CN115836850A

CN115836850A - PWV data analysis system and method for arterial blood flow characteristic parameters

Info

Publication number: CN115836850A
Application number: CN202211409357.6A
Authority: CN
Inventors: 曹阳; 梁冰; 彭伟龙; 曹悦
Original assignee: Zhigu Medical Technology Guangzhou Co ltd
Current assignee: Zhigu Medical Technology Guangzhou Co ltd
Priority date: 2022-11-11
Filing date: 2022-11-11
Publication date: 2023-03-24

Abstract

The invention belongs to the technical field of biological fluid dynamics data processing, and discloses a system and a method for acquiring, measuring and analyzing pulse wave propagation speed data of arterial blood flow characteristic parameters, wherein a high-frequency and double-channel pressure pulse waveform of a peripheral artery is acquired by connecting double sensors in series; calculating PWV of the measuring point by identifying the characteristic point of the arterial pressure waveform, calculating the blood vessel characteristic parameter of the measuring point according to the PWV, a pulsation tree pulsation fluid dynamics model and a fluid dynamics law, and calculating peripheral arterial blood flow volume and blood flow velocity; establishing an arterial elasticity curve, a systolic arterial pressure waveform shear rate curve and a diastolic arterial pressure shear rate curve based on the peripheral arterial pressure time function, the peripheral arterial blood flow and the blood flow velocity; establishing an arterial blood flow characteristic parameter system of a curve function; and establishing an arterial blood flow perfusion state analysis mathematical model. The invention completes the artery blood flow characteristic physiological parameters and the calculated blood flow perfusion function characteristic parameters.

Description

PWV data analysis system and method for arterial blood flow characteristic parameters

Technical Field

The invention belongs to the technical field of biological fluid dynamics data processing, and relates to a peripheral artery waveform analysis technology, and the technical field of acquisition, calculation and analysis of main artery blood vessel and blood flow characteristics such as pressure, flow, resistance, impedance, stiffness, elasticity and the like of a peripheral artery blood vessel, in particular to a PWV data analysis system and method of artery blood flow characteristic parameters.

Background

At present, the biological basis of the blood flow law of the heart and the vascular system of the biological fluid dynamics, and the blood flow, the flow speed and the resistance based on the characteristics of the blood vessels and the blood flow, the coupling effect of the blood flow, the flow speed and the resistance on the heart and the influence on tissue perfusion are lack of research;

the core calculation index of characteristic parameters such as blood flow, flow velocity and resistance based on blood vessel and blood flow characteristics is PWV, and the existing PWV measurement method is inconvenient for obtaining other electrophysiological methods based on an ultrasonic technology and cannot be directly used in clinic.

Parameters such as stiffness of blood vessels, impedance of the blood vessels, elasticity of the blood vessels, blood flow resistance and the like can be calculated based on PWV, and basic parameters of biological fluid dynamics are provided;

based on the blood vessel pressure and flow parameters, a hemodynamics model can be generated, so that the perfusion work condition of the blood vessel to the tissue through blood flow can be reflected in real time, the tissue perfusion characteristic of a circulatory system is reflected, the perfusion is the target of the whole circulatory system, and the curve and the mathematical model obtained by the curve have practical values for understanding the functional state of the circulatory system, predicting the change trend of the cardiac function and formulating a treatment scheme;

arterial stiffness is a property of the artery itself, reflecting the elasticity or compliance of the artery. A carotid-femoral pulse wave velocity value above 10 m/s in middle-aged hypertensive patients indicates the presence of asymptomatic cardiovascular damage. Arterial stiffness cannot be measured directly in the human body, but can be obtained indirectly by (1) assessing arterial Pulse Wave Velocity (PWV);

can reflect the characteristic information of blood flow contractility, vascular resistance, blood flow and the like, has important values in accurately applying treatment means and treatment medicines, judging treatment effect, improving treatment scheme, predicting treatment outcome and the like in the cardiovascular function treatment process, and is a key characteristic which is needed clinically at present.

The main defect of the current clinical parameter index systems such as blood, cardiac output and the like is that the characteristics of peripheral arterial blood flow, perfusion pressure, vascular resistance and the like which reflect perfusion characteristics and are the most critical cannot be coupled, and the perfusion efficiency of the system is comprehensively calculated, so that the system is a key technical breakthrough;

many studies have investigated the effect of age on aortic PWV and AI, most of which show that PWV and AI are both linear and age-related; however, the central AI and the aortic PWV are not always linearly related, but are affected differently by age.

Data from a large healthy population of ozuge-cadov cooperative tests (ACCTs) show that the pattern of age-related changes differs between the central AI and the aortic PWV. The change in AI was more pronounced in younger (< 50 years) individuals, while the change in aortic PWV was more pronounced in individuals over 50 years of age. Thus, in young people, central AI may be a more sensitive marker of arterial aging, while aortic PWV is more sensitive in people over the age of 50. Also, mitchell et al report that in the elderly, AI varies less with age and actually declines after the age of 60.

McEnery et al have proposed a hypothesis for these various reactions.

In young people, the pressure increase is due to an increase in the wave reflection amplitude, rather than an increase in the wave velocity, whereas in older people, the pressure increase is driven by the earlier return of the reflected wave and the less compliant aorta, rather than the main change in the wave reflection amplitude. Age-related dilatation usually occurs in aortic arteriosclerosis, so PWV increases, but the effect on impedance is minimal. Although this problem is still controversial, aortic stiffness in women has been reported to increase with age, particularly at menopause. In particular, postmenopausal women have an AI that is about 7% higher than men, in part because women are taller and thus have a closer physical distance between the heart and reflex sites.

Another report found that the pulse pressure in women was more elevated after menopause with respect to age. The central PWV of women is also faster with age than men, approximately around 45 years of age, while femoral PWV has no significant difference between gender.

Recently, a korean vascular research group conducted Korean Arterial Aging Study (KAAS) to determine the effect of aging on stiffness parameters (e.g., AI and PWV) of apparently healthy individuals with or without cardiovascular risk factors. 1750 subjects aged 17-87 years (mean 46.5 years) were included in the study, who appeared healthy and did not take any medications for hypertension, diabetes or dyslipidemia. The same trend was observed for our aging and gender effects. At younger ages (

quartiles

1 and 2 years), both central and peripheral pulse pressures are lower in women than in men, but around age 45, the pulse pressure rises so sharply that at

ages

50 and 50 women is higher than in men. There is also a tendency for the velocity of the central pulse wave to increase rapidly.

It has been shown that stroke volume can be quantified from arterial pressure pulse contours taking into account the time of blood flow through the arterial tree, different volume-pressure and volume-flow relationships. The stroke volume thus calculated is substantially identical to the stroke volume determined by the dye injection method, with a mean difference within ± 8%. This method allows to track the pulse-to-pulse volume changes and is therefore very suitable for the follow-up judgment of sudden changes in cardiovascular dynamics.

Attempts to apply this method to the human body have been plagued by a number of difficulties. In humans, the arterial volume varies greatly due to aging and atherosclerosis. The aortic pressure of the human body cannot be directly recorded and it is also unclear whether the peripheral arterial pulse coincides completely with the aortic pulse, so that it can be used to calculate the stroke volume from the pulse contour.

