CN111048203B - Brain blood flow regulator evaluation device - Google Patents
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Abstract
The application is applicable to machine learning algorithm technical field, provides a cerebral blood flow regulation function evaluation device, includes: the acquisition module is used for acquiring blood pressure and cerebral blood flow velocity in a preset time period; the calculation module is used for calculating the incidence relation between the blood pressure and the cerebral blood flow velocity according to the blood pressure and the cerebral blood flow velocity in the preset time period; and the evaluation module is used for inputting the incidence relation into the regulation function evaluation model so as to output the corresponding cerebral blood flow regulation function, wherein the regulation function evaluation model is generated by the model training module, the model training module is used for acquiring training samples, and training the classification model according to the training samples so as to obtain the regulation function evaluation model, and the training samples comprise the incidence relation between the blood pressure and the cerebral blood flow speed and the corresponding cerebral blood flow regulation function. The cerebral blood flow regulator evaluating device has the advantages of high evaluating efficiency, high accuracy and universality.
Description
Technical Field
The application belongs to the technical field of machine learning algorithms, and particularly relates to a cerebral blood flow regulation function evaluation device.
Background
The existing method for evaluating the cerebral blood flow regulation function generally adopts artificial judgment according to the collected blood pressure and the collected cerebral blood flow velocity, and because the relation between the blood pressure and the cerebral blood flow velocity is complex, the efficiency of the cerebral blood flow regulation function is low through the artificial evaluation of the blood pressure and the cerebral blood flow velocity, the accuracy cannot be ensured, and the universality among different individuals cannot be ensured.
Disclosure of Invention
In view of this, the embodiments of the present application provide a cerebral blood flow regulation function evaluation device, so as to solve the problems of low efficiency, low accuracy and non-universality among different individuals of the cerebral blood flow regulation function in the prior art.
The embodiment of the application provides a cerebral blood flow regulation function evaluation device, including:
the acquisition module is used for acquiring blood pressure and cerebral blood flow velocity in a preset time period;
the calculation module is used for calculating the incidence relation between the blood pressure and the cerebral blood flow velocity according to the blood pressure and the cerebral blood flow velocity in the preset time period;
and the evaluation module is used for inputting the incidence relation into a regulation function evaluation model so as to output the corresponding cerebral blood flow regulation function, wherein the regulation function evaluation model is generated by a model training module, the model training module is used for acquiring a training sample, and training a classification model according to the training sample so as to obtain the regulation function evaluation model, and the training sample comprises the incidence relation between blood pressure and cerebral blood flow speed and the corresponding cerebral blood flow regulation function.
In one possible implementation, the calculation module includes:
the first calculation unit is used for calculating a first relational expression of the blood pressure and the cerebral blood flow according to the blood pressure and the cerebral blood flow velocity in the preset time period;
and the second calculation unit is used for calculating a second relational expression of the blood pressure and the cerebral blood flow according to the blood pressure and the cerebral blood flow velocity in the preset time period.
In a possible implementation manner, the first computing unit is specifically configured to:
calculating corresponding transfer functions of the blood pressure and the cerebral blood flow according to the blood pressure and the cerebral blood flow velocity;
the gain and phase of the transfer function are calculated.
In a possible implementation manner, the first computing unit is further specifically configured to:
and calculating a coherent function of the blood pressure and the cerebral blood flow velocity according to the blood pressure power spectrum and the cerebral blood flow power spectrum.
In a possible implementation manner, the second computing unit is specifically configured to:
establishing a series model of blood pressure and cerebral blood flow velocity;
fitting coefficients of the series model according to the blood pressure and the corresponding cerebral blood flow velocity;
and generating an optimized series model according to the coefficient of the fitted series model.
In one possible implementation manner, the classification model includes a feature extraction model and a classifier model, and the model training module includes a first training unit and a second training unit;
the first training unit is used for training the feature extraction model to obtain an optimized feature extraction model;
the second training unit is used for training the optimized feature extraction model and the classifier model.
In a possible implementation manner, the feature extraction model is an encoder model, and the first training unit is specifically configured to:
constructing an encoder and a decoder of an encoder model;
extracting the characteristics of the incidence relation between the blood pressure and the cerebral blood flow speed of the training sample through the encoder;
inputting the extracted features of the incidence relation into a decoder to obtain an output value of the decoder;
optimizing the parameters of the encoder and the parameters of the decoder according to the output value of the decoder and the incidence relation between the blood pressure and the cerebral blood flow speed of the training sample;
and taking the encoder model corresponding to the optimized parameters of the encoder as an optimized feature extraction model.
In one possible implementation, the encoder model is a stacked self-encoder model or a variational self-encoder model.
