Methods, devices, equipment, and media for estimating airway resistance and compliance

Abstract

The present invention discloses a method for estimating airway resistance and compliance. The method includes: obtaining multiple sets of sampling data and obtaining a preset discrete linear airway resistance model; based on the multiple sets of sampling data and a pre-established parameter update model, solving for preset parameters in the discrete linear airway resistance model to obtain parameter values of the preset parameters; and based on the parameter values and substitution relationships between the preset parameters and airway resistance, lung compliance, and breathing circuit compliance, solving to obtain airway resistance, lung compliance, and breathing circuit compliance. In addition, a device, equipment, and storage medium for estimating airway resistance and compliance are also proposed. Airway resistance and lung compliance are used to control the ventilation modes of anesthetic machines and ventilators to suit various usage scenarios of different users. In the above solution, since the influence of breathing circuit compliance on tidal volume is considered, the finally obtained airway resistance and lung compliance are accurate enough and can be applied to multiple ventilation modes.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a method, device, equipment and medium for estimating airway resistance and compliance. Background Art

[0002] In the control of anesthetic machines and ventilators, various physical signs need to be monitored. By correctly monitoring the airway resistance (R value) and lung compliance (C value), the ventilation modes of anesthetic machines and ventilators can be effectively controlled to suit different situations, and the complications caused by the use of anesthetic machines and ventilators can be reduced. Usually, the formula R = ΔP1 / ΔF is used to calculate the airway resistance, where ΔP1 = Ppeak - Pplat, Ppeak is the peak inspiratory pressure, Pplat is the airway plateau pressure, and ΔF is the peak flow rate; the formula C = ΔV / ΔP2 is used to calculate the lung compliance, where ΔP2 = Ppeak - PEEP, PEEP is the positive end-expiratory pressure, and ΔV is the tidal volume. However, since the respiratory circuit compliance (Cr value) is much smaller than the lung compliance C, the above scheme ignores Cr when calculating the tidal volume, resulting in the obtained tidal volume ΔV being slightly larger than the actual value, and further resulting in low accuracy in calculating the airway resistance R and lung compliance C. Moreover, the above method can only be calculated in the volume controlled ventilation (VCV) control mode with a breath-holding function, with great limitations and cannot be applied to various ventilation modes. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device, equipment and medium for estimating airway resistance and compliance in view of the above problems.

[0004] A method for estimating airway resistance and compliance, the method comprising:

[0005] Obtaining a plurality of sets of sampling data, and obtaining a preset discrete linear airway resistance model. A set of sampling data includes the airway pressure sampled at three consecutive sampling times and the total flow rate sampled at the last two of the three consecutive sampling times. The discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance and respiratory circuit compliance and the discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance and respiratory circuit compliance;

[0006] According to the plurality of sets of sampling data, based on a pre-established parameter update model, solving the preset parameters in the discrete linear airway resistance model to obtain the parameter values of the preset parameters, where the parameter update model is a model for updating the preset parameters;

[0007] According to the parameter values and the substitution relationships between the preset parameters and airway resistance, lung compliance, and breathing circuit compliance, the airway resistance, lung compliance, and breathing circuit compliance are solved and obtained.

[0008] In one embodiment, the obtaining of the preset discrete linear airway resistance model includes:

[0009] Obtain the dynamic equation of the linear airway resistance model, and construct a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the dynamic equation of the linear airway resistance model;

[0010] Perform data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model.

[0011] In one embodiment, the dynamic equation of the linear airway resistance model includes a total flow rate equation, a first equation, and a second equation. The total flow rate equation includes the breathing circuit flow rate and the lung flow rate. The first equation is the relationship equation between the airway pressure and the breathing circuit flow rate, and the second equation is the relationship equation between the airway pressure and the lung flow rate. The constructing of a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the dynamic equation of the linear airway resistance model includes:

[0012] Determine the alternative expression of the breathing circuit flow rate according to the first equation, and determine the alternative expression of the lung flow rate according to the total flow rate equation and the alternative expression of the breathing circuit flow rate;

[0013] Substitute the alternative expression of the lung flow rate into the second equation to obtain a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance.

[0014] In one embodiment, the performing of data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model includes:

[0015] Differentiate the linear airway resistance model to obtain the obtained differential equation;

[0016] Substitute the airway resistance, lung compliance, and breathing circuit compliance in each term of the differential equation with a number of intermediate values to obtain the substituted differential equation and the first substitution relationship;

[0017] Discretize the substituted differential equation to obtain the discretized differential equation:

[0018] Arrange the discretized differential equation into a target expression, and there is at least one coefficient in the target expression that is 1;

[0019] Replace the coefficient of one of the remaining terms in the target expression with a preset parameter to obtain a second substitution relationship and the discrete linear air resistance model, where the remaining term is a term with a coefficient not equal to 1, and the coefficient of the remaining term is composed of the intermediate values.

[0020] In one embodiment, the solving for airway resistance, lung compliance, and breathing circuit compliance according to the parameter value and the substitution relationship between the preset parameter and airway resistance, lung compliance, and breathing circuit compliance includes:

[0021] Solve for the intermediate value according to the parameter value and the second substitution relationship, and calculate airway resistance, lung compliance, and breathing circuit compliance according to the solved intermediate value and the first substitution relationship.

[0022] In one embodiment, the discrete linear air resistance model is:

[0023] P aw (k) = θ1P aw (k - 1) + θ2P aw (k - 2) + θ3 flow(k) + θ4 flow(k - 1)

[0024] where θ1, θ2, θ3, and θ4 are the preset parameters, P aw (k), P aw (k - 1), P aw (k - 2) are the airway pressures sampled at three consecutive sampling times, and flow(k), flow(k - 1) are the total flow rates sampled at the last two sampling times among the three consecutive sampling times.

[0025] In one embodiment, the solving for the parameter values of the preset parameters in the discrete linear air resistance model according to the multiple sets of sampling data based on a pre-established parameter update model includes:

[0026] Substitute the multiple sets of sampling data into the discrete linear air resistance model, and update the initial values of the preset parameters based on the parameter update model to minimize an index function, where the index function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated by the discrete linear air resistance model.

[0027] An apparatus for estimating airway resistance and compliance, the apparatus includes:

[0028] An acquisition module for acquiring multiple sets of sampling data and a preset discrete linear airway resistance model. One set of sampling data includes airway pressures sampled at three consecutive sampling moments and total flow rates sampled at the last two of the three consecutive sampling moments. The discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and breathing circuit compliance and discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and breathing circuit compliance;

[0029] An identification module for solving for the parameter values of the preset parameters in the discrete linear airway resistance model based on a pre-established parameter update model according to the multiple sets of sampling data. The parameter update model is a model for updating the preset parameters; and solving for airway resistance, lung compliance, and breathing circuit compliance according to the parameter values and the substitution relationship between the preset parameters and airway resistance, lung compliance, and breathing circuit compliance.

[0030] A computer-readable storage medium stores a computer program, which when executed by a processor causes the processor to execute the steps of the method for estimating airway resistance and compliance described above.

[0031] An apparatus for estimating airway resistance and compliance includes a memory and a processor. The memory stores a computer program, which when executed by the processor causes the processor to execute the steps of the method for estimating airway resistance and compliance described above.

[0032] The present invention provides a method, device, apparatus, and medium for estimating airway resistance and compliance. By acquiring multiple sets of sampling data and a preset discrete linear airway resistance model, one set of sampling data includes airway pressures sampled at three consecutive sampling moments and total flow rates sampled at the last two of the three consecutive sampling moments. The discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and breathing circuit compliance and discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and breathing circuit compliance; solving for the parameter values of the preset parameters in the discrete linear airway resistance model based on a pre-established parameter update model according to the multiple sets of sampling data; and solving for airway resistance, lung compliance, and breathing circuit compliance according to the parameter values and the substitution relationship between the preset parameters and airway resistance, lung compliance, and breathing circuit compliance. Airway resistance and lung compliance are used to effectively control the ventilation modes of an anesthesia machine and a ventilator to suit various usage situations of different users. In the above solution, since the influence of breathing circuit compliance on tidal volume is considered, the finally obtained airway resistance and lung compliance are accurate enough and can be applied to multiple ventilation modes. Brief Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0034] Among them:

[0035] Figure 1 is the principle block diagram of RCC;

[0036] Figure 2 is the flowchart of the method for estimating airway resistance and compliance in one embodiment;

[0037] Figure 3 is the structural schematic diagram of the device for estimating airway resistance and compliance in one embodiment;

[0038] Figure 4 is the structural block diagram of the device for estimating airway resistance and compliance in one embodiment. Detailed implementation manners

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0040] To avoid confusion, first, the parameters and corresponding symbols appearing in this solution are exemplified. Among them, airway resistance is represented as R, lung compliance is represented as C, respiratory circuit compliance is represented as C T , total flow rate is represented as flow, airway pressure is represented as P aw , tidal volume is represented as V T , data sampling period is represented as T s .

[0041] Airway resistance refers to the pressure difference generated by unit flow rate in the airway. Lung compliance refers to the ease with which the lung changes under the action of external force. The large compliance of the lung indicates its strong deformation ability, that is, a large deformation can be caused under a small external force. The values of R and C play a decisive role in the accuracy and precision of pressure control ventilation and its extended ventilation modes. By correctly monitoring the values of R and C, the complications caused by users when using anesthetic machines and ventilators can be effectively reduced.

[0042] To calculate accurate values of R and C in this application, a discrete linear airway resistance model needs to be constructed in advance. The construction process is as follows: First, based on the Figure 1 principle block diagram of RCC (i.e., airway resistance R, lung compliance C, and breathing circuit compliance C T ), the dynamic equation of the linear airway resistance model is constructed as follows

[0043]

[0044] Among them, the first column of equations is the total flow rate equation, flow(t) is the total flow rate, flow T(t) is the breathing circuit flow rate, and flow C(t) is the lung flow rate. The second column of equations is the first equation, which is the relationship equation between airway pressure and breathing circuit flow rate. The third column is the second equation, which is the relationship equation between airway pressure and lung flow rate.

[0045] Based on the first equation, an alternative expression for the breathing circuit flow rate is determined as:

[0046]

[0047] Then, based on the total flow rate equation and the alternative expression of the breathing circuit flow rate, an alternative expression for the lung flow rate is determined as:

[0048]

[0049] Substitute the alternative expression of the lung flow rate into the second equation to obtain the linear airway resistance model for airway resistance, lung compliance, and breathing circuit compliance as:

[0050]

[0051] Then, differentiate the above linear airway resistance model to obtain the differential equation as:

[0052]

[0053] Replace the airway resistance, lung compliance, and breathing circuit compliance in each term of the differential equation with a number of intermediate values to obtain the substituted differential equation as:

[0054]

[0055] The first substitution relationship at this time is:

[0056]

[0057] Correspondingly, this first substitution relationship can also be expressed as:

[0058]

[0059] Then, discretize the substituted differential equation by setting the sampling time as T s , and adopt the first-order backward difference method (for example, set ΔP aw (k) = P aw (k) - P aw (k - 1)) to discretize the substituted differential equation and obtain the discretized differential equation as follows:

[0060]

[0061] That is:

[0062] (1 + T s k3)P aw (k) - (2 + T s k3)P aw (k - 1) + P aw (k - 2)

[0063] =(1 + T s k2)T s k1flow(k) - T s k1flow(k - 1) (10)

[0064] Then, organize the discretized differential equation into the target expression. In the target expression, there is at least one coefficient equal to 1, which is expressed as:

[0065]

[0066] In this target expression, the remaining terms are those with coefficients not equal to 1, such as P aw (k - 1), P aw (k - 2), and the coefficients of the remaining terms are composed of the intermediate value k. Substitute the coefficient of one of the remaining terms in the target expression with a preset parameter θ to obtain the second substitution relationship and the discrete linear air resistance model. Finally, the discrete linear air resistance model is expressed as:

[0067] P aw (k) = θ1P aw (k - 1) + θ2P aw (k - 2) + θ3 flow(k) + θ4 flow(k - 1) (12)

[0068] where θ1, θ2, θ3, θ4 are preset parameters, and P aw (k), P aw (k - 1), P aw (k - 2) are the airway pressures sampled at three consecutive sampling times, and flow(k), flow(k - 1) are the total flow velocities sampled at the last two of the three consecutive sampling times.

[0069] Correspondingly, the second substitution relationship is expressed as:

[0070]

[0071] Meanwhile, the second substitution relationship can also be expressed as:

[0072]

[0073] As Figure 2 shown, Figure 2 is a schematic flowchart of a method for estimating airway resistance and compliance in an embodiment. The steps provided by the method for estimating airway resistance and compliance in this embodiment include:

[0074] Step 202, obtain multiple sets of sampling data and obtain a preset discrete linear airway resistance model.

[0075] Therefore, the preset discrete linear airway resistance model is:

[0076] P aw (k) = θ1P aw (k - 1)+θ2P aw (k - 2)+θ3 flow(k)+θ4 flow (k - 1) (12)

[0077] This discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and breathing circuit compliance and discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and breathing circuit compliance.

[0078] Based on the above discrete linear airway resistance model, a set of sampling data includes the airway pressures P aw (k), P aw (k - 1), and P aw (k - 2) sampled at three consecutive sampling moments, and also includes the total flow rates flow(k) and flow(k - 1) sampled at the last two sampling moments among the three consecutive sampling moments. It is set that a total of N sets of sampling data are obtained in this embodiment, and N should be as large as possible to ensure the accuracy of the identification of the preset parameters.

[0079] Step 204, according to multiple sets of sampling data, solve the preset parameters in the discrete linear airway resistance model based on a pre-established parameter update model to obtain the parameter values of the preset parameters.

[0080] Among them, the parameter update model is a model for updating the preset parameters. The solution process of the preset parameters is:

[0081] After obtaining N sets of sampling data and substituting them into the discrete linear air resistance model, the discrete linear air resistance model can be represented by a matrix as follows:

[0082]

[0083] Among them, represents the input observation of the i-th group of data, i ∈ N. In this embodiment, n = 4, corresponding to P aw (k - 1), P aw (k - 2), flow(k), flow(k - 1);

[0084] represents n preset parameters, corresponding to θ1, θ2, θ3, θ4. y i represents the output observation of the i-th group of data. In this embodiment, it corresponds to P aw (k). According to the least squares criterion, when the index function is minimized, that is, when the following formula is the smallest, the parameter values of the preset parameters are solved.

[0085]

[0086] This index function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated by the discrete linear air resistance model. At this time, the parameter values of the preset parameters are:

[0087] On this basis, in this embodiment, the parameter update model is expressed as:

[0088]

[0089] Among them represents the parameter value solved under the N-th group of sampling data, represents the parameter value solved under the N - 1-th group of sampling data, is the correction amount, y(N) is equivalent to y N , is equivalent to K(N) is the gain matrix, satisfying the following formula:

[0090]

[0091] In the above formula, the covariance matrix The initial value of the covariance matrix P(k) is generally given as follows: P(0) = αI, where α is a sufficiently large integer (10 4 ~10 10 ).

[0092] The initial value of the preset parameter is set to ξ is a zero vector or a sufficiently small real vector. Based on the initial value of the preset parameter and the parameter update model for recursion until the parameter value is obtained

[0093] Step 206, according to the parameter value, and the substitution relationship between the preset parameter and airway resistance, lung compliance, and respiratory circuit compliance, solve to obtain airway resistance, lung compliance, and respiratory circuit compliance.

[0094] After solving to obtain (including θ1, θ2, θ3, θ4), first obtain the intermediate value based on the following second substitution relationship:

[0095]

[0096] Then calculate the airway resistance, lung compliance, and respiratory circuit compliance based on the solved intermediate value and the following first substitution relationship.

[0097]

[0098] The above method for estimating airway resistance and compliance obtains multiple sets of sampling data and obtains a preset discrete linear airway resistance model. One set of sampling data includes airway pressures sampled at three consecutive sampling times and total flow rates sampled at the last two of the three consecutive sampling times. The discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by substituting the preset parameter for airway resistance, lung compliance, and respiratory circuit compliance; based on multiple sets of sampling data, solve the preset parameter in the discrete linear airway resistance model according to the pre-established parameter update model to obtain the parameter value of the preset parameter; according to the parameter value and the substitution relationship between the preset parameter and airway resistance, lung compliance, and respiratory circuit compliance, solve to obtain airway resistance, lung compliance, and respiratory circuit compliance. Among them, airway resistance and lung compliance are used to effectively control the ventilation mode of the anesthesia machine and ventilator to suit various usage situations of different users. In the above solution, due to considering the influence of respiratory circuit compliance on tidal volume, the finally obtained airway resistance and lung compliance are accurate enough and can be applied to various ventilation modes.

[0099] In one embodiment, as Figure 3 shown, a device for estimating airway resistance and compliance is proposed. The device includes:

[0100] An acquisition module 302 is configured to acquire multiple sets of sampling data and a preset discrete linear airway resistance model. One set of sampling data includes airway pressures sampled at three consecutive sampling times and total flow rates sampled at the last two of the three consecutive sampling times. The discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and breathing circuit compliance and discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and breathing circuit compliance.

[0101] An identification module 304 is configured to solve for the preset parameters in the discrete linear airway resistance model based on a pre-established parameter update model according to multiple sets of sampling data, so as to obtain the parameter values of the preset parameters. The parameter update model is a model for updating the preset parameters. According to the parameter values and the substitution relationship between the preset parameters and airway resistance, lung compliance, and breathing circuit compliance, airway resistance, lung compliance, and breathing circuit compliance are solved.

[0102] In one embodiment, the acquisition module 302 is specifically configured to: acquire the dynamic equation of the linear airway resistance model, and construct a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the dynamic equation of the linear airway resistance model; perform data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model.

[0103] In one embodiment, the acquisition module 302 is further specifically configured to: determine an alternative expression of the breathing circuit flow rate according to a first equation, and determine an alternative expression of the lung flow rate according to the total flow rate equation and the alternative expression of the breathing circuit flow rate; substitute the alternative expression of the lung flow rate into a second equation to obtain a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance.

[0104] In one embodiment, the acquisition module 302 is further specifically configured to: differentiate the linear airway resistance model to obtain a differential equation; substitute airway resistance, lung compliance, and breathing circuit compliance in each term of the differential equation with a plurality of intermediate values to obtain a substituted differential equation and a first substitution relationship; discretize the substituted differential equation to obtain a discretized differential equation; organize the discretized differential equation into a target expression, where at least one coefficient in the target expression is 1; substitute the coefficient of one of the remaining terms in the target expression with a preset parameter to obtain a second substitution relationship and the discrete linear airway resistance model, where the remaining terms are terms with coefficients not equal to 1, and the coefficients of the remaining terms are composed of intermediate values.

[0105] In one embodiment, the identification module 304 is specifically configured to: solve for the intermediate values according to the parameter values and the second substitution relationship, and calculate airway resistance, lung compliance, and breathing circuit compliance according to the solved intermediate values and the first substitution relationship.

[0106] In one embodiment, the identification module 304 is further specifically configured to: substitute multiple sets of sampling data into the discrete linear airway resistance model, and update the initial value of the preset parameter based on the parameter update model to minimize the index function, so as to obtain the parameter value of the preset parameter, where the index function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated by the discrete linear airway resistance model.

[0107] Figure 4 The internal structure diagram of a device for estimating airway resistance and compliance in one embodiment is shown. As Figure 4 shown, the device for estimating airway resistance and compliance includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the device for estimating airway resistance and compliance stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the method for estimating airway resistance and compliance. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can execute the method for estimating airway resistance and compliance. Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the device for estimating airway resistance and compliance to which the solution of the present application is applied. The specific device for estimating airway resistance and compliance may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0108] A device for estimating airway resistance and compliance includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: obtaining multiple sets of sampling data, and obtaining a preset discrete linear airway resistance model, where a set of sampling data includes the airway pressure sampled at three consecutive sampling times and the total flow rate sampled at the last two of the three consecutive sampling times. Among them, the discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and respiratory circuit compliance; according to multiple sets of sampling data, solving the preset parameters in the discrete linear airway resistance model based on a pre-established parameter update model to obtain the parameter values of the preset parameters, where the parameter update model is a model for updating the preset parameters; according to the parameter values and the substitution relationship between the preset parameters and airway resistance, lung compliance, and respiratory circuit compliance, solving to obtain airway resistance, lung compliance, and respiratory circuit compliance.

[0109] In one embodiment, obtaining a preset discrete linear airway resistance model includes: obtaining the dynamic equation of the linear airway resistance model, and constructing a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the dynamic equation of the linear airway resistance model; performing data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model.

[0110] In one embodiment, the dynamic equation of the linear airway resistance model includes a total flow rate equation, a first equation, and a second equation. The total flow rate equation includes the breathing circuit flow rate and the lung flow rate. The first equation is the relationship equation between the airway pressure and the breathing circuit flow rate, and the second equation is the relationship equation between the airway pressure and the lung flow rate. Constructing a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the dynamic equation of the linear airway resistance model includes: determining an alternative expression of the breathing circuit flow rate according to the first equation, and determining an alternative expression of the lung flow rate according to the total flow rate equation and the alternative expression of the breathing circuit flow rate; substituting the alternative expression of the lung flow rate into the second equation to obtain a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance.

[0111] In one embodiment, performing data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model includes: differentiating the linear airway resistance model to obtain the obtained differential equation; substituting airway resistance, lung compliance, and breathing circuit compliance in each term of the differential equation with a plurality of intermediate values to obtain the substituted differential equation and the first substitution relationship; discretizing the substituted differential equation to obtain the discretized differential equation: arranging the discretized differential equation into a target expression, where at least one coefficient in the target expression is 1; substituting the coefficient of one of the remaining terms in the target expression with a preset parameter to obtain the second substitution relationship and the discrete linear airway resistance model, the remaining terms being the terms with coefficients not equal to 1, and the coefficients of the remaining terms being composed of intermediate values.

[0112] In one embodiment, solving for airway resistance, lung compliance, and breathing circuit compliance according to the parameter value and the substitution relationship between the preset parameter and airway resistance, lung compliance, and breathing circuit compliance includes: solving for the intermediate value according to the parameter value and the second substitution relationship, and calculating airway resistance, lung compliance, and breathing circuit compliance according to the solved intermediate value and the first substitution relationship.

[0113] In one embodiment, based on a pre-established parameter update model and according to multiple sets of sampling data, the preset parameters in the discrete linear airway resistance model are solved to obtain the parameter values of the preset parameters, including: substituting the multiple sets of sampling data into the discrete linear airway resistance model, and updating the initial values of the preset parameters based on the parameter update model to minimize an index function, thereby obtaining the parameter values of the preset parameters. The index function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated by the discrete linear airway resistance model.

[0114] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented: obtaining multiple sets of sampling data, and obtaining a preset discrete linear airway resistance model. One set of sampling data includes the airway pressure sampled at three consecutive sampling times and the total flow rate sampled at the last two of the three consecutive sampling times. The discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and breathing circuit compliance and the discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and breathing circuit compliance; based on a pre-established parameter update model and according to multiple sets of sampling data, solving the preset parameters in the discrete linear airway resistance model to obtain the parameter values of the preset parameters. The parameter update model is a model for updating the preset parameters; according to the parameter values and the substitution relationship between the preset parameters and airway resistance, lung compliance, and breathing circuit compliance, solving to obtain airway resistance, lung compliance, and breathing circuit compliance.

