Method, device, apparatus and medium for estimating airway resistance and compliance

Abstract

The present invention discloses a method for estimating airway resistance and compliance, comprising: obtaining multiple sets of sampled data and a discrete nonlinear air resistance model; solving the preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters; solving the airway resistance, lung compliance, and breathing circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance. The linear airway resistance and lung compliance are used to control the ventilation mode of the anesthesia machine and the ventilator to adapt to various usage situations of different users. In the above scheme, since the influence of the breathing circuit compliance on the tidal volume is taken into account, the final required linear airway resistance and lung compliance are sufficiently accurate. In addition, a device, equipment, and medium for estimating airway resistance and compliance are also proposed.

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] Monitoring various vital signs is essential for anesthesia machine and ventilator control. By accurately monitoring airway resistance (R) and lung compliance (C), effective control of ventilation methods can be achieved to adapt to various situations and reduce complications associated with their use. Airway resistance is typically calculated using the formula R = ΔP1 / ΔF, where ΔP1 = Ppeak - Pplat, where Ppeak is peak inspiratory pressure, Pplat is plateau airway pressure, and ΔF is peak flow rate. Lung compliance is calculated using the formula C = ΔV / ΔP2, where ΔP2 = Ppeak - PEEP, where PEEP is positive end-expiratory pressure and ΔV is pressure tidal volume. However, because breathing circuit compliance (Cr) is much smaller than lung compliance (C), the aforementioned scheme ignores Cr when calculating tidal volume. This results in a slightly larger tidal volume (ΔV) than the actual value, leading to inaccurate calculations of airway resistance (R) and lung compliance (C). Moreover, the above method can only be calculated in the volume controlled ventilation (VCV) control mode with breath-holding function, which has 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 to address the above problems.

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

[0005] Acquiring multiple sets of sampled data and a preset discrete nonlinear air resistance model, wherein one set of sampled data includes airway pressure and total flow rate sampled at multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses a nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters;

[0006] According to the multiple sets of sampled data, based on a pre-established parameter update model, the preset parameters in the discrete nonlinear air resistance model are solved to obtain parameter values of the preset parameters, wherein the parameter update model is a model for updating the preset parameters;

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

[0008] In one embodiment, obtaining a preset discrete nonlinear air resistance model includes:

[0009] Obtaining a dynamic equation of a nonlinear air resistance model, and constructing a nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance according to the dynamic equation of the nonlinear air resistance model;

[0010] The nonlinear air resistance model is subjected to data discretization and relationship substitution to obtain the discrete nonlinear air resistance model.

[0011] In one embodiment, the dynamic equation of the nonlinear air 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 nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance is constructed based on the dynamic equation of the nonlinear air resistance model, including:

[0012] determining an alternative expression for the breathing circuit flow rate based on the first equation, and determining an alternative expression for the lung flow rate based on the total flow rate equation and the alternative expression for the breathing circuit flow rate;

[0013] The alternative expression for the pulmonary flow rate is substituted into the second equation to obtain a nonlinear airway resistance model with respect to linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance.

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

[0015] Removing coupling terms and higher-order terms from the nonlinear air resistance model to obtain a simplified model;

[0016] Differentiating the simplified model to obtain a differential equation;

[0017] Substituting a plurality of intermediate values for each of the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance in the differential equation to obtain a substituted differential equation and a first substitution relationship;

[0018] The substituted differential equation is discretized to obtain the discretized differential equation:

[0019] Arranging the discretized differential equation into a target expression, wherein the coefficient of at least one term in the target expression is 1;

[0020] Replacing a coefficient of a remaining term in the target expression with a preset parameter to obtain a second substitution relationship and the discrete nonlinear air resistance model after substitution, wherein the remaining term is a term whose coefficient is not 1, and the coefficient of the remaining term is composed of the intermediate value;

[0021] Solving the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance includes:

[0022] The intermediate value is solved according to the parameter value and the second substitution relationship, and the linear airway resistance, nonlinear airway resistance, lung compliance and breathing circuit compliance are calculated according to the solved intermediate value and the first substitution relationship.