The assessment of peripheral arterial systolic and diastolic properties is the basis for improving the understanding of the pathophysiology and therapeutics of the arterial circulatory system, in particular of circulatory failure. Systolic and diastolic pressure related parametric analysis has been derived from measurements of transient peripheral arterial pressure, and over the past few decades, with advances in invasive and non-invasive techniques for measuring pressure, these data have become a common method of basic research in circulatory physiology, transforming medicine, and clinical researchers. Arterial pressure pulse wave velocity is the basis of modern cardiovascular research, and some early key researches, such as the improved widkersell model established by otto frank in 1896, are the earliest established relationship curve of diastolic pressure and flow rate, in order to describe the elastic characteristics, compliance characteristics and how comprehensive impedance affects blood flow and blood flow velocity of the circulatory system. Studies in the 80 s of the 20 th century and early 90 s of the 20 th century elucidated some features of these relationships.

Through the above analysis, the problems and defects of the prior art are as follows:

(1) Clinically, a real-time and accurate relationship curve between arterial pressure and flow is not currently available.

(2) The arterial pressure contains blood flow information and also contains comprehensive impedance characteristics of a blood vessel wall and a far-end blood vessel, the characteristics are in dynamic change along with the influence of factors such as age, diseases, blood flow volume change, medicines and the like, the more critical a patient is, the more obvious the change is, and the change of blood flow and perfusion characteristics along with the change of the characteristics directly influences the oxygen delivery and circulation ending;

because the blood flow characteristics change greatly with the clinical state, the blood flow dynamics model established based on the pulse contour analysis in the prior art cannot really reflect the essential characteristics of the blood flow and perfusion data information, thereby misleading diagnosis and even delaying treatment.

(3) Peripheral arterial pressure measurements are widely used and have proven accurate. However, the coupling of pressure and flow is the essence of the circulatory system to complete the perfusion target, the arterial pressure information can be well matched based on the blood flow data corrected clinically, and the arterial pressure flow curve generated by the intelligent algorithm is a graphical circulatory state and circulatory function curve, and can be used for predicting the result of blood flow perfusion. But the accuracy of the matching data obtained by the prior art is low.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a PWV data analysis system and method for arterial blood flow characteristic parameters.

The invention is realized in such a way that a PWV data analysis method of the artery blood flow characteristic parameters comprises the following steps:

acquiring peripheral arterial pressure waveforms, generating an arterial pressure time function, calibrating pressure pulse waves based on the arterial pressure time function, identifying characteristic points, and extracting n characteristic variables by using the characteristic points;

for the established arterial waveform analysis mathematical model, reversely deducing and adjusting blood vessel blood flow characteristic parameters in the harmonic model by using logic data flow, and calculating and obtaining an arterial flow time function and an arterial blood flow velocity time function;

respectively establishing an arterial pressure flow time curve, an arterial pressure flow speed time curve and an arterial flow speed time curve based on the arterial pressure time function, the arterial flow time function and the arterial blood flow speed time function;

and calculating the physiological parameters of the arterial blood flow characteristics and the blood perfusion function characteristics based on the established arterial pressure flow time curve, the arterial pressure flow velocity time curve and the arterial flow velocity time curve. PWV = L/PTT; where L refers to the distance of the heart beat pulse across 2 pressure sensors and PTT refers to the time difference between the heart beat pulse across 2 pressure sensors.

Further, in the process of acquiring peripheral arterial pressure waveforms and generating arterial pressure time functions, based on specific acquisition points of the peripheral arterial pressure pulse waveforms, a pressure pulse acquisition module is connected by using a series pressure transducer with a fixed distance and fixed connection catheter impedance, so as to acquire peripheral arterial pressure pulses coupled in pairs in each heart cycle and convert the peripheral arterial pressure pulses into paired pressure time functions;

the specific acquisition part comprises proximal and distal limbs arteries which can acquire peripheral arterial pressure pulses, and aortic, common carotid and vertebral artery pressure pulses acquired by a direct pressure pulse sensor;

acquiring peripheral arterial pressure pulses also includes arterial pressure pulses acquired using impedance techniques, arterial pressure pulses acquired using ultrasound techniques.

Further, in acquiring paired coupled peripheral arterial pressure pulses per heart cycle, converting to paired pressure time functions, calculating the time difference of each pair of coupled waveforms as the waveform propagation time, calculating the velocity of waveform propagation PWVmea by the distance between the two sensors in series, PWVmea = PWV + PWV catheter.

Further, the waveform analysis method based on superposition of characteristic impedance of the connecting catheter corrects and acquires the velocity PWVmean of local waveform propagation of the peripheral artery by using the characteristic impedance of the connecting catheter connected between the series sensors.

Further, based on the calculated peripheral artery PWVmea, performing baseline correction on PWVmea using blood pressure to obtain corrected PWVmea; p = α PWV ² + β, α and β depend on the material properties and geometry of the artery (C, A) ₁ 、ρ、R ₀ And H ₀ ) And will be determined by experimentation.

Further, the calculating and obtaining the arterial flow time function and the arterial blood flow velocity time function specifically includes the following steps:

firstly, establishing an artery waveform analysis mathematical model, and calculating PWV, vessel impedance Zc, vessel compliance C and blood flow resistance R vessel/blood flow characteristic parameters by utilizing key characteristics in a logic data flow reverse-estimation and set harmonic model;

calculating PWV from formula PWV = L/PTT and passing P _mea And PWV calculation Z _c R, C; improving a ternary Windkessel model, connecting the Windkessel model with a wave transmission model, and analyzing an improved waveform decomposition function;

secondly, setting the morphological characteristics of the reflected wave and establishing an improved harmonic model;

and thirdly, comparing and analyzing the parameters obtained by model calculation with Vigileo and TEE parameters, and determining the effectiveness and the authenticity of the harmonic model in clinical practical application through TEE correction parameters while coupling related intervention.

Further, the second step of setting the morphological characteristics of the reflected wave and establishing the improved harmonic model comprises the following steps:

establishing reflected wave form characteristic parameters including reflected wave advancing, reflected wave amplitude increasing and reflected wave form characteristic change;

the method comprises the steps of carrying out clinical matching on the reflected wave advance, the reflected wave amplitude increase and the reflected wave form characteristic change, comparing anesthesia and vasoactive drugs influencing the reflected wave Pb form and position change, setting a series of related parameters such as a stiffness and reflected wave displacement relation K1 value, a stiffness and reflected wave amplitude relation K2 value, a stiffness and reflected wave average amplitude relation K3 value and the like, and determining a form and position mathematical model of the reflected wave under the condition of characteristic blood vessel stiffness;

P _res(t) ＝K2·P _rest (t+K1)；

based on the pulse waveform of wave separation, a stiffness change label is used for reversely pushing the wave crest and displacement of the reflected wave, the amplitude form characteristics of the reflected wave are calibrated, and the comprehensive reflected wave is separated, positioned and determined.

Further, the second step of setting the morphological characteristics of the reflected wave and establishing the improved harmonic model further comprises the following steps:

analyzing the harmonic characteristics of blood flow forward wave, reflected wave and counterpulsation wave by using a forward blood flow and reflected blood flow analysis method, recalculating the forward blood flow waveform in the harmonic function on the basis of correcting the reflected wave, and forming an arterial pressure harmonic model with clinical logic characteristics coupled by prediction factors, arterial pressure waveform harmonic characteristics and outcome factors.

