CN113724879B - Method for establishing cardiovascular disease identification model by using mucoid optimization algorithm - Google Patents
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- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 41
- 208000024172 Cardiovascular disease Diseases 0.000 title claims abstract description 34
- 238000005457 optimization Methods 0.000 title claims abstract description 24
- 230000004089 microcirculation Effects 0.000 claims abstract description 35
- 230000017531 blood circulation Effects 0.000 claims abstract description 28
- 210000000707 wrist Anatomy 0.000 claims abstract description 10
- 238000010801 machine learning Methods 0.000 claims abstract description 8
- 230000036541 health Effects 0.000 claims abstract description 6
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- 235000021251 pulses Nutrition 0.000 description 56
- 206010020772 Hypertension Diseases 0.000 description 7
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- 210000000748 cardiovascular system Anatomy 0.000 description 2
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
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- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
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- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention discloses a method for establishing a recognition model of cardiovascular diseases by using a mucoid optimization algorithm, which is used for collecting wrist pressure pulse waves and finger tip volume pulse waves; establishing a volume pulse blood flow model of finger tip microcirculation; the wrist pressure pulse wave and the finger tip volume pulse wave are respectively used as the actual input and the expected output of the volume pulse blood flow model, then the uncertainty of model parameters is reduced by using a mucosae optimization algorithm, the final parameters of the volume pulse blood flow model are estimated, and the extraction of microcirculation physiological state information is realized; and then establishing an identification model of the cardiovascular disease based on a machine learning algorithm so as to judge the health state of the cardiovascular disease. The invention can obtain higher accuracy and better recognition effect.
Description
Technical Field
The invention relates to the technical field of blood pressure prediction methods, in particular to a method for establishing a cardiovascular disease identification model by using a mucosae optimization algorithm.
Background
In the cardiovascular system, microcirculation provides an important place for substance exchange between blood and tissue, being the only way blood flows from arteries into veins. According to clinical studies, the blood flow change of the finger tip microcirculation (or the nail fold microcirculation) can reflect the state of important components in the cardiovascular system such as heart, artery and the like, and the state of the microcirculation is obviously related to cardiovascular diseases.
The microcirculation is a complex system, the microcirculation states of different human bodies have larger uncertainty, and the established model may not completely simulate the structure of the microcirculation, so that a plurality of local optimal solutions may appear when model parameters are estimated, and larger recognition errors exist in the recognition model. Therefore, the improvement is made by the inventor, and a method for establishing the identification model of the cardiovascular disease by using the mucoid optimization algorithm is provided.
Disclosure of Invention
In order to solve the technical problems, the invention provides the following technical scheme:
the method for establishing the identification model of the cardiovascular disease by using the mucoid optimization algorithm comprises the following steps:
step 1, collecting wrist pressure pulse waves and finger tip volume pulse waves;
step 2, establishing a volume pulse blood flow model of finger tip microcirculation;
step 3, taking wrist pressure pulse waves and finger tip volume pulse waves as actual input and expected output of a volume pulse blood flow model respectively, then reducing uncertainty of model parameters by using a mucosae optimization algorithm, estimating final parameters of the volume pulse blood flow model, and realizing extraction of microcirculation physiological state information;
and 4, establishing an identification model of the cardiovascular disease based on a machine learning algorithm so as to judge the health state of the cardiovascular disease.
As a preferable technical scheme of the invention, the method for establishing the volume pulse blood flow model of the finger tip microcirculation is to establish an electrical network model of cardiovascular simulation,
inductance L represents the difficulty of the blood flow change in the microcirculation, representing the flow inertia of the blood in the micro arteries; capacitance C represents the rate of change of the total volume of blood vessels in the microcirculation with blood pressure P, representing the overall compliance of the microcirculation; the resistance R represents the total resistance that the blood receives when flowing in the circulation;
from this model, the following mathematical expression can be established:
the finishing model equation is as follows:
to obtain the parameter values of the model, an optimal R, L, C value is solved within a set parameter range by applying a mucosae optimization algorithm.
