CN112927803A - Consciousness assessment method and system for cerebrovascular patient - Google Patents
Consciousness assessment method and system for cerebrovascular patient Download PDFInfo
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G—PHYSICS
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The invention discloses a method and a system for assessing consciousness of a cerebrovascular patient, wherein the method comprises the following steps: inputting, by the central control module, the set of respiratory information data, the set of pain stimulus reflectance signal data, and the set of consciousness information data into the multivariate linear regression model to obtain a first result, the first result comprising a first coefficient, a second coefficient, and a third coefficient; obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model; and inputting the first respiratory information, the first pain stimulation reflection signal and the first consciousness information into the consciousness assessment model to obtain a first consciousness assessment result. The technical problem that in the prior art, consciousness assessment of a cerebrovascular patient is inaccurate, and later treatment rehabilitation is influenced is solved.
Description
Technical Field
The invention relates to the field of consciousness assessment, in particular to a method and a system for assessing consciousness of a cerebrovascular patient.
Background
Consciousness assessment is the main content of clinical diagnosis and treatment activities of cerebrovascular doctors, and is mostly based on behaviors or observation, namely whether a patient has repeatable, directional and autonomous behavior ability for various stimuli is judged through repeated examination, but due to the special state of the patient, the accurate assessment of the consciousness of the patient has certain difficulty.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the prior art has the technical problem that consciousness of a cerebrovascular patient is inaccurately evaluated, and then later treatment rehabilitation is influenced.
Disclosure of Invention
The embodiment of the application provides a method and a system for assessing consciousness of a cerebrovascular patient, solves the technical problem that in the prior art, consciousness assessment of the cerebrovascular patient is inaccurate, and further later treatment rehabilitation is influenced, achieves the purpose of performing consciousness assessment on the patient through the significance of combination of a coefficient threshold and a multiple linear regression model, improves the accuracy of assessment results, and provides a value reference technical effect for later treatment of the patient.
In view of the foregoing problems, embodiments of the present application provide a method and a system for assessing consciousness of a cerebrovascular patient.
In a first aspect, the present application provides a method for assessing consciousness of a cerebrovascular patient, the method including: constructing a user data set; obtaining a respiratory information data set of all users in the user data set through the respiratory monitoring module; obtaining, by the pain stimulus reflex module, a pain stimulus reflex signal dataset for all users in the user dataset; acquiring consciousness information data sets of all users in the user data sets through the consciousness monitoring module; constructing a multiple linear regression model; obtaining an initial consciousness assessment result data set of all users in the user data set; inputting the respiration information data set, the pain stimulus reflectance signal data set and the consciousness information data set into the multiple linear regression model through the central control module, taking the initial consciousness assessment result data set as output data, and training the multiple linear regression model to obtain a first result, wherein the first result comprises a first coefficient, a second coefficient and a third coefficient; obtaining a first accuracy influence degree; determining a first coefficient threshold, a second coefficient threshold and a third coefficient threshold according to the first accuracy influence degree and the first coefficient, the second coefficient and the third coefficient; obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model; judging whether the consciousness evaluation model is remarkably regressed; if the consciousness evaluation model is remarkably regressed, obtaining first respiratory information, a first pain stimulation reflection signal and first consciousness information of a first user; and inputting the first respiratory information, the first pain stimulation reflection signal and the first consciousness information into the consciousness assessment model to obtain a first consciousness assessment result.
In another aspect, the present application also provides a cerebrovascular patient consciousness assessment system, the system comprising: a first construction unit for constructing a user data set; a first obtaining unit, configured to obtain, by the respiration monitoring module, respiration information datasets of all users in the user dataset; a second obtaining unit for obtaining a pain stimulus reflex signal dataset of all users in the user dataset by the pain stimulus reflex module; a third obtaining unit, configured to obtain, by the consciousness monitoring module, consciousness information datasets of all users in the user dataset; a second construction unit for constructing a multiple linear regression model; a fourth obtaining unit, configured to obtain an initial consciousness assessment result dataset of all users in the user dataset; a fifth obtaining unit, configured to input the respiration information data set, the pain stimulus reflection signal data set, and the consciousness information data set into the multiple linear regression model through the central control module, train the multiple linear regression model using the initial consciousness assessment result data set as output data, and obtain a first result, where the first result includes a first coefficient, a second coefficient, and a third coefficient; a sixth obtaining unit configured to obtain a first accuracy influence degree; a first determination unit configured to determine a first coefficient threshold, a second coefficient threshold, and a third coefficient threshold from the first accuracy influencing amount and the first coefficient, the second coefficient, and the third coefficient; a seventh obtaining unit, configured to obtain a consciousness assessment model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold, and the trained multiple linear regression model; a first judging unit configured to judge whether the consciousness assessment model is regression-significant; an eighth obtaining unit, configured to obtain first respiratory information, a first pain stimulus reflex signal, and first consciousness information of the first user if the consciousness assessment model regresses significantly; a ninth obtaining unit configured to input the first respiration information, the first pain stimulus reflection signal, and the first consciousness information into the consciousness assessment model, and obtain a first consciousness assessment result.
