Sputum obstruction detection method and system based on lung binary tree model, terminal and medium

Abstract

The invention discloses a sputum obstruction detection method and system based on a lung binary tree model, a terminal and a medium. The sputum obstruction detection method based on the lung binary tree model comprises the steps that a normal lung impedance curve of a subject under the normal condition of the lung is obtained; establishing a lung binary tree model, and performing parameter calibration on the lung binary tree model according to the normal lung impedance curve to obtain normal values of geometric parameters of the airway; and acquiring a current lung impedance curve of the forced oscillation test of the subject during sputum accumulation detection, and determining the sputum blockage position and degree of the subject according to the current lung impedance curve, the normal lung impedance curve and the normal value of the airway geometric parameter. By simplifying the lung binary tree model and quantitatively analyzing the accumulation condition of the sputum in the lung, the sputum blockage position and degree can be accurately identified, and the comfort level of a patient is improved by adopting forced oscillation measurement, so that the physical and psychological burden of sputum suction on the patient is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of physiological signal processing and modeling analysis, and in particular to a sputum obstruction detection method, system, terminal and medium based on a lung binary tree model. Background Art

[0002] Mechanical ventilation is one of the key technologies in the clinical treatment of critically ill patients. However, mechanical ventilation usually irritates the airway mucosa, leading to an increase in respiratory sputum. During mechanical ventilation, patients are usually unable to expel sputum from the body through natural physiological reactions, resulting in sputum deposition in the patient's lungs. Sputum deposition can lead to many adverse reactions and induce complications, and frequent suctioning can bring physical and psychological burdens to patients. Therefore, the identification of sputum accumulation can provide medical staff with the appropriate time to suction sputum to reduce the physical and psychological burden of suction on patients.

[0003] At present, the clinical judgment of sputum accumulation mainly relies on doctors' auscultation, which takes a lot of doctors' time. Pulmonary function testing is the gold standard for diagnosing lung obstruction, but this examination requires the subject's high cooperation and strict and strong breathing movements, which is difficult to complete smoothly for critically ill patients.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] The main purpose of the present application is to provide a sputum obstruction detection method, system, terminal and medium based on a lung binary tree model, aiming to solve the problem in the prior art that the clinical judgment of sputum accumulation relies on the doctor's auscultation and the cooperation of the subjects, and some subjects are unable to cooperate, resulting in the inability to accurately identify the location and degree of sputum obstruction.

[0006] A first aspect of an embodiment of the present application provides a sputum obstruction detection method based on a lung binary tree model, and the sputum obstruction detection method based on a lung binary tree model comprises the following steps: obtaining a normal lung impedance curve of a subject when the lungs are normal; establishing a lung binary tree model, and calibrating parameters of the lung binary tree model according to the normal lung impedance curve to obtain normal values ​​of airway geometry parameters; obtaining a current lung impedance curve of the subject during a forced oscillation test during sputum accumulation detection, and determining the location and degree of sputum obstruction of the subject according to the current lung impedance curve, the normal lung impedance curve and the normal values ​​of airway geometry parameters.

[0007] Optionally, in one embodiment of the present application, the obtaining of the normal lung impedance curve of the subject when the lungs are in normal condition specifically includes: obtaining the pressure and flow signals at the mouth of the subject when the lungs are undergoing a forced oscillation test when the lungs are in normal condition; preprocessing the pressure and the flow signals to obtain preprocessed pressure and preprocessed flow signals; and determining the normal lung impedance curve based on the preprocessed pressure and the preprocessed flow signals.

[0008] Optionally, in one embodiment of the present application, determining a normal lung impedance curve based on the preprocessed pressure and the preprocessed flow signal specifically includes: for each frequency point, calculating the pressure autopower spectrum corresponding to the preprocessed pressure under each sliding window, the flow autopower spectrum and the cross-power spectrum corresponding to the preprocessed flow signal; calculating the first average value corresponding to the pressure autopower spectrum and the second average value corresponding to the cross-power spectrum under all sliding windows; calculating the impedance value at each frequency point based on the first average value and the second average value; and obtaining a normal lung impedance curve based on multiple impedance values ​​at different frequency points.

[0009] Optionally, in one embodiment of the present application, the parameters of the lung binary tree model are calibrated according to the normal lung impedance curve to obtain normal values ​​of airway geometry parameters, specifically including: determining the parameters to be optimized in the lung binary tree model; taking the root mean square error between the simulated impedance curve fitted by the lung binary tree model and the normal lung impedance curve as a fitness function; based on the fitness function, optimizing the parameters to be optimized to obtain normal values ​​of airway geometry parameters.

