CN101627905B - Noninvasive integrative monitoring analytical method and noninvasive integrative monitoring analytic device for intracranial pressure - Google Patents

Abstract

The invention discloses a noninvasive integrative monitoring analytical method and a noninvasive integrative monitoring analytical device for intracranial pressure. In the invention, physiological and biomechanical parameters are obtained by a flash visual evoked potential measurement subsystem connected with a computer and a skull Doppler monitoring subsystem, and an intracranial pressure noninvasive integrative monitoring mathematical model and software carry out seamless fusion and processing analysis on the parameters to obtain the noninvasive quantitative detection value and the dynamic change process of the intracranial pressure. The mathematical model starts from the angles of pathology and biomechanics, adopts the two subsystems to detect the characteristic physiological parameters of a patient, extracts a relevant relationship between different symptoms causing intracranial hypertension and the characteristic value of a noninvasive monitoring method of the intracranial pressure based on methods of data exploitation and system identification and is established by constructing a novel database management mechanism and a model training mechanism; in the invention, multi-functional data collection equipment is utilized so as to enable the device to realize better calibration, and a detection result more truly reflects the value and the change trend of the intracranial pressure; and the noninvasive quantitative detection and the long-time monitoring of the intracranial pressure are realized.

Description

A kind of intracranial pressure noinvasive comprehensive monitoring analytical method and device

Technical field

The present invention relates to biomedical engineering field, be specifically related to a kind of intracranial pressure noinvasive comprehensive monitoring analytical method and device based on data mining.

Background technology

Intracranial pressure (ICP) is relevant with cerebral perfusion pressure (CPP), ICP increases and will cause the reduction of CPP, if do not add control, to cause vomiting, headache or blurred vision, or even lose consciousness, ICP continues rising may cause nonvolatil brain injury, under the serious situation, will cause patient disabled even dead.Therefore, ICP is an important indicator of reflection brain function state.Whether normal, be the direct foundation of correctly diagnosis of medical department, active treatment and therapeutic evaluation if promptly and accurately detecting patient ICP.

At present, though the someone has proposed multiple ICP noinvasive detection method, but the ICP monitoring method of the most normal employing has at present remained the wound method, wears method or opens the cranium manometry as waist, not only cerebral tissue is had certain damage, and may cause accompanying infection, and medical expense is higher, requires the doctor that higher technology is arranged, and has increased patient's misery, limited the freedom of patient activity, thereby clinical practice is subjected to great limitation.

Based on different detection principles, also there is the people to propose the noninvasively estimating method of multiple ICP.The common thought of these non-invasive methodses is to change the noinvasive testing result that certain relevant physical descriptor obtains intracranial pressure indirectly by detecting with ICP.As based on CT, transcranial doppler (TransCranial Doppler, the noinvasive of intracranial hypertension TCD) detects, based on the preclinical variation of N2 ripple and the intracranial mesohigh detection of the positive correlation of intracranial pressure of flash visual evoked potential, based on tympanum displacement method (tympanic membrane displacement, TMD) low intracranial pressure detects and based on bregma pressure measurement (AnteriorFontanel Pressure, AFP) intracranial pressure that is applicable to neonate and infant of method detects, and the intracranial pressure noinvasive detection method of different principle respectively has its pluses and minuses and the scope of application.As ZL01135697.9 " non-wound intracrenial pressure monitor ", the ZL02104049.4 disclosed intracranial pressure noinvasive detection techniques such as " a kind of intra cranial pressure measurers " of Chinese patent bulletin, also announced multinomial intracranial pressure noinvasive detection technique abroad.But, have on the existing market to be exclusively used in the equipment that the intracranial pressure noinvasive detects few, detect and assessment though CT, TCD can be used for the noinvasive of ICP, be not specially at the intracranial pressure non-invasive monitoring, and because instrument itself, use is very limited; Though detect the intracranial pressure non-invasive detector device of principle based on flash visual evoked potential on sale on market, but it is based on single ICP noinvasive detection method, thereby unavoidably has a principle defective, and also there is following deficiency in prior art: (1) price is relatively costly, and hardware is formed complicated; (2) signal processing method is more simple, and instrument repeatability degree is not high, needs the manual intervention diagnostic result; (3) robustness of the intracranial hypertension situation that various disease is caused is not strong; (4) the open degree of instrument is not enough, information management and ability to exchange a little less than; (5) existing method does not lie in them in the defective that exists aspect the intracranial pressure non-invasive monitoring and does not find the variable relevant with intracranial pressure, and is to lack the data handling system that these variablees of calibration obtain the absolute wave numerics of intracranial pressure.