In a third aspect, the present invention provides a cerebrovascular patient consciousness assessment system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
obtaining a first result as a result of using the multiple linear regression model to input the respiratory information dataset, the pain stimulus reflectance signal dataset, and the awareness information dataset into the central control module, the first result comprising a first coefficient, a second coefficient, and a third coefficient; obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model; the first respiratory information, the first pain stimulation reflection signal and the first consciousness information are input into the consciousness assessment model, a first consciousness assessment result is obtained, and then the consciousness assessment of the patient is carried out through the combination of the coefficient threshold and the multiple linear regression model, the accuracy of the assessment result is improved, and the technical effect of value reference is provided for the later treatment of the patient.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for assessing consciousness of a cerebrovascular patient according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a system for assessing consciousness of cerebrovascular patients according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first constructing unit 11, a first obtaining unit 12, a second obtaining unit 13, a third obtaining unit 14, a second constructing unit 15, a fourth obtaining unit 16, a fifth obtaining unit 17, a sixth obtaining unit 18, a first determining unit 19, a seventh obtaining unit 20, a first judging unit 21, an eighth obtaining unit 22, a ninth obtaining unit 23, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the application provides a system and a method for assessing consciousness of a cerebrovascular patient, solves the technical problem that in the prior art, consciousness assessment of the cerebrovascular patient is inaccurate, and further later treatment rehabilitation is influenced, achieves the purpose of performing consciousness assessment on the patient through the significance of combination of a coefficient threshold and a multiple linear regression model, improves the accuracy of assessment results, and provides a value reference technical effect for later treatment of the patient. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Consciousness assessment is the main content of clinical diagnosis and treatment activities of cerebrovascular doctors, and is mostly based on behaviors or observation, namely whether a patient has repeatable, directional and autonomous behavior ability for various stimuli is judged through repeated examination, but due to the special state of the patient, the accurate assessment of the consciousness of the patient has certain difficulty. But the prior art has the technical problem that the consciousness of the cerebrovascular patient is inaccurately evaluated, and the later treatment rehabilitation is influenced.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a consciousness assessment method for a cerebrovascular patient, which comprises the following steps: constructing a user data set; obtaining a respiratory information data set of all users in the user data set through the respiratory monitoring module; obtaining, by the pain stimulus reflex module, a pain stimulus reflex signal dataset for all users in the user dataset; acquiring consciousness information data sets of all users in the user data sets through the consciousness monitoring module; constructing a multiple linear regression model; obtaining an initial consciousness assessment result data set of all users in the user data set; inputting the respiration information data set, the pain stimulus reflectance signal data set and the consciousness information data set into the multiple linear regression model through the central control module, taking the initial consciousness assessment result data set as output data, and training the multiple linear regression model to obtain a first result, wherein the first result comprises a first coefficient, a second coefficient and a third coefficient; obtaining a first accuracy influence degree; determining a first coefficient threshold, a second coefficient threshold and a third coefficient threshold according to the first accuracy influence degree and the first coefficient, the second coefficient and the third coefficient; obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model; judging whether the consciousness evaluation model is remarkably regressed; if the consciousness evaluation model is remarkably regressed, obtaining first respiratory information, a first pain stimulation reflection signal and first consciousness information of a first user; and inputting the first respiratory information, the first pain stimulation reflection signal and the first consciousness information into the consciousness assessment model to obtain a first consciousness assessment result.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a method for assessing consciousness of a cerebrovascular patient, wherein the method includes:
step S100: constructing a user data set;
specifically, the user data set is a cerebrovascular patient set which needs consciousness assessment, and refers to various diseases of cerebral vessels, including cerebral atherosclerosis, thrombosis, stenosis, occlusion, cerebral arteritis, cerebral artery injury, cerebral aneurysm, intracranial vascular malformation, cerebral arteriovenous fistula and the like, and the patient data set is constructed by patients with cerebral ischemia or hemorrhagic accidents.
Step S200: obtaining a respiratory information data set of all users in the user data set through the respiratory monitoring module;
step S300: obtaining, by the pain stimulus reflex module, a pain stimulus reflex signal dataset for all users in the user dataset;
specifically, the respiration monitoring module is a module capable of accurately measuring parameter information such as the user respiration volume and the respiration frequency, and the respiration information data set of all patients in the user data set is obtained through the respiration monitoring module. The pain stimulus reflex module is a reverse response of a stimulated object to the stimulated object, namely a reverse response test module of a patient to the pain stimulus, and is a natural phenomenon, the pain stimulus reflex of the patient is a regular response of an organism to the stimulation of internal and external environments under the participation of a central nervous system, and a pain stimulus reflex signal data set of all users in the user data set is obtained through the pain stimulus reflex module.
Step S400: acquiring consciousness information data sets of all users in the user data sets through the consciousness monitoring module;
specifically, the consciousness monitoring module is a module for detecting the cognition and perception abilities of the user to the surrounding environment and the self state, and is a comprehensive expression of the functional activities of the higher nerve center of the brain, the consciousness activities mainly comprise five aspects of cognition, thinking, emotion, memory and orientation force, the consciousness disturbance can be represented as somnolence, lethargy and coma according to the consciousness clearness degree and the consciousness disturbance range of the user, such as the consciousness disturbance of consciousness, the content of the consciousness disturbance is represented as the status of confusion and delirium, and further different consciousness expressions are provided, and the consciousness information data set of all patients in the user data set is obtained through the consciousness monitoring module.
Step S500: constructing a multiple linear regression model;
further, in the step S500 of constructing the multiple linear regression model in the embodiment of the present application, specifically:
wherein x is1Representing first respiratory information; x is the number of2Representing a first pain stimulus reflectance signal; x is the number of3Representing first awareness information;
Specifically, the formula is a multiple linear regression model, the variation of the dependent variable is often influenced by several important factors, at this time, two or more influencing factors are required to be used as independent variables to explain the variation of the dependent variable, which is the multiple linear regression model, and when the multiple independent variables and the dependent variables are in linear relation, the regression analysis is the multiple linear regression. Wherein, beta1，β2，β3Is a constant number, x1Representing first respiratory information; x is the number of2Representing a first pain stimulus reflectance signal; x is the number of3The first consciousness information is represented, the regression model has three influencing factor independent variables, the formula is specifically positioned in the ternary linear regression model, consciousness evaluation is carried out on the patient through the multivariate linear regression model, and the technical effect of ensuring that the regression model has excellent interpretability and prediction is achieved.
Step S600: obtaining an initial consciousness assessment result data set of all users in the user data set;
step S700: inputting the respiration information data set, the pain stimulus reflectance signal data set and the consciousness information data set into the multiple linear regression model through the central control module, taking the initial consciousness assessment result data set as output data, and training the multiple linear regression model to obtain a first result, wherein the first result comprises a first coefficient, a second coefficient and a third coefficient;
specifically, the initial consciousness assessment result data set is the result of initial consciousness assessment of all users in the user data set by clinical technical means such as scale assessment and neuroimaging, and the respiration information data set, the pain stimulus reflex signal data set and the consciousness information data set are input into the multiple linear regression model through the central control module, i.e. the module for centralized management and control, which is x in the regression model1、x2And x3Taking the initial consciousness assessment result data set as output data, namely output Y values, training the multiple linear regression model to respectively obtain the trained dataResulting in a first coefficient beta1Second coefficient of2And a third coefficient beta3The method achieves the technical effects of determining the coefficients of the multiple linear regression model and ensuring the regression model to be more accurate.
Step S800: obtaining a first accuracy influence degree;
further, in step S800 of obtaining the first accuracy influence degree, the method further includes:
step S810: obtaining a first assessment context of a first consciousness assessment result;
step S820: analyzing the first evaluation environment to obtain a first interference factor;
step S830: obtaining a first accuracy influence degree of the first interference factor on the first consciousness assessment result.
Specifically, the first evaluation environment of the first consciousness assessment result is a site state condition under which the consciousness of the user is assessed, the first interference factor is a factor which can interfere with the evaluation result after the first evaluation environment is analyzed, such as the gas mobility of the environment, the intensity of light, the magnetic field, the air humidity and temperature, and noise, the first accuracy influencing degree is the influence degree of the first interference factor on the first consciousness assessment result, if the relative humidity suitable for human life is in the range of 30-75%, if the humidity is too high, people can feel suffocating and suffocating, and if the humidity is too low, people can feel dry and uncomfortable in the oral cavity and nostrils, therefore, the evaluation result is influenced, and the technical effect that the influence of environmental factors on the consciousness evaluation of the patient is considered is achieved, so that the evaluation result is more accurate.