Furthermore, in analyzing the harmonic characteristics of the forward wave, the reflected wave and the dicrotic wave of the blood flow by using a forward blood flow and reflected blood flow analysis method, a matching function relationship between the forward blood flow of the artery and the stroke volume of the heart needs to be established.

Further, the third step of comparing and analyzing the parameters obtained by model calculation with Vigileo and TEE parameters comprises the following steps:

1) Constructing warning scores, visual indexes and intelligent adverse event identification software, and establishing a cardiovascular disease database;

2) Carrying out correlation analysis on the cardiac function characteristic parameters and the clinical outcome data;

3) Constructing a warning score, a visual index and an intelligent adverse event/complication identification software system;

4) Establishing a cardiovascular disease occurrence and development mechanism decision support database.

Further, the blood perfusion function characteristic parameters include: time rise minimum shear rate of arterial pressure in dP/dt _Min (ii) a Time-rise maximum shear rate of arterial pressure in dP/dt _Max (ii) a Time rise minimum shear rate of arterial flow in dF/dt _Min (ii) a Time-rise maximum shear rate of arterial flow in dP/dt _Max (ii) a End-diastolic arterial pressure EDPa; arterial end-diastolic volume EDVa; end-systolic arterial pressure ESPa; end-systolic arterial volume ESVa; arterial stroke volume ASV;

a vascular impedance strain rate; vascular impedance elastic strain/PP ratio; arterial compliance/PP ratio; vascular impedance elastic strain/MAP ratio; arterial compliance/MAP ratio; vascular impedance elastic strain/forward wave peak ratio; arterial compliance/forward peak ratio; vascular resistance strain rate; vascular resistance/PP ratio; vascular resistance/MAP ratio; vascular resistance/reflection peak ratio.

Further, the calculating the physiological parameter of arterial blood flow characteristics includes: calculating the output blood volume AO of the artery per minute; the total amount of blood ejected from the artery in one minute is the arterial stroke volume ASV multiplied by the heart rate HR; AO = ASV × HR;

the arterial diastolic compliance is calculated as: EDVa Filling VolumeComp dV/dPa;

elasticity of artery Ea: the relationship between the ventricular end-systolic blood pressure and stroke volume.

Another object of the present invention is to provide a PWV data analysis system for arterial blood flow characteristic parameters, comprising:

the high-frequency pressure time function acquisition module is used for acquiring a high-frequency pressure time function based on peripheral arteries;

the blood flow time function and blood flow velocity time function calculation module is used for calculating a peripheral artery blood flow time function and a blood flow velocity time function by using an intelligent algorithm;

the curve function acquisition module is used for establishing an arterial pressure flow time curve, an arterial pressure flow rate time curve and an arterial flow rate time curve based on a peripheral arterial pressure time function, a peripheral arterial blood flow rate time function and a blood flow rate time function;

the arterial blood flow characteristic parameter system building module is used for building an arterial blood flow characteristic parameter system of a curve function based on the curve built by the curve function obtaining module;

and the perfusion state analysis model building module is used for building an arterial blood flow perfusion state analysis mathematical model.

Further, the PWV data analysis system for arterial blood flow characteristic parameters further includes: and the high-frequency two-way series connection acquisition module is used for acquiring the position information of the peripheral arterial pressure pulse.

Further, the high-frequency pressure time function obtaining module comprises:

the pressure time function pairing module is used for acquiring peripheral arterial pressure pulses coupled in pairing mode in each heart cycle and converting the peripheral arterial pressure pulses into paired pressure time functions by utilizing a series pressure transducer with fixed distance and fixed connection catheter impedance based on a specific acquisition point of a peripheral arterial pressure pulse waveform and connecting the pressure pulse acquisition module; the specific acquisition site comprises a site capable of acquiring peripheral arterial pressure pulses;

and the waveform propagation speed acquisition module is used for calculating the time difference of each pair of coupled waveforms in the process of acquiring the peripheral arterial pressure pulse coupled in pair in each heart cycle and converting the peripheral arterial pressure pulse into the paired pressure time function to be used as the waveform propagation time, and the velocity PWVmean of the waveform propagation is calculated through the distance between the two series sensors.

And the waveform propagation speed correction module is used for correcting and acquiring the local waveform propagation speed PWVmean of the peripheral artery by utilizing the characteristic impedance of the connecting catheter connected between the series sensors based on the waveform analysis method of the characteristic impedance superposition of the connecting catheter.

The blood flow time function and blood flow velocity time function calculation module comprises:

the artery waveform analysis mathematical model establishing module is used for calculating PWV, vascular impedance Zc, vascular compliance C, blood flow resistance R, vascular/blood flow characteristic parameters by utilizing key characteristics in the logic data flow reverse-estimation and adjustment harmonic model and analyzing and improving a waveform decomposition function;

the improved harmonic model establishing module is used for adjusting the morphological characteristics of the reflected wave and establishing an improved harmonic model;

and the harmonic model determining module is used for comparing and analyzing the parameters obtained by model calculation with Vigileo and TEE parameters, and determining the effectiveness and the authenticity of the harmonic model in clinical practical application through TEE correction parameters while coupling related intervention.

The improved harmonic model building module comprises:

the comprehensive reflected wave determining module is used for establishing reflected wave form characteristic parameters including reflected wave advancing, reflected wave amplitude increasing and reflected wave form characteristic changing;

the method comprises the steps of carrying out clinical matching on the reflected wave advance, the reflected wave amplitude increase and the reflected wave form characteristic change, comparing anesthesia and vasoactive drugs influencing the reflected wave Pb form and position change, setting a series of related parameters such as a stiffness and reflected wave displacement relation K1 value, a stiffness and reflected wave amplitude relation K2 value, a stiffness and reflected wave average amplitude relation K3 value and the like, and determining a form and position mathematical model of the reflected wave under the condition of characteristic blood vessel stiffness; based on the pulse waveform of wave separation, reversely pushing the wave crest and displacement of the reflected wave by using a stiffness change label, calibrating the amplitude and form characteristics of the reflected wave, and separating and positioning to determine a comprehensive reflected wave;

the arterial pressure harmonic model acquisition module is used for analyzing the harmonic characteristics of blood flow forward waves, reflected waves and dicrotic waves by utilizing a forward blood flow and reflected blood flow analysis method, recalculating the forward blood flow waveform in a harmonic function on the basis of correcting the reflected waves, and forming an arterial pressure harmonic model with clinical logic characteristics and coupled prediction factors, arterial pressure waveform harmonic characteristics and outcome factors;

the matching function relationship module of the forward blood flow and the heart stroke volume is used for analyzing the harmonic characteristics of the forward wave, the reflected wave and the dicrotic wave of the blood flow by utilizing a forward blood flow and reflected blood flow analysis method and establishing the matching function relationship of the forward blood flow and the heart stroke volume.

The harmonic model determination module includes:

the cardiovascular disease database construction module is used for constructing warning scores, visual indexes and intelligent adverse event identification software and establishing a cardiovascular disease database;

the analysis module of the cardiac function characteristic parameters and the clinical outcome data is used for carrying out correlation analysis of the cardiac function characteristic parameters and the clinical outcome data;

the software system construction module is used for constructing a warning score, a visual index and an intelligent adverse event/complication identification software system;

and the decision support database establishing module is used for establishing a cardiovascular disease occurrence and development mechanism decision support database.