Summary of the invention

At the prior art above shortcomings, the purpose of this invention is to provide a kind of precision height, capacity of resisting disturbance is strong, cost is low, intelligent degree is high intracranial pressure noinvasive comprehensive monitoring analytical method and device, to realize that the intracranial pressure non-invasive quantitative detects and long time is guarded.

The object of the present invention is achieved like this: intracranial pressure noinvasive comprehensive monitoring analytical method, it is characterized in that, obtain physiological parameter and biomechanical parameter by flash visual evoked potential measurement subsystem and the transcranial doppler monitoring subsystem that links to each other with computer, by intracranial pressure noinvasive comprehensive monitoring mathematical model and software parameter is carried out seamless fusion and Treatment Analysis, obtain the non-invasive quantitative detected value and the dynamic changing process of intracranial pressure;

Wherein, described intracranial pressure noinvasive comprehensive monitoring mathematical model is from pathology and biomechanics angle, adopt flash visual evoked potential and transcranial doppler monitoring patient characteristics physiological parameter, based on data mining and system identifying method, extract the different syndromes that causes intracranial hypertension and the dependency relation between the intracranial pressure non-invasive monitoring method feature value, by making up novel data base administration mechanism and the foundation of model training mechanism; The reflection of intracranial pressure noinvasive comprehensive monitoring mathematical model has the dependency relation between wound intracranial pressure value and the Noninvasive intracranial pressure monitor value, realization improves the precision of intracranial pressure non-invasive monitoring to the seamless fusion and the integrated application of the different pathological that causes intracranial hypertension and biomechanics control parameter profound level;

Described intracranial pressure noinvasive comprehensive monitoring mathematical model and software (3) are realized as follows:

1. use the flash visual evoked potential measurement subsystem, patient's intracranial pressure of utilizing described flash visual evoked potential monitoring intracranial pressure mathematical model to obtain change with flash visual evoked potential variation incubation period between functional relationship, obtain patient's intracranial pressure noinvasive detected value; And be worth correction input value as transcranial doppler monitoring intracranial pressure mathematical model with this;

2. utilize the transcranial doppler monitoring subsystem, the hemodynamic parameter of monitoring patient middle cerebral artery, and in arteriotony together as the input of described transcranial doppler monitoring intracranial pressure mathematical model, thereby obtain the non-invasive monitoring value and the dynamic changing process of patient's intracranial pressure continuously;

3. utilized the flash visual evoked potential measurement subsystem to obtain a patient's intracranial pressure noinvasive detected value every 5 minutes, and revise the continuous variation tendency of the intracranial pressure that obtains by the transcranial doppler monitoring subsystem with this and estimate;

4. repeat said process, the patient's intracranial pressure non-invasive monitoring value that at every turn obtains is handled by intracranial pressure noinvasive comprehensive monitoring software (3), obtain patient's the continuous dynamic changing process of intracranial pressure, show by display (2).

