per_70-end_int_p=6,9, VLF_per_int_p=11,9, VLF_per_20_int_p=0,01, VLF_per_20-70_int_p=0,01, VLF_per_70-100_int_p=0.49, VLF_per_100-70_int_p=0.01, VLF_per_70-end_int_p=11.87, LF_per_int_p=30.05, LF_per_20_int_p=0.01, LF_per_20-70_int_p=0.02, LF_per_70-100_int_p=5.22, LF_per_100-70_int_p, LF_per_70-end_int_p, LF_per_70-100_int_p=5,22, LF_per_100-70_int_p=0,01, LF_per_70-end_int_p=27,17, HF_per_int_p=49.67, HF_per_20_int_p=0,01, HF_per_20-70_int_p=0,02, HF_per_70-100_int_p=12,50, HF_per_100-70_int_p=0,02, HF_per_70-end_int_p=48,33, ULF_per_int_n=4.39, ULF_per_20_int_n=0.01, ULF_per_20-70_int_n=0.88, ULF_per_70-100_int_n=2.96, ULF_per_100-70_int_n=0.01, ULF_per_70-end_int_n=3.69, VLF_per_int_n=9.69, VLF_per_20_int_n=0.01, VLF_per_20-70_int_n=3.41, VLF_per_70-100_int_n=5.04, VLF_per_100-70_int_n=0.01, VLF_per_70-end_int_n=7.92, LF_per_int_n=25.79, LF_per_20_int_n=0.01, LF_per_20-70_int_n=15.43, LF_per_70-100_int_n,20.49, LF_per_100-70_int_n=0.01, LF_per_70-end_int_n=24.77, HF_per_int_n=56.58, HF_per_20_int_n=0.01, HF_per_20-70_int_n=40.78, HF_per_70-100_int_n=25.00, HF_per_100-70_int_n=0.00, HF_per_70-end_int_n=59.63, Delta_per=98.21, Delta_per_20=97.71, Delta_per_20-70=94.13, Delta_per_70-100=96.85, Delta_per_100-70=93.87, Delta_per_70-end=92.17, Teta_per=1.51, Teta_per_20=1.37, Teta_per_20-70=4.16, Teta_per_70-100=2.27, Teta_per_100-70=3.74, Teta_per_70-end=5.81, Alpha_per=0.16, Alpha_per_20=0.40, Alpha_per_20-70=0.65, Alpha_per_70-100=0.44, Alpha_per_100-70=0.79, Alpha_per_70-end=0.94, Beta_per=0.09, Beta_per_20=0.38, Beta_per_20-70=0.34, Beta_per_70-100=0.32, Beta_per_100-70=0.60, Beta_per_70-end=0.74,.....]. fractal ffr(x)=[0.6,0.52,0.45,0.52,0.61,0.64,.....], optimal fop(x)=[1.2,1.4,10.6,0.7,0.8,0.17,..... To the resulting matrix y(x), we add the calculated complex fc(x) indicators PARS-AO=3 - moderate functional stress, PARS-AOI=4 - moderate functional stress and IFSP=4 - pre-neurological state, resulting in a matrix of states, IWCF=2 normotension, vegetative balance (IWCF= - 2 - + 2), IWCF=1 vegetative regulation (IWCF= 1,....-1), ICSudHG=2 moderate sympathicotonia (ICSudHG= +3 - +5), ICSudHG=1 autonomic regulation (ICSudHG= 1,...,-1). fs(x)=G of the subject fao(x)ft(x)fsp(x)fm(x)fop(x)ffr(x)fc(x)fs(x)fruf(x)…=y(x)

[0013] As a result of applying the above-mentioned analysis methods ft(x)fsp(x)fm(x)fop(x)ffr(x) to the signal fao(x), a matrix vector y(x) is obtained, represented by indicators, semantic expressions, and functions. Machine learning methods are applied to the obtained vector y(x) – the classification task ZcI(y) = [cardiovascular diseases - 38%, hypertension - 10%, hypertension - 20%, lung diseases - 9%, mental (neurological) disorders - 3%, arrhythmia - 17%, COVID-19 - 12%, regression modeling Zr(y) = blood parameters (blood glucose = 5, endothelin = 4,....), dynamic state of the endothelium = 6,