[0010] Optionally, in one embodiment of the present application, the parameters to be optimized are optimized based on the fitness function to obtain normal values ​​of airway geometry parameters, specifically including: initializing the position and velocity of each particle; wherein the position of the particle represents a combination of the parameters to be optimized, and the velocity of the particle represents the direction and magnitude of the update of the parameters to be optimized; in each iteration, the fitness value of each particle is calculated according to the fitness function, and the individual historical optimal position and the global optimal position are updated according to the fitness value to obtain the current individual historical optimal position and the current global optimal position; according to the current individual historical optimal position and the current global optimal position, the position and velocity of each particle are updated to obtain the current position and current velocity of each particle; when the current number of iterations reaches the maximum number of iterations, or the iteration is stopped when the fitness value corresponding to the current position is less than a preset threshold, the current global optimal position corresponding to the current position is used as the normal value of the airway geometry parameters.

[0011] Optionally, in one embodiment of the present application, the location and degree of sputum obstruction are obtained according to the current pulmonary impedance curve, the normal pulmonary impedance curve and the normal values ​​of the airway geometry parameters, specifically including: calculating the root mean square error value of the current pulmonary impedance curve and the normal pulmonary impedance curve at all frequencies; setting a sputum obstruction threshold, if the root mean square error value is greater than the sputum obstruction threshold, determining the current airway geometry parameter fitting value according to the current pulmonary impedance curve; determining the location and degree of sputum obstruction according to the current airway geometry parameter fitting value and the normal values ​​of the airway geometry parameters.

[0012] Optionally, in one embodiment of the present application, the calculating of the root mean square error value between the current lung impedance curve and the normal lung impedance curve at all frequencies further includes: if the root mean square error value is less than the sputum blockage threshold, determining that the subject does not have sputum blockage.

[0013] The second aspect of the embodiment of the present application further provides a sputum obstruction detection system based on a lung binary tree model, wherein the sputum obstruction detection system based on a lung binary tree model comprises: A lung impedance calculation module, used to obtain a normal lung impedance curve of a subject under normal lung conditions; A lung model parameter calibration module, used to establish a lung binary tree model, calibrate the parameters of the lung binary tree model according to the normal lung impedance curve, and obtain normal values ​​of airway geometric parameters; The sputum obstruction identification module is used to obtain the current lung impedance curve of the subject during the forced oscillation test during sputum accumulation detection, and determine the location and degree of sputum obstruction of the subject based on the current lung impedance curve, the normal lung impedance curve and the normal values ​​of the airway geometry parameters.

[0014] The third aspect of an embodiment of the present application also provides a terminal, wherein the terminal includes: a memory, a processor, and a sputum blockage detection program based on a pulmonary binary tree model stored in the memory and executable on the processor, wherein the sputum blockage detection program based on a pulmonary binary tree model implements the steps of the sputum blockage detection method based on a pulmonary binary tree model as described above when executed by the processor.

[0015] The fourth aspect of an embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a sputum blockage detection program based on a lung binary tree model, and when the sputum blockage detection program based on a lung binary tree model is executed by a processor, the steps of the sputum blockage detection method based on a lung binary tree model as described above are implemented.

[0016] Beneficial effects: The present application provides a sputum blockage detection method, system, terminal and medium based on a lung binary tree model. The present application establishes a lung binary tree model by combining forced oscillation technology and respiratory dynamics of the lung physiological structure, quantitatively analyzes the accumulation of sputum in the lungs, and can accurately identify the location and degree of sputum blockage, providing medical staff with a reference for whether to perform suction operations on patients. The use of forced oscillation measurement improves the patient's comfort, thereby reducing the physical and psychological burden of suction on patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a preferred embodiment of the sputum obstruction detection method based on the lung binary tree model of the present application; Figure 2 It is a flow chart of preprocessing pressure and flow signals in a preferred embodiment of the sputum obstruction detection method based on the lung binary tree model of the present application; Figure 3 It is the impedance curve calculation process in the preferred embodiment of the sputum obstruction detection method based on the lung binary tree model of the present application; Figure 4 It is a single airway circuit analog diagram in a preferred embodiment of the sputum obstruction detection method based on the lung binary tree model of the present application; Figure 5 It is a simplified binary tree circuit analogy diagram of the lung in a preferred embodiment of the sputum obstruction detection method based on the lung binary tree model of the present application; Figure 6 It is the lung sputum obstruction identification process in the preferred embodiment of the sputum obstruction detection method based on the lung binary tree model of the present application; Figure 7 It is a structural diagram of a preferred embodiment of the sputum obstruction detection system based on the lung binary tree model of the present application; Figure 8 This is a structural diagram of a preferred embodiment of the terminal of this application.

[0019] Description of reference numerals: 100. Lung impedance calculation module; 200. Lung model parameter calibration module; 300. Sputum obstruction identification module. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and effect of the present application clearer and more specific, the technical scheme in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. The described embodiments are only possible technical implementations of the present application, not all possible implementations. Based on the embodiments in the present application, those skilled in the art can completely combine the embodiments of the present application to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of the present application.