Compared to existing technology, the present invention has following advantage:

I, utilize multifunctional data acquiring equipment, make system realize better calibration, thereby reach higher precision;

II, two kinds of intracranial pressure noinvasive detection method coordinative roles, detected parameters is unified in the data base management system by intracranial pressure noinvasive comprehensive monitoring software, the comprehensive comparison and the processing of unifying realize profound seamless fusion, the value and the variation tendency of the truer reflection intracranial pressure of testing result;

III, software and hardware separation, convenient upgrading;

IV, software can be used as stand-alone program and use, and are used for calculating and detection, remote detection and the consultation of doctors with visual evoked potential data readback, Doppler parameter and envelope demonstration, medical record management, patient information inquiry, intracranial pressure;

V, the non-invasive quantitative detection that can realize intracranial pressure and long time monitoring;

VI, can cooperate abundant software kit, realize the time-domain analysis of visual evoked potential data, frequency-domain analysis, frequency band extracts, each ripple incubation period of flash visual evoked potential and Doppler parameter obtain automatic calculating with the intracranial pressure value, functions such as computer-aided diagnosis automatically;

VII, patient's visual evoked potential and Doppler signal record can be done to preserve for a long time in hard disk, and also can transcribing imprints CDs does nonvolatil preservation;

VIII, can the network savvy by computer realize remote detection, diagnosis and the consultation of doctors of intracranial pressure.

IX, simple in structure, cost is lower.

Description of drawings

Fig. 1 is the software mathematical model training process block diagram of intracranial pressure noinvasive comprehensive monitoring analytical method of the present invention;

Fig. 2 is that the local linear model tree of the present invention is about dividing the spatial algorithm example of input variable iteratively;

Fig. 3 is the model framework of intracranial pressure noinvasive comprehensive monitoring analytical method of the present invention;

Fig. 4 realizes intracranial pressure noinvasive composite monitoring device functional-block diagram of the present invention;

Fig. 5 is the software flow block diagram of intracranial pressure noinvasive comprehensive monitoring analytical method of the present invention.

Among the figure: 1 computer, 2 display, 3 intracranial pressure noinvasive integrated monitoring softwares, 4 flashers, 5 acquisition electrodes, 6 Switching Power Supplies, 7 PRE-VEP type amplifiers, 8 data collecting cards and timer conter, 9 ultrasonic transductions probe, 10 ultrasonic signal control units, amplify and demodulator circuit in 11 ultrasonic signal broadbands, 12 data collecting cards, 13 printers.

The specific embodiment

Below in conjunction with drawings and Examples intracranial pressure noinvasive comprehensive monitoring analytical method of the present invention is described further:

As shown in Figure 4, realize the device of intracranial pressure noinvasive comprehensive monitoring analytical method of the present invention, mainly comprise computer 1 and display 2 thereof, flasher 4 and acquisition electrode 5, ultrasonic transduction probe 9, also comprise Switching Power Supply 6, PRE-VEP type amplifier 7 and data collecting card and timer conter 8, amplify and demodulator circuit 11 and data collecting card 12 in ultrasonic signal control unit 10, ultrasonic signal broadband; Acquisition electrode 5 is connected with computer 1 with PRE-VEP type amplifier 7, data collecting card and timer conter 8 respectively with flasher 4; And ultrasonic transduction probe 9 is connected with computer 1 with demodulator circuit 11 and data collecting card 12 by ultrasonic signal control unit 10, the amplification of ultrasonic signal broadband.Described flasher 4 is made up of two groups of light emitting diode matrixs, can be by intracranial pressure noinvasive comprehensive monitoring software 3 by data collecting card and timer conter 8 to its control, make its according to the frequency scintillation of setting to obtain flash stimulation.

Wherein, the PRE-VEP type amplifier 7 that is used for the amplification of visual evoked potential signal adopts the isolated amplification modulate circuit of the PRE-ISO.VEP 50 μ V of Beijing auspicious cloud Computer Company production, and amplifier output signal is by data collecting card and timer conter 8 and computer 1 communication;

Amplify in the ultrasonic signal broadband and demodulator circuit 11 is controlled and treatment circuit for self-control, can adopt circuit unit as shown in Figure 5; This unitary output signal is by data collecting card 12 and computer communication.

Described data collecting card and timer conter 8 can adopt the popular in the market data acquisition unit based on PCI or usb bus, only require it to have 16 road A/D ALT-CH alternate channels, and 16, sample rate＞250KHz has 16 bit timing technology devices more than 1.

Use this intracranial pressure noinvasive comprehensive detection monitoring analysis device, for the flash visual evoked potential signal processing, 4 acquisition electrodes (can use general bridge-type electrode or circular electrode in the existing electroencephalogram detection) are placed on patient's glabella respectively as ground electrode, forehead hairline place is as the reference electrode, and is placed in occipital bone place, the left and right sides and is used for extracting the visual evoked potential signal; Then, glisten to give the pathways for vision flash stimulation with certain flash stimulation FREQUENCY CONTROL flasher, the visual evoked potential signal that collects from occipital bone through PRE-VEP type amplifier after, be converted to digital quantity by data collecting card and timer conter, after computer carries out further digital filtering processing with the visual evoked potential data that receive, the result is presented on the display, also can be kept in the hard disk of computer.

For the doppler ultrasound test section, ultrasonic transduction probe 9 places middle cerebral artery (MCA) position to its continuous monitoring, by 10 pairs of ultrasonic transduction probe 9 controls of ultrasonic signal control unit, obtain the cerebral blood flow velocity signal with the further processing of broadband amplification and demodulator circuit unit 11, be converted to digital quantity by the data collecting card 12 that is inserted in the computer PCI slot, computer 1 will receive, and the ultrasonic multispectral signal of reining in is presented on the display 2 with the form of image and numerical value, and the while is saved in data on the hard disk.

Intracranial pressure noinvasive comprehensive detection monitor device of the present invention utilizes the intracranial pressure noinvasive comprehensive monitoring software based on the method for the invention exploitation, positive correlation according to flash visual evoked potential and intracranial pressure, ultrasonicly multispectrally rein in the cerebral blood flow velocity extracted in the signal and the nonlinear mapping functional relationship of intracranial pressure is carried out integrated treatment to two class data, height according to intracranial pressure, patient age, sex, factors such as the cause of disease are judged and are handled, the last continuous changing trend diagram that demonstrates more accurate intracranial pressure noinvasive detected value and intracranial pressure on display, detailed process as shown in Figure 6.

Software work of the present invention adopts the programming of Visual C++ 6.0 programming languages in the Windows of Microsoft operating system platform, can work under the operating system platform of Windows Vista and later release at Windows 2000/XP/NT.Software as the inventive method is realized, allows it to break away from hardware components independent operating of the present invention.By the network savvy of computer, can realize the remote detection and the networking consultation of doctors of patient's intracranial pressure.

In particular for obtaining from the data handling system of intracranial pressure correlated variables acquisition intracranial pressure non-invasive monitoring quantitative values, the present invention is from pathology and biomechanics angle, adopt flash visual evoked potential and transcranial doppler monitoring patient characteristics physiological parameter, based on data mining and system identifying method, extract the different syndromes that causes intracranial hypertension and the dependency relation between the intracranial pressure non-invasive monitoring method feature value, make up novel data base administration mechanism and model training mechanism, set up learning through thinning processing, can remember mathematical model, intracranial pressure noinvasive comprehensive monitoring mathematical model has reflected the dependency relation that has between wound intracranial pressure value and the Noninvasive intracranial pressure monitor value, realization is to the seamless fusion and the integrated application of the different pathological that causes intracranial hypertension and biomechanics control parameter profound level, obtain and intracranial pressure relevant variable and the intracranial pressure effective calibration steps between changing, improve the precision of intracranial pressure non-invasive monitoring.

Intracranial pressure noinvasive comprehensive monitoring mathematical model of the present invention is based on that date processing framework that general biological medical signals estimates realizes.This date processing framework comprises a Signals Data Base, a model training process and a signal estimation process.Signals Data Base is made up of a plurality of data clauses and subclauses, and each clauses and subclauses is made up of wound intracranial pressure and physiological signal and the hemodynamic parameter dynamic variable signal relevant with the intracranial pressure variation by one section.Obtain the model parameter and the calibration process of the mathematical model that each correlated variables and intracranial pressure change by the described model training process of Fig. 1, realize the accurate measurement and the monitoring of Noninvasive intracranial pressure.

The variable relevant with the intracranial pressure variation that the present invention relates to comprises clinical each physiological parameter, as body temperature, arteriotony, heart rate and flash visual evoked potential signal and hematodinamics characteristic parameter.

The training process of described model is: at first, take out a data base entries, utilize faint biomedicine signals analytical method, from above-mentioned each variable signal, extract characteristic vector, and, obtain the error of a Noninvasive intracranial pressure that obtains based on the dynamics mathematical model of using this data base entries with of the input of this characteristic vector as a nonlinear mapping function; Secondly, based on data mining technology, these error variances are further decided optimum data base entries with the database processing function; At last, selected data base entries is used to construct intracranial pressure noinvasive comprehensive monitoring mathematical model, thereby realizes the non-invasive monitoring of intracranial pressure.

Each data base entries of the present invention is according to the clinical data of surveying, and utilizes the self-adapting signal segmentation algorithm to determine each data segment stably, and then constructs Signals Data Base with it.The purpose of signal subsection is to utilize a linear autoregression that has extraneous input stably (Auto Regressive with eXternal input, ARX) coefficient of model is as characteristic vector.Signal Processing and judgement are comprised following key step:

1. utilize segmentation algorithm to find change point to arterial blood pressure signal (ABP) and cerebral blood flow velocity (CBFV) signal, the signal segment between the change point belongs to a steady section continuously;

2., then it is regarded as instantaneous and will not adopt if the length of steady section was less than 90 seconds;

3., then also will not adopt if the intracranial pressure signal in the steady section has the noise that causes such as reasons such as cough, nursing.

A plurality of Kalman filterings that are based on the signal subsection algorithm realize.In order to use the equation expression segmentation problem, the present invention the ARX coefficient as the time become, can further be written as the regression problem of modified line when general a: y _n=φ _n ^Tθ _n+ e _nWherein, y _nBe the output of model, time point n, e _nBe that (covariance is R to the zero-mean white Gaussian noise ₂), φ _nBe regression variable, form θ by the input and output sample of the model in past _nIt is model coefficient vector at transition point n.The I/O model of supposing ABP and CBFV is that segmentation is stable, θ _nOnly change with probability q:

θ _n+1＝θ _n+ω _n

Wherein, v _nBe that (covariance is R to zero-mean Gaussian noise sequence ₁).The signal subsection problem is exactly to find corresponding time point when this variation takes place.The present invention is based on the high-efficient algorithm that Andersson proposes.In this algorithm, approach θ with a gauss hybrid models (number is M) _nPosterior probability density:

$P\left({\mathrm{\θ}}_n\right|\{y_1,\·\·\·,y_{n-1}\})=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{a}_{i,n|n-1}G({\mathrm{\θ}}_n,{\stackrel{\‾}{\mathrm{\θ}}}_{i,n|n-1},P_{i,n|n-1})$

A wherein _{I, n|n-1}Become when being, be and i the weight that Gauss model is related." n|n-1 " meaning is that what to use at time n is n-1 data constantly.Function G is represented the Gaussian probability-density function PDF of standard, and (average is Covariance matrix is P _{I, n|n-1}).Further derivation can obtain,

$P\left({\mathrm{\θ}}_{n+1}\right|\{y_1,...y_n\})=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{\mathrm{\α}}_{i,n|n}\{\mathrm{qG}({\mathrm{\θ}}_n,{\stackrel{\‾}{\mathrm{\θ}}}_{i,n|n},P_{i,n|n})+(1-q)G({\mathrm{\θ}}_n,{\stackrel{\‾}{\mathrm{\θ}}}_{i,n|n},P_{i,n|n}+R_1)\}$

Wherein, P _{I, n|n}And α _{I, n|n}Can be from new data sample y _nAnd φ _n, and

P _{I, n|n-1}And α _{I, n|n-1}, upgrade iterative part with the time in the Kalman filtering algorithm of standard and obtain.