As a preferable technical scheme of the invention, the method for solving the optimal R, L, C value in the set parameter range by applying the mucosae optimization algorithm comprises the following specific steps of searching the R, L, C value position:
step (1), setting an initial value of a population;
step (2), calculating a current fitness value and sequencing;
step (3), updating the positions of the population;
step (4), calculating the fitness value again, and updating the optimal position in the population;
judging the end condition of the optimizing process, and executing the steps (2) to (5) again if the end condition is not met;
as a preferable technical scheme of the present invention, the formula for updating the position in the step (3) is as follows:
where the sum is the upper and lower boundaries of the search range, respectively, t is the current iteration,to simulate the position with the highest current odor concentration in the feeding course of the coliform, the method comprises the following steps of->In the range of [ -a, a]，/> For two positions selected randomly, W represents the weight of the position, < ->Linearly decreasing from 1 to 0. Further, the expression of the parameter p is as follows:
p＝tanh|S(i)-DF|
wherein S (i) isDF is the best fitness in all iterations. The expression of parameter a is as follows:
wherein maxT is the maximum iteration number, and W is as follows:
where r is a random value in [0,1], bF and wF are the best fitness and worst fitness, respectively, in the current iteration process, and the condition is the population of S (i) in the first 50% of the ranking.
As a preferred technical solution of the present invention, the method for establishing the recognition model of cardiovascular disease based on the machine learning algorithm in the step 4 is to establish the recognition model of cardiovascular disease through Random Forest (RF), wherein the difference between the two is that:
(1) Gini's non-purity function is used to scale when node-slicing of decision trees,
such as formulaAs shown in the drawing,
p mk the probability of the occurrence of the target variable value in the current node training sample is lower when the sample class at the node is more definite;
(2) And determining a final classification result by using a voting method, and taking the mode of each decision tree as the final classification result according to the classification result of each decision tree.
The beneficial effects of the invention are as follows: the method for establishing the cardiovascular disease identification model of the mucoid optimization algorithm comprises the steps of firstly collecting wrist pressure pulse waves and finger tip volume pulse waves; establishing a volume pulse blood flow model of finger tip microcirculation; the wrist pressure pulse wave and the finger tip volume pulse wave are respectively used as the actual input and the expected output of the volume pulse blood flow model, then the uncertainty of model parameters is reduced by using a mucosae optimization algorithm, the final parameters of the volume pulse blood flow model are estimated, and the extraction of microcirculation physiological state information is realized; and then establishing an identification model of the cardiovascular disease based on a machine learning algorithm so as to judge the health state of the cardiovascular disease. The invention can obtain higher accuracy and better recognition effect.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings;
FIG. 1 is a flow diagram of a R, L, C parameter estimation flow chart of the present invention;
FIG. 2 is a flow chart of the establishment of a cardiovascular disease identification model according to the present invention;
FIG. 3 is an electrical network diagram of a volume pulse flow model of the microcirculation of the present invention;
FIG. 4 is a diagram of a method of calibrating pulse waves;
FIG. 5 is a diagram of a method of calibrating pressure pulse waves;
FIG. 6 is Q after calibration m Comparing with CO;
FIG. 7 is a graph of simulated volume pulse waves versus raw volume pulse waves;
FIG. 8 is a graph of the prediction results of a cardiovascular disease recognition model;
fig. 9 is a graph of cardiovascular disease classifier versus experimental results.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Examples: the invention relates to a method for establishing a cardiovascular disease identification model of a mucoid optimization algorithm, which comprises the following steps:
step 1, collecting wrist pressure pulse waves and finger tip volume pulse waves;
step 2, establishing a volume pulse blood flow model of finger tip microcirculation;
step 3, taking wrist pressure pulse waves and finger tip volume pulse waves as actual input and expected output of a volume pulse blood flow model respectively, then reducing uncertainty of model parameters by using a mucosae optimization algorithm, estimating final parameters of the volume pulse blood flow model, and realizing extraction of microcirculation physiological state information;
and 4, establishing an identification model of the cardiovascular disease based on a machine learning algorithm so as to judge the health state of the cardiovascular disease.