Step S900: determining a first coefficient threshold, a second coefficient threshold and a third coefficient threshold according to the first accuracy influence degree and the first coefficient, the second coefficient and the third coefficient;
further, wherein a first coefficient threshold, a second coefficient threshold, and a third coefficient threshold are determined according to the first accuracy influencing degree and the first coefficient, the second coefficient, and the third coefficient, and step S900 in this embodiment of the present application further includes:
step S910: taking the first coefficient as first input information;
step S920: taking the second coefficient as second input information;
step S930: taking the third coefficient as third input information;
step S940: taking the first accuracy influence degree as fourth input information;
step S950: respectively taking the first input information, the second input information, the third input information and the fourth input information as input information, inputting a threshold estimation model for training, wherein the threshold estimation model is obtained by training multiple groups of training data, and each group of data in the multiple groups of training data comprises: one of the first input information, the second input information, the third input information, and the fourth input information and identification information to identify a second result;
step S960: obtaining output information of the threshold estimation model, the output information including a second result, the second result including one of the first coefficient threshold, the second coefficient threshold, and the third coefficient threshold.
Specifically, the threshold estimation model is a Neural network model, i.e., a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), which reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (ANN), is a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the first input information and the fourth input information into a neural network model through training of a large amount of training data, and outputting a second result, wherein the second result comprises the first coefficient threshold.
More specifically, the training process is essentially a supervised learning process, each group of supervised data includes the first input information, the fourth input information, and identification information for identifying a second result, the first input information and the fourth input information are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information for identifying the second result, and the group of supervised learning is ended until the obtained second result is consistent with the identification information, and the next group of supervised learning is performed; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. And similarly, inputting the second input information, the third input information and the fourth input information into a neural network model respectively, and outputting the second coefficient threshold value and the third coefficient threshold value. Through supervised learning of the neural network model, the neural network model can process the input information more accurately, the output coefficient threshold value is more reasonable and accurate, and the technical effects of enabling the coefficient threshold value to be more accurate and improving the accuracy of an evaluation result are achieved.
Step S1000: obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model;
further, in an embodiment of the present invention, the consciousness assessment model is obtained according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold, and the trained multiple linear regression model, and step S1000 in the embodiment of the present application specifically includes:
wherein x is1Representing first respiratory information; x is the number of2Representing a first pain stimulus reflectance signal; x is the number of3Representing first awareness information;
β’1，β’2，β’3respectively representing a first coefficient threshold, a second coefficient threshold, and a third coefficient threshold.
Specifically, the consciousness assessment model is a multivariate linear regression model, wherein β'1Is a first coefficient threshold value, beta'2Is a second coefficient threshold value, beta'3Is a third coefficient threshold, x1Representing first respiratory information; x is the number of2Representing a first pain stimulus reflectance signal; x is the number of3And expressing first consciousness information, namely the regression model has three influencing factor independent variables, Y is the user consciousness assessment result, and the specific position of the formula is the ternary linear regression model, so that consciousness assessment is performed on the patient through the multivariate linear regression model, and the technical effect of ensuring that the regression model has excellent interpretation capability and prediction is achieved.
Step S1100: judging whether the consciousness evaluation model is remarkably regressed;
further, in step S1100 of the embodiment of the present application, the determining whether the consciousness assessment model regresses significantly further includes:
step S1110: obtaining a predetermined coefficient threshold;
step S1120: judging whether the first coefficient threshold, the second coefficient threshold and the third coefficient threshold all accord with the preset coefficient threshold;
step S1130: if the first coefficient threshold, the second coefficient threshold and the third coefficient threshold all accord with the preset coefficient threshold, determining that the consciousness assessment model does not regress significantly;
step S1140: and if the first coefficient threshold, the second coefficient threshold and the third coefficient threshold do not all accord with the preset coefficient threshold, determining that the consciousness evaluation model is remarkably regressed.
Specifically, the predetermined coefficient threshold bit is a predetermined coefficient rangeIf the coefficient grade of the consciousness evaluation model is set to be between 0 and 1, if all the first coefficient threshold value, the second coefficient threshold value and the third coefficient threshold value accord with the preset coefficient threshold value, namely beta'1，β′2，β′3And within the preset coefficient threshold value, the consciousness evaluation model does not regress significantly, and the data information is relatively discrete at the moment, so that the consciousness evaluation model data are not linearly related. If the first coefficient threshold value, the second coefficient threshold value and the third coefficient threshold value do not all accord with the preset coefficient threshold value, namely beta'1，β′2，β’3And if the data of the consciousness evaluation model is not all within the preset coefficient threshold, the consciousness evaluation model is remarkably regressed, and the data of the consciousness evaluation model are linearly related and have influence on a Y value, namely the consciousness evaluation result of the user, and the model is meaningfully available, so that the significance of the evaluation model is tested by determining the coefficient threshold, and the consciousness evaluation model is ensured to have excellent interpretability and prediction technical effects.