It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:

firstly, acquiring peripheral arterial pressure waveforms in series by using a high-frequency double path to generate an arterial pressure time function; calibrating the pressure pulse wave, identifying required characteristic points, and extracting n characteristic variables;

calculating to obtain an arterial flow time function and an arterial blood flow speed time function;

establishing an arterial pressure flow time curve, an arterial pressure flow velocity time curve and an arterial flow velocity time curve;

and step four, based on the curve in the step three, realizing the artery blood flow characteristic physiological parameters and the blood flow perfusion function characteristic parameters calculated according to the parameters.

It is a further object of the present invention to provide a computer readable storage medium, storing a computer program which, when executed by a processor, causes the processor to execute the PWV data analysis method of arterial blood flow characteristic parameters.

Another object of the present invention is to provide an information data processing terminal, which is used for implementing the PWV data analysis method of arterial blood flow characteristic parameters.

By combining all the technical schemes, the invention has the advantages and positive effects that:

first, aiming at the technical problems existing in the prior art and the difficulty in solving the problems, the technical problems to be solved by the technical scheme of the present invention are closely combined with results, data and the like in the research and development process, and some creative technical effects are brought after the problems are solved. The specific description is as follows:

1. the potential value of PWV as a biomarker for cardiovascular disease has been demonstrated in a number of clinical trials. However, based on PWV between the carotid-femoral artery, or other four-limb arteries, there are significant drawbacks: (1) because the human vascular tree is complex to walk, the distance L between 2 measuring points cannot be accurately measured, and the traditional measuring scheme depends on the estimation of the distance L; (2) the arteries include the aorta with a diameter of 3 cm, the aorta or the elasticity artery with a diameter of 3 cm to 5 mm, the medium or muscle artery with a diameter of 5 mm to 2 mm, the arteriole with a diameter of 2 mm to 150 μm, the arteriole with a diameter of less than 150 μm, and the anterior capillary artery with a diameter of 50 μm. The PWV of the artery measured by the method represents the average PWV of the human artery system among the measuring points, and the PWV measured by any 2 artery measuring points on the body surface represents the average PWV of the area; (3) although the average PWV presents certain biomarker characteristics, the arteries cannot be accurately positioned, and the fluid dynamics characteristics of local vessel walls caused by arteriosclerosis and the change characteristics of blood flow generated along with the arteriosclerosis cannot be accurately revealed through the PWV, so that the discovery and the disclosure of the fluid dynamics rules of the local vessels and the blood flow are greatly limited; (4) the average PWV is affected by the arterial pressure in the measurement area, and the pressure variation of the arteries at different parts (calibre) makes the average PWV unable to normalize the blood pressure, thus unable to accurately measure the difference and value of PWV among individuals.

The invention utilizes the series double sensors, high-frequency sampling characteristics and the basic principle of physics to accurately position and measure the PWV of the local artery, thereby fundamentally solving the measurement defect and value defect of the average PWV.

Secondly, considering the technical scheme as a whole or from the perspective of products, the technical effect and advantages of the technical scheme to be protected by the invention are specifically described as follows:

1. the PWV of the artery is measured accurately and in a fixed point manner, on one hand, the PWV can be used as a biomarker to predict the pathophysiological process and prognosis of diseases from the clinical angle, and on the other hand, the PWV can be used as an important measurement basis for revealing the characteristics of arterial blood vessels and blood flow from the research of the nature of the diseases, so that a researcher is helped to establish a hydrodynamic model of the artery, and the hydrodynamic rule of the artery and the influence rule of the hydrodynamic rule on tissue organ perfusion are further revealed;

2. the technical characteristics of the invention do not depend on large-scale equipment or complex equipment, and the artery PWV is taken as an important parameter for revealing the fluid dynamics rule, so that the PWV is accurately and conveniently obtained and is applied to scientific research or clinical work, and the value is huge;

third, as inventive supplementary proof of the claims of the present invention, there are several important aspects as follows:

(1) The expected income and commercial value after the technical scheme of the invention is converted are as follows: as a scheme, a system and a product formed after technical transformation, an auxiliary diagnosis system and equipment taking PWV as a biomarker can be formed, and related special consumables including hypertension grading, diabetes grading, coronary heart disease grading, cerebrovascular disease grading, nephropathy grading and the like can be contained, and the PWV-based diagnosis system, the PWV-based diagnosis system and the PWV-based diagnosis equipment can be used as basic monitoring equipment for family and community health management to provide wide-coverage life monitoring and support. The continuous real-time monitoring based on PWV can also produce the tissue organ perfusion fluid dynamics monitoring system and series of parameters and relevant equipment and special consumables based on blood vessel and blood flow, mainly includes:

time-rising minimum shear Rate of arterial pressure (dP/dt) _Min ): an arterial waveform rise occurs, at which time the arterial pressure rises to the end, and blood flow into the segment of the artery decreases, at a very high rate of pressure drop.

Time-rise maximum shear rate (dP/dt) of arterial pressure _Max ): occurs in the initial middle segment of the arterial waveform rise, where the blood flow from the heart rushes into this segment, producing the largest pressure change, so the arterial pressure rise rate is very high.

Time-rising minimum shear rate (dF/dt) of arterial flow _Min ): occurs in the minimum ascending segment of the time function waveform of the arterial flow, at the ascending end segment of the arterial flow, the blood flow into the segment of the artery is reduced, and the descending rate of the flow is very large.

Time-rising maximum shear rate (dP/dt) of arterial flow _Max ): the method occurs in the initial middle section of the rise of the arterial flow time function waveform, and at the moment, blood flow from the heart flows into the section to generate the maximum flow change, so that the rising rate of the arterial flow is very high.

End Diastolic Pressure (EDPa): pressure at the end of arterial diastole.

End Diastolic Volume (End-diastole Volume, EDVa): volume at end diastole of the artery.

End-Systolic Pressure (ESPa): end-systolic pressure values.

End-Systolic Volume (End-Systolic Volume area, ESVa): end-systolic volume value.

Arterial Stroke Volume (ASV): during a cardiac cycle, the ventricles eject a volume of blood into the arteries.

(2) The technical scheme of the invention fills the technical blank in the industry at home and abroad:

the invention belongs to an artery PWV measuring scheme with accurate positioning and accurate measurement initiated internationally, and is a technical breakthrough of measuring key bottom parameters of biological fluid dynamics of blood vessels and blood flow.

(3) The technical scheme of the invention solves the technical problems which are always desired to be solved but are not successful:

since the clinical prediction value of the average PWV has been written in the european cardiovascular disease guideline, the parameter has a measurement defect for a long time, and accurate measurement cannot be realized on a human body, and the method cannot be used for accurate prediction of diseases and related fluid dynamics management research.

Drawings

Fig. 1 is a flowchart of a PWV data analysis method for arterial blood flow characteristic parameters according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of a PWV data analysis system for arterial blood flow characteristic parameters according to an embodiment of the present invention.

In the figure: 1. a high-frequency pressure time function acquisition module; 2. a blood flow time function and blood flow velocity time function calculation module; 3. a curve function acquisition module; 4. an arterial blood flow characteristic parameter system construction module; 5. and a perfusion state analysis model building module.

FIG. 3 is a graph of arterial pressure for a single pulse cycle, provided by an embodiment of the present invention.

Fig. 4 is a schematic diagram of acquiring a high-frequency dual-channel arterial waveform according to an embodiment of the present invention.

FIG. 5 is a schematic illustration of local PWV measurements for 5 patients at various stages provided by an embodiment of the present invention.

Fig. 6 is a schematic diagram of the measured pressures of the phases, the uncorrected reservoir waves and the corrected reservoir waves of the patient 4, provided by an embodiment of the present invention.

Fig. 7 is a schematic diagram of measured pressure, uncorrected reservoir waves, and corrected reservoir waves at various stages of the patient 4, as provided by an embodiment of the present invention.

Fig. 8 is a schematic diagram of measured pressure, uncorrected reservoir waves and corrected reservoir waves at various stages of the patient 4 according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

1. Illustrative embodiments are explained. This section is an explanatory embodiment expanding on the claims so as to fully understand how the present invention is embodied by those skilled in the art.

In the invention, PWV (cfPWV) between the carotid artery and the femoral artery mainly reflects the stiffness of larger elastic arteries such as the aorta and the carotid artery, and is a measurement index which is applied earlier and is researched more. PWV can predict the occurrence of cardiovascular and cerebrovascular complications of natural population and patients with hypertension and end-stage renal disease. However, cfPWV is greatly affected by age and blood pressure, and its predictive value is significantly reduced or even disappeared after age and blood pressure are corrected, so that its predictive value independent of factors such as blood pressure still needs to be studied intensively. When blood pressure drops, cfPWV drops significantly. In patients treated with different types of antihypertensive drugs, the difference between cfpwvs is not significant, and other cardiovascular drug treatment means other than antihypertensive treatment have less influence on cfpwvs. PWV from carotid artery to brachial or radial artery mainly reflects stiffness of subclavian and brachial arteries (cbPWV or crppv); the PWV from the femoral artery to the dorsum of the foot may then reflect the stiffness of the arterial vessels of the lower extremities (fdPWV). In addition, the PWV of the ankle brachii artery (baPWV) can also be measured, which includes an assessment of partial muscular arterial stiffness. There has been clear evidence that baPWV can predict the occurrence of cardiovascular and cerebrovascular complications. PWV = L/PTT; where L refers to the distance of the heart beat pulse across 2 pressure sensors and PTT refers to the time difference between the heart beat pulse across 2 pressure sensors.

The PWV values of different arteries differ greatly, but the correlation between them is good. The index for measuring the muscular arterial stiffness can more sensitively reflect the curative effect of various cardiovascular drug treatment methods. Therefore, although in some consensus documents and even guidelines, aortic PWV is often used as the primary indicator for measuring arterial stiffness and assessing risk stratification, the clinical significance of other PWVs is not negligible. The difference of the prediction values of the artery detection indexes on the cardiovascular and cerebrovascular complications and the evaluation significance of the treatment effect in the population and various cardiovascular patients need to be analyzed in a comparison manner.

As shown in fig. 1, a PWV data analysis method for artery blood flow characteristic parameters according to an embodiment of the present invention includes:

s101, obtaining a high-frequency pressure time function based on peripheral arteries with or without wound;

s102, calculating a peripheral artery blood flow time function and a blood flow velocity time function by using an intelligent algorithm;

s103, establishing an arterial pressure flow time curve, an arterial pressure flow rate time curve and an arterial flow rate time curve based on the peripheral arterial pressure time function, the peripheral arterial blood flow volume time function and the blood flow rate time function;

s104, establishing an arterial blood flow characteristic parameter system based on the curve function;

and S105, establishing an arterial blood flow perfusion state analysis mathematical model.

As shown in fig. 2, a PWV data analysis system for artery blood flow characteristic parameters according to an embodiment of the present invention includes:

the high-frequency pressure time function acquisition module 1 is used for acquiring a high-frequency pressure time function based on a peripheral artery invasively or non-invasively;

the blood flow time function and blood flow velocity time function calculation module 2 is used for calculating a peripheral artery blood flow time function and a blood flow velocity time function by using an intelligent algorithm;

the curve function acquisition module 3 is used for establishing an arterial pressure flow time curve, an arterial pressure flow rate time curve and an arterial flow rate time curve based on a peripheral arterial pressure time function, a peripheral arterial blood flow volume time function and a blood flow rate time function;

the arterial blood flow characteristic parameter system building module 4 is used for building an arterial blood flow characteristic parameter system based on the curve function;

and the perfusion state analysis model building module 5 is used for building an arterial blood flow perfusion state analysis mathematical model.

Further, the high-frequency pressure time function obtaining module comprises:

a waveform propagation velocity acquisition module for calculating the time difference of each pair of coupled waveforms as the waveform propagation time in acquiring the coupled peripheral arterial pressure pulse pair per heart cycle, converting into the paired pressure time function, and calculating the velocity of the waveform propagation PWVmean by the distance between the two sensors in series, PWVmean = PWV + PWV catheter.

The waveform propagation velocity correction module is used for performing baseline correction on the PWVmean by using blood pressure based on a waveform analysis method for connecting catheter characteristic impedance superposition to obtain the corrected PWVmean; p = α PWV ² + β, α and β depend on the material properties and geometry of the artery (C, A) ₁ 、ρ、R ₀ And H ₀ ) And will be determined by experiment

The improved harmonic model building module comprises:

the arterial pressure harmonic model acquisition module is used for analyzing the harmonic characteristics of the blood flow forward wave, the reflected wave and the dicrotic wave by utilizing a forward blood flow and reflected blood flow analysis method, recalculating the forward blood flow waveform in the harmonic function on the basis of correcting the reflected wave, and forming an arterial pressure harmonic model with clinical logic characteristics and coupled with prediction factors, arterial pressure waveform harmonic characteristics and ending factors;

The harmonic model determination module includes:

The invention uses the series connection of double sensors to obtain the high-frequency (more than 512 HZ) and double-channel pressure pulse waveform (pressure time function P) of the peripheral artery _A (t)); calculating PWV of a measuring point by identifying characteristic points of an arterial pressure waveform, such as a systolic period starting point, a first peak point, a second peak point, a rebroadcast incisional point, a systolic period maximum shear rate point, a diastolic period maximum shear rate point and the like, calculating blood vessel characteristic parameters of the measuring point, such as blood vessel wall elasticity (Ea), blood vessel wall characteristic impedance (Zc), total blood vessel compliance (C), total peripheral resistance (R) and the like, according to the PWV, a pulsatile tree pulsatile fluid dynamics model (winkssel model and the like) and a fluid dynamics law (Poiseue law and the like), and calculating peripheral arterial blood flow volume and blood flow velocity by using an intelligent algorithm; based on peripheral arterial pressure time function P _A (t) peripheral arterial blood flow volume and blood flow velocity, and establishing an arterial elasticity curve, a systolic arterial pressure waveform shear rate curve and a diastolic arterial pressure shear rate curve; an arterial blood flow characteristic parameter system of a curve function is established based on the curve established by the curve function acquisition module; and establishing an arterial blood flow perfusion state analysis mathematical model. The method is based on the established arterial pressure flow time curve, the arterial pressure flow velocity time curve and the arterial flow velocity time curve; the physiological parameters of arterial blood flow characteristics and the blood flow perfusion function characteristic parameters calculated according to the parameters can be completed.

The technical solution of the present invention is further described below with reference to specific examples.

Example 1

The embodiment of the invention provides a PWV data analysis method of artery blood flow characteristic parameters, which comprises the following steps:

respectively establishing an arterial pressure flow time curve, an arterial pressure flow rate time curve and an arterial flow rate time curve based on the arterial pressure time function, the arterial flow time function and the arterial blood flow rate time function;

and calculating the physiological parameters of the arterial blood flow characteristics and the blood perfusion function characteristics based on the established arterial pressure flow time curve, the arterial pressure flow velocity time curve and the arterial flow velocity time curve.

In a preferred embodiment of the invention, in acquiring peripheral arterial pressure waveforms and generating arterial pressure time functions, based on specific acquisition points of the peripheral arterial pressure pulse waveforms, a pressure pulse acquisition module is connected by using a series pressure transducer with fixed distance and fixed connection catheter impedance, so as to acquire peripheral arterial pressure pulses coupled in pairs in each cardiac cycle and convert the peripheral arterial pressure pulses into paired pressure time functions;

In a preferred embodiment of the invention, the time difference of each pair of coupled waveforms is calculated as the waveform propagation time in obtaining the coupled peripheral arterial pressure pulse pair per heart cycle, converted to the coupled pressure time function, and the velocity PWVmea of the waveform propagation is calculated from the distance between the two sensors in series.

In a preferred embodiment of the invention, the characteristic impedance of the connecting catheter between the series-connected sensors is used to correct and acquire the local waveform propagation velocity PWVmean of the peripheral artery based on the waveform analysis method of the characteristic impedance superposition of the connecting catheter.

In a preferred embodiment of the present invention, the corrected PWVmea is obtained by performing the baseline correction of the PWVmea using the mean arterial pressure based on the calculated peripheral artery PWVmea.

In a preferred embodiment of the present invention, the calculating and obtaining the arterial flow time function and the arterial blood flow velocity time function specifically includes the following steps:

firstly, establishing an artery waveform analysis mathematical model, and calculating PWV, vessel impedance Zc, vessel compliance C and blood flow resistance R vessel/blood flow characteristic parameters by utilizing key characteristics in a logic data flow reverse-estimation and adjustment harmonic model;

In a preferred embodiment of the present invention, the second step of setting the morphological characteristics of the reflected wave and establishing the improved harmonic model includes the following steps:

establishing reflected wave form characteristic parameters including reflected wave advance, reflected wave amplitude increase and reflected wave form characteristic change;

P _res(t) ＝K2·P _rest (t+K1)；

In a preferred embodiment of the present invention, the second step of setting the morphological characteristics of the reflected wave and establishing the improved harmonic model further includes the following steps:

In a preferred embodiment of the present invention, further, in analyzing the harmonic features of the forward wave, the reflected wave and the dicrotic wave of the blood flow by using the forward blood flow and reflected blood flow analysis method, a matching function relationship between the forward blood flow of the artery and the stroke volume of the heart needs to be established.

In a preferred embodiment of the present invention, the third step of comparing the parameters obtained by model calculation with the Vigileo and TEE parameters comprises:

In a preferred embodiment of the present invention, the blood perfusion function characteristic parameters include: time rise minimum shear rate of arterial pressure in dP/dt _Min (ii) a Time-rise maximum shear rate of arterial pressureIn the unit dP/dt _Max (ii) a Time rise minimum shear rate of arterial flow in dF/dt _Min (ii) a Time-rise maximum shear rate of arterial flow in dP/dt _Max (ii) a End diastolic arterial pressure EDPa; end-diastolic arterial volume EDVa; end-systolic arterial pressure ESPa; end-systolic arterial volume ESVa; arterial stroke volume ASV;

In a preferred embodiment of the present invention, the calculating the physiological parameter of arterial blood flow characteristics includes: calculating the arterial blood output per minute AO; the total amount of blood ejected from the artery in one minute is the arterial stroke volume ASV multiplied by the heart rate HR; AO = ASV × HR;

arterial elasticity Ea = ESP/SV ventricular end systolic blood pressure divided by stroke volume.

Example 2

The PWV data analysis method for the arterial blood flow characteristic parameters provided by the embodiment of the invention comprises the following steps:

1) And a special acquisition module is utilized, and a high-frequency double-path is connected in series to acquire a peripheral arterial pressure waveform and generate an arterial pressure time function. Calibrating the pressure pulse wave, identifying required characteristic points, and extracting n characteristic variables;

2) Calculating to obtain an arterial flow time function and an arterial blood flow speed time function;

3) Establishing an arterial pressure flow time curve, an arterial pressure flow velocity time curve and an arterial flow velocity time curve;

4) Based on the curve, the arterial blood flow characteristic physiological parameters and the blood flow perfusion function characteristic parameters calculated according to the parameters can be completed, and the method mainly comprises the following steps:

Time-rise maximum shear rate (dP/dt) of arterial pressure _Max ): it occurs in the initial middle segment of the arterial waveform rise, where the blood flow from the heart flows into this segment, generating the largest pressure change, so the arterial pressure rise rate is very high.

End Diastolic Pressure (EDPa): pressure at the end of arterial diastole.

End-Systolic Pressure (ESPa): end-systolic pressure values.

The formula is as follows:

arterial Output blood volume per minute (AO): the total amount of blood ejected from the artery in one minute, the Arterial Stroke Volume (ASV) multiplied by the Heart Rate (Heart Rate, HR). AO = ASV × HR;

arterial diastolic compliance: the formula is as follows: EDVa Filling VolumeComp dV/dPa

Arterial elasticity (Effective Arterial elasticity, ea): for the relationship between ventricular end-systolic blood pressure and stroke volume, the formula is as follows:

example 3

During a complete cardiac cycle, when arterial blood flow is abnormally perfused, the change in Pa-Fa curre is:

for example, in systemic sepsis, peripheral vascular stiffness is altered by inflammatory factors, blood flow in the blood vessels is increased, vascular tone and blood pressure are decreased, tissue edema, increased capillary permeability, and anterior capillary sphincter and arteriolar dysfunction due to inflammation, pa-Fa curve produces a decrease in the peak of the closed loop of the curve (systolic pressure) and a broadening of the curve, representing an increase in flow.

When affected by a vasoconstrictor such as norepinephrine, pa-Facurve produces an increase in the peak of the closed loop of the curve (systolic pressure) in the graph, while the curve narrows, representing a decrease in flow. The intervention predicts an increase in arterial stiffness of 1/Ea, while arterial compliance C decreases, resulting in a decrease in flow of this segment of blood flow. The state that the EDVa is reduced and the arterial pressure is increased is opposite to the state of the heart and the aorta, the state is the change rule of the peripheral arterial pressure and the blood flow when the sepsis causes the infectious shock, and the rule is reproduced in real time to greatly help the early judgment of the sepsis state, the prediction of the disease change trend and the guidance of treatment. The expression on Pa-Fa curve is:

(1) ring morphology: the intercept between the EDVa and the ESVa is reduced, namely the ASV is reduced, and the width of the ring is narrowed; an increase in arterial pressure may cause the height of the curve to be shorter. While dP/dtMax becomes smaller, suggesting a decrease in arterial compliance.

(2) Position of the ring: with the aggravation of the septic shock, the curve shifts downwards and rightwards in the early stage, which indicates that the tension of the blood vessel is reduced, the flow is increased, the curve is flattened, and the arterial contraction is weak. With the use of vasoconstrictors, the curve begins to shift upward and hasten the inflammatory blockage of the downstream arterioles, arterioles and capillaries, the arterial flow begins to decrease, and the curve begins to shift to the left.

(3) Area of the curve: work ASW = ASV HR due to arterial perfusion. The decrease in ASV results in a decrease in arterial stroke. The area enclosed by the artery Pa-Fa curve is reduced. Accordingly, at late stages of sepsis, the effective work of arterial perfusion is reduced. As shown in the graph of arterial pressure for a single pulse cycle in fig. 3.

Specifically, the invention provides a PWV data analysis method of the characteristic parameters of the arterial blood flow, which comprises the following steps:

in the first step, peripheral arterial waveform data are acquired, as shown in fig. 4.

The serial high-frequency waveform acquisition and data optimization equipment (module) which is completed by using earlier-stage research selects a patient with increased arterial stiffness under general anesthesia, acquires high-frequency (500 HZ) double-path (series) radial artery waveform data, and performs filtering and noise reduction. Not less than 30 arterial periodic waveforms with good consistency are acquired in each acquisition time period. Collecting time: before anesthesia, in the anesthesia induction period, at the intervention time point of the main blood vessel stiffness (such as using vasoactive drugs) in the operation, in the anesthesia resuscitation stage and the like.

And secondly, establishing an artery waveform analysis mathematical model, and using the logic data flow to reversely presume key characteristics in the harmonic model.

(1) Calculating the characteristic parameters of the blood vessel/blood flow, such as PWV, the impedance of the blood vessel (Zc), the compliance of the blood vessel (C), the resistance of the blood flow (R), and the like

Calculating PWV from formula PWV = L/PTT and passing P _mea And PWV calculation Z _c R and C. And improving a ternary Windkessel model, connecting the Windkessel model with a wave transmission model, and analyzing an improved waveform decomposition function.

Thirdly, setting the morphological characteristics of the reflected wave and establishing an improved harmonic model

Using Z _c R and C, performing clinical matching on the reflected wave advance, reflected wave amplitude increase, reflected wave form characteristic change and the like, comparing anesthesia and vasoactive drugs influencing reflected wave (Pb) form and position change, setting a series of related parameters such as a stiffness and reflected wave displacement relation K1 value, a stiffness and reflected wave amplitude relation K2 value, a stiffness and reflected wave average amplitude relation K3 value and the like, and determining a mathematical model of the form and the position of the reflected wave under the condition of characteristic vascular stiffness。

P _res(t) ＝K2·P _rest (t+K1)；

Based on the pulse waveform of wave separation, using stiffness change labels, reversely pushing the wave crest and displacement of the reflected wave, calibrating the amplitude width of the reflected wave and other morphological characteristics, separating and positioning to determine the comprehensive reflected wave.

Analyzing the harmonic characteristics of the blood flow forward wave, the reflected wave, the dicrotic wave and the like, recalculating the forward blood flow waveform in the harmonic function on the basis of correcting the reflected wave, and forming an arterial pressure harmonic model with clinical logic characteristics coupled by prediction factors, arterial pressure waveform harmonic characteristics and outcome factors.

Blood flow information is collected at an anesthesia key node by using a transesophageal ultrasound (TEE) technology, and characteristic cardiac function parameters calculated by a harmonic model, including indexes such as front and back loads, myocardial contraction force, heart work effect and the like, are corrected.

In the standardized case set, the function key parameters complete the closed-loop processing of synchronous coupling intervention information, case outcome information and harmonic function characteristic information (reflected waves), and complete the clinical verification of a harmonic model.

And fourthly, comparing and analyzing the parameters obtained by model calculation with Vigileo and TEE parameters, and determining the effectiveness and the authenticity of the harmonic model in clinical practical application through TEE correction parameters while coupling related intervention.

(1) Constructing warning score, visual index and intelligent adverse event identification software, and establishing a cardiovascular disease support scheme;

(2) Correlation analysis of cardiac function characteristic parameters and clinical outcomes;

(3) Designing warning scores, visual indexes and intelligent adverse event/complication identification software;

(4) Establishing a decision support scheme for the occurrence and development mechanism of cardiovascular diseases (arterial stiffness characteristics).

2. Application examples. In order to prove the creativity and the technical value of the technical scheme of the invention, the part is the application example of the technical scheme of the claims on specific products or related technologies.

As shown in fig. 5, to analyze the specificity and sensitivity of PWVex for the single-point measurement method. As shown in the figure, the results of measuring PWVex and its variation for 5 subjects at preset 5 sampling time periods were compared with the results of PWV calculation after correction. As shown, the PWV value and trend of Patient 3 in the T4 time period are different from other cases, possibly reflecting that Patient 3 is more sensitive to vasoconstrictor drugs. The mean value of PWVex of the above 5 time periods was 3.10. + -. 0.82, and the mean value of PWV was 10.53. + -. 5.02.

Figure Local PWV measurement and for 5 patients of each stages

Local PWV measurements for 5 patients at each stage

3. Evidence of the relevant effects of the examples. The embodiment of the invention achieves some positive effects in the process of research and development or use, and has great advantages compared with the prior art, and the following contents are described by combining data, diagrams and the like in the test process.

As shown in fig. 5, an example of the radial artery pressure waveform analysis process performed by the artirial reservoir model for 1 subject over 5 sampling periods is given below (fig. 6-8). The waveform parameters of each stage are labeled in the figure. At the same stage, pr and Ar of the corrected reservoir wave are smaller than the uncorrected reservoir wave, and tr is larger than the uncorrected reservoir wave.

FIG. 6 measured phase pressures, uncorrected reservoir waves, and corrected reservoir waves for patient 4. The figure shows a section and the parameters Pr, pr/PP for each stage.

Figure 7 patient 4 measures pressure, uncorrected reservoir waves, and corrected reservoir waves at various stages. The segmentation and the parameters tr, tr/T of the phases are shown in the figure.

Figure 8 patient 4 measured pressure, uncorrected reservoir wave, and corrected reservoir wave at each stage. The figure shows a segment and the parameters Ar, ar/a for each stage.

It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.

The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims

1. A PWV data analysis method of artery blood flow characteristic parameters is characterized by comprising the following steps:

calculating the physiological parameters of arterial blood flow characteristics and the characteristic parameters of blood perfusion functions based on the established arterial pressure flow time curve, the arterial pressure flow rate time curve and the arterial flow rate time curve; PWV = L/PTT; where L refers to the distance of the heart beat pulse across 2 pressure sensors and PTT refers to the time difference between the heart beat pulse across 2 pressure sensors.

2. The PWV data analysis method for arterial blood flow characteristics of claim 1, wherein in acquiring peripheral arterial pressure waveform and generating arterial pressure time function, based on specific acquisition point of peripheral arterial pressure pulse waveform, connecting pressure pulse acquisition module with serial pressure transducer with fixed distance and fixed connecting catheter impedance, acquiring peripheral arterial pressure pulse coupled in pairs for each cardiac cycle and converting to paired pressure time function;

the specific acquisition part comprises a proximal extremity artery and a distal extremity artery which can acquire peripheral arterial pressure pulses, and aortic, common carotid and vertebral artery pressure pulses acquired by a direct pressure pulse sensor;

3. The PWV data analysis method for arterial blood flow characteristic parameters according to claim 2, wherein in acquiring peripheral arterial pressure pulses coupled in pairs per heart cycle, converting them into paired pressure time functions, calculating the time difference of each pair of coupled waveforms as the waveform propagation time, and calculating the velocity PWVmea of the waveform propagation by the distance between two sensors connected in series;

a waveform analysis method based on superposition of characteristic impedance of a connecting catheter corrects and acquires the velocity PWVmean of local waveform propagation of peripheral arteries by using the characteristic impedance of the connecting catheter connected between series sensors, and PWVmean = PWV + PWV catheter.

4. As in claimThe PWV data analysis method for arterial blood flow characteristic parameters according to claim 3, wherein the PWVmea is subjected to baseline correction using blood pressure based on the calculated peripheral artery PWVmea, and the corrected PWVmea is obtained; p = α PWV ² + β, α and β depend on the material properties and geometry of the artery (C, A) ₁ 、ρ、R ₀ And H ₀ ) And will be determined by experimentation.

5. The PWV data analysis method for arterial blood flow characteristic parameters according to claim 1, wherein the calculation and obtaining of the arterial flow time function and the arterial blood flow velocity time function specifically comprises the following steps:

calculating PWV from formula PWV = L/PTT and passing through P _mea And PWV calculation Z _c R, C; improving a ternary Windkessel model, connecting the Windkessel model with a wave transmission model, and analyzing an improved waveform decomposition function;

6. The PWV data analysis method for arterial blood flow characteristic parameters according to claim 5, wherein the second step of setting reflected wave morphology characteristics, establishing an improved harmonic model comprises the steps of:

P _res(t) ＝K2·P _rest (t+K1)；

based on the pulse waveform of wave separation, reversely pushing the wave crest and displacement of the reflected wave by using a stiffness change label, calibrating the amplitude and form characteristics of the reflected wave, and separating and positioning to determine a comprehensive reflected wave;

the second step of setting the morphological characteristics of the reflected wave and establishing the improved harmonic model further comprises the following steps:

analyzing the harmonic characteristics of blood flow forward waves, reflected waves and dicrotic waves by using a forward blood flow and reflected blood flow analysis method, recalculating the forward blood flow waveforms in the harmonic functions on the basis of correcting the reflected waves, and forming an arterial pressure harmonic model with clinical logic characteristics coupled by prediction factors, arterial pressure waveform harmonic characteristics and ending factors;

in analyzing the harmonic characteristics of blood flow forward wave, reflected wave and counterpulsation wave by using a forward blood flow and reflected blood flow analysis method, a matching function relationship between the arterial forward blood flow and the heart stroke volume needs to be established;

in the third step, the comparison analysis of the parameters obtained by model calculation with Vigileo and TEE parameters comprises the following steps:

7. The method for PWV data analysis of arterial blood flow characteristic parameters of claim 1 wherein the blood flow is perfusedAnnotating functional characteristic parameter and including: time rise minimum shear rate of arterial pressure in dP/dt _Min (ii) a Time-rise maximum shear rate of arterial pressure in dP/dt _Max (ii) a Minimum shear rate of arterial flow time rise in dF/dt _Min (ii) a Time-rise maximum shear rate of arterial flow in dP/dt _Max (ii) a End diastolic arterial pressure EDPa; arterial end-diastolic volume EDVa; end-systolic arterial pressure ESPa; end-systolic arterial volume ESVa; arterial stroke volume ASV;

a vascular impedance strain rate; vascular impedance elastic strain/PP ratio; arterial compliance/PP ratio; vascular impedance elastic strain/MAP ratio; arterial compliance/MAP ratio; vascular impedance elastic strain/forward wave peak ratio; arterial compliance/forward peak ratio; vascular resistance strain rate; vascular resistance/PP ratio; vascular resistance/MAP ratio; vascular resistance/reflected wave peak ratio;

the calculating the physiological parameter of the arterial blood flow characteristic comprises the following steps: calculating the arterial blood output per minute AO; the total amount of blood ejected from the artery in one minute is the arterial stroke volume ASV multiplied by the heart rate HR; AO = ASV × HR;

the calculating the physiological parameter of the arterial blood flow characteristic further comprises: the arterial diastolic compliance is calculated as: EDVa Filling VolumeComp dV/dPa;

the calculating the physiological parameter of the arterial blood flow characteristic further comprises: arterial elasticity Ea = ESP/SV ventricular end systolic blood pressure divided by stroke volume.

8. A PWV data analysis system for an arterial blood flow characteristic parameter, which implements the PWV data analysis method for an arterial blood flow characteristic parameter according to any one of claims 1 to 7, the PWV data analysis system for an arterial blood flow characteristic parameter comprising:

the high-frequency pressure time function acquisition module is used for acquiring a pressure time function based on peripheral arteries;

the perfusion state analysis model building module is used for building an arterial blood flow perfusion state analysis mathematical model;

the PWV data analysis system for the arterial blood flow characteristic parameters further comprises: and the high-frequency two-way series connection acquisition module is used for acquiring the position information of the peripheral arterial pressure pulse.

9. The PWV data analysis system for arterial blood flow characteristic parameters according to claim 8, wherein the high frequency pressure time function acquisition module comprises:

The waveform propagation speed correction module is used for correcting and acquiring the local waveform propagation speed PWVmean of the peripheral artery by utilizing the characteristic impedance of the connecting catheter connected between the series-connected sensors based on a waveform analysis method for connecting catheter characteristic impedance superposition;

10. The PWV data analysis system for arterial blood flow characteristic parameters according to claim 9 wherein the modified harmonic model building module comprises:

the method comprises the steps of carrying out clinical matching on the reflected wave advance, the reflected wave amplitude increase and the reflected wave form characteristic change, comparing anesthesia and vasoactive drugs influencing the reflected wave Pb form and position change, setting a series of related parameters such as a stiffness and reflected wave displacement relation K1 value, a stiffness and reflected wave amplitude relation K2 value, a stiffness and reflected wave average amplitude relation K3 value and the like, and determining a form and position mathematical model of the reflected wave under the condition of characteristic blood vessel stiffness; based on the pulse waveform of wave separation, reversely pushing the wave crest and displacement of the reflected wave by using a stiffness change label, calibrating the amplitude and width morphological characteristics of the reflected wave, and separating and positioning to determine a comprehensive reflected wave;

the matching function relationship module of the forward blood flow and the heart stroke volume is used for analyzing the harmonic characteristics of the forward wave, the reflected wave and the dicrotic wave of the blood flow by utilizing a forward blood flow and reflected blood flow analysis method and establishing the matching function relationship of the forward blood flow and the heart stroke volume;

the harmonic model determination module includes: