Methods, apparatus, devices, and media to estimate airway resistance and compliance

Abstract

This invention discloses a method for estimating airway resistance and compliance. The method includes: acquiring multiple sets of sampled data and acquiring a preset discrete nonlinear airway resistance model. Each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a single sampling time. Based on the multiple sets of sampled data and a pre-established parameter update model, preset parameters in the discrete nonlinear airway resistance model are solved to obtain parameter values. Based on the parameter values ​​and the substitution relationships between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, linear airway resistance, nonlinear airway resistance, and lung compliance are solved. The linear airway resistance and lung compliance are used to effectively control the ventilation modes of anesthesia machines and ventilators to suit various usage situations for different users. Furthermore, devices, equipment, and media for estimating airway resistance and compliance are also proposed.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to methods, apparatus, devices and media for estimating airway resistance and compliance. Background Technology

[0002] In the control of anesthesia machines and ventilators, it is necessary to monitor various vital signs. By accurately monitoring airway resistance (R value) and lung compliance (C value), the ventilation mode of the anesthesia machine and ventilator can be effectively controlled to suit various situations, reducing complications caused by the use of anesthesia machines and ventilators. Typically, airway resistance is calculated using the formula R = ΔP1 / ΔF, where ΔP1 = Ppeak - Pplat, Ppeak is the peak inspiratory pressure, Pplat is the airway plateau pressure, and ΔF is the peak flow rate. Lung compliance is calculated using the formula C = ΔV / ΔP2, where ΔP2 = Ppeak - PEEP, PEEP is the positive end-expiratory pressure, and ΔV is the pressure tidal volume. However, because respiratory circuit compliance (Cr value) is much smaller than lung compliance (C), the above scheme ignores Cr when calculating tidal volume, resulting in a slightly larger tidal volume (ΔV) than the actual value, thus leading to low accuracy in calculating airway resistance (R) and lung compliance (C). Moreover, the above method can only be calculated in volume-controlled ventilation (VCV) control mode with breath-holding function, which has great limitations and cannot be applied to multiple ventilation modes. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, device, and medium for estimating airway resistance and compliance to address the above-mentioned problems.

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

[0005] Multiple sets of sampling data are acquired, and a preset discrete nonlinear air resistance model is also acquired. Each set of sampling data includes airway pressure, total flow rate, and tidal volume sampled at a sampling time. The discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by replacing linear airway resistance, nonlinear airway resistance, and lung compliance with preset parameters.

[0006] Based on the multiple sets of sampled data, and based on the 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. The parameter update model is a model for updating the preset parameters.

[0007] Based on the parameter values ​​and the substitution relationships between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, the linear airway resistance, nonlinear airway resistance, and lung compliance are solved.

[0008] In one embodiment, before obtaining the preset discrete nonlinear air resistance model, the method further includes:

[0009] A first nonlinear airway resistance model is constructed to express the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance with airway pressure, total flow rate, and tidal volume.

[0010] The first linear air resistance model is discretized and its relationships are replaced to obtain the discrete nonlinear air resistance model.

[0011] In one embodiment, the step of discretizing and replacing the data of the first linear air resistance model to obtain the discrete nonlinear air resistance model includes:

[0012] Discretize the first linear air resistance model to obtain the discretized equation;

[0013] The coefficient of one term in the discretized equation is replaced with a preset parameter to obtain the first substitution relationship and the discrete nonlinear air resistance model.

[0014] In one embodiment, the step of solving for linear airway resistance, nonlinear airway resistance, and lung compliance based on the parameter values ​​and the substitution relationship between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance includes:

[0015] Based on the parameter values ​​and the first substitution relationship, linear airway resistance, nonlinear airway resistance, and lung compliance are obtained.

[0016] In one embodiment, the discrete nonlinear air resistance model includes:

[0017]

[0018] in, The preset parameters are... This represents the pressure constant, which is also one of the preset parameters. The airway pressure, For the total flow rate, The tidal volume is mentioned.

[0019] In one embodiment, the method further includes:

[0020] A second nonlinear air resistance model is constructed, which is obtained by fitting the first nonlinear air resistance model. The second nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume.

[0021] The second linear air resistance model is discretized and its relationships are replaced to obtain the discrete nonlinear air resistance model.

[0022] In one embodiment, the discrete nonlinear air resistance model includes:

[0023]

[0024] in, The preset parameters are... This represents the pressure constant, which is also one of the preset parameters. The airway pressure, For the total flow rate, The tidal volume is mentioned.

[0025] An apparatus for estimating airway resistance and compliance, the apparatus comprising:

[0026] The acquisition module is used to acquire multiple sets of sampled data and to acquire a preset discrete nonlinear air resistance model. Each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a sampling time. The discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by replacing linear airway resistance, nonlinear airway resistance, and lung compliance with preset parameters.

[0027] Based on the multiple sets of sampled data, and based on the 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. 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 linear airway resistance, nonlinear airway resistance, and lung compliance, linear airway resistance, nonlinear airway resistance, and lung compliance are solved.

[0028] A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method for estimating airway resistance and compliance described above.

[0029] An apparatus for estimating airway resistance and compliance includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method for estimating airway resistance and compliance described above.

[0030] This invention provides a method, apparatus, device, and medium for estimating airway resistance and compliance. It acquires multiple sets of sampled data and a preset discrete nonlinear airway resistance model. Each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a single sampling time. The discrete nonlinear airway resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by substituting preset parameters for linear airway resistance, nonlinear airway resistance, and lung compliance. Based on the multiple sets of sampled data and a pre-established parameter update model, the preset parameters in the discrete nonlinear airway resistance model are solved to obtain parameter values. The parameter update model updates the preset parameters. Based on the parameter values ​​and the substitution relationships between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, linear airway resistance, nonlinear airway resistance, and lung 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 usage situations for different users. This approach does not consider the relationship between tidal volume and flow rate. Instead, it uses the measured airway pressure, total flow rate, and tidal volume to estimate airway resistance and compliance. It employs a pre-defined discrete nonlinear air resistance model and parameter update model to accurately estimate airway resistance and compliance. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] in:

[0033] Figure 1 This is a flowchart illustrating a method for estimating airway resistance and compliance in one embodiment;

[0034] Figure 2 This is a schematic diagram of a device for estimating airway resistance and compliance in one embodiment;

[0035] Figure 3 This is a structural block diagram of a device for estimating airway resistance and compliance in one embodiment. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] To avoid confusion, we will first provide examples of the parameters and their corresponding symbols involved in this scheme. Linear airway resistance is expressed as... The unit is Nonlinear airway resistance is expressed as The unit is Lung compliance is expressed as The unit is The total flow velocity is expressed as The unit is Airway pressure is expressed as The unit is Tidal volume is expressed as The unit is The data sampling period is expressed as The unit is .

[0038] Airway resistance refers to the pressure difference generated per unit flow rate within the airway. Lung compliance refers to the ease with which the lungs change shape under external force. High lung compliance indicates strong deformability, meaning that a relatively small external force can induce a large deformation. R-value and C-value play a decisive role in the accuracy and precision of pressure-controlled ventilation and its extended ventilation modes. By correctly monitoring R-value and C-value, complications in users using anesthesia machines and ventilators can be effectively reduced.

[0039] To calculate accurate R and C values, this application requires the prior construction of a discrete nonlinear air resistance model. In one embodiment, the construction process of the discrete nonlinear air resistance model is as follows:

[0040] First, a first nonlinear airway resistance model is constructed. This first nonlinear airway resistance model is used to express the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance with airway pressure, total flow rate, and tidal volume, and is expressed as follows:

[0041] (1)

[0042] Discretizing the first nonlinear air resistance model yields the discretized equation:

[0043] (2)

[0044] Under the same dimensions, it is expressed as:

[0045] (3)

[0046] Replacing the coefficient of one term in the above discretized equation with a preset parameter, the resulting discrete nonlinear air resistance model is expressed as:

[0047] (4)

[0048] Therefore, the first substitution relation corresponding to the above discretized equation is:

[0049]

[0050] This first substitution relationship can also be expressed as:

[0051]

[0052] In another embodiment, the process of constructing the discrete nonlinear air resistance model is as follows:

[0053] By fitting the first nonlinear airway resistance model, a suitable exponential factor n can be obtained to replace the nonlinear airway resistance term, ultimately yielding the second nonlinear airway resistance model. This second nonlinear airway resistance model expresses the nonlinear relationship between linear airway resistance and lung compliance and airway pressure, total flow rate, and tidal volume, as follows:

[0054] (7)

[0055] Discretizing the second nonlinear air resistance model yields the discretized equation:

[0056] (8)

[0057] Under the same dimensions, it is expressed as:

[0058] (9)

[0059] Replacing the coefficient of one term in the above discretized equation with a preset parameter, the resulting discrete nonlinear air resistance model is expressed as:

[0060] (10)

[0061] Therefore, the first substitution relation corresponding to the above discretized equation is:

[0062]

[0063] The first substitution relation at this point can also be expressed as:

[0064]

[0065] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for estimating airway resistance and compliance in one embodiment. The steps provided by the method for estimating airway resistance and compliance in this embodiment include:

[0066] Step 102: Obtain multiple sets of sampled data and obtain a preset discrete nonlinear air resistance model.

[0067] Therefore, in one embodiment, the obtained discrete nonlinear air resistance model is:

[0068] (4)

[0069] In another embodiment, the obtained discrete nonlinear air resistance model is:

[0070] (10)

[0071] As can be seen, both of the above discrete nonlinear airway resistance models express the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by replacing linear airway resistance, nonlinear airway resistance, and lung compliance with