If be initialized as M gauss hybrid models, need carry out M constantly at n ⁿInferior Kalman filtering.Andersson has proposed a kind of approximate data of seldom calculating of only needing.Specifically, change moment at each, it has kept mixed coefficint M (only separate best Gauss model, and the worst new model that replaces with).At moment n, the contribution of each Gauss distribution is by α _{I, n|n}Judge, because most probable model should have maximum α _{I, n|n}

Therefore can use

Weighted sum try to achieve.Above-mentioned obtaining

Algorithm can promptly find the kinetic model change point with the segmentation problem that solves in the patent of the present invention.Particularly, if a Gauss distribution of newly obtaining remains optimum at next iteration point, this just means that this is a change point constantly.Signal between adjacent two change points promptly is a stationary signal section.When collecting each sample of signal, utilize this algorithm decision to separate whether the new Gauss model that obtains is current best model.If the current time is designated as change point.Because finish in each sampling moment segmentation, the adaptivity of this segmentation algorithm is tangible.

Nonlinear mapping function of the present invention is based on the linear fuzzy model of local, thereby obtains better data digging method.The linear fuzzy model of local (Local Linear Fuzzy Model, general type LLFM) is:

$y=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}(w_{i,0}+w_{i,1}{u}_1+,\·\·\·,+w_{i,p}{u}_p){\mathrm{\Φ}}_i\left(u\right)$

Wherein, m represents the quantity of local linear model, and p is the dimension of input vector u, vectorial w _i={ w _{I, 0}..., w _{I, p}Be the coefficient of i linear model, Φ _i(u) be the member function (relevant with i linear model) of u, it is controlling the ratio of i linear model to the contribution of output.For u arbitrarily,

Make among the present invention So Φ _i(u) two parameters are arranged.Vector c _iThe center that can regard i linear model as, variances sigma _{I, j}Control the excursion of model on j the direction.For above-mentioned form, the output of LLFM is that weighted sum input vector u from the local linear model output of m is to center c _iDistance decide weight.

The linear fuzzy model of local (LLFM) is a nonlinear model, and its model parameter obtains from inputoutput data by a linear model tree in part (LoLiMoT) algorithm.LoLiMoT is one and divides the spatial algorithm of input variable iteratively.In Fig. 2, be that two situation illustrates this algorithm with an input variable dimension.When algorithm began, whole input variable space can be divided.Space dividing is to carry out at orthogonal x and y direction, and modeling is come with a linear function in each subspace.Therefore, iteration has two kinds of divisions (a/b and c/d) for the first time, and division optimum in these two kinds of divisions will be adopted.The optimality of dividing is to decide by its degree of fitting to data.After the decision optimal dividing, enter next iteration.At this moment, need select a sub spaces further to divide.This sub spaces choose the degree of its corresponding linear function that be based on to data fitting in the subspace, promptly the poorest subspace of degree of fitting is with selected.

The ultimate principle that above subspace is divided is exactly to come modeling is carried out in the subspace of complexity with polyteny model more.When determining the corresponding member function in each subspace

${\mathrm{\Φ}}_i\left(u\right)=\frac{G_i\left(u\right)}{\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{G}_j\left(u\right)},$ $G_i\left(u\right)=\underset{j=1}{\overset{p}{\mathrm{\Π}}}\exp \left(\frac{-1}{2}\frac{{(u_j-c_{i,j})}^2}{{{\mathrm{\σ}}^2}_{i,j}}\right),$

The present invention uses the central point of subspace as c _{I, j}, with the k of subspace in j axle size _σDoubly as σ _{I, j}When the degree of fitting of model did not change in 10 iteration, above iteration stopped.