[0021] In the related art, a method predicts the patient's lung function based on CT image data. First, CT image data is obtained, airway data is extracted, and an airway model is generated using three-dimensional reconstruction technology; then the statistical data of lung function is input into the normal model and the stenosis model for simulation to generate simulation data; finally, the simulation data is input into the lung function prediction model to output the predicted value. The disadvantages of this method are: it depends on CT image data, the cost is high, and the processing process is complicated and the calculation amount is large. However, the present application does not rely on CT image data to simulate the lungs. It measures the lung impedance curve through forced oscillation, and fits the changes in the geometric parameters of each airway of the patient through a simplified lung binary tree model and a particle swarm optimization algorithm, thereby identifying the location and degree of pulmonary sputum obstruction. Therefore, the present application has a lower cost of use than the above method, and the simplified binary tree model has less calculation amount.

[0022] In the related art, another method is to use a sleep breathing monitoring and obstruction positioning system implanted in the nasal cavity, using a carrier catheter integrated with a pressure sensor array to monitor the contact pressure signal in the respiratory tract, and analyzing the pressure signal through a processing module to obtain the pressure change in the respiratory tract to monitor the airway obstruction during sleep. The disadvantage of this method is that it is an invasive measurement method, which may cause discomfort to the patient, and is mainly used for airway obstruction monitoring in the sleeping state, and has a limited scope of application. The present application uses a non-invasive measurement method to measure the impedance of the lungs. Although it cannot directly obtain the obstruction in the respiratory tract, it can indirectly reflect the obstruction by fitting the airway geometric parameters through lung modeling and parameter optimization methods, which has a higher comfort level in practical applications. In addition, the present application is mainly aimed at locating sputum in the lungs, while the above-mentioned scheme is used for airway obstruction monitoring in the sleeping state.

[0023] Forced Oscillation Technique (FOT) is a non-invasive method for measuring the respiratory mechanical properties of the lungs that does not rely on forced exhalation. The principle is to apply a pressure wave of a certain frequency to the respiratory system during quiet breathing, and measure the pressure and flow signals at the patient's mouth to calculate the impedance of the respiratory system. By comparing the key parameters of the respiratory system impedance with empirical values, such as resistance at 5 Hz (R5), resistance at 20 Hz (R20), resonant frequency (Fres), resistance at 5 Hz (X5), and resistance at 20 Hz (X20), a qualitative judgment of lung obstruction can be achieved. However, this method cannot make quantitative judgments on the specific location and degree of obstruction. For this reason, this application combines a simplified binary tree circuit model of the lungs and a particle swarm optimization algorithm to solve the above problems.

[0024] This application has the following innovations: First, simplifying the airway binary tree model: Due to the complex structure of the lungs, accurate lung modeling and simulation calculations are large. In practical applications, the calculation complexity is reduced by reducing the calculation accuracy by simplifying the model. Combined with the transmission line theory, the lung model is analogized to a circuit model. At the same time, it is further assumed that the lungs are symmetrical and uniform binary tree structures and each level of airway satisfies a specific proportional relationship. The model is further simplified, the number of model parameters is reduced, and the practicality is stronger. Second, the lung airway parameter fitting algorithm is used: In order to make the impedance curve of the simulation model as consistent as possible with the actual measured impedance value of the forced oscillation, the particle swarm optimization algorithm in the random optimization algorithm is used to optimize the airway parameters in the model. This method has the advantages of a small number of parameters and strong global search capabilities. The optimized airway radius parameters are compared with the airway radius parameters under normal conditions, so as to reflect the specific location and degree of obstruction.

[0025] The following describes the sputum obstruction detection method, system, terminal and medium based on the pulmonary binary tree model of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the judgment of sputum accumulation in the above-mentioned related technology relies on the doctor's auscultation and the cooperation of the subject, and some subjects cannot cooperate, resulting in the inability to accurately identify the position and degree of sputum obstruction, the present application provides a sputum obstruction detection method based on the pulmonary binary tree model, in which a pulmonary binary tree model is established by combining forced oscillation technology and respiratory dynamics of the physiological structure of the lungs, and the accumulation of sputum in the lungs is quantitatively analyzed, and the position and degree of sputum obstruction can be accurately identified, providing medical staff with a reference for whether to perform sputum suction on the patient, and using forced oscillation measurement to improve the patient's comfort, so as to reduce the physical and psychological burden of sputum suction on the patient, and the simplified model and optimized algorithm reduce the computational complexity, improve the practicality, and do not rely on CT image data, reducing the cost of use. Thus, the technical problem that the judgment of sputum accumulation in the related technology relies on the doctor's auscultation and the cooperation of the subject, and some subjects cannot cooperate, resulting in the inability to accurately identify the position and degree of sputum obstruction is solved.

[0026] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0027] The sputum obstruction detection method based on the lung binary tree model described in the preferred embodiment of the present application is as follows: Figure 1 As shown, the sputum obstruction detection method based on the lung binary tree model includes the following steps: In step S101, a normal lung impedance curve of a subject under normal lung conditions is obtained.

[0028] It is worth noting that the present application can calculate the human lung impedance from the pressure and flow signals collected by the forced oscillation test, and combine the simplified lung binary tree model to complete the identification of the lung airway geometric parameters, and by comparing with the airway geometric parameters under normal conditions, it can realize the identification of the location and degree of sputum blockage.

[0029] In one possible implementation, the pressure and flow signals at the mouth of the subject undergoing a forced oscillation test when the lungs are normal are obtained; the pressure and flow signals are preprocessed to obtain preprocessed pressure and preprocessed flow signals; and a normal lung impedance curve is determined based on the preprocessed pressure and the preprocessed flow signals.

[0030] Specifically, lung impedance calculation: the pressure and flow signals at the mouth of the subject under normal lung conditions are collected during the forced oscillation process, and the lung impedance curve of the subject under normal conditions is calculated after the pressure and flow signals are preprocessed. That is, the pressure and flow signals of the subject in the forced oscillation test are input, and then the signals are preprocessed, the impedance curve is calculated, and the lung impedance curve under normal conditions is output.

[0031] Further, if Figure 2 As shown, during the preprocessing of the pressure and flow signals, the pressure and flow signals are downsampled to 128 Hz to reduce the amount of subsequent calculations; the pressure and flow changes caused by spontaneous breathing are extracted from the pressure and flow signals using a moving average filter, and the pressure and flow changes caused by spontaneous breathing are subtracted from the pressure and flow signals to obtain purer pressure and flow changes caused by forced oscillations.

[0032] In a possible implementation, for each frequency point, the pressure autopower spectrum corresponding to the preprocessed pressure under each sliding window is calculated. G pp , the flow autopower spectrum corresponding to the preprocessed flow signal G ff and cross power spectrum G fp ; Calculate the first average value corresponding to the pressure autopower spectrum and the second average value corresponding to the cross-power spectrum under all sliding windows; Based on the first average value G pp_avg and the second mean G fp_avg , calculating the impedance value at each frequency point; and obtaining a normal lung impedance curve based on the multiple impedance values ​​at different frequency points.

[0033] Further, if Figure 3 As shown in the figure, during the calculation of the lung impedance curve, for a certain frequency in the forced oscillation test f The pressure and flow signals under each sliding window are calculated by using a sliding window method to calculate the autopower spectrum and cross-power spectrum of the pressure and flow signals under each sliding window. G pp , G ff , G fp ;in, G pp , G ff , G fp Represent the auto-power spectrum of pressure, auto-power spectrum of flow and cross-power spectrum respectively; calculate the average value of the auto-power spectrum of pressure, auto-power spectrum of flow and cross-power spectrum under each window Gpp_avg , G ff_avg , G fp_avg To improve the accuracy of impedance calculation; according to the definition of impedance, impedance , calculate the impedance value at each frequency point; impedance is a complex number, calculate Z The real part of the respiratory resistance ,calculate Z The imaginary part of the breathing reactance is , Im Represents an imaginary unit; calculates different frequencies in turn f Respiratory resistance R and respiratory reactance X The final complete impedance curve can be obtained, which describes the impedance characteristics of the lungs at different frequencies.

[0034] In step S102, a lung binary tree model is established, and parameters of the lung binary tree model are calibrated according to the normal lung impedance curve to obtain normal values ​​of airway geometric parameters.

[0035] It should be noted that the present application can effectively combine forced oscillation technology and respiratory dynamics modeling of lung physiological structure, and quantitatively analyze the accumulation of sputum in the lungs through a system identification method, so as to provide a reference for medical staff to determine whether to perform sputum suction on patients. The present invention first calculates the lung impedance curve under normal conditions by using the pressure and flow signals recorded by the forced oscillation test when there is no sputum obstruction in the lungs of the subject. Combined with the simplified binary tree circuit model of the lungs, the airway geometry parameters (radius, length) under normal conditions are fitted to complete the calibration of the model parameters. In the sputum accumulation detection, the subject is again subjected to a forced oscillation test, the lung impedance curve of the subject is calculated, and the airway geometry parameters are refitted. The impedance curve and the airway geometry parameters are compared with the impedance curve and the airway geometry parameters under normal conditions to complete the identification of the location of sputum accumulation and the degree of sputum obstruction.

[0036] In one possible implementation, the parameters to be optimized in the pulmonary binary tree model are determined; the root mean square error between the simulated impedance curve fitted by the pulmonary binary tree model and the normal pulmonary impedance curve is used as a fitness function; based on the fitness function, the parameters to be optimized are optimized to obtain normal values ​​of airway geometry parameters.