Step S1200: if the consciousness evaluation model is remarkably regressed, obtaining first respiratory information, a first pain stimulation reflection signal and first consciousness information of a first user;
step S1300: and inputting the first respiratory information, the first pain stimulation reflection signal and the first consciousness information into the consciousness assessment model to obtain a first consciousness assessment result.
In particular, if the consciousness assessment model regresses significantly, indicating that the consciousness assessment model data is linearly related, the first respiratory information x of the user is applied1The first pain stimulus reflex signal x2And the first consciousness information x3Inputting the first consciousness assessment result Y value into the consciousness assessment model. And then the consciousness of the patient is evaluated through the significance of the combination of the coefficient threshold and the multiple linear regression model, the accuracy of the evaluation result is improved, and the technical effect of providing value reference for the later treatment of the patient is achieved.
Further, after obtaining the first accuracy influence of the first interference factor on the first consciousness assessment result, step S830 in this embodiment of the present application further includes:
step S831: judging whether the first accuracy influence degree exceeds a preset threshold value or not;
step S832: if the first accuracy influence exceeds the predetermined threshold, obtaining a second evaluation environment, wherein the second accuracy influence of a second interference factor of the second evaluation environment on the first consciousness assessment result is lower than the first accuracy influence of the first interference factor of the first evaluation environment on the first consciousness assessment result;
step S833: and acquiring first reminding information according to the second evaluation environment, wherein the first reminding information is used for reminding the first user to perform consciousness evaluation in the second evaluation environment.
Specifically, the predetermined threshold is a preset critical value of the influence degree of the disturbance factor on the consciousness assessment result, the second assessment environment is a place state condition when the consciousness of the user is assessed, may be a nearby place where the interference factor is smaller than the first interference factor of the first evaluation environment, obtaining the second evaluation environment when the first accuracy impact exceeds the predetermined threshold, the first reminding information is used for reminding the first user of consciousness assessment in the second assessment environment, namely, consciousness assessment is carried out on the user in the environment with proper influence factors such as gas mobility, light intensity, air humidity, temperature, noise and the like, so that the influence of the environment factors on the consciousness assessment of the patient is reduced, and the technical effect of enabling the assessment result to be more accurate is achieved.
Further, after obtaining the second evaluation environment if the first accuracy influence exceeds the predetermined threshold, step S832 according to this embodiment of the present application further includes:
step S3821: judging whether the second evaluation environment is vacant;
step S3822: if the second evaluation environment is vacant, obtaining a first route information set;
step S3823: obtaining first transfer route information according to the first route information set, wherein the transfer route information is that the distance in the first route information set is shortest;
step S3824: judging whether an interfering object exists in the first transfer route information or not;
step S3825: determining the transfer route information if no interferent is present in the first transfer route information;
specifically, whether the second evaluation environment is vacant is judged, if the vacancy is available, the user can be transferred, the obtained first route information set is a route set for transferring the user to the second evaluation environment, the first transfer route information is the shortest route in the first route information set, the interfering object is a factor which cannot pass through the first transfer route due to interference, and if the first transfer route is not provided with the interfering object, the first transfer route information is determined, which indicates that the user can be subjected to transfer evaluation through the route, so that the technical effects of determining the transfer route in advance and carrying out consciousness evaluation on the patient in a proper environment are achieved.
In summary, the method and the system for assessing consciousness of cerebrovascular patients provided by the embodiment of the present application have the following technical effects:
1. obtaining a first result as a result of using the multiple linear regression model to input the respiratory information dataset, the pain stimulus reflectance signal dataset, and the awareness information dataset into the central control module, the first result comprising a first coefficient, a second coefficient, and a third coefficient; obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model; the first respiratory information, the first pain stimulation reflection signal and the first consciousness information are input into the consciousness assessment model, a first consciousness assessment result is obtained, and then the consciousness assessment of the patient is carried out through the combination of the coefficient threshold and the multiple linear regression model, the accuracy of the assessment result is improved, and the technical effect of value reference is provided for the later treatment of the patient.
2. Due to the fact that the mode that the coefficient and the first accuracy influence degree are input into the neural network model is adopted, the neural network model can process the input information more accurately, the output coefficient threshold value is more reasonable and accurate, and the technical effects that the coefficient threshold value is more accurate and the accuracy of the evaluation result is improved are achieved.