The method for establishing the volume pulse blood flow model of the finger tip microcirculation is to establish an electrical network model of cardiovascular simulation, as shown in figure 3,
inductance L represents the difficulty of the blood flow change in the microcirculation, representing the flow inertia of the blood in the micro arteries; capacitance C represents the rate of change of the total volume of blood vessels in the microcirculation with blood pressure P, representing the overall compliance of the microcirculation; the resistance R represents the total resistance that the blood receives when flowing in the circulation;
from this model, the following mathematical expression can be established:
the finishing model equation is as follows:
to obtain the parameter values of the model, an optimal R, L, C value is solved within a set parameter range by applying a mucosae optimization algorithm.
As shown in fig. 1, the method for solving the best R, L, C value within the set parameter range by applying the mucosae optimization algorithm comprises the following specific steps of finding the R, L, C value position:
step (1), setting an initial value of a population;
step (2), calculating a current fitness value and sequencing;
step (3), updating the positions of the population;
step (4), calculating the fitness value again, and updating the optimal position in the population;
and (5) judging the end condition of the optimizing process, and executing the steps (2) to (5) again if the end condition is not reached.
The formula for updating the position in the step (3) is as follows:
where the sum is the upper and lower boundaries of the search range, respectively, t is the current iteration,to simulate the position with the highest current odor concentration in the feeding course of the coliform, the method comprises the following steps of->In the range of [ -a, a]，/> For two positions selected randomly, W represents the weight of the position, < ->Linearly decreasing from 1 to 0. Further, the expression of the parameter p is as follows:
p＝tanh|S(i)-DF|
wherein S (i) isDF is the best fitness in all iterations. The expression of parameter a is as follows:
wherein maxT is the maximum iteration number, and W is as follows:
where r is a random value in [0,1], bF and wF are the best fitness and worst fitness, respectively, in the current iteration process, and the condition is the population of S (i) in the first 50% of the ranking.
The method for establishing the cardiovascular disease identification model based on the machine learning algorithm in the step 4 is to establish the cardiovascular disease identification model through a random forest classification algorithm (RandomForest, RF), wherein the method is basically consistent with the structure of a classification algorithm of a traditional random forest classification algorithm and a regression algorithm, and the difference between the classification algorithm and the regression algorithm is that:
(1) Gini's non-purity function is used to scale when node-slicing of decision trees,
such as formulaAs shown in the drawing,
p mk the probability of the occurrence of the target variable value in the current node training sample is lower when the sample class at the node is more definite;
(2) And determining a final classification result by using a voting method, and taking the mode of each decision tree as the final classification result according to the classification result of each decision tree.
The signals acquired by the pulse sensor can be used for approximating pulse signals representing the time-varying parameters such as intravascular pressure, volume and the like, but the signals are represented by voltage variation, and the amplitude of the signals has no specific physiological significance. Therefore, these curves need to be calibrated before they can be used in parameter estimation of the cardiovascular simulation model.
The signals of the pressure pulse wave and the volume pulse wave are shown in fig. 4 and 5, wherein the signals comprise a direct current component and a pulsation component. The signal of the pressure pulse wave comes from the change in the blood pressure inside the arterial vessel, so the curve can be calibrated using the blood pressure value [46]. In the pulsation cycle of the ventricle, the maximum pressure in the artery is the systolic pressure, the minimum pressure is the diastolic pressure, and the maximum pressure and the minimum pressure respectively correspond to the maximum value and the minimum value in the pulse wave, so that a calibration formula of the original monocycle pressure pulse wave can be obtained:
wherein M is s 、M d The blood ejected from left ventricle with maximum and minimum values in original waveform flows into microcirculation of human body via artery under the action of pulse wave pressure and blood perfusion, and exchanges substance with tissue cells in microcirculation, while the volume pulse wave of finger tip microcirculation reflects the variation of blood volume in micro artery and capillary along with heart beat, so that the average value Q of volume pulse wave can be obtained m The blood volume output by the left ventricle, i.e. cardiac output, is calibrated. According to the definition of hemodynamics, peripheral resistance R (PRU), cardiac output CO (mL/s) and P of the blood vessel m The mean arterial pressure (mmHg) has the following relationship:
wherein P is m Computable, Q m (mL/s) is the average pulse blood flow, SV (mL/bean) is the stroke volume, is the pulse wave period, and the calculation formula is as follows
Thus, it can be deduced that:
in the formula, K, K' is a waveform characteristic quantity of the pressure pulse wave and the volume pulse wave.
Under the assumption of linearization, the direct current component and the pulsation component of the pressure pulse wave and the volume pulse wave in the above method are respectively corresponding, so that the maximum value and the minimum value of the volume pulse wave are calibrated as follows,
the calibration formula of the obtained volume pulse wave is consistent with the calibration method of the pressure pulse wave
The cardiovascular disease identification model selects a common hypertension sample with higher risk as a target object. In the process of establishing the identification model, as shown in FIG. 2, features are first extracted from the pulse wave samples, and then the estimated R, L, C model parameters, and the systolic pressure Ps, diastolic pressure Pd, average pressure Pm, CO cardiac output, Q are used max Maximum blood flow and Q min And establishing a feature set of the minimum blood flow, and finally establishing an identification model of health, hypertension and coronary heart disease based on a random forest algorithm.
In the experiment, firstly, the pulse wave is calibrated, and the average blood flow calculated by the calibrated volume pulse wave is compared with the cardiac output theory calculated by the calibrated pressure pulse wave. As shown in FIG. 6, the two methods have good consistency, and the average absolute error is 11.10+/-8.02 mL/s, which indicates that the calibration method has certain accuracy.
As shown in fig. 7, to verify the effect of performing parameter uncertainty quantification of the volume pulse wave blood flow model by using the mucosae optimization algorithm, the difference between the simulated volume pulse wave output by the model after parameter optimization and the original volume pulse wave actually acquired is compared. It can be seen that the similarity of the waveforms is high, which indicates that the mucosae optimization algorithm searches for a better position in the R, L, C parameter space, so that the estimated model parameter value can basically represent the actual physiological state of the microcirculation. RF, SVM, KNN and DT algorithms are widely applied to classifying and identifying pulse waves, and for verifying the classifying performance of RF, cardiovascular disease identification models are established by using the algorithms respectively for comparison analysis. The performance of each classifier was tested in the experiment using a 3-fold cross-test method, and the average accuracy, average accuracy and average recall of 3 experiments were calculated, with the results shown in fig. 9. Wherein the RF average accuracy rate is improved by 2.8% compared with suboptimal KNN, and 91.96% is achieved; the average accuracy is improved by 3 percent and reaches 92.13 percent; the average recall rate is improved by 2.49 percent and reaches 92.05 percent. The result shows that RF has good classification performance in four classification algorithms, and better distinguishing capability is provided for cardiovascular parameter characteristics with more cross overlaps in space, so that the RF algorithm is finally selected to establish a cardiovascular disease identification model.