[0037] It can be understood that the lung binary tree model is fitted according to the normal lung impedance curve to obtain a simulated impedance curve.

[0038] Specifically, lung model parameter calibration: according to the physiological structure and respiratory aerodynamics of the lungs, the lungs are modeled as a simplified binary tree model related to the airway geometric parameters, and the impedance curve calculated under normal conditions is used as the standard value through the system recognition method to fit the optimal airway geometric parameters as the normal value of the airway geometric parameters. That is, the impedance curve calculated under normal conditions is input, the impedance curve is fitted using the binary tree model, the optimal airway geometric parameters are found through the particle swarm optimization algorithm, and the normal value of the airway geometric parameters is output.

[0039] The simplified binary tree model of the lungs is established based on the physiological structure of the lungs and the principles of respiratory aerodynamics. The lungs are simplified into a binary tree model determined by the geometric parameters of the airways. This model is used to simulate the gas flow in the airways of the lungs. Specifically, a single airway is regarded as an axially symmetrical cylindrical pipe, and the gas is regarded as a Newtonian fluid; the gas flow in the airway is analogized to a circuit using the transmission line theory, and the respiratory resistance is calculated. R , Compliance C and gas inertia L According to the physiological structure of the lungs, the human airway is divided into 23 levels, from the main airway (level 0) to the terminal airway (level 23). Assuming that each bifurcation of the airway is two-forked and symmetrical, the lungs are modeled as a circuit diagram of a series-parallel structure. In order to simplify the calculation, it is assumed that the radius between two adjacent airways is r ,length l and wall thickness h The ratio is the same, respectively a, b, c express.

[0040] Furthermore, in the process of establishing the simplified binary tree model of the lungs, the movement of gas in a single trachea is regarded as the flow of a cylindrical pipe along the axial symmetry, the gas is regarded as a Newtonian fluid, the flow process is analogized to a circuit, and combined with the transmission line theory, the following can be established: Figure 4 The circuit diagram shown is used to model the gas movement in a single airway. Q represents the airway flow, P represents the airway pressure, and the subscripts represent the different positions of the airway. Figure 4 In, Q 1 Indicates the first-order branch of the main airway, Q 2 represents the second-level branch, P 1 represents the pressure of the first branch, P 2 Indicates the pressure of the second-level branch and respiratory resistance R , Compliance C and gas inertia L It can be calculated by the pipe geometry parameters and the physical properties of the gas; breathing resistance ; Compliance ; Gas inertia ;in lis the length of the trachea, r is the radius of the trachea, h is the thickness of the tracheal wall, μ is the viscosity of the gas, ρ is the density of the gas, E is the effective elastic modulus, ν is the Poisson coefficient, δ For r The dimensionless constants involved, M 1 , ε 1 are defined as the modulus and phase angle of the first-order Bessel function of the first kind, respectively. According to the physiological structure of the lungs, the human airway is divided into 23 levels. Level 0 is the main airway. According to the airway bifurcation, the airway level increases step by step. Level 23 airways are terminal airways connected to alveoli. In order to simplify the calculation, it is assumed that each bifurcation of the airway is a two-fork bifurcation, and the left and right bifurcations are symmetrical. Then the lungs can be further modeled as follows Figure 5 The circuit diagram of the series-parallel structure shown in the figure, R represents respiratory resistance, L represents inertial resistance, C represents elastic resistance, and the subscript represents the corresponding airway level. Figure 5 In, R 0 Indicates the main airway respiratory resistance, L 0 Indicates the main airway inertial resistance, C 0 Represents the main airway elastic resistance, R 1 Indicates the respiratory resistance of the first branch of the main airway, L 1 represents the inertial resistance of the first-order branch of the main airway, C 1 Represents the elastic resistance of the first-order branches of the main airway, R 23 Represents the respiratory resistance of the twenty-third branch, L 23 represents the inertial resistance of the 23rd branch, C 23 represents the elastic resistance of the 23rd level branch, where the radius of each level of airway is r ,length l and wall thickness h is an undetermined parameter; in order to further reduce the number of parameters, it is assumed that the radius between two adjacent airways r ,length l , and wall thickness h The scales are the same, and the scale factors are a , b , c .

[0041] In a possible implementation, the position and velocity of each particle are initialized; wherein the position of the particle represents a combination of parameters to be optimized, and the velocity of the particle represents the direction and magnitude of the update of the parameters to be optimized; in each iteration, the fitness value of each particle is calculated according to the fitness function, and the individual historical optimal position and the global optimal position are updated according to the fitness value to obtain the current individual historical optimal position and the current global optimal position; according to the current individual historical optimal position and the current global optimal position, the position and velocity of each particle are updated to obtain the current position and current velocity of each particle; the iteration is stopped until the current number of iterations reaches the maximum number of iterations, or the fitness value corresponding to the current position is less than a preset threshold, and the current global optimal position corresponding to the current position is used as the normal value of the airway geometry parameter.