3. Because the coefficient of the linear regression model is designed in a complicated way and becomes a coefficient threshold value, consciousness evaluation is carried out on the patient in a way of combining the threshold value and the trained model, and the significance of the evaluation model is checked by determining the coefficient threshold value, so that the consciousness evaluation model is ensured to have excellent interpretability and predictive technical effect.
Example two
Based on the same inventive concept as the method for assessing consciousness of cerebrovascular patients in the foregoing embodiment, the present invention further provides a system for assessing consciousness of cerebrovascular patients, as shown in fig. 2, the system comprising:
a first construction unit 11, said first construction unit 11 being configured to construct a user data set;
a first obtaining unit 12, where the first obtaining unit 12 is configured to obtain, through a respiration monitoring module, respiration information data sets of all users in the user data set;
a second obtaining unit 13, wherein the second obtaining unit 13 is configured to obtain the pain stimulus reflection signal data sets of all users in the user data set through a pain stimulus reflection module;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain, through an awareness monitoring module, awareness information data sets of all users in the user data set;
a second constructing unit 15, wherein the second constructing unit 15 is used for constructing a multiple linear regression model;
a fourth obtaining unit 16, wherein the fourth obtaining unit 16 is configured to obtain an initial consciousness assessment result dataset of all users in the user dataset;
a fifth obtaining unit 17, configured to input the respiration information data set, the pain stimulus reflection signal data set, and the consciousness information data set into the multiple linear regression model through a central control module, train the multiple linear regression model using the initial consciousness assessment result data set as output data, and obtain a first result, where the first result includes a first coefficient, a second coefficient, and a third coefficient;
a sixth obtaining unit 18, where the sixth obtaining unit 18 is configured to obtain the first degree of accuracy influence;
a first determining unit 19, configured to determine a first coefficient threshold, a second coefficient threshold, and a third coefficient threshold according to the first accuracy influencing amount and the first coefficient, the second coefficient, and the third coefficient;
a seventh obtaining unit 20, where the seventh obtaining unit 20 is configured to obtain a consciousness assessment model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold, and the trained multiple linear regression model;
a first judging unit 21, configured to judge whether the consciousness assessment model is regression-significant or not;
an eighth obtaining unit 22, wherein the eighth obtaining unit 22 is configured to obtain the first respiration information, the first pain stimulus reflection signal and the first consciousness information of the first user if the consciousness assessment model regresses significantly;
a ninth obtaining unit 23, where the ninth obtaining unit 23 is configured to input the first respiration information, the first pain stimulus reflection signal, and the first consciousness information into the consciousness assessment model, and obtain a first consciousness assessment result.
Further, the system further comprises:
a tenth obtaining unit for obtaining a first evaluation environment of the first consciousness evaluation result;
an eleventh obtaining unit, configured to analyze the first evaluation environment to obtain a first interference factor;
a twelfth obtaining unit, configured to obtain a first accuracy influence degree of the first disturbance factor on the first consciousness assessment result.
Further, the system further comprises:
a second determination unit configured to determine whether the first accuracy influence exceeds a predetermined threshold;
a thirteenth obtaining unit configured to obtain a second evaluation environment if the first accuracy influence exceeds the predetermined threshold, wherein a second accuracy influence of a second disturbance factor of the second evaluation environment on the first consciousness assessment result is lower than a first accuracy influence of the first disturbance factor of the first evaluation environment on the first consciousness assessment result;
a fourteenth obtaining unit, configured to obtain first reminding information according to the second evaluation environment, where the first reminding information is used to remind the first user of performing consciousness evaluation in the second evaluation environment.
Further, the system further comprises:
a first as unit for taking the first coefficient as first input information;
a second as unit for taking the second coefficient as second input information;
a third acting unit configured to take the third coefficient as third input information;
a fourth as unit for taking the first accuracy influence as fourth input information;
a first input unit, configured to input a threshold estimation model to be trained by using the first input information, the second input information, the third input information, and the fourth input information as input information, respectively, where the threshold estimation model is obtained by training multiple sets of training data, and each set of data in the multiple sets of training data includes: one of the first input information, the second input information, the third input information, and the fourth input information and identification information to identify a second result;
a fifteenth obtaining unit configured to obtain output information of the threshold estimation model, the output information including a second result, the second result including one of the first coefficient threshold, the second coefficient threshold, and the third coefficient threshold.
Further, the system further comprises:
a sixteenth obtaining unit configured to obtain a predetermined coefficient threshold;
a third determining unit, configured to determine whether all of the first coefficient threshold, the second coefficient threshold, and the third coefficient threshold meet the predetermined coefficient threshold;
a second determination unit, configured to determine that the consciousness assessment model regression is not significant if all of the first coefficient threshold, the second coefficient threshold, and the third coefficient threshold match the predetermined coefficient threshold;
a third determination unit, configured to determine that the consciousness assessment model regresses significantly if all of the first coefficient threshold, the second coefficient threshold, and the third coefficient threshold do not meet the predetermined coefficient threshold.
Various changes and embodiments of the cerebrovascular patient consciousness assessment method in the first embodiment of FIG. 1 are also applicable to the cerebrovascular patient consciousness assessment system of the present embodiment, and those skilled in the art can clearly understand the implementation method of the cerebrovascular patient consciousness assessment system in the present embodiment through the foregoing detailed description of the cerebrovascular patient consciousness assessment method, so for the sake of brevity of the description, detailed descriptions thereof are omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of a method for assessing cerebrovascular consciousness of a cerebrovascular patient as in the previous embodiment, the present invention further provides a system for assessing cerebrovascular patient consciousness, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the computer program implements the steps of any one of the methods for assessing cerebrovascular patient consciousness as described above.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides a consciousness assessment method for a cerebrovascular patient, which comprises the following steps: constructing a user data set; obtaining a respiratory information data set of all users in the user data set through the respiratory monitoring module; obtaining, by the pain stimulus reflex module, a pain stimulus reflex signal dataset for all users in the user dataset; acquiring consciousness information data sets of all users in the user data sets through the consciousness monitoring module; constructing a multiple linear regression model; obtaining an initial consciousness assessment result data set of all users in the user data set; inputting the respiration information data set, the pain stimulus reflectance signal data set and the consciousness information data set into the multiple linear regression model through the central control module, taking the initial consciousness assessment result data set as output data, and training the multiple linear regression model to obtain a first result, wherein the first result comprises a first coefficient, a second coefficient and a third coefficient; obtaining a first accuracy influence degree; determining a first coefficient threshold, a second coefficient threshold and a third coefficient threshold according to the first accuracy influence degree and the first coefficient, the second coefficient and the third coefficient; obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model; judging whether the consciousness evaluation model is remarkably regressed; if the consciousness evaluation model is remarkably regressed, obtaining first respiratory information, a first pain stimulation reflection signal and first consciousness information of a first user; and inputting the first respiratory information, the first pain stimulation reflection signal and the first consciousness information into the consciousness assessment model to obtain a first consciousness assessment result. The technical problem that consciousness assessment of a cerebrovascular patient is inaccurate and later treatment rehabilitation is influenced in the prior art is solved, the consciousness assessment of the patient is achieved through the significance of combination of the coefficient threshold and the multiple linear regression model, the accuracy of an assessment result is improved, and a value reference is provided for later treatment of the patient.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method for assessing consciousness of a cerebrovascular patient, wherein the method is applied to an intelligent wearable device, the device comprises a pain stimulus reflection module, a consciousness monitoring module, a respiration monitoring module and a central control module, wherein the pain stimulus reflection module, the consciousness monitoring module and the respiration monitoring module are in communication connection with the central control module, and the method comprises the following steps:
constructing a user data set;
obtaining a respiratory information data set of all users in the user data set through the respiratory monitoring module;
obtaining, by the pain stimulus reflex module, a pain stimulus reflex signal dataset for all users in the user dataset;
acquiring consciousness information data sets of all users in the user data sets through the consciousness monitoring module;
constructing a multiple linear regression model;
obtaining an initial consciousness assessment result data set of all users in the user data set;
inputting the respiration information data set, the pain stimulus reflectance signal data set and the consciousness information data set into the multiple linear regression model through the central control module, taking the initial consciousness assessment result data set as output data, and training the multiple linear regression model to obtain a first result, wherein the first result comprises a first coefficient, a second coefficient and a third coefficient;
obtaining a first accuracy influence degree;
determining a first coefficient threshold, a second coefficient threshold and a third coefficient threshold according to the first accuracy influence degree and the first coefficient, the second coefficient and the third coefficient;
obtaining a consciousness evaluation model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model;
judging whether the consciousness evaluation model is remarkably regressed;
if the consciousness evaluation model is remarkably regressed, obtaining first respiratory information, a first pain stimulation reflection signal and first consciousness information of a first user;
and inputting the first respiratory information, the first pain stimulation reflection signal and the first consciousness information into the consciousness assessment model to obtain a first consciousness assessment result.
2. The method of claim 1, wherein said obtaining a first accuracy affecting amount comprises:
obtaining a first assessment context of a first consciousness assessment result;
analyzing the first evaluation environment to obtain a first interference factor;
obtaining a first accuracy influence degree of the first interference factor on the first consciousness assessment result.