As shown in fig. 8, the prediction result of the cardiovascular disease recognition model established on the microcirculation state parameter feature set by using the RF algorithm is shown, and the recognition accuracy of various samples reaches more than 88%. The identification accuracy of the coronary heart disease sample is highest and is 95.51%, the average identification accuracy of the healthy sample is 92.11%, and the average identification accuracy of the hypertension sample is 88.55%. The reason for the lower accuracy of the hypertension sample identification is that: some normal high blood pressure is present in the healthy sample, and the pulse wave and the parameters of the cardiovascular model may show similar characteristics, whereas patients with coronary heart disease usually have a history of hypertension diseases, and thus may also show similar characteristics. In the whole, the established model has better distinguishing capability on the characteristic parameters under different blood vessel health states, and can realize effective identification of hypertension and coronary heart disease.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. The method for establishing the identification model of the cardiovascular disease by using the mucoid optimization algorithm is characterized by comprising the following steps of:
step 1, collecting wrist pressure pulse waves and finger tip volume pulse waves;
step 2, establishing a volume pulse blood flow model of finger tip microcirculation;
the method for establishing the volume pulse blood flow model of the finger tip microcirculation is to establish an electrical network model of cardiovascular simulation,
inductance L represents the difficulty of the blood flow change in the microcirculation, representing the flow inertia of the blood in the micro arteries; capacitance C represents the rate of change of the total volume of blood vessels in the microcirculation with blood pressure P, representing the overall compliance of the microcirculation; the resistance R represents the total resistance that the blood receives when flowing in the circulation;
from this model, the following mathematical expression can be established:
the finishing model equation is as follows:
to obtain the parameter values of the model, an optimal R, L, C value is solved in a set parameter range by applying a mucosae optimization algorithm;
the method for solving the optimal R, L, C value in the set parameter range by applying the mucosae optimization algorithm comprises the following specific steps of searching R, L, C value positions:
step (1), setting an initial value of a population;
step (2), calculating a current fitness value and sequencing;
step (3), updating the positions of the population;
step (4), calculating the fitness value again, and updating the optimal position in the population;
judging the end condition of the optimizing process, and executing the steps (2) to (5) again if the end condition is not met;
the formula for updating the position in the step (3) is as follows:
where the sum is the upper and lower boundaries of the search range, respectively, t is the current iteration,to simulate the position with the highest current odor concentration in the feeding course of the coliform, the method comprises the following steps of->In the range of [ -a, a]，/> For two positions selected randomly, W represents the weight of the position, < ->Linearly decreasing from 1 to 0; further, the expression of the parameter p is as follows:
p＝tanh|S(i)-DF|
wherein S (i) isDF is the best fitness in all iterations; the expression of parameter a is as follows:
wherein maxT is the maximum iteration number, and W is as follows:
wherein r is a random value in [0,1], bF and wF are respectively the best fitness and the worst fitness in the current iteration process, and the condition is the population of S (i) in the first 50% in the sorting process;
step 3, taking wrist pressure pulse waves and finger tip volume pulse waves as actual input and expected output of a volume pulse blood flow model respectively, then reducing uncertainty of model parameters by using a mucosae optimization algorithm, estimating final parameters of the volume pulse blood flow model, and realizing extraction of microcirculation physiological state information;
and 4, establishing an identification model of the cardiovascular disease based on a machine learning algorithm so as to judge the health state of the cardiovascular disease.
2. The method for establishing the recognition model of the cardiovascular disease by the mucosae optimization algorithm according to claim 1, wherein the method for establishing the recognition model of the cardiovascular disease based on the machine learning algorithm in the step 4 is to establish the recognition model of the cardiovascular disease by a random forest classification algorithm, wherein the classification algorithm is basically identical to the structure of a regression algorithm in the conventional random forest classification algorithm, and the difference between the classification algorithm and the regression algorithm is that:
(1) Gini's non-purity function is used to scale when node-slicing of decision trees,
such as formulaAs shown in the drawing,
p mk the probability of the occurrence of the target variable value in the current node training sample is lower when the sample class at the node is more definite;
(2) And determining a final classification result by using a voting method, and taking the mode of each decision tree as the final classification result according to the classification result of each decision tree.
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