[0042] Using system identification technology, the parameters of the binary tree model are fitted through the actual measured impedance curve to obtain the airway geometry parameters under normal conditions. Specifically, the six parameters to be optimized in the binary tree model are determined ( r，l，h， a, b, c ); The root mean square error between the actual measured impedance curve and the impedance curve fitted by the model is used as the fitness function; Initialize the particle swarm and set the number of particles M , maximum number of iterations T max , inertia weight w , Individual learning factor c 1 and social learning factors c 2 , randomly initialize the position and speed of each particle. In each iteration, calculate the fitness value of each particle, update the individual historical optimal position and the global optimal position, and update the particle's speed and position according to the speed update formula and the position update formula. When the maximum number of iterations is reached or the fitness value change of the global optimal position is less than the threshold, stop the iteration. Finally, return the global optimal position as the best parameter fitting result of the model, that is, the airway geometry parameters under normal conditions.

[0043] Furthermore, in the process of system identification, based on the above binary tree circuit model established for the physiological structure of the lung, a total of 6 parameters to be optimized are included: r, l, h, a, b, c The optimization goal is to minimize the error between the final fitting result of the binary tree model and the actual measured value. In system identification, the particle swarm optimization method in the random optimization method is used, and the fitness function is set as the root mean square error between the actual measured impedance curve and the impedance curve fitted by the model. x is the parameter vector to be optimized; during the initialization process, the number of particles M Set to 20, the maximum number of iterations is set to position Tmax , inertia weight w Set to 0.4, individual learning factor c 1 and social learning factors c 2 are all set to 1.5, the position of each particle x i Initialized randomly in the search space, the velocity v of each particle i Initialized to 0, individual historical best position pbest i Initialize to x i , the global optimal position gbest Initialize to the best fitness in the group pbest i ; In each iteration process t For each particle, calculate the current fitness value f ( x i ),like , then update pbest i =x i , in all pbest i Find the best one if ,renew gbest , for each particle i ,speed v i Update according to the following formula: ; rand () is a random number in [0, 1]. Position x i Update according to the following formula: x i =x i +v i ; The iteration stops when one of the following conditions is met: (1) The maximum number of iterations T is reached max , (2) gbest The fitness value change is less than the threshold 1e-5 ; Finally return to the global optimal position gbest , which is the optimal parameter fitting result of the model (i.e., the airway geometric parameters under normal conditions).

[0044] It is understandable that the system identification method in this application uses the particle swarm optimization method in the random optimization method to optimize the unknown parameters in the model, which has the advantages of a small number of parameters, fast convergence speed, and strong global search capability. Other optimization methods such as genetic algorithms, gradient descent, and simulated annealing can also be used to optimize model parameters.

[0045] In step S103, the current lung impedance curve of the subject during the forced oscillation test is obtained, and the location and degree of sputum obstruction of the subject are determined based on the current lung impedance curve, the normal lung impedance curve and the normal values ​​of the airway geometry parameters.

[0046] In one possible implementation, the root mean square error value between the current lung impedance curve and the normal lung impedance curve at all frequencies is calculated; a sputum blockage threshold is set, and if the root mean square error value is greater than the sputum blockage threshold, the current airway geometry parameter fitting value is determined according to the current lung impedance curve; and the location and degree of sputum blockage are determined according to the current airway geometry parameter fitting value and the normal value of the airway geometry parameter.

[0047] Specifically, identification of sputum obstruction: During sputum accumulation detection, the system performs a forced oscillation test on the subject again, and calculates the lung impedance curve and the best airway geometry parameter fitting value of the forced oscillation test, and compares them with the lung impedance curve and airway geometry parameter values ​​under normal conditions to complete the identification of the location and degree of sputum obstruction. That is, the pressure and flow signals during sputum accumulation detection, as well as the calibrated normal airway geometry parameters, are input, the impedance curve and airway geometry parameter fitting value during sputum accumulation are calculated, and compared with the normal values, and the location and degree of sputum obstruction are output.

[0048] In a possible implementation, if the root mean square error value is smaller than the sputum blockage threshold, it is determined that the subject does not have sputum blockage.