[0023] In one embodiment, the discrete nonlinear air resistance model is:

[0024]

[0025] Among them, θ1, θ2, θ3, θ4 are the preset parameters, ΔP aw (k), ΔP aw (k-1), P aw (k) includes the airway pressure sampled at three consecutive sampling moments, and Δflow(k), flow(k) include the total flow rate sampled at the last two sampling moments of the three consecutive sampling moments.

[0026] In one embodiment, the step of performing data discretization and relationship substitution on the nonlinear air resistance model to obtain the discrete nonlinear air resistance model further includes:

[0027] Obtaining a correspondence between a total flow rate and a tidal volume, and replacing an integral term of the total flow rate in the nonlinear air resistance model with the tidal volume according to the correspondence between the total flow rate and the tidal volume, to obtain a nonlinear general model;

[0028] The nonlinear general model is discretized and relationally replaced to obtain the discrete nonlinear air resistance model:

[0029]

[0030] Among them, θ1, θ2, θ3, θ4, θ5, θ6 are the preset parameters, ΔP aw (k), P aw (k) includes the airway pressure sampled at two consecutive sampling moments, flow(k) is the total flow rate sampled at one sampling moment, V T (k) is the tidal volume, P con is the pressure constant.

[0031] In one embodiment, solving the preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters includes:

[0032] The multiple sets of sampled data are substituted into the discrete nonlinear air resistance model, and the initial values of the preset parameters are updated based on the parameter update model so as to minimize an index function and obtain parameter values of the preset parameters. The index function is used to measure the error between the airway pressure in the sampled data and the airway pressure calculated by the discrete nonlinear air resistance model.

[0033] A device for estimating airway resistance and compliance, the device comprising:

[0034] an acquisition module, configured to acquire multiple sets of sampled data and a preset discrete nonlinear air resistance model, wherein a set of sampled data includes airway pressure sampled at multiple consecutive sampling moments and a total flow rate sampled at the multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses a nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters;

[0035] An identification module is configured to solve preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters, wherein the parameter update model is a model for updating the preset parameters; and to solve and obtain the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance.

[0036] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor is caused to perform the steps of the above-mentioned method for estimating airway resistance and compliance.

[0037] A device for estimating airway resistance and compliance comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for estimating airway resistance and compliance.

[0038] The present invention provides a method, apparatus, device and medium for estimating airway resistance and compliance, by obtaining multiple sets of sampling data and a preset discrete nonlinear air resistance model, wherein a set of sampling data includes the airway pressure sampled at multiple consecutive sampling moments and the total flow rate sampled at multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance with preset parameters; according to the multiple sets of sampling data, based on a pre-established parameter update model, the preset parameters in the discrete nonlinear air resistance model are solved to obtain parameter values of the preset parameters, and 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 the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance, the airway resistance, lung compliance and respiratory circuit compliance are solved. Linear airway resistance and lung compliance are used to effectively control the ventilation modes of anesthesia machines and ventilators to suit various user scenarios. By considering the impact of breathing circuit compliance on tidal volume in the above solution, the required linear airway resistance and lung compliance are sufficiently accurate and applicable to various ventilation modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] in:

[0041] Figure 1 This is the RCC principle block diagram;

[0042] Figure 2 1 is a flow chart of a method for estimating airway resistance and compliance in one embodiment;

[0043] Figure 3 FIG1 is a schematic structural diagram of an apparatus for estimating airway resistance and compliance in one embodiment;

[0044] Figure 4FIG. 1 is a structural block diagram of an apparatus for estimating airway resistance and compliance in one embodiment. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] To avoid confusion, the parameters and corresponding symbols involved in this scheme are first explained with examples. Linear airway resistance is represented by R1, nonlinear airway resistance is represented by R2, lung compliance is represented by C, and breathing circuit compliance is represented by C T , the total flow rate is expressed as flow, and the airway pressure is expressed as P aw , tidal volume is expressed as V T , the data sampling period is expressed as T s .

[0047] Airway resistance refers to the pressure difference generated by a unit of airflow within the airway. Lung compliance refers to how easily the lungs change shape in response to external forces. High lung compliance indicates a strong ability to deform, meaning it can deform significantly under relatively small external forces. The R1 and C values are crucial to the accuracy and precision of pressure-controlled ventilation and its extended modes. Proper monitoring of the R1 and C values can effectively reduce complications associated with the use of anesthesia machines and ventilators.

[0048] In order to calculate accurate R1 and C values, this application needs to pre-build a discrete nonlinear air resistance model. The construction process is as follows: First, based on the following Figure 1 The RCC shown (i.e., airway resistance R, lung compliance C, and breathing circuit compliance C) T ) principle block diagram, and construct the dynamic equations of the nonlinear air resistance model as shown below.

[0049]

[0050] The first column of equations is the total flow rate equation, where 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 represents the relationship between airway pressure and breathing circuit flow rate. The third column is the second equation, which represents the relationship between airway pressure and lung flow rate.

[0051] An alternative expression for the breathing circuit flow rate based on the first equation is:

[0052]

[0053] Based on the total flow rate equation and the alternative expression of the breathing circuit flow rate, the alternative expression of the lung flow rate is determined as:

[0054]

[0055] Substituting this alternative expression for lung flow rate into the second equation yields a nonlinear airway resistance model with respect to linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance:

[0056]

[0057] From the above formula, we can see that there are flow and P in the nonlinear air resistance model. aw There are two ways to deal with the coupling terms and higher-order terms to obtain a discrete nonlinear air resistance model.

[0058] Among them, the first treatment method is to convert the nonlinear air resistance model into Item and P aw The coupling terms with flow are ignored.

[0059] Considering C T is much smaller than C, and R2 is also smaller, so the nonlinear air resistance model can be Item and P aw Ignoring the coupling term with flow, the simplified model is:

[0060]

[0061] Differentiating the simplified model, the obtained differential equation is:

[0062]

[0063] The linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance of each term in the differential equation are replaced by several intermediate values k to obtain the differential equation after substitution:

[0064]

[0065] The first substitution relationship at this time is:

[0066]

[0067] Correspondingly, the first substitution relationship can also be expressed as:

[0068]

[0069] Then the replaced differential equation is discretized by setting the sampling time to T s , using the first-order backward difference method (for example, assuming ΔP aw (k) = P aw (k)-P aw (k-1)) Discretize the replaced differential equation to obtain the discretized differential equation, which is:

[0070]

[0071] The discretized differential equation is then organized into a target expression, in which there is at least one term with a coefficient of 1, expressed as:

[0072]

[0073] In this target expression, the remaining terms are terms with coefficients other than 1, such as ΔP aw (k-1), Δflow(k), and the coefficients of the remaining terms are composed of the intermediate value k. The coefficient of one of the remaining terms in the target expression is replaced by a preset parameter θ to obtain the second substitution relationship and the discrete nonlinear air resistance model. Finally, the discrete nonlinear air resistance model is expressed as:

[0074]

[0075] Among them, θ1, θ2, θ3, and θ4 are preset parameters, ΔP aw (k), ΔP aw (k-1), P aw (k) includes the airway pressure sampled at three consecutive sampling moments, and Δflow(k), flow(k) include the total flow rate sampled at the last two sampling moments of the three consecutive sampling moments.

[0076] Correspondingly, the second substitution relation is expressed as:

[0077]

[0078] At the same time, the second substitution relationship can also be expressed as:

[0079]

[0080] The second treatment method is: do not use the nonlinear air resistance model Item and P aw The coupling terms with flow are ignored.

[0081] In this processing method, the corresponding relationship between the total flow rate and the tidal volume must be obtained first, that is:

[0082] V T(t)=∫flow(t)dt (15)

[0083] Then, according to the corresponding relationship between the total flow rate and the tidal volume, the integral term of the total flow rate in the nonlinear air resistance model is replaced by the tidal volume, and the nonlinear general model is obtained, which is expressed as:

[0084]

[0085] Then the nonlinear general model is discretized and the relation is replaced to obtain:

[0086]

[0087] Among them, θ1, θ2, θ3, θ4, θ5, and θ6 are preset parameters, and ΔP aw (k), P aw (k) includes the airway pressure sampled at two consecutive sampling moments, flow(k) is the total flow rate sampled at one sampling moment, V T (k) is the tidal volume sampled at a sampling moment.

[0088] However, when the derived model is actually used, there may be data errors. For example, the integral of the total flow rate used in the solution process may differ from the tidal volume value used, and this difference may gradually increase over time. Based on this, the derived model is modified and Pcon (pressure constant) is introduced into the formula to correct the error. The final discrete nonlinear air resistance model is:

[0089]

[0090] The substitution relationship between the existing preset parameters and linear airway resistance, nonlinear airway resistance, lung compliance and breathing circuit compliance is:

[0091]

[0092] The corresponding expression is:

[0093]

[0094] like Figure 2 As shown, Figure 2 FIG. 5 is a flow chart of a method for estimating airway resistance and compliance in one embodiment. The method for estimating airway resistance and compliance in this embodiment includes the following steps:

[0095] Step 202: Acquire multiple sets of sampled data and obtain a preset discrete nonlinear air resistance model.

[0096] Therefore, in one embodiment, the obtained discrete nonlinear air resistance model is:

[0097]

[0098] In another embodiment, the obtained discrete nonlinear air resistance model is:

[0099]

[0100] As can be seen, the two discrete nonlinear air resistance models above both express the nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters.

[0101] The difference is that the second discrete nonlinear air resistance model is more robust than the first discrete nonlinear air resistance model, but the computational complexity is greater. When the first discrete nonlinear air resistance model is selected, a set of sampling data includes the airway pressure ΔP sampled at three consecutive sampling moments. aw (k), ΔP aw (k-1), P aw (k), the total flow rate Δflow(k), flow(k) sampled at the last two sampling moments of the three sampling moments. When the second discrete nonlinear air resistance model is selected, a set of sampling data includes the airway pressure ΔP sampled at the two sampling moments aw (k), P aw (k), the total flow rate flow(k) sampled at a sampling moment and the tidal volume V sampled at a sampling moment T (k).

[0102] It is assumed that a total of N groups of sampling data are obtained in this embodiment. N should be as large as possible to ensure the accuracy of identifying the preset parameters.

[0103] Step 204 : solving preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters.

[0104] The parameter updating model is a model for updating preset parameters. In this embodiment, the following discrete nonlinear air resistance model is first used to illustrate the solution, and the solution ideas of other discrete nonlinear air resistance models are the same.

[0105]

[0106] The solution process of the preset parameters is:

[0107] After obtaining N sets of sampled data and substituting them into the discrete nonlinear air resistance model, the discrete nonlinear air resistance model can be expressed in a matrix as follows:

[0108]

[0109] in, represents the input observation of the i-th group of data, i∈N, n=4 in this embodiment, corresponding to ΔP aw (k-1), Δflow(k), flow(k), flow(k)Δflow(k);

[0110] Represents n preset parameters, corresponding to θ1, θ2, θ3, θ4. i represents the output observation of the i-th group of data, which corresponds to ΔP in this embodiment. aw (k). According to the least squares criterion, when the indicator function is minimized, that is, when the following formula is minimized, the parameter value of the preset parameter is obtained.

[0111]

[0112] This indicator function is used to measure the error between the airway pressure in the sampled data and the airway pressure calculated by the discrete nonlinear air resistance model. The parameter values of the preset parameters are:

[0113] On this basis, in this embodiment, the parameter update model is expressed as:

[0114]

[0115] in Indicates the parameter value solved under the Nth set of sampling data, Indicates the parameter value solved under the N-1th set of sampling data, is the correction amount, y(N) is equivalent to y N , Equivalent to K(N) is the gain matrix, which satisfies the following formula:

[0116]

[0117] In the above formula, the covariance matrix The initial value of the covariance matrix P(k) is generally given as follows: P(0) = cI, where α is a sufficiently large integer (10 4 ~10 10 ).

[0118] The initial values of the preset parameters are 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 is recursively deduced until the parameter value is obtained

[0119] When the second discrete nonlinear air resistance model is selected for solution, n=7 in the above process, and the rest of the solution ideas are the same.

[0120] Step 206 , based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance and breathing circuit compliance, the linear airway resistance, nonlinear airway resistance, lung compliance and breathing circuit compliance are obtained by solving.

[0121] In one embodiment, after solving After (including θ1, θ2, θ3, θ4), the intermediate value is first obtained based on the following second substitution relationship:

[0122]

[0123] Then, the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance are calculated based on the solved intermediate value and the following first alternative relationship.