In a possible implementation manner, the second training unit is specifically configured to:
extracting the characteristics of the training sample through the optimized characteristic extraction model;
inputting the extracted features of the training samples into the classifier model to output a classification result;
and optimizing parameters of the optimized feature extraction model and parameters of the classifier model according to the classification result and the cerebral blood flow regulator of the training sample.
In one possible implementation, the classifier model is a single hidden layer feedforward neural network classifier.
Compared with the prior art, the embodiment of the application has the advantages that: the acquisition module acquires the blood pressure and the cerebral blood flow velocity in a preset time period, and the calculation module calculates the correlation between the blood pressure and the cerebral blood flow velocity according to the blood pressure and the cerebral blood flow velocity in the preset time period, so that the complete variable of the cerebral blood flow regulation function can be acquired, and the accuracy of an evaluation result is ensured; the evaluation module inputs the incidence relation into the regulatory function evaluation model, so that the corresponding cerebral blood flow regulatory function can be output, and the efficiency is high; and the regulation function evaluation model is obtained by training the classification model through a training sample by the model training module, so that the evaluation of the cerebral blood flow regulation function has universality.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below.
Fig. 1 is a schematic view of a cerebral blood flow regulation function evaluation device according to an embodiment of the present application;
fig. 2 is a schematic diagram of a feature extraction model and a classifier model provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In addition, in the description of the present application, the terms "first," "second," and the like are used solely for distinguishing between the descriptions and are not intended to indicate or imply relative importance.
Referring to fig. 1, the cerebral blood flow regulation function evaluation apparatus according to the embodiment of the present application is described below, and includes an obtaining module 10, a calculating module 20, an evaluating module 30, and a training module 40.
The obtaining module 10 is used for obtaining blood pressure and cerebral blood flow velocity within a preset time period.
Specifically, the continuous blood pressure and the cerebral blood flow velocity measured in a preset time period of a sample to be evaluated are obtained.
The calculating module 20 is configured to calculate an association relationship between the blood pressure and the cerebral blood flow velocity according to the blood pressure and the cerebral blood flow velocity in the preset time period.
In a possible implementation manner, the association relationship between the blood pressure and the cerebral blood flow velocity includes a first relationship between the blood pressure and the cerebral blood flow and a second relationship between the blood pressure and the cerebral blood flow, and correspondingly, the calculating module 20 includes a first calculating unit 21 and a second calculating unit 22.
The first calculating unit 21 is configured to calculate a first relational expression between blood pressure and cerebral blood flow according to the blood pressure and the cerebral blood flow velocity in the preset time period.
Specifically, the first relation includes a transfer function of blood pressure and cerebral blood flow and a gain and a phase of the transfer function, and is used for representing a linear relation of the blood pressure and the blood flow. Wherein the transfer function is represented by the formula
Optionally, the first relation further includes a coherence function of blood pressure and cerebral blood flow velocity for characterizing a linear relation of blood pressure and blood flow, wherein the coherence function is expressed by a formula
MSC(f)＝spv(f)2/[spp(f)svv(f)]Calculation, where MSC (f) represents the coherence function, svv(f) A self-powered spectrum representing blood flow is calculated from the continuous blood flow. The coherent function can calculate the time domain impulse response and the step response by inversion and convolution, and the specific calculation process is the prior art and is not described herein again.
The second calculating unit 22 is configured to calculate a second relational expression between the blood pressure and the cerebral blood flow according to the blood pressure and the cerebral blood flow velocity in the preset time period.
Specifically, the second relation is a series model of blood pressure and cerebral blood flow velocity, and is used for representing the nonlinear relation of the blood pressure and the blood flow velocity. Firstly, establishing a series model of blood pressure and brain blood flow velocity:
wherein v isM(t) represents the blood flow velocity, p (t-m), p (t-m1), p (t-m2) represent the developed form of blood pressure, and k represents the blood pressure0、k1(m)、k2(m1, m2) represents coefficients of the series model. Fitting coefficients of the series model according to the blood pressure of the sample and the corresponding cerebral blood flow velocity, and generating the optimized series model according to the fitted coefficients of the series model.
The evaluation module 30 is configured to input the association relationship into a regulatory function evaluation model to output a corresponding cerebral blood flow regulatory function, where the regulatory function evaluation model is generated by a model training module 40, the model training module 40 is configured to collect a training sample, and train a classification model according to the training sample to obtain the regulatory function evaluation model, where the training sample includes the association relationship between blood pressure and cerebral blood flow velocity and the corresponding cerebral blood flow regulatory function.
Specifically, the model training module 40 trains out a regulation function evaluation model, and the regulation function evaluation model processes the input correlation between the blood pressure and the cerebral blood flow velocity to output the cerebral blood flow regulation function, which includes normal and abnormal.
In one possible implementation, the classification model includes a feature extraction model and a classifier model, and the model training module 40 includes a first training unit 41 and a second training unit 42.
The first training unit 41 is configured to train the feature extraction model to obtain an optimized feature extraction model; the second training unit 42 is used for training the optimized feature extraction model and the classifier model.
By way of example and not limitation, the feature extraction model is an encoder model, which may be a stacked self-encoder model or a variational self-encoder model.
In a possible implementation manner, the encoder model is a stacked self-encoder model, and correspondingly, the first training unit 41 is specifically configured to:
constructing an encoder and a decoder of an encoder model, and extracting the characteristics of the incidence relation between the blood pressure and the cerebral blood flow speed of the training sample through the encoder; and inputting the extracted characteristics of the association relationship into a decoder to obtain an output value of the decoder. Wherein, the training sample is generated according to the incidence relation between the blood pressure and the brain blood flow speed of different individuals; the correlation between the blood pressure and the cerebral blood flow velocity is obtained by acquiring the blood pressure and the cerebral blood flow velocity of different individuals and calculating according to the first relational expression and the second relational expression. Part of the training samples are the correlation between blood pressure and cerebral blood flow velocity and the corresponding cerebral blood flow regulation function, namely the training samples with labels; part of the training samples only have the incidence relation between the blood pressure and the cerebral blood flow velocity, namely the training samples without labels.
Optimizing parameters of the encoder and parameters of the decoder according to the incidence relation between the output value of the decoder and the blood pressure and cerebral blood flow velocity of the training sample; and taking the encoder model corresponding to the optimized parameters of the encoder as an optimized feature extraction model. Specifically, a loss function is set
Calculating corresponding encoder parameters and decoder parameters when the loss function L is the minimum value, wherein y represents the incidence relation of the blood pressure and the cerebral blood flow velocity, namely the input value of the encoder, z is the extracted characteristic,
is the output value of the decoder. And when the loss function is the minimum value, the corresponding encoder parameter is the optimized encoder parameter, namely the parameter of the feature extraction model. Optionally, a layer-by-layer greedy training method is used to train parameters of the feature extraction model.
When the encoder model is a stacked self-encoder model, the second training unit 42 is specifically configured to extract the features of the training samples through the optimized feature extraction model; inputting the extracted features of the training samples into the classifier model to output a classification result; and optimizing parameters of the optimized feature extraction model and parameters of the classifier model according to the classification result and the cerebral blood flow regulator of the training sample. Optionally, the classifier model is a single hidden layer feedforward neural network classifier, where the loss function is:
L＝-Y*logh-(1-Y)*log(1-h) Wherein Y represents the cerebral blood flow regulation function of the training sample and is a probability value hRepresenting the probability values of the classifier model outputs. The probability value corresponds to the cerebral blood flow regulation function, for example, the probability greater than 50% is normal, and the probability less than or equal to 50% is abnormal. Specifically, the features of the training samples extracted by the optimized feature extraction model are input into the classifier, and the corresponding encoder parameters and classifier parameters when the loss function L is the minimum value are respectively used as the parameters of the optimized feature extraction model and the parameters of the optimized classifier model.
In another possible implementation, the encoder model is a variational self-encoder model, which is different from the previous possible implementation in that the first training unit 41 sets a loss function as:
L＝IEz～Q[logP(X|z)]+DKL[Q(z|X)||P(z)]where P (X | z) denotes the output probability distribution function of the decoder, P (z) denotes the prior probability of the features extracted by the encoder, obeying a normal distribution, Q (z | X) denotes the conditional probability of the input values of the encoder, IEz～QRepresenting the desired operation of successive probability distributions, DKLRepresenting the divergence calculation. During the training process of the classifier model, the second training unit 42 sets the loss function as:
L＝-Y*logh-(1-Y)*log(1-h)+DKL[Q(z|X)||P(z)]。
as shown in fig. 2, a schematic diagram of a feature extraction model and a classifier model is shown, where a denotes an input value of an encoder, B denotes an extracted feature, and C denotes an output probability of a classifier. Combining the trained feature extraction model and the classifier model to obtain a regulation function evaluation model, sequentially passing the incidence relation between the blood pressure and the cerebral blood flow speed corresponding to the evaluation sample to be tested through the trained encoder model and the classifier model, and judging the cerebral blood flow regulation function according to the output probability, wherein the output probability is normal when more than 50 percent and abnormal when less than or equal to 50 percent, and meanwhile, the output probability can also represent the probability that the cerebral blood flow regulation function is abnormal.
In the above embodiment, the obtaining module obtains the blood pressure and the cerebral blood flow velocity in the preset time period, and the calculating module calculates the association relationship between the blood pressure and the cerebral blood flow velocity according to the blood pressure and the cerebral blood flow velocity in the preset time period, so that the complete variable of the cerebral blood flow regulation function can be obtained, and the accuracy of the evaluation result is ensured; the evaluation module inputs the incidence relation into the regulatory function evaluation model, so that the corresponding cerebral blood flow regulatory function can be output, and the efficiency is high; and the regulation function evaluation model is obtained by training the classification model through a training sample by the model training module, so that the evaluation of the cerebral blood flow regulation function has universality.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An apparatus for evaluating a cerebral blood flow regulation function, comprising:
the acquisition module is used for acquiring blood pressure and cerebral blood flow velocity in a preset time period;
the calculation module is used for calculating the association relationship between the blood pressure and the cerebral blood flow velocity according to the blood pressure and the cerebral blood flow velocity in the preset time period, wherein the association relationship comprises a first relation expression of the blood pressure and the cerebral blood flow and a second relation expression of the blood pressure and the cerebral blood flow, the first relation expression of the blood pressure and the cerebral blood flow is used for representing the linear relation between the blood pressure and the blood flow, and the second relation expression of the blood pressure and the cerebral blood flow is used for representing the nonlinear relation between the blood pressure and the blood flow;
and the evaluation module is used for inputting the incidence relation into a regulation function evaluation model so as to output the corresponding cerebral blood flow regulation function, wherein the regulation function evaluation model is generated by a model training module, the model training module is used for acquiring a training sample, and training a classification model according to the training sample so as to obtain the regulation function evaluation model, and the training sample comprises the incidence relation between blood pressure and cerebral blood flow speed and the corresponding cerebral blood flow regulation function.
2. The cerebral blood flow regulation function evaluation device according to claim 1, wherein the calculation module includes:
the first calculation unit is used for calculating a first relational expression of the blood pressure and the cerebral blood flow according to the blood pressure and the cerebral blood flow velocity in the preset time period;
and the second calculation unit is used for calculating a second relational expression of the blood pressure and the cerebral blood flow according to the blood pressure and the cerebral blood flow velocity in the preset time period.
3. The cerebral blood flow regulation function evaluation device according to claim 2, wherein the first calculation unit is specifically configured to:
calculating corresponding transfer functions of the blood pressure and the cerebral blood flow according to the blood pressure and the cerebral blood flow velocity;
the gain and phase of the transfer function are calculated.
4. The cerebral blood flow regulation function evaluation device according to claim 2, wherein the first calculation unit is further specifically configured to:
and calculating a coherent function of the blood pressure and the cerebral blood flow velocity according to the blood pressure power spectrum and the cerebral blood flow power spectrum.
5. The cerebral blood flow regulation function evaluation device according to claim 2, wherein the second calculation unit is specifically configured to:
establishing a series model of blood pressure and cerebral blood flow velocity;
fitting coefficients of the series model according to the blood pressure and the corresponding cerebral blood flow velocity;
and generating an optimized series model according to the coefficient of the fitted series model.
6. The cerebral blood flow regulation function evaluation device according to claim 1, wherein the classification model includes a feature extraction model and a classifier model, and the model training module includes a first training unit and a second training unit;
the first training unit is used for training the feature extraction model to obtain an optimized feature extraction model;
the second training unit is used for training the optimized feature extraction model and the classifier model.
7. The cerebral blood flow regulation function evaluation device according to claim 6, wherein the feature extraction model is an encoder model, and the first training unit is specifically configured to:
constructing an encoder and a decoder of an encoder model;
extracting the characteristics of the incidence relation between the blood pressure and the cerebral blood flow speed of the training sample through the encoder;
inputting the extracted features of the incidence relation into a decoder to obtain an output value of the decoder;
optimizing the parameters of the encoder and the parameters of the decoder according to the output value of the decoder and the incidence relation between the blood pressure and the cerebral blood flow speed of the training sample;
and taking the encoder model corresponding to the optimized parameters of the encoder as an optimized feature extraction model.
8. The cerebral blood flow regulation function evaluation device according to claim 7, wherein the encoder model is a stacked self-encoder model or a variational self-encoder model.
9. The cerebral blood flow regulation function evaluation device according to claim 6, wherein the second training unit is specifically configured to:
extracting the characteristics of the training sample through the optimized characteristic extraction model;
inputting the extracted features of the training samples into the classifier model to output a classification result;
and optimizing parameters of the optimized feature extraction model and parameters of the classifier model according to the classification result and the cerebral blood flow regulator of the training sample.
10. The cerebral blood flow regulation function evaluation device according to claim 9, wherein the classifier model is a single hidden layer feedforward neural network classifier.
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