[0049] Specifically, the impedance curve of the second measurement is compared with the impedance curve under normal conditions, and the root mean square error (RMS) of the two at all frequencies is calculated. RMSE ) to achieve this; set a threshold value to determine whether the change in the impedance curve is sufficient to indicate the occurrence of sputum obstruction. RMSE If the value is less than the threshold, it is considered that there is no sputum obstruction and the subject may be in a normal state or the degree of sputum obstruction is mild, which is not enough to cause a significant change in the impedance curve; RMSE If the value is greater than the threshold, it is considered that sputum obstruction has occurred, and further analysis is needed on the location and extent of sputum obstruction. Locating sputum obstruction: Assume that sputum obstruction mainly changes the parameters in the model. r (airway radius), while other parameters (such as l，h ) may be less affected or the change of radius is mainly considered in this scheme; r As the parameters to be optimized, a particle swarm optimization algorithm or other optimization algorithms are used to fit the optimal airway geometry parameters under sputum obstruction. By comparing the radius under normal conditions rand optimal radius in case of sputum obstruction r’ , the change of airway radius parameter caused by sputum obstruction can be identified. According to the location of the radius parameter change (i.e. which level or levels of airway radius have changed significantly), the location of sputum obstruction can be inferred.

[0050] Further, if Figure 6 As shown in Figure 2, in the process of identifying the location and degree of sputum obstruction, the calibrated model parameters have been obtained through the impedance curve under normal conditions. r , l , h ,in r , l , h They represent the radius, length and wall thickness of the airway from level 0 to level 23 under normal conditions respectively; when performing sputum obstruction detection, the forced oscillation experiment is performed again, and the impedance curve of the second measurement (i.e., the current lung impedance curve) is obtained according to the same steps as step S101, and the impedance curve of the second measurement is compared with the impedance curve under normal conditions, and the root mean square error of the two at all frequencies is calculated. E ; ; in, N is the total number of frequency points, and Represent the respiratory resistance and respiratory reactance under normal conditions. E If the value is less than the threshold, it is considered that no sputum obstruction occurs; E If it is greater than the threshold, it is considered that sputum obstruction occurs. It is further assumed that sputum obstruction only changes the parameters in the model. r , then r As the parameter to be optimized, the particle swarm optimization algorithm in step S102 is used again to obtain the optimal parameter r’ (Right now Figure 6 The model radius of r and r’ By comparison, the change in airway radius parameters caused by sputum blockage can be obtained, and the location of sputum blockage can be further obtained, thereby providing corresponding suggestions to doctors.

[0051] Next, the sputum obstruction detection system based on the lung binary tree model proposed in accordance with the embodiment of the present application is described with reference to the accompanying drawings.

[0052] Figure 7 It is a structural diagram of a sputum obstruction detection system based on a lung binary tree model according to an embodiment of the present application.

[0053] like Figure 7As shown, the sputum blockage detection system based on the lung binary tree model includes: a lung impedance calculation module 100, a lung model parameter calibration module 200 and a sputum blockage identification module 300.

[0054] Specifically, the lung impedance calculation module 100 is used to obtain a normal lung impedance curve of a subject when the lungs are in normal condition; A lung model parameter calibration module 200 is used to establish a lung binary tree model, and to calibrate the parameters of the lung binary tree model according to the normal lung impedance curve to obtain normal values ​​of airway geometric parameters; The sputum blockage identification module 300 is used to obtain the current lung impedance curve of the subject during the forced oscillation test during sputum accumulation detection, and determine the location and degree of sputum blockage of the subject based on the current lung impedance curve, the normal lung impedance curve and the normal values ​​of the airway geometry parameters.

[0055] Figure 8 This is a structural diagram of a terminal provided in an embodiment of the present application. The terminal may include: A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0056] When the processor 502 executes the program, the sputum obstruction detection method based on the lung binary tree model provided in the above embodiment is implemented.

[0057] Furthermore, the terminal further includes: The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0058] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0059] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0060] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EIS) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0061] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0062] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0063] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the sputum obstruction detection method based on the lung binary tree model as described above is implemented.

[0064] One embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the present application Figure 1 Any of the corresponding embodiments provides a sputum obstruction detection method based on a lung binary tree model.

[0065] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to user analysis data, user storage data, user display data, etc.) and signals involved in the present invention are all information, data and signals authorized by the user or fully authorized by all parties; and the collection, use and processing of relevant information, data and signals comply with the laws, regulations and standards of relevant countries and regions.

[0066] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0067] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0068] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0069] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0070] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0071] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0072] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0073] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

[0074] It should be understood that the application of the present application is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to this application.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A sputum obstruction detection method based on a lung binary tree model, characterized in that: The sputum obstruction detection method based on the lung binary tree model comprises: Obtaining a normal lung impedance curve of the subject under normal lung conditions; Establishing a lung binary tree model, and calibrating parameters of the lung binary tree model according to the normal lung impedance curve to obtain normal values ​​of airway geometric parameters; The current lung impedance curve of the subject during the forced oscillation test is obtained, and the location and degree of sputum obstruction of the subject are determined based on the current lung impedance curve, the normal lung impedance curve and the normal values ​​of the airway geometry parameters.