[0124]

[0125] In another embodiment, after solving (Including θ1, θ2, θ3, θ4, θ5, θ6, P con ), the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance can be directly solved based on the substitution relationship between the following preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance:

[0126]

[0127] The above-mentioned method for estimating airway resistance and compliance obtains multiple sets of sampled data and a preset discrete nonlinear air resistance model, wherein the sampled data set includes airway pressure sampled at multiple consecutive sampling moments and total flow rate sampled at multiple consecutive sampling moments. The discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance with preset parameters. Based on the multiple sets of sampled data, the preset parameters in the discrete nonlinear air resistance model are solved based on a pre-established parameter update model to obtain parameter values of the preset parameters, wherein the parameter update model is a model for updating the preset parameters. The airway resistance, lung compliance, and breathing circuit compliance are solved based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance. The linear airway resistance and lung compliance are used to effectively control the ventilation mode of anesthesia machines and ventilators to adapt to various usage scenarios of different users. In the above scheme, since the influence of the compliance of the breathing circuit on the tidal volume is taken into consideration, the final required linear airway resistance and lung compliance are sufficiently accurate and applicable to various ventilation modes.

[0128] In one embodiment, Figure 3 As shown, a device for estimating airway resistance and compliance is proposed, the device comprising:

[0129] an acquisition module 302 configured to acquire multiple sets of sampled data and a preset discrete nonlinear air resistance model, wherein a set of sampled data includes airway pressure sampled at multiple consecutive sampling moments and total flow rate sampled at multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses the nonlinear relationship between the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters;

[0130] The identification module 304 is used to solve the preset parameters in the discrete nonlinear air resistance model based on multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters. The parameter update model is a model for updating the preset parameters; based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance, the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance are solved to obtain.

[0131] In one embodiment, the acquisition module 302 is specifically used to: obtain the dynamic equation of the nonlinear air resistance model, and construct a nonlinear air resistance model about linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance based on the dynamic equation of the nonlinear air resistance model; discretize the data and replace the relationship of the nonlinear air resistance model to obtain a discrete nonlinear air resistance model.

[0132] In one embodiment, the acquisition module 302 is further specifically used to: determine an alternative expression for the breathing circuit flow rate based on the first equation, determine an alternative expression for the lung flow rate based on the total flow rate equation and the alternative expression for the breathing circuit flow rate; and substitute the alternative expression for the lung flow rate into the second equation to obtain a nonlinear airway resistance model related to linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance.

[0133] In one embodiment, the acquisition module 302 is further specifically configured to: remove coupling terms and higher-order terms in the nonlinear air resistance model to obtain a simplified minimalist model; differentiate the minimalist model to obtain a differential equation; replace the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance of each term in the differential equation with a plurality of intermediate values to obtain a replaced differential equation and a first replacement relationship; discretize the replaced differential equation to obtain a discretized differential equation: organize the discretized differential equation into a target expression, wherein at least one term in the target expression has a coefficient of 1; replace the coefficient of one remaining term in the target expression with a preset parameter to obtain a second replacement relationship and a replaced discrete nonlinear air resistance model, wherein the remaining terms are terms with coefficients not equal to 1, and the coefficients of the remaining terms are composed of intermediate values; and the identification module 304 is specifically configured to: solve the intermediate value based on the parameter value and the second replacement relationship, and calculate the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance based on the solved intermediate value and the first replacement relationship.

[0134] In one embodiment, the identification module 304 is further specifically used to: obtain the corresponding relationship between the total flow rate and the tidal volume, replace the integral term of the total flow rate in the nonlinear air resistance model with the tidal volume according to the corresponding relationship between the total flow rate and the tidal volume, and obtain a nonlinear general model; discretize and replace the relationship of the nonlinear general model to obtain a discrete nonlinear air resistance model.

[0135] In one embodiment, the identification module 304 is further specifically used to: substitute multiple sets of sampled data into a discrete nonlinear air resistance model, and update the initial values of preset parameters based on a parameter update model to minimize an indicator function and obtain parameter values of the preset parameters. The indicator function is used to measure the error between the airway pressure in the sampled data and the airway pressure calculated by the discrete nonlinear air resistance model.