Utilize the method for the invention to develop intracranial pressure non-invasive monitoring software (3), its model framework as shown in Figure 3.In this model framework, rectangular block is represented each main software module; 6 kinds of data that the circle representative is main, their preserve separately are in different data bases.Cause influence to other data accesses for fear of changing a kind of data format, the visit of each data all realizes by a specific software module, represents with thick arrow in Fig. 3.Have between the interactional module and link to each other with a thin arrow line.

Succeeded in developing a kind of precision height, capacity of resisting disturbance is strong, cost is low, intelligent degree is high intracranial pressure noinvasive comprehensive detection monitoring analysis software based on the present invention, its seamless fusion and integrated application based on the intracranial pressure noinvasive detection method of flash visual evoked potential and transcranial doppler.

Use the device of intracranial pressure noinvasive comprehensive detection monitoring analysis method of the present invention: at first, application includes the protective eye lens of led array in patient's eyes, and the flasher (4) that produces certain pulsewidth and certain frequency by computer (1), intracranial pressure noinvasive comprehensive monitoring software (3) control data capture card and timer conter (8) acts on patient's eyes; Secondly, utilize the circular electrode (5) of the electroencephalogram collection usefulness that is placed on patient's occipital bone place and forehead to constitute flash visual evoked potential acquisition electrode, reference electrode and ground electrode respectively, gather the flash visual evoked potential signal of the left and right visual pathway of patient; Then, the PRE-VEP type amplifier (7) of the flash visual evoked potential signal process high-gain that collects changes digital quantity into by data collecting card and timer conter (8) and enters computer (1), and shows the flash visual evoked potential waveform by display (2); According to the wave character of flash visual evoked potential, intracranial pressure noinvasive comprehensive monitoring software (3) can automatically or semi-automatically be determined the incubation period of N2 ripple in the flash visual evoked potential again; At last, utilize flash visual evoked potential of the present invention and dependency relation y=ax between the wound intracranial pressure value is arranged ^b+ c obtains patient's intracranial pressure noinvasive detected value.Y is the noinvasive estimated value of patient's intracranial pressure in the formula, and x is the incubation period of flash visual evoked potential N2 ripple, and a, b, c are the model parameters of being determined by mathematical model of the present invention.

The present invention is based on the preclinical variation of N2 ripple of flash visual evoked potential and the positive correlation of intracranial pressure, obtain the estimated value that intracranial pressure noinvasive sometime detects, utilize transcranial doppler continuous monitoring middle cerebral artery, based on the hematodinamics feature that extracts from the arteriotony ABP that measures and cerebral blood flow velocity CBFV, obtain the parameter relevant, finally realize the noinvasive continuous monitoring of intracranial pressure variation tendency with intracranial hypertension.Wherein, the estimated value of utilizing flash visual evoked potential to obtain is used as correction value, guarantees that the output of intracranial pressure trend monitoring model has the situation of change of high precision and correct reflection intracranial pressure.

Claims (3)

1. intracranial pressure noinvasive comprehensive monitoring analytical method, it is characterized in that, obtain physiological parameter and biomechanical parameter by flash visual evoked potential measurement subsystem and the transcranial doppler monitoring subsystem that links to each other with computer (1), by intracranial pressure noinvasive comprehensive monitoring mathematical model and software (3) parameter is carried out seamless fusion and Treatment Analysis, obtain the non-invasive quantitative detected value and the dynamic changing process of intracranial pressure;

Wherein, described intracranial pressure noinvasive comprehensive monitoring mathematical model is from pathology and biomechanics angle, adopt flash visual evoked potential and transcranial doppler monitoring patient characteristics physiological parameter, based on data mining and system identifying method, extract the different syndromes that causes intracranial hypertension and the dependency relation between the intracranial pressure non-invasive monitoring method feature value, by making up novel data base administration mechanism and the foundation of model training mechanism;

Described intracranial pressure noinvasive comprehensive monitoring mathematical model and software (3) are realized as follows:

1. use the flash visual evoked potential measurement subsystem, patient's intracranial pressure of utilizing described flash visual evoked potential monitoring intracranial pressure mathematical model to obtain change with flash visual evoked potential variation incubation period between functional relationship, obtain patient's intracranial pressure noinvasive detected value; And be worth correction input value as transcranial doppler monitoring intracranial pressure mathematical model with this;

2. utilize the transcranial doppler monitoring subsystem, the hemodynamic parameter of monitoring patient middle cerebral artery, and in arteriotony together as the input of described transcranial doppler monitoring intracranial pressure mathematical model, thereby obtain the non-invasive monitoring value and the dynamic changing process of patient's intracranial pressure continuously;

3. utilized the flash visual evoked potential measurement subsystem to obtain a patient's intracranial pressure noinvasive detected value every 5 minutes, and revise the continuous variation tendency of the intracranial pressure that obtains by the transcranial doppler monitoring subsystem with this and estimate;

4. repeat said process, the patient's intracranial pressure non-invasive monitoring value that at every turn obtains is handled by intracranial pressure noinvasive comprehensive monitoring software (3), obtain patient's the continuous dynamic changing process of intracranial pressure, show by display (2).

2. realize the device of the described intracranial pressure noinvasive of claim 1 comprehensive monitoring analytical method, it is characterized in that described flash visual evoked potential measurement subsystem comprises flasher (4), acquisition electrode (5), visual evoked potential signal isolation amplification conditioning module (7), data collecting card and timer counter controller (8) and Switching Power Supply (6);

Flasher (4) links to each other with computer (1) by data collecting card and timer counter controller (8); Acquisition electrode (5) is isolated amplification conditioning module (7) by the visual evoked potential signal successively and is linked to each other with computer (1) with data collecting card and timer counter controller (8); Described flasher (4) comprises two groups of light emitting diode matrixs, by intracranial pressure noinvasive comprehensive monitoring software (3) by data collecting card and timer conter (8) to flasher (4) control, make its according to the frequency scintillation of setting to obtain the flash stimulation signal;

Described acquisition electrode (5) collection flash visual evoked potential signal changes digital quantity through visual evoked potential signal isolation amplification conditioning module (7) into by data collecting card and timer conter (8) and enters computer (1), and by display (2) demonstration flash visual evoked potential waveform;

Switching Power Supply (6) is isolated amplification conditioning module (7) with the visual evoked potential signal and is linked to each other;

It is 20,000 times that described visual evoked potential signal is isolated the amplification of amplifying conditioning module (7), and input reference signal is 0.5 μ V～50 μ V, and the signal band scope is 1～300Hz, and common mode rejection ratio is 120dB, the double T trap, and trueness error is ± 0.2%.

3. realize the device of the described intracranial pressure noinvasive of claim 1 comprehensive monitoring analytical method, it is characterized in that, described transcranial doppler measurement subsystem comprises ultrasonic transduction probe (9), and ultrasonic transduction probe (9) links to each other with computer (1) with demodulator circuit (11) and data collecting card (12) by ultrasonic signal control unit (10), broadband amplification successively;

The transcranial doppler monitoring subsystem, utilize the ultrasonic pulse signal that produces 2MHz by ultrasonic signal control unit (10) control ultrasonic transduction probe (9), act on patient's middle cerebral artery, and utilize ultrasonic transduction probe (9) to receive the echo-signal of blood vessel, by ultrasonic signal control unit (10) and broadband amplifying and demodulator circuit (11) obtains frequency shift signal between ultrasonic pulsative signal and the echo-signal, be transformed into digital signal by data collecting card (12), enter computer (1);

The frequency shift signal that the transcranial doppler monitoring system collects is handled the back by intracranial pressure noinvasive comprehensive monitoring software (3) and show its waveform and spectrogram in display (2), is obtained each hemodynamics characteristic parameter of patient by spectrogram; The input of the hemodynamics characteristic parameter that obtains, obtain patient's intracranial pressure non-invasive monitoring value again as described transcranial doppler monitoring intracranial pressure mathematical model.

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