2. The sputum obstruction detection method based on the lung binary tree model according to claim 1, characterized in that: The step of obtaining a normal lung impedance curve of the subject when the lungs are in normal condition specifically includes: Obtaining pressure and flow signals at the mouth of the subject when the subject is undergoing a forced oscillation test under normal lung conditions; Preprocessing the pressure and the flow signal to obtain a preprocessed pressure and a preprocessed flow signal; A normal lung impedance curve is determined according to the preprocessed pressure and the preprocessed flow signal.

3. The sputum obstruction detection method based on the lung binary tree model according to claim 2, characterized in that: Determining a normal pulmonary impedance curve according to the preprocessed pressure and the preprocessed flow signal specifically includes: For each frequency point, calculate the pressure autopower spectrum corresponding to the preprocessed pressure under each sliding window, the flow autopower spectrum and the cross-power spectrum corresponding to the preprocessed flow signal; Calculate a first average value corresponding to the pressure autopower spectrum and a second average value corresponding to the cross-power spectrum under all sliding windows; Calculating the impedance value at each frequency point according to the first average value and the second average value; A normal lung impedance curve is obtained according to the multiple impedance values ​​at different frequency points.

4. The sputum obstruction detection method based on the lung binary tree model according to claim 1, characterized in that: The step of calibrating the parameters of the lung binary tree model according to the normal lung impedance curve to obtain normal values ​​of airway geometric parameters specifically includes: Determining parameters to be optimized in the lung binary tree model; The root mean square error between the simulated impedance curve fitted by the pulmonary binary tree model and the normal pulmonary impedance curve is used as a fitness function; Based on the fitness function, the parameters to be optimized are optimized to obtain normal values ​​of airway geometric parameters.

5. The sputum obstruction detection method based on the lung binary tree model according to claim 4, characterized in that: The optimizing the parameters to be optimized based on the fitness function to obtain normal values ​​of airway geometric parameters specifically includes: Initialize the position and speed of each particle; wherein the position of the particle represents a combination of the parameters to be optimized, and the speed of the particle represents the direction and magnitude of the update of the parameters to be optimized; In each iteration, the fitness value of each particle is calculated according to the fitness function, and the individual historical optimal position and the global optimal position are updated according to the fitness value to obtain the current individual historical optimal position and the current global optimal position; According to the current individual historical optimal position and the current global optimal position, the position and speed of each particle are updated to obtain the current position and current speed of each particle; When the current number of iterations reaches the maximum number of iterations, or the fitness value corresponding to the current position is less than a preset threshold, the iteration is stopped, and the current global optimal position corresponding to the current position is used as the normal value of the airway geometric parameter.

6. The sputum obstruction detection method based on the lung binary tree model according to claim 1, characterized in that: The determining the location and degree of sputum obstruction of the subject according to the current pulmonary impedance curve, the normal pulmonary impedance curve and the normal value of the airway geometry parameter specifically includes: Calculating the root mean square error value of the current lung impedance curve and the normal lung impedance curve at all frequencies; Setting a sputum blockage threshold, and if the root mean square error value is greater than the sputum blockage threshold, determining a current airway geometry parameter fitting value according to the current lung impedance curve; The location and degree of sputum obstruction are determined according to the current airway geometry parameter fitting value and the normal value of the airway geometry parameter.

7. The sputum obstruction detection method based on the lung binary tree model according to claim 6, characterized in that: The step of calculating the root mean square error between the current lung impedance curve and the normal lung impedance curve at all frequencies further includes: If the root mean square error value is less than the sputum blockage threshold, it is determined that the subject does not have sputum blockage.

8. A sputum obstruction detection system based on a lung binary tree model, characterized in that: The sputum obstruction detection system based on the lung binary tree model includes: A lung impedance calculation module, used to obtain a normal lung impedance curve of a subject under normal lung conditions; A lung model parameter calibration module, used to establish a lung binary tree model, calibrate the parameters of the lung binary tree model according to the normal lung impedance curve, and obtain normal values ​​of airway geometric parameters; The sputum obstruction identification module is used to obtain the current lung impedance curve of the subject during the forced oscillation test during sputum accumulation detection, and determine the location and degree of sputum obstruction of the subject based on the current lung impedance curve, the normal lung impedance curve and the normal values ​​of the airway geometry parameters.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a sputum obstruction detection program based on a pulmonary binary tree model stored in the memory and executable on the processor. When the sputum obstruction detection program based on a pulmonary binary tree model is executed by the processor, the steps of the sputum obstruction detection method based on a pulmonary binary tree model as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a sputum obstruction detection program based on a pulmonary binary tree model, and when the sputum obstruction detection program based on a pulmonary binary tree model is executed by a processor, the steps of the sputum obstruction detection method based on a pulmonary binary tree model as described in any one of claims 1-7 are implemented.

Images