[0136] Figure 4 FIG. 1 shows an internal structure diagram of a device for estimating airway resistance and compliance in one embodiment. Figure 4 As shown, the device for estimating airway resistance and compliance includes a processor, a memory, and a network interface connected via a system bus. 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 may implement a 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 may implement a method for estimating airway resistance and compliance. Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the device for estimating airway resistance and compliance to which the scheme of the present application is applied. The specific device for estimating airway resistance and compliance may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0137] 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 sampled data and obtaining a preset discrete nonlinear air resistance model, wherein one set of sampled data includes airway pressure and total flow rate sampled at multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters; solving the preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters, wherein the parameter update model is a model for updating the preset parameters; and solving the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance.

[0138] In one embodiment, obtaining a preset discrete nonlinear air resistance model includes: obtaining a dynamic equation of the nonlinear air resistance model, constructing a nonlinear air resistance model about linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance according to the dynamic equation of the nonlinear air resistance model; discretizing the data and performing relationship substitution on the nonlinear air resistance model to obtain a discrete nonlinear air resistance model.

[0139] In one embodiment, the dynamic equation of the nonlinear air 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. A nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance is constructed based on the dynamic equation of the nonlinear air resistance model, including: determining an alternative expression for the breathing circuit flow rate based on the first equation, determining an alternative expression for the lung flow rate based on the total flow rate equation and the alternative expression for the breathing circuit flow rate; and substituting the alternative expression for the lung flow rate into the second equation to obtain a nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance.

[0140] In one embodiment, the nonlinear air resistance model is discretized and replaced with a relation to obtain a discrete nonlinear air resistance model, including: removing coupling terms and higher-order terms in the nonlinear air resistance model to obtain a simplified minimalist model; differentiating the minimalist model to obtain a differential equation; replacing each linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance in the differential equation with a number of intermediate values to obtain a replaced differential equation and a first replacement relation; discretizing the replaced differential equation to obtain a discretized differential equation; organizing the discretized differential equation into a target expression, wherein there is at least one coefficient in the target expression. is 1; the coefficient of a remaining term in the target expression is replaced by a preset parameter to obtain a second substitution relationship and a discrete nonlinear air resistance model after substitution, the remaining terms are terms whose coefficients are not 1, and the coefficients of the remaining terms are composed of intermediate values; according to the parameter value and the substitution relationship between the preset parameter and the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance, the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance are solved, including: solving the intermediate value according to the parameter value and the second substitution relationship, and calculating the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance according to the solved intermediate value and the first substitution relationship.

[0141] In one embodiment, data discretization and relationship substitution are performed on the nonlinear air resistance model to obtain a discrete nonlinear air resistance model, which also includes: obtaining a corresponding relationship between the total flow rate and the tidal volume, and replacing the integral term of the total flow rate in the nonlinear air resistance model with the tidal volume according to the corresponding relationship between the total flow rate and the tidal volume to obtain a nonlinear general model.

[0142] In one embodiment, according to multiple sets of sampling data, based on a pre-established parameter update model, the preset parameters in the discrete nonlinear air resistance model are solved to obtain the parameter values of the preset parameters, including: substituting the multiple sets of sampling data into the discrete nonlinear air resistance model, and updating the initial values of the preset parameters based on the parameter update model to minimize the indicator function to obtain the parameter values of the preset parameters, where the indicator function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated by the discrete nonlinear air resistance model.

[0143] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the following steps: obtaining multiple sets of sampled data and a preset discrete nonlinear air resistance model, wherein one set of sampled data includes airway pressure and total flow rate sampled at multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters; solving the preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters, wherein the parameter update model is a model for updating the preset parameters; and solving the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance.

[0144] In one embodiment, obtaining a preset discrete nonlinear air resistance model includes: obtaining a dynamic equation of the nonlinear air resistance model, constructing a nonlinear air resistance model about linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance according to the dynamic equation of the nonlinear air resistance model; discretizing the data and performing relationship substitution on the nonlinear air resistance model to obtain a discrete nonlinear air resistance model.

[0145] In one embodiment, the dynamic equation of the nonlinear air 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. A nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance is constructed based on the dynamic equation of the nonlinear air resistance model, including: determining an alternative expression for the breathing circuit flow rate based on the first equation, determining an alternative expression for the lung flow rate based on the total flow rate equation and the alternative expression for the breathing circuit flow rate; and substituting the alternative expression for the lung flow rate into the second equation to obtain a nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance.

[0146] In one embodiment, the nonlinear air resistance model is discretized and replaced with a relation to obtain a discrete nonlinear air resistance model, including: removing coupling terms and higher-order terms in the nonlinear air resistance model to obtain a simplified minimalist model; differentiating the minimalist model to obtain a differential equation; replacing each linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance in the differential equation with a number of intermediate values to obtain a replaced differential equation and a first replacement relation; discretizing the replaced differential equation to obtain a discretized differential equation; organizing the discretized differential equation into a target expression, wherein there is at least one coefficient in the target expression. is 1; the coefficient of a remaining term in the target expression is replaced by a preset parameter to obtain a second substitution relationship and a discrete nonlinear air resistance model after substitution, the remaining terms are terms whose coefficients are not 1, and the coefficients of the remaining terms are composed of intermediate values; according to the parameter value and the substitution relationship between the preset parameter and the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance, the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance are solved, including: solving the intermediate value according to the parameter value and the second substitution relationship, and calculating the linear airway resistance, nonlinear airway resistance, lung compliance and respiratory circuit compliance according to the solved intermediate value and the first substitution relationship.

[0147] In one embodiment, data discretization and relationship substitution are performed on the nonlinear air resistance model to obtain a discrete nonlinear air resistance model, which also includes: obtaining a corresponding relationship between the total flow rate and the tidal volume, and replacing the integral term of the total flow rate in the nonlinear air resistance model with the tidal volume according to the corresponding relationship between the total flow rate and the tidal volume to obtain a nonlinear general model.

[0148] In one embodiment, according to multiple sets of sampling data, based on a pre-established parameter update model, the preset parameters in the discrete nonlinear air resistance model are solved to obtain the parameter values of the preset parameters, including: substituting the multiple sets of sampling data into the discrete nonlinear air resistance model, and updating the initial values of the preset parameters based on the parameter update model to minimize the indicator function to obtain the parameter values of the preset parameters, where the indicator function is used to measure the error between the airway pressure in the sampling data and the airway pressure calculated by the discrete nonlinear air resistance model.

[0149] It should be noted that the above-mentioned methods, devices, equipment and computer-readable storage media for estimating airway resistance and compliance belong to a general inventive concept, and the contents of the embodiments of the methods, devices, equipment and computer-readable storage media for estimating airway resistance and compliance are applicable to each other.

[0150] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, which 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0151] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0152] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for estimating airway resistance and compliance, characterized in that: The method comprises: Acquiring multiple sets of sampled data and a preset discrete nonlinear air resistance model, wherein one set of sampled data includes airway pressure and total flow rate sampled at multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses a nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters; According to the multiple sets of sampled data, based on a pre-established parameter update model, the preset parameters in the discrete nonlinear air resistance model are solved to obtain parameter values of the preset parameters, wherein the parameter update model is a model for updating the preset parameters; Solving and obtaining the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance; Wherein, the discrete nonlinear air resistance model is: in, is the preset parameter, Including the airway pressure sampled at three consecutive sampling moments, Including the total flow rate sampled at the last two sampling moments of the three consecutive sampling moments; in, ; in, ; Where, is the sampling time; is the compliance of the breathing circuit; is the linear airway resistance; C is the lung compliance; is nonlinear airway resistance.

2. The method according to claim 1, characterized in that The obtaining of a preset discrete nonlinear air resistance model includes: Obtaining a dynamic equation of a nonlinear air resistance model, and constructing a nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance according to the dynamic equation of the nonlinear air resistance model; The nonlinear air resistance model is subjected to data discretization and relationship substitution to obtain the discrete nonlinear air resistance model.

3. The method according to claim 2, characterized in that The dynamic equation of the nonlinear air resistance model includes a total flow rate equation, a first equation, and a second equation. The total flow rate equation includes a breathing circuit flow rate and a lung flow rate. The first equation is a relationship equation between the airway pressure and the breathing circuit flow rate. The second equation is a relationship equation between the airway pressure and the lung flow rate. The nonlinear air resistance model regarding linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance is constructed based on the dynamic equation of the nonlinear air resistance model, including: determining an alternative expression for the breathing circuit flow rate based on the first equation, and determining an alternative expression for the lung flow rate based on the total flow rate equation and the alternative expression for the breathing circuit flow rate; The alternative expression for the pulmonary flow rate is substituted into the second equation to obtain a nonlinear airway resistance model with respect to linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance.

4. The method according to claim 2, characterized in that The step of performing data discretization and relationship replacement on the nonlinear air resistance model to obtain the discrete nonlinear air resistance model includes: Removing coupling terms and higher-order terms from the nonlinear air resistance model to obtain a simplified model; Differentiating the simplified model to obtain a differential equation; Substituting a plurality of intermediate values for each of the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance in the differential equation to obtain a substituted differential equation and a first substitution relationship; The substituted differential equation is discretized to obtain the discretized differential equation: Arranging the discretized differential equation into a target expression, wherein the coefficient of at least one term in the target expression is 1; Replacing a coefficient of a remaining term in the target expression with a preset parameter to obtain a second substitution relationship and the discrete nonlinear air resistance model after substitution, wherein the remaining term is a term whose coefficient is not 1, and the coefficient of the remaining term is composed of the intermediate value; Solving the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance includes: The intermediate value is solved according to the parameter value and the second substitution relationship, and the linear airway resistance, nonlinear airway resistance, lung compliance and breathing circuit compliance are calculated according to the solved intermediate value and the first substitution relationship.

5. The method according to claim 2, characterized in that The performing data discretization and relationship replacement on the nonlinear air resistance model to obtain the discrete nonlinear air resistance model further includes: Obtaining a correspondence between a total flow rate and a tidal volume, and replacing an integral term of the total flow rate in the nonlinear air resistance model with the tidal volume according to the correspondence between the total flow rate and the tidal volume, to obtain a nonlinear general model; The nonlinear general model is discretized and relationally replaced to obtain the discrete nonlinear air resistance model: in, is the preset parameter, Including the airway pressure sampled at two consecutive sampling moments, is the total flow rate sampled at a sampling moment, is the tidal volume, is the pressure constant.

6. The method according to claim 1, characterized in that Solving the preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters includes: The multiple sets of sampled data are substituted into the discrete nonlinear air resistance model, and the initial values of the preset parameters are updated based on the parameter update model so as to minimize an index function and obtain parameter values of the preset parameters. The index function is used to measure the error between the airway pressure in the sampled data and the airway pressure calculated by the discrete nonlinear air resistance model.

7. A device for estimating airway resistance and compliance, characterized in that The device comprises: an acquisition module, configured to acquire multiple sets of sampled data and a preset discrete nonlinear air resistance model, wherein a set of sampled data includes airway pressure sampled at multiple consecutive sampling moments and a total flow rate sampled at the multiple consecutive sampling moments, wherein the discrete nonlinear air resistance model expresses a nonlinear relationship between linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance and the discretized airway pressure and total flow rate by replacing the linear airway resistance, nonlinear airway resistance, lung compliance, and respiratory circuit compliance with preset parameters; an identification module, configured to solve preset parameters in the discrete nonlinear air resistance model based on the multiple sets of sampled data and a pre-established parameter update model to obtain parameter values of the preset parameters, wherein the parameter update model is a model for updating the preset parameters; and to solve and obtain the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance based on the parameter values and the substitution relationship between the preset parameters and the linear airway resistance, nonlinear airway resistance, lung compliance, and breathing circuit compliance; Wherein, the discrete nonlinear air resistance model is: in, is the preset parameter, Including the airway pressure sampled at three consecutive sampling moments, Including the total flow rate sampled at the last two sampling moments of the three consecutive sampling moments; in, ; in, ; Where, is the sampling time; is the compliance of the breathing circuit; is the linear airway resistance; C is the lung compliance; is nonlinear airway resistance.

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 perform the steps of the method according to any one of claims 1 to 6.

9. A device 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 perform the steps of the method according to any one of claims 1 to 6.

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