[0115] In one embodiment, obtaining a preset discrete linear airway resistance model includes: obtaining the kinetic equation of the linear airway resistance model, and constructing a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the kinetic equation of the linear airway resistance model; performing data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model.

[0116] In one embodiment, the kinetic equation of the linear airway resistance model includes a total flow rate equation, a first equation, and a second equation. The total flow rate equation includes the breathing circuit flow rate and the lung flow rate. The first equation is the relationship equation between the airway pressure and the breathing circuit flow rate, and the second equation is the relationship equation between the airway pressure and the lung flow rate. Constructing a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the kinetic equation of the linear airway resistance model includes: determining the substitution expression of the breathing circuit flow rate according to the first equation, and determining the substitution expression of the lung flow rate according to the total flow rate equation and the substitution expression of the breathing circuit flow rate; substituting the substitution expression of the lung flow rate into the second equation to obtain a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance.

[0117] In one embodiment, data discretization and relationship substitution are performed on the linear air resistance model to obtain a discrete linear air resistance model, including: differentiating the linear air resistance model to obtain the resulting differential equation; substituting the airway resistance, lung compliance, and respiratory circuit compliance in each term of the differential equation with a number of intermediate values to obtain the substituted differential equation and the first substitution relationship; discretizing the substituted differential equation to obtain the discretized differential equation; arranging the discretized differential equation into a target expression, where at least one coefficient in the target expression is 1; substituting the coefficient of one of the remaining terms in the target expression with a preset parameter to obtain the second substitution relationship and the discrete linear air resistance model, the remaining terms being the terms with coefficients not equal to 1, and the coefficients of the remaining terms being composed of intermediate values.

[0118] In one embodiment, according to the parameter values and the substitution relationships between the preset parameter and the airway resistance, lung compliance, and respiratory circuit compliance, the airway resistance, lung compliance, and respiratory circuit compliance are solved, including: solving the intermediate values according to the parameter values and the second substitution relationship, and calculating the airway resistance, lung compliance, and respiratory circuit compliance according to the solved intermediate values and the first substitution relationship.

[0119] In one embodiment, according to multiple sets of sampling data, based on a pre-established parameter update model, the preset parameter in the discrete linear air resistance model is solved to obtain the parameter value of the preset parameter, including: substituting the multiple sets of sampling data into the discrete linear air resistance model, and updating the initial value of the preset parameter based on the parameter update model to minimize the index function, where the index function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated through the discrete linear air resistance model.

[0120] It should be noted that the above methods, devices, equipment, and computer-readable storage media for estimating airway resistance and compliance belong to a general inventive concept, and the contents in the embodiments of the methods, devices, equipment, and computer-readable storage media for estimating airway resistance and compliance are mutually applicable.

[0121] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. This program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0122] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0123] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for estimating airway resistance and compliance, characterized in that, The method includes: Obtaining multiple sets of sampling data and obtaining a preset discrete linear airway resistance model. One set of sampling data includes the airway pressure sampled at three consecutive sampling moments and the total flow rate sampled at the last two of the three consecutive sampling moments. Among them, the discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and breathing circuit compliance and the discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and breathing circuit compliance; According to the multiple sets of sampling data, based on a pre-established parameter update model, solve for the preset parameters in the discrete linear airway resistance model to obtain the parameter values of the preset parameters. The parameter update model is a model for updating the preset parameters; According to the parameter values and the substitution relationship between the preset parameters and airway resistance, lung compliance, and breathing circuit compliance, solve for airway resistance, lung compliance, and breathing circuit compliance; The discrete linear airway resistance model is: Among them, , , , are the preset parameters, , , are the airway pressures sampled at three consecutive sampling moments, , are the total flow rates sampled at the last two sampling moments among the three consecutive sampling moments; The airway resistance, lung compliance, and breathing circuit compliance are obtained by the following formula: ， ， ； ， ， ； Among them, is the sampling time, is the airway resistance, is the lung compliance, is the breathing circuit compliance.

2. The method according to claim 1, wherein The obtaining of the preset discrete linear airway resistance model includes: Obtaining the kinetic equation of the linear airway resistance model, and constructing a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the kinetic equation of the linear airway resistance model; Performing data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model.

3. The method according to claim 2, characterized in that, The kinetic equation of the linear airway resistance model includes a total flow rate equation, a first equation, and a second equation. The total flow rate equation includes the breathing circuit flow rate and the lung flow rate. The first equation is the relationship equation between the airway pressure and the breathing circuit flow rate. The second equation is the relationship equation between the airway pressure and the lung flow rate. The constructing of a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance according to the kinetic equation of the linear airway resistance model includes: Determining the alternative expression of the breathing circuit flow rate according to the first equation, and determining the alternative expression of the lung flow rate according to the total flow rate equation and the alternative expression of the breathing circuit flow rate; Substituting the alternative expression of the lung flow rate into the second equation to obtain a linear airway resistance model regarding airway resistance, lung compliance, and breathing circuit compliance; Among them, the relationship equation between the airway pressure and the breathing circuit flow rate is obtained by the following formula: wherein, is the relational equation between the airway pressure and the respiratory circuit flow rate, is the respiratory circuit flow rate, is the respiratory circuit compliance.

4. The method according to claim 2, wherein The performing of data discretization and relationship substitution on the linear airway resistance model to obtain the discrete linear airway resistance model includes: Differentiating the linear airway resistance model to obtain the obtained differential equation; Substituting airway resistance, lung compliance, and breathing circuit compliance in each term of the differential equation with several intermediate values to obtain the substituted differential equation and the first substitution relationship; Discretizing the substituted differential equation to obtain the discretized differential equation: Rearranging the discretized differential equation into a target expression, and there is at least one coefficient in the target expression that is 1; Replace the coefficient of a remaining term in the target expression with a preset parameter to obtain a second substitution relationship and the discrete linear airway resistance model, where the remaining term is a term with a coefficient not equal to 1, and the coefficient of the remaining term is composed of the intermediate values.

5. The method according to claim 4, characterized in that, The solving for airway resistance, lung compliance, and respiratory circuit compliance according to the parameter value and the substitution relationship between the preset parameter and airway resistance, lung compliance, and respiratory circuit compliance includes: Solving for the intermediate value according to the parameter value and the second substitution relationship, and calculating airway resistance, lung compliance, and respiratory circuit compliance according to the solved intermediate value and the first substitution relationship.

6. The method according to claim 1, wherein The solving for the parameter value of the preset parameter in the discrete linear airway resistance model based on a pre-established parameter update model according to the multiple sets of sampling data includes: Substitute the multiple sets of sampling data into the discrete linear airway resistance model, and update the initial value of the preset parameter based on the parameter update model to minimize an objective function, where the objective function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated through the discrete linear airway resistance model.

7. An apparatus for estimating airway resistance and compliance, which is applied to the method for estimating airway resistance and compliance as described in claim 1, characterized in that, The device includes: An acquisition module, configured to acquire multiple sets of sampling data and acquire a preset discrete linear airway resistance model. One set of sampling data includes the airway pressure sampled at three consecutive sampling moments and the total flow rate sampled at the last two of the three consecutive sampling moments. Among them, the discrete linear airway resistance model expresses the linear relationship between airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by substituting preset parameters for airway resistance, lung compliance, and respiratory circuit compliance. An identification module, configured to solve for the parameter value of the preset parameter in the discrete linear airway resistance model based on a pre-established parameter update model according to the multiple sets of sampling data, where the parameter update model is a model for updating the preset parameter; and solve for airway resistance, lung compliance, and respiratory circuit compliance according to the parameter value and the substitution relationship between the preset parameter and airway resistance, lung compliance, and respiratory circuit compliance.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 6.

9. An apparatus for estimating airway resistance and compliance, comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 6.

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