[0014] Anxiety level ±12%, depression ±6%, central and peripheral hemodynamics (pulse rate PX=0.088, great artery tone m.=0.18, blood volume=63.3, total peripheral resistance=1023,....), cluster analysis Zcla(y)=[PcULF_per=12, PcAlpha_per_20=10, PcTeta_per=7, Pi70-end=17,....] and semantic analysis Zsem(y)=[Id=6, It=4,.....,]. Based on the results of the obtained indicator and assessment matrix Zcl(y)Zr(y)Zcla(y)Zsem(y), the expert system creates the patient's state matrix Exp(n). Zcl(y)Zr(y)Zcla(y)Zsem(y)…=Exp(n)

[0015] Depending on the user’s specialty, the expert system creates a matrix of the patient’s states Exp(n)

[0016] For the patient, Exp(n) = maintaining a healthy lifestyle.

[0017] A report based on the matrix Zcl(y) = [Cardiovascular diseases - 38%, hypertension - 10%, hypertension - 20%, diseases of the pulmonary system - 9%, mental (neurological) diseases - 3%, arrhythmia - 17%, COVID-19 - 12%, regression modeling of Zr(y) = blood parameters (blood glucose = 5, endothelin = 4,....), dynamic state of the endothelium = 6, anxiety level ±12%, depression ±6%, central and peripheral hemodynamics (pulse width distribution time PX=0.088, tone of the great artery m.=0.18, blood volume = 63.3, total peripheral resistance = 1023,....], cluster analysis Zcla(y)=[PcULF_per=12, PcAl-pha_per_20=10, PcTeta_per=7, Pi70-end=17,....] and semantic analysis Zsem(y)=[Id=6, It=4,.....,].

[0018] For a cardiologist: the contents of the matrices y(x), Zcl(y)Zr(y)Zcla(y)Zsem(y) are suggested

[0019] For the admission of a pediatrician to physical education classes: The contents of the matrices Zcl(y) and fruf(x) are proposed

[0020] The processor 5, which operates according to the specified algorithm, can be either built into the tonometer or external - a personal computer processor is used, which is connected to the analog-to-digital converter 4 of the tonometer. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited non-patent literature

[0000] https: / / technosova.ru / dlja-zdorovja / tonometr / podkachivaet-vozduhpovtorno /

[0002]

Claims

[] Blood pressure monitor with a pneumatic cuff pneumatically connected to a pressure transducer, the output of which is connected to an analog-to-digital converter, the output of which is connected to a display via a processor, characterized by that it uses a processor with an algorithm: fao(x)⋅ft(x)⋅fsp(x)⋅fm(x)⋅fop(x)⋅ffr(x)=y(x), where f ao (x)f ao (x) - values ​​of the electrical signal recorded by the sensor, including arterial oscillations; f t (x)f t (x) - values ​​of indicators by the time-based analysis method; f sp (x)f sp (x) - values ​​of indicators by the method of spectral analysis; f m (x)f m (x) - values ​​of indicators by the morphological analysis method; f fr (x)f fr (x) - values ​​of indicators using the method of fractal analysis; f op (x)f op (x) - values ​​of indicators resulting from the ratio of interval and amplitude components; y(x)y(x) - an integral matrix containing the above values ​​and matrix functions. With the subsequent formation of the state matrix Exp(n) of the trougher using the following algorithm: Zcl(y)⋅Zr(y)⋅Zcla(y)⋅Zsem(y)=Exp(n) where Z cl (y) - values ​​and functions based on machine learning methods - a classification task; Z r (y) - values ​​and functions based on regression modeling methods; Z cla (y)Z cl (y) - values ​​and functions based on cluster analysis methods; Z sem (y) - values ​​and functions based on methods of semantic analysis.

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