3. The method of claim 2, wherein said obtaining a first accuracy impact of the first interference factor on the first consciousness assessment result comprises:
judging whether the first accuracy influence degree exceeds a preset threshold value or not;
if the first accuracy influence exceeds the predetermined threshold, obtaining a second evaluation environment, wherein the second accuracy influence of a second interference factor of the second evaluation environment on the first consciousness assessment result is lower than the first accuracy influence of the first interference factor of the first evaluation environment on the first consciousness assessment result;
and acquiring first reminding information according to the second evaluation environment, wherein the first reminding information is used for reminding the first user to perform consciousness evaluation in the second evaluation environment.
4. The method of claim 1, wherein the determining a first coefficient threshold, a second coefficient threshold, and a third coefficient threshold from the first accuracy influencing quantity and the first coefficient, the second coefficient, and the third coefficient comprises:
taking the first coefficient as first input information;
taking the second coefficient as second input information;
taking the third coefficient as third input information;
taking the first accuracy influence degree as fourth input information;
respectively taking the first input information, the second input information, the third input information and the fourth input information as input information, inputting a threshold estimation model for training, wherein the threshold estimation model is obtained by training multiple groups of training data, and each group of data in the multiple groups of training data comprises: one of the first input information, the second input information, the third input information, and the fourth input information and identification information to identify a second result;
obtaining output information of the threshold estimation model, the output information including a second result, the second result including one of the first coefficient threshold, the second coefficient threshold, and the third coefficient threshold.
5. The method according to claim 1, wherein the constructing of the multiple linear regression model is specifically:
wherein x is1Representing first respiratory information; x is the number of2Representing a first pain stimulus reflectance signal; x is the number of3Representing first awareness information;
β1，β2，β3is a constant.
6. The method according to claim 1, wherein the consciousness assessment model is obtained according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold and the trained multiple linear regression model, and specifically:
wherein x is1Representing first respiratory information; x is the number of2Representing a first pain stimulus reflectance signal; x is the number of3Representing first awareness information;
β’1，β’2，β’3respectively representing a first coefficient threshold, a second coefficient threshold, and a third coefficient threshold.
7. The method of claim 1, wherein the determining whether the consciousness assessment model regresses significantly comprises:
obtaining a predetermined coefficient threshold;
judging whether the first coefficient threshold, the second coefficient threshold and the third coefficient threshold all accord with the preset coefficient threshold;
if the first coefficient threshold, the second coefficient threshold and the third coefficient threshold all accord with the preset coefficient threshold, determining that the consciousness assessment model does not regress significantly;
and if the first coefficient threshold, the second coefficient threshold and the third coefficient threshold do not all accord with the preset coefficient threshold, determining that the consciousness evaluation model is remarkably regressed.
8. A cerebrovascular patient consciousness assessment system, wherein said system comprises:
a first construction unit for constructing a user data set;
a first obtaining unit for obtaining a breathing information dataset of all users in the user dataset through a breathing monitoring module;
a second obtaining unit for obtaining a pain stimulus reflex signal dataset of all users in the user dataset by a pain stimulus reflex module;
a third obtaining unit, configured to obtain, by a consciousness monitoring module, consciousness information datasets of all users in the user dataset;
a second construction unit for constructing a multiple linear regression model;
a fourth obtaining unit, configured to obtain an initial consciousness assessment result dataset of all users in the user dataset;
a fifth obtaining unit, configured to input the respiration information data set, the pain stimulus reflection signal data set, and the consciousness information data set into the multiple linear regression model through a central control module, train the multiple linear regression model using the initial consciousness assessment result data set as output data, and obtain a first result, where the first result includes a first coefficient, a second coefficient, and a third coefficient;
a sixth obtaining unit configured to obtain a first accuracy influence degree;
a first determination unit configured to determine a first coefficient threshold, a second coefficient threshold, and a third coefficient threshold from the first accuracy influencing amount and the first coefficient, the second coefficient, and the third coefficient;
a seventh obtaining unit, configured to obtain a consciousness assessment model according to the first coefficient threshold, the second coefficient threshold, the third coefficient threshold, and the trained multiple linear regression model;
a first judging unit configured to judge whether the consciousness assessment model is regression-significant;
an eighth obtaining unit, configured to obtain first respiratory information, a first pain stimulus reflex signal, and first consciousness information of the first user if the consciousness assessment model regresses significantly;
a ninth obtaining unit configured to input the first respiration information, the first pain stimulus reflection signal, and the first consciousness information into the consciousness assessment model, and obtain a first consciousness assessment result.
9. A cerebrovascular patient consciousness assessment system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the program.
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| CN111767963A (en) * | 2020-07-07 | 2020-10-13 | 南通市第二人民医院 | Method and device for improving quality assessment based on endoscope screening |
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