CN107992452A - Calculate method, apparatus, storage medium and the equipment of central hemodynamics index - Google Patents
Calculate method, apparatus, storage medium and the equipment of central hemodynamics index Download PDFInfo
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Abstract
The present invention provides a kind of method, apparatus, storage medium and equipment for calculating central hemodynamics index.Wherein calculate central hemodynamics and refer to calibration method, include the following steps：The radial artery pulse wave signal of collection is demarcated using the brachial arterial pressure of collection, obtains radial artery wave shape；According to the radial artery wave shape, central aortic waveform morphology is predicted using linear regression model (LRM)；According to the central aortic waveform morphology, the corresponding radial artery wave shape is selected, according to the transmission function, to utilize the radial artery pulse wave signal reconstruction central aortic pressure wave shape to the transmission function of central aortic pressure wave shape；Central hemodynamics index is calculated according to the central aortic pressure wave shape.Present invention only requires extraction radial artery pulse wave can obtain the hemodynamic indexs such as central aortic pressure, and radial artery pulse wave is more stable and is easy to extract, and security risk is not present in human test.
Description
Technical field
The present invention relates to biomedical engineering technology field, and in particular to a kind of side for calculating central hemodynamics index
Method, device, computer-readable recording medium, computer equipment and computer program product.
Background technology
Central aortic pressure refers to the arterial pressure close with heart.The high blood of heart of Europe association (ESC) in 2003 and Europe
Pressure association (ESH) guide is pointed out：Central aortic pressure has differences with brachial artery pressure, and brachial arterial pressure is aroused in interest in cannot replacing completely
Pulse pressure.The Strong Heart researchs in the U.S. show that the central aortic pulse pressure measured with non-invasive methods can be more than Brachial Artery Pulse Pressure
Cardiovascular endpoints event is predicted well.The Major Vessels of heart, kidney and supply brain are exposed to sustainer rather than arteria brachialis
Pressure, therefore, central aortic pressure energy, which more accurately reflects, is applied to left ventricle, coronary artery and cerebrovascular load condition.
So measuring center angiosthenia has great importance in clinical practice.
At present, the e measurement technology of central aortic pressure has two methods：Noninvasive method and invasive method.Invasive method is by fluid filled catheter
Center aortic root is sent into through femoral artery or radial artery, conduit is outer to be connected with pressure sensor, and sensor records blood vessel in real time
Interior pressure, and then accurate acquisition aortic pressure Reeb.Invasive method is mainly used for the fields such as first aid and intensive care unit, can
As " goldstandard " for assessing noninvasive method accuracy.But this method, there are traumatic and risk, operation difficulty is big, is not suitable for
General patient and healthy population.Noninvasive method mainly by analyzing big-sample data, is built between central aortic and peripheral arterial
Mathematical model, i.e. transfer function model.From the human body radial artery wave shape measured, center is obtained using the transmission function
Arterial pressure waveform.
Karamanoglu et al. constructs sustainer to peripheral arterial transmission function based on Fourier transformation method, but this
The transmission function frequency spectrum of kind of method structure there is higher variation, it is necessary to longer data sequence reduces this variation, and
Since pressure wave is along arterial tree from sustainer to the positive transmission of peripheral arterial, reconstructed using above-mentioned transmission function from peripheral arterial
, it is necessary to which transmission function is inverted during aortic pressure waveform, the complexity of calculating is added, and needs wave filter to reduce signal
Unstability.For this reason, Fetics et al. utilizes external (ARX, the autoregressive exogenous) model construction of autoregression
Radial artery is to sustainer reverse transfer functions, it is not necessary to is inverted transmission function and can obtain aortic pressure using wave filter
Waveform, adds the applicability of transmission function.However, the model does not consider the influence factor of transmission function, it may be decreased
The precision of transmission function.
The content of the invention
The application's aims to overcome that the above problem or solves or extenuate to solve the above problems at least in part.
Central hemodynamics are calculated the present invention provides one kind and refer to calibration method, are included the following steps：
The radial artery pulse wave signal of collection is demarcated using the brachial arterial pressure of collection, obtains radial artery wave
Shape；
According to the radial artery wave shape, central aortic waveform morphology is predicted using linear regression model (LRM)；
According to the central aortic waveform morphology, the corresponding radial artery wave shape is selected to central aortic pressure wave
The transmission function of shape, according to the transmission function, utilizes the radial artery pulse wave signal reconstruction central aortic pressure wave shape；
Central hemodynamics index is calculated according to the central aortic pressure wave shape.
Further, wherein, the brachial arterial pressure using collection is to the radial artery pulse wave signal of collection into rower
The step of determining, obtaining radial artery wave shape specifically includes：
For the radial artery pulse wave signal of collection, by filtering out the noise of the radial artery pulse wave signal, institute is removed
The baseline drift of radial artery pulse wave signal is stated, obtains radial pulse waveform；
The radial pulse waveform is demarcated using the brachial arterial pressure, obtains the radial artery wave
Shape.
Further, the step of predicting central aortic waveform morphology using linear regression model (LRM) specifically includes：
Radial artery Augmentation index is calculated according to the radial artery wave shape；
The radial artery Augmentation index and age are inputted into the linear regression model (LRM), central aortic Augmentation index is obtained and estimates
Value；
The central aortic waveform morphology is predicted using the central aortic Augmentation index valuation.
Further, wherein, the transmission function obtains in accordance with the following steps：
(1) sample data is divided into by several groups according to the central aortic waveform morphology of sample data reality；
(2) data concentrated to the estimated data in each group, radial artery wave shape is translated backward on a timeline,
Radial artery wave shape is built respectively to the individual of central aortic pressure wave shape using the external model of autoregression in System Discrimination
Transmission function, is averaged the superposition of personal transmission function, obtains the prediction model with the transmission function of the group；
(3) using the data in the validation data set in each group, the prediction model of transmission function is verified, if not
Meet accuracy requirement, then repeat step (2), if meeting accuracy requirement, the prediction model be with the group it is actual in
The corresponding transmission function of heart arterial waveform morphology.
Further, wherein, it is described according to the central aortic waveform morphology, corresponding transmission function is selected, according to institute
Transmission function is stated, is specifically included using the step of radial artery pulse wave signal reconstruction central aortic pressure wave shape：
According to the central aortic waveform morphology, the corresponding radial artery wave shape is selected to central aortic pressure wave
The transmission function of shape, the transmission function are：
Wherein：Na and nb is the order of transmission function, and nk is the time delay of transmission function, and q accords with for time shift operation, a1To anaAnd b1
To bnbFor the polynomial coefficient accorded with the time shift operation；
Pass through formula FC(ω)=FR(ω)*TFR→CThe central aortic pressure wave shape is reconstructed, wherein, FR(ω) represents oar
The frequency-domain function of arterial pulse wave signal, FC(ω) represents the frequency-domain function of central aortic pressure wave shape.
Further, wherein, the central hemodynamics index includes one or more in following index：Center
Arterial systolic blood pressure, central aortic diastolic pressure, central aortic pulse pressure, the pressure that increases, ejection time, back wave growth indices, contraction
Phase pressure time integration, diastole pressure time integration and subendocardiac muscle vigor rate.
Present invention also offers a kind of device for calculating central hemodynamics index, including following module：
Demarcating module, is configured as brachial arterial pressure using collection to the radial artery pulse wave signal of collection into rower
It is fixed, obtain radial artery wave shape；
Prediction module, is configured as the radial artery wave shape according to the demarcating module, utilizes linear regression mould
Type predicts central aortic waveform morphology；
Reconstructed module, according to the central aortic waveform morphology of the prediction module, selects the corresponding radial artery
Transmission function between pressure waveform and central aortic pressure wave shape, according to the transmission function, utilizes the radial pulse
Ripple signal reconstruction central aortic pressure wave shape；
Computing module, calculates central hemodynamics according to the central aortic pressure wave shape of the reconstructed module and refers to
Mark.
Further, wherein, the reconstructed module specifically includes：
Function selecting module, is configured as the central aortic waveform morphology according to prediction module, selects corresponding radial artery
Transmission function between pressure waveform and central aortic pressure wave shape；
Frequency-domain transform module, is configured as converting radial pulse signal from time domain using discrete time Fourier transform
To frequency domain；
Computing module, is configured as the transmission function selected according to function selecting module, by the frequency of radial pulse signal
Domain signal transforms to time domain again after carrying out computing with transmission function, obtains central aortic pressure wave shape.
Present invention also offers a kind of computer-readable recording medium, is stored with computer program, the computer
Program realizes that above-mentioned calculating central hemodynamics refer to calibration method when executed by the processor.
Present invention also offers a kind of computer equipment, including memory, processor and it is stored in the memory simultaneously
The computer program that can be run by the processor, wherein, the processor is realized above-mentioned when performing the computer program
Calculate central hemodynamics and refer to calibration method.
Computational methods are optimized present invention employs transfer function model, are effectively improved the accurate of result of calculation
Property.
Further, the radial artery pulse wave signal of collection is demarcated using the brachial arterial pressure of collection, be conducive to
Improve the accuracy of subsequent result.
Further, transfer function model is selected according to central aortic waveform morphology, so as to according to all ages and classes, body
Matter selects different transmission functions, improves the applicability and flexibility of this method.
Further, the application employs linear regression model (LRM), and calculation amount is small, and calculating speed is fast, while take into account oar
The factors such as arterial pressure waveform, actual central aortic pressure wave shape and age so that obtained linear regression model (LRM) is more
Accurately.
Further, the application uses autoregression external (ARX) model construction transmission function of the radial artery to sustainer,
Take into full account the influence factor of transmission function, therefore improved the precision of transmission function, and then improve the standard of result of calculation
True property.
Further, the application employs the mode centering pulse pressure waveform aroused in interest of signal reconstruction and is estimated, so that
Realize based on the noninvasive method for obtaining central aortic pressure wave shape of radial artery pulse wave signal, the convenient letter of process of gathered data
It is single, and save the time.
Brief description of the drawings
According to the accompanying drawings to the detailed description of the specific embodiment of the application, those skilled in the art will be more
Understand above-mentioned and other purposes, the advantages and features of the application.
Some specific embodiments of the application are described in detail by way of example, and not by way of limitation with reference to the accompanying drawings hereinafter.
Identical reference numeral denotes same or similar component or part in attached drawing.It should be appreciated by those skilled in the art that these
What attached drawing was not necessarily drawn to scale.In attached drawing：
Fig. 1 shows the flow chart of the embodiment of the method for the calculating central hemodynamics index of the present invention；
Fig. 2 a show A class central aortic waveform morphology figures；
Fig. 2 b show C class central aortic waveform morphology figures；
Fig. 3 shows the schematic diagram of central aortic haemodynamics index of correlation；
Fig. 4 shows that the present invention calculates the structure diagram of device one embodiment of central hemodynamics index；
Fig. 5 shows that the present invention calculates the prediction model generation in device one embodiment of central hemodynamics index
The structure diagram of module.
Embodiment
The invention will now be described in detail with reference to the accompanying drawings.
According to an aspect of the invention, there is provided a kind of central hemodynamics that calculate refer to calibration method, including it is as follows
Step, as shown in Figure 1：
1. being demarcated using the brachial arterial pressure of collection to the radial artery pulse wave signal of collection, radial artery pressure is obtained
Waveform；
2. according to radial artery wave shape, central aortic waveform morphology is predicted using linear regression model (LRM)；
3. according to central aortic waveform morphology, select between corresponding radial artery wave shape and central aortic pressure wave shape
Transmission function, according to transmission function, utilize radial artery pulse wave signal reconstruction central aortic pressure wave shape；
4. central hemodynamics index is calculated according to central aortic pressure wave shape.
Preferably, the 1st step can comprise the following steps：
1.1 for collection radial artery pulse wave signal, by filtering out the noise of radial artery pulse wave signal, remove oar and move
The baseline drift of arteries and veins pulse wave signal, obtains radial pulse waveform；
1.2 demarcate radial pulse waveform using brachial arterial pressure, obtain radial artery wave shape.
Specifically, electronic sphygmomanometer can be utilized to obtain the brachial arterial pressure of human body, pulse signal sensor can be utilized
Collection obtains the radial pulse waveform of human body.
Human body brachial arterial pressure can include arteria brachialis systolic pressure SBP and arteria brachialis diastolic pressure DBP, be shunk according to arteria brachialis
Pressure SBP and arteria brachialis diastolic pressure DBP can calculate arteria brachialis mean pressure MAP, and the computational methods of arteria brachialis mean pressure are MAP=
DBP+0.4*(SBP-DBP)。
Since in measurement, extraneous various noises are very big on the influence of radial pulse waveform, this is unfavorable for follow-up signal
Analysis and index calculating, it is therefore desirable to noise is filtered.Radial pulse waveform is generally non-stationary and does not fix week
Phase signal, spectrum energy are concentrated mainly on below 10Hz, and interference component mainly has two classes, when high-frequency noise, second, baseline floats
Move.For high-frequency noise, one or more in following filter method can be used：Wiener filtering, Kalman filtering, FIR
Filtering, wavelet transformation etc..For baseline drift, morphology operations can be used to remove.To the radial artery pulse wave signal of collection
After carrying out above-mentioned processing, radial pulse waveform is obtained.
Then, radial pulse waveform is demarcated using brachial arterial pressure, obtains radial artery wave shape.Due to adopting
The radial pulse waveform of collection is no dimension, it is therefore desirable to which it is demarcated.Using brachial arterial pressure to radial artery arteries and veins
The radial artery wave shape obtained after waveform is demarcated of fighting has blood pressure dimension, unit mmHg, so as to obtain with physiology
The pressure waveform of meaning.It is for instance possible to use arteria brachialis diastolic pressure DBP and arteria brachialis mean pressure MAP are to radial pulse waveform
Demarcated.Calibration concretely comprises the following steps：
1.2.1 arteria brachialis diastolic pressure DBP and arteria brachialis mean pressure MAP is obtained；
1.2.2 the radial pulse waveform p (t) of a cycle is obtained；
1.2.3 the average value p of radial pulse waveform p (t) is calculatedmeanWith minimum value pmin；
1.2.4 radial artery wave shape P (t)=DBP+ (MAP-DBP) * (p (t)-pmin)/(pmean-pmin)。
Preferably, the 2nd step can comprise the following steps：
2.1 calculate radial artery Augmentation index according to radial artery wave shape；
2.2 by radial artery Augmentation index and age input linear regression model, obtains central aortic Augmentation index valuation；
2.3 predict central aortic waveform morphology using the valuation of central aortic Augmentation index.
Wherein, the structure of linear regression model (LRM) can comprise the following steps：
(1) radial artery wave shape and actual central aortic pressure wave shape and their year of m subjects is obtained
Age age.Preferably, subject is the people of same nationality, for example, being Chinese.Preferably, m >=200 are taken；
(2) radial artery Augmentation index rAIx is calculated according to radial artery wave shape, according to actual central aortic pressure wave
Shape calculates central aortic Augmentation index cAIx.
(3) linear regression is built according to radial artery Augmentation index rAIx, age age and central aortic Augmentation index cAIx
Model, the undetermined parameter of the regression model is obtained by Least Square Method.
Wherein, linear regression model (LRM) can use the form such as formula (1)：
Wherein,For the valuation of linear regression analysis valuation, i.e. central aortic Augmentation index；a、b1、b2For regression model
Undetermined parameter；RAIx is radial artery Augmentation index.It is logical using the known data samples (cAIx, rAIx and age) of m subjects
Cross Least Square Method and obtain the undetermined parameter of the regression model, wherein solving the equation group of the undetermined parameter of regression model such as
Shown in formula (2)：
After the undetermined parameter of model determines, the structure to linear regression model (LRM) is completed.
In use, the radial artery Augmentation index rAIx of measured people and age can be inputted regression model, you can
To central aortic Augmentation index valuation.The central aortic waveform shape of measurement people is predicted further according to central aortic Augmentation index valuation
State.
The central aortic waveform morphology of human body can be divided using multiple standards.For example, can be according to central aortic
The size of Augmentation index cAIx is classified.In a preferred embodiment, for example, central aortic waveform morphology can be divided into
A classes, B classes and C classes.The standard of division is, if central aortic Augmentation index cAIx is more than or equal to zero, to show aroused in interest in the people
Arteries and veins waveform morphology is A classes or B classes；If central aortic Augmentation index cAIx is less than zero, show the central aortic waveform shape of the people
State is C classes.
Typical central aortic waveform morphology as shown in Figure 2 a and 2 b, as shown in Figure 2 a, A classes waveform and B class waveform (its
Middle B classes waveform is not shown) pip be located at before peak point, then central aortic Augmentation index cAIx be all higher than be equal to zero；Such as
Shown in Fig. 2 b, the pip of C class waveforms is located at after peak point, then central aortic Augmentation index cAIx is less than zero.Therefore, can be with
Central aortic waveform morphology is judged according to the value of central aortic Augmentation index cAIx.
It should be appreciated that other methods structure regression model can also be used, for example, structure nonlinear regression model (NLRM).
More parameters structure linear regression model (LRM) can also be used, is estimated with centering heart arterial waveform morphology.Central aortic ripple
The classification of shape form can also use other appropriate standards.
Preferably, the structure of the transmission function in the 3rd step can comprise the following steps：
(1) sample data is divided into by several groups according to the central aortic waveform morphology of sample data reality；
(2) data concentrated to the estimated data in each group, radial artery wave shape is translated backward on a timeline,
Radial artery wave shape is built respectively to the individual of central aortic pressure wave shape using the external model of autoregression in System Discrimination
Transmission function, is averaged the superposition of personal transmission function, obtains the prediction model with the transmission function of the group；
(3) using the data in the validation data set in each group, the prediction model of transmission function is verified, if not
Meet accuracy requirement, then repeat step (2), if meeting accuracy requirement, the prediction model be with the group it is actual in
The corresponding transmission function of heart arterial waveform morphology.
The construction step (1) to (3) of above-mentioned transmission function can be realized especially by procedure below；
(1) sample data of n subjects is obtained, which includes aroused in interest in radial artery wave shape and reality
Pulse pressure waveform.
In a preferred embodiment, first, the central aortic waveform and radial artery waveform of n subjects is gathered, so
Two-way waveform is pre-processed afterwards, pretreatment includes filtering out the noise of pulse wave signal, removes the baseline drift of pulse wave signal
Move, obtain radial pulse waveform, and with arteria brachialis diastolic pressure DBP and arteria brachialis mean pressure MAP to radial pulse waveform into
Rower obtains actual central aortic pressure wave shape and radial artery wave shape surely.
Preferably, subject is the people of same nationality, for example, being Chinese.Preferably, n >=200.Preferably, it is actual
Central aortic pressure wave shape using subject arteria carotis pressure waveform.
Central aortic Augmentation index cAIx is calculated according to actual central aortic pressure wave shape, and then obtains central aortic ripple
Shape form, is divided into several groups according to central aortic waveform morphology by sample data.Refer to for example, can be strengthened according to central aortic
Subject is divided into A groups and C groups by the size of number cAIx, and further, the central waveform form of A group subjects is assigned to as A classes
Or B classes, the central waveform form of C group subjects is C classes.So as to fulfill the central aortic waveform morphology according to sample data reality
Sample data is divided into A groups and C groups.
(2) data concentrated to the estimated data in A groups and C groups, build the specific steps of the prediction model of transmission function
Including：
A. radial artery wave shape is translated backward on a timeline, i.e., using radial artery wave shape as input signal,
The signal of approximate center arterial pressure waveform is exported after translation.
For A groups and C groups, it is utilized respectively ARX model and builds radial artery wave shape to the biography of central aortic pressure wave shape
Delivery function.Since the direction of propagation of arterial blood is transmitted from central aortic to radial artery, in the present invention transmission function that builds with
The direction of propagation is on the contrary, i.e. using radial artery as input signal, and central aortic is as output signal, oar theoretical according to system signal
Before arterial signal should appear in central aortic signal, to ensure the causality of system.For this reason, it may be necessary to by radial artery wave
Shape translates backward on a timeline.Preferably, the value range of the numerical value translated on a timeline is 200 milliseconds to 300 milliseconds.
B. utilize the external model of autoregression (ARX model) in System Discrimination to build oar respectively in A groups and C group crowds to move
Arteries and veins is to central aortic transmission function.ARX model is a linear polynomial model, it is retouched according to past output and input of system
State system property.ARX model structure such as formula (3)：
Wherein：Na and nb is the order of transmission function, and nk is the time delay of transmission function, and q accords with for time shift operation, a1 to anaWith
And b1 to bnbFor the polynomial coefficient accorded with time shift operation, TFR→CFor transmission function.
The central aortic pressure wave shape is reconstructed by formula (4)：
FC(ω)=FR(ω)*TFR→C (4)
Wherein, FR(ω) represents the frequency-domain function of radial artery pulse wave signal, FC(ω) represents central aortic pressure wave shape
Frequency-domain function.The method of reconstruct includes but not limited to following steps：
C. the same formula of form (4) of a people's transmission function, time-domain expression (5) is rewritten into by formula (4)：
y(t)+a1y(t-1)+...+anaY (t-na)=b1u(t-nk)+...+bnbu(t-nb-nk+1)+e(t) (5)
In formula, y (t) is that personal radial artery pulse wave signal passes through the output after transfer function model in t moment, expression t moment
The central aortic pressure wave shape of people；U (t) is input of the personal radial artery pulse wave signal in t moment；Na is limit number；nb
Add 1 for zero point number；Nk is the time delay of transmission function；U (t-nk) is to delay the input after nk；E (t) is white noise disturbed value,
Residual error i.e. between the output of reality output and transfer function model estimation；a1To anaAnd b1To bnbFor undetermined parameter.
D. loss function V and Akaike ' s in system identification theory is used finally to predict that error FPE determines that model is optimal
Order (na and nb), loss function V is the sum of normalized squared prediction error of model, calculation formula such as (6)：
Wherein, det represents determinant, ε (t, θN) it is to predict error, i.e. residual error, θNRepresent the parameter of estimation, i.e. na and nb,
N is the numerical value number that estimated data concentrates personal data.
Akaike ' s finally predict the definition such as formula (7) of error FPE：
In formula, V is loss function, and d is the number of parameters of estimation, and it is that estimated data concentrates personal data to take na+nb, N
Numerical value number.
Graph of a relation is made by the function of formula (7), wherein, the value of final prediction error FPE is made as the longitudinal axis, d values
For transverse axis, the function curve of final prediction error FPE and d are drawn, smoothed curve is obtained after being fitted to the function curve, it is right
Point on smoothed curve makees tangent line, the minimum value d of corresponding d values when taking tangent line parallel to transverse axis1, take na=nb=0.5d1Make
For the order that model is optimal.It is understood that preferably, na and nb are equal, na and nb can also be selected proportional.
E. the estimation time delay on the basis of definite ARX model order.To each object, i.e. each subject, difference
Calculate the corresponding loss function V of each time delay and final prediction error FPE.Choose loss function V and final prediction error FPE most
The time delay of hour is as final mask time delay.For example, when estimating loss function V and final prediction error FPE, low-order mode is used
Type, that is, na=nb=2, estimates initial delay.After Optimal order is determined, using time delay as a unknown parameter, na=nb=
Optimal order, sets time delay range=5-250ms, determines optimal time delay within the range.
F. determine model order and when delay, according to identification technology estimate ARX model undetermined parameter a1To ana
And b1To bnb.Introduce transfer function model parameter θ and regression vectorConcrete form is referring to following equation (8) and (9)：
θ=[a1 a2 ... ana b 1b2 ... bnb]T (8)
Wherein, na and nb is the model order of transmission function, and u (t) is the radial artery pulse wave signal of input, and y (t) is defeated
The central aortic pressure wave shape gone out, it is preferable that central aortic pressure wave shape can be arteria carotis signal pressure waveform.Then lose letter
Number V is changed into formula (10), parameters of model parameter θ when taking loss function minimum as ARX model.
In formula, N concentrates the numerical value number of personal data, Z for estimated dataNTo map, such as formula (11)：
ZN=[y (1), u (1), y (2), u (2) ..., y (N), u (N)] (11)
The estimate of personal transfer function model parameter θ is calculated according to formula (12),
Wherein, variate-value when arg min represent to be minimized object function, thus obtains undetermined parameter a1To anaWith
And b1To bnbEstimate.So as to determine personal transmission function.
G. respectively to all in A groups and C groups from radial artery wave shape to central aortic pressure wave shape to personal pass
Delivery function superposition be averaging, obtain respectively with the actual corresponding transmission function of central aortic waveform morphology of A groups and C groups
Prediction model.
(3) data in the validation data set in A groups and C groups are utilized, the prediction model of transmission function is verified, if
Be unsatisfactory for accuracy requirement, then each step in repeat step (2), if meeting accuracy requirement, the prediction model be with
The actual corresponding transmission function of central aortic waveform morphology of the group.
The judgement of accuracy can include but is not limited to following method：
For example, by the actual central aortic wavy curve of each sample in validation data set and prediction model can be passed through
The shaded area of the actual central aortic wavy curve lap estimated and actual central aortic wavy curve
Estimate accuracy of the ratio of area as the sample, if the estimate accuracy of all samples meets standardized normal distribution,
Then think to meet accuracy requirement, otherwise it is assumed that accuracy requirement is unsatisfactory for, each step in repeat step (2).
In addition to this it is possible to using data analysing methods such as variance analysis, regression analysis, cluster analyses to transmission function
Prediction model verified.
Preferably, the 3rd step can obtain in accordance with the following steps：
3.1 according to central aortic waveform morphology, select corresponding radial artery wave shape and central aortic pressure wave shape it
Between transmission function；
Radial pulse signal is transformed from the time domain to frequency domain by 3.2 using discrete time Fourier transform；
3.3 according to the transmission function of selection, after the frequency-region signal of radial pulse signal and transmission function are carried out computing
Time domain is transformed to again, obtains central aortic pressure wave shape.
Specifically, according to central aortic waveform morphology, corresponding radial artery wave shape and central aortic pressure wave are selected
Transmission function between shape；The waveform of radial artery pulse wave signal is changed into frequency domain by discrete time Fourier transform, is utilized
Formula (4) obtains the frequency-domain function F of central aortic pressure wave shapeC(ω)；Again by FC(ω) transforms to time domain, obtains central aortic
Pressure waveform, so as to fulfill the reconstruct of centering pulse pressure waveform aroused in interest.
Preferably, the 4th step can obtain in accordance with the following steps：
According to the central aortic pressure wave shape of reconstruct can calculate one in following central hemodynamics index or
It is multiple：Central aortic systolic pressure cSBP, central aortic diastolic pressure cDBP, central aortic pulse pressure cPP, when increasing pressure AP, penetrating blood
Between Ed, back wave growth indices AIx, systole phase pressure time integration SPTI, the diastole pressure time integration DPTI and internal membrane of heart
Lower myocardial viability rate SEVR.
Specifically, as shown in figure 3, arterial systolic blood pressure centered on cSBP, corresponding waveform peak point.Artery relaxes centered on cDBP
Open pressure, corresponding waveform trough point.Arterial pulse pressure centered on cPP, its calculation formula are cPP=cSBP-cDBP.AP presses to increase
Power, the increment of corresponding first shoulder wave point to blood pressure between peak point.AIx is back wave growth indices, its calculation formula is AIx=
AP/cPP.Ed is ejection time, duration corresponding pressure Wave-shrinking phase.SPTI integrates for systole phase pressure time, corresponding
The region area that the pressure waveform systole phase surrounds.DPTI integrates for diastole pressure time, and corresponding pressure waveform diastole surrounds
Region area.Subendocardiac muscle vigor rate SEVR is the ratio of SPTI and DPTI.
According to another aspect of the present invention, there is provided a kind of device for calculating central hemodynamics index, including such as
Lower module, as shown in Figure 4：
1. demarcating module, brachial arterial pressure using collection is configured as to the radial artery pulse wave signal of collection into rower
It is fixed, obtain radial artery wave shape；
2. prediction module, is configured as the radial artery wave shape according to demarcating module, is predicted using linear regression model (LRM)
Central aortic waveform morphology；
3. reconstructed module, is configured as the central aortic waveform morphology according to prediction module, utilizes corresponding pressure of the radial artery
Transmission function between Reeb shape and central aortic pressure wave shape, based on radial artery pulse wave signal reconstruction central aortic pressure wave
Shape；
4. computing module, is configured as being referred to according to the central aortic pressure wave shape of reconstructed module calculating central hemodynamics
Mark.
Preferably, demarcating module can include following module：
1.1 radial artery pulse wave signal processing modules, are configured as the radial artery pulse wave signal for collection, pass through filter
Except the noise of radial artery pulse wave signal, the baseline drift of radial artery pulse wave signal is removed, obtains radial pulse waveform；
1.2 radial artery wave shape generation modules, be configured as using brachial arterial pressure to radial artery pulse wave signal at
The radial pulse waveform of reason module is demarcated, and obtains radial artery wave shape.Preferably, wherein brachial arterial pressure includes the upper arm
Arterial systolic blood pressure and arteria brachialis diastolic pressure.
Preferably, prediction module can include following module：
2.1 radial artery Augmentation index computing modules, are configured as calculating oar according to the radial artery wave shape of demarcating module
Artery Augmentation index；
2.2 estimation modules, are configured as the radial artery Augmentation index at age and radial artery Augmentation index computing module is defeated
Enter linear regression model (LRM), obtain central aortic Augmentation index valuation；
2.3 prediction modules, are configured as predicting central aortic using the central aortic Augmentation index valuation of estimation module
Waveform morphology.
Preferably, the device of the calculating central hemodynamics index can also include linear regression model (LRM) structure module,
Linear regression model (LRM) structure module can include following module：
(1) data acquisition module, is configured as obtaining the radial artery wave shape of some subjects and actual center
Arterial pressure waveform and their age；
(2) central aortic Augmentation index computing module, is configured as the radial artery wave shape according to data acquisition module
Radial artery Augmentation index rAIx is calculated, central aortic Augmentation index cAIx is calculated according to actual central aortic pressure wave shape；
(3) model and parameter determination module, are configured as being increased according to the radial artery of central aortic Augmentation index computing module
The age structure linear regression model (LRM) of the acquisition of strong index rAIx, central aortic Augmentation index cAIx and data acquisition module, leads to
Cross Least Square Method and obtain the undetermined parameter of the regression model.
Preferably, the device of the calculating central hemodynamics index can also include transmission function structure module, the biography
Delivery function structure module can include following module：
(1) grouping module, is configured as that sample data is divided into several groups according to actual central aortic waveform morphology；
(2) prediction model generation module, is configured as the number concentrated for the estimated data in each group of grouping module
According to radial artery wave shape is translated backward on a timeline, is built respectively using the external model of autoregression in System Discrimination
Radial artery wave shape is averaged the superposition of personal transmission function, obtains to the personal transmission function of central aortic pressure wave shape
To the prediction model of the transmission function with the group；
(3) authentication module, is configured as verifying the prediction model of prediction model generation module generation, if being unsatisfactory for
Accuracy requirement, then will regenerate prediction model using prediction model generation module (2), should if meeting accuracy requirement
Prediction model is the corresponding transmission function of actual central aortic waveform morphology with the group.
Preferably, can include with reference to Fig. 5, prediction model generation module：
A. translation module, is configured as on a timeline translating radial artery wave shape backward；
B. model determining module, be configured as utilize System Discrimination in the external model of autoregression (ARX model) A groups with
Radial artery is built in C group crowds respectively to central aortic transmission function；
C. a people's translation of transfer function module, is configured as rewriting the personal transmission function identical with transmission function form
Into time-domain expression；
D. optimal order determining module, is configured with loss function V and Akaike ' s in system identification theory most
Prediction error FPE determines the optimal order of model (na and nb) eventually；
E. time delay estimation module, is configured as calculating the corresponding loss function V of each time delay and final prediction error FPE,
Loss function V and the final time delay predicted when error FPE is minimum are chosen as final mask time delay；
F. undetermined parameter determining module, is configured as estimating according to identification technology the undetermined parameter a of ARX model1Extremely
anaAnd b1To bnb；
G. prediction model generation module, be configured to all in A groups and C groups from radial artery wave shape into
The personal transmission function superposition that pulse pressure waveform aroused in interest arrives is averaging, and obtains the actual central aortic ripple respectively with A groups and C groups
The prediction model of the corresponding transmission function of shape form；
Preferably, authentication module can include：
Shaded area computing module, is configured as the actual central aortic waveform of each sample in validation data set is bent
Line and the actual central aortic wavy curve lap that is estimated by the prediction model of prediction model generation module
Estimate accuracy of the ratio of the area of shaded area and actual central aortic wavy curve as the sample；
Accuracy judgment module, is configured as the estimate accuracy to shaded area computing module and judges, if institute
The estimate accuracy for having sample meets standardized normal distribution, then it is assumed that meets accuracy requirement, otherwise it is assumed that being unsatisfactory for accurately
Property require, then return prediction model generation module.
Preferably, reconstructed module can include following module：
3.1 function selecting modules, are configured as the central aortic waveform morphology according to prediction module, select corresponding oar to move
Transmission function between pulse pressure waveform and central aortic pressure wave shape；
3.2 frequency-domain transform modules, are configured as radial pulse signal using discrete time Fourier transform from time domain
Transform to frequency domain；
3.3 computing modules, are configured as the transmission function selected according to function selecting module, by radial pulse signal
Frequency-region signal transforms to time domain again after carrying out computing with transmission function, obtains central aortic pressure wave shape.
Preferably, computing module can calculate following central aortic blood flow according to the central aortic pressure wave shape of reconstructed module
One or more in dynamics index：
Central aortic systolic pressure cSBP, central aortic diastolic pressure cDBP, central aortic pulse pressure cPP, increase pressure AP, penetrate blood
Time Ed, back wave growth indices AIx, systole phase pressure time integrate SPTI, diastole pressure time integrates DPTI and intracardiac
Myocardial viability rate SEVR under film.
The computational methods that modules in device provided by the invention are related to can with it is each in method provided by the invention
The computational methods that a step uses are same or similar, and details are not described herein.
According to another aspect of the present invention, there is provided a kind of computer-readable recording medium, is stored with computer
Program, computer program realize that above-mentioned calculating central hemodynamics refer to calibration method when executed by the processor.
According to another aspect of the present invention, there is provided a kind of computer equipment, including memory, processor and be stored in
In memory and the computer program that can be run by processor, wherein, when processor execution computer program, realizes above-mentioned calculating
Central hemodynamics refer to calibration method.
According to another aspect of the present invention, there is provided a kind of computer program product, including computer-readable code, when
When the computer-readable code is performed by computer equipment, above computer equipment is caused to perform above-mentioned calculating center blood flow
The method of mechanical index.
The present invention is corrected traditional transfer function model, effectively improves the applicability of transfer function model.This hair
Compared with the prior art, beneficial effect includes but not limited to the following aspects to the technical solution of bright use：
(1) transmission function of the present invention according to central aortic waveform morphology structure specific modality, can effectively improve transmission
The precision of function.
(2) present invention structure linear regression model (LRM) prediction human body central aortic waveform morphology, further selects convenient form
Transfer function model so that the central aortic waveform of reconstruct is more accurate.
(3) present invention only requires extraction radial artery pulse wave can obtain the hemodynamic indexs such as central aortic pressure,
Radial artery pulse wave is more stable and is easy to extract, and security risk is not present in human test.
The present invention is described in detail above in association with attached drawing, but those of ordinary skill in the art should be known that specification only
It is to be used to interpret the claims.But protection scope of the present invention is not limited to specification.It is any to be familiar with the art
Technical staff is in the technical scope that the present invention discloses, the change or replacement that can readily occur in, should all cover the present invention's
Within protection domain.Therefore, protection scope of the present invention should be subject to the protection domain of claims.
Claims (10)
1. one kind, which calculates central hemodynamics, refers to calibration method, it is characterised in that includes the following steps：
The radial artery pulse wave signal of collection is demarcated using the brachial arterial pressure of collection, obtains radial artery wave shape；
According to the radial artery wave shape, central aortic waveform morphology is predicted using linear regression model (LRM)；
According to the central aortic waveform morphology, the corresponding radial artery wave shape is selected to central aortic pressure wave shape
Transmission function, according to the transmission function, utilizes the radial artery pulse wave signal reconstruction central aortic pressure wave shape；
Central hemodynamics index is calculated according to the central aortic pressure wave shape.
2. calculating central hemodynamics according to claim 1 refer to calibration method, wherein, the upper arm using collection moves
The step of arteries and veins blood pressure is demarcated to the radial artery pulse wave signal of collection, obtains radial artery wave shape specifically includes：
For the radial artery pulse wave signal of collection, by filtering out the noise of the radial artery pulse wave signal, the oar is removed
The baseline drift of arterial pulse wave signal, obtains radial pulse waveform；
The radial pulse waveform is demarcated using the brachial arterial pressure, obtains the radial artery wave shape.
3. it is described according to the radial artery wave shape according to the method described in claim 1, wherein, utilize linear regression mould
Type predicts that the step of central aortic waveform morphology specifically includes：
Radial artery Augmentation index is calculated according to the radial artery wave shape；
The radial artery Augmentation index and age are inputted into the linear regression model (LRM), obtain central aortic Augmentation index valuation；
The central aortic waveform morphology is predicted using the central aortic Augmentation index valuation.
4. calculating central hemodynamics according to claim 1 refer to calibration method, wherein, the transmission function is according to such as
Lower step obtains：
(1) sample data is divided into by several groups according to the central aortic waveform morphology of sample data reality；
(2) data concentrated to the estimated data in each group, radial artery wave shape is translated backward on a timeline, is utilized
The individual that the external model of autoregression in System Discrimination builds radial artery wave shape to central aortic pressure wave shape respectively transmits
Function, is averaged the superposition of personal transmission function, obtains the prediction model with the transmission function of the group；
(3) using the data in the validation data set in each group, the prediction model of transmission function is verified, if being unsatisfactory for
Accuracy requirement, then repeat step (2), if meeting accuracy requirement, the prediction model be with the group it is actual in it is aroused in interest
The corresponding transmission function of arteries and veins waveform morphology.
5. calculating central hemodynamics according to claim 1 refer to calibration method, wherein, it is described according to aroused in interest in described
Arteries and veins waveform morphology, selects corresponding transmission function, according to the transmission function, using in the radial artery pulse wave signal reconstruction
The step of pulse pressure waveform aroused in interest, specifically includes：
According to the central aortic waveform morphology, the corresponding radial artery wave shape is selected to central aortic pressure wave shape
Transmission function, the transmission function are：
Wherein：Na and nb is the order of transmission function, and nk is the time delay of transmission function, and q accords with for time shift operation, a1To anaAnd b1
To bnbFor the polynomial coefficient accorded with the time shift operation；
Pass through formula FC(ω)=FR(ω)*TFR→CThe central aortic pressure wave shape is reconstructed, wherein, FR(ω) represents radial artery
The frequency-domain function of pulse wave signal, FC(ω) represents the frequency-domain function of central aortic pressure wave shape.
6. calculating central hemodynamics according to any one of them of claim 1 to 5 refers to calibration method, wherein, in described
Heart hemodynamic index includes one or more in following index：Central aortic systolic pressure, central aortic diastolic pressure, in
Heart arterial pulse pressure, increase pressure, ejection time, back wave growth indices, systole phase pressure time integration, diastole pressure time
Integration and subendocardiac muscle vigor rate.
7. a kind of device for calculating central hemodynamics index, it is characterised in that including following module：
Demarcating module, is configured as demarcating the radial artery pulse wave signal of collection using the brachial arterial pressure of collection, obtained
To radial artery wave shape；
Prediction module, is configured as the radial artery wave shape according to the demarcating module, pre- using linear regression model (LRM)
Measured center arterial waveform morphology；
Reconstructed module, according to the central aortic waveform morphology of the prediction module, selects the corresponding radial artery pressure
Waveform, according to the transmission function, utilizes the radial artery pulse wave signal weight to the transmission function of central aortic pressure wave shape
Structure central aortic pressure wave shape；
Computing module, central hemodynamics index is calculated according to the central aortic pressure wave shape of the reconstructed module.
8. the device according to claim 7 for calculating central hemodynamics index, wherein, the reconstructed module is specifically wrapped
Include：
Function selecting module, is configured as the central aortic waveform morphology according to prediction module, selects corresponding radial artery pressure
Transmission function between waveform and central aortic pressure wave shape；
Frequency-domain transform module, is configured as that radial pulse signal is transformed from the time domain to frequency using discrete time Fourier transform
Domain；
Computing module, is configured as the transmission function selected according to function selecting module, the frequency domain of radial pulse signal is believed
Number with transmission function carry out computing after transform to time domain again, obtain central aortic pressure wave shape.
9. a kind of computer-readable recording medium, is stored with computer program, the computer program is held by processor
Realize that the calculating central hemodynamics as any one of claim 1 to 6 refer to calibration method during row.
10. a kind of computer equipment, including memory, processor and it is stored in the memory and can be transported by the processor
Capable computer program, wherein, the processor is realized such as any one of claim 1 to 6 when performing the computer program
The calculating central hemodynamics refer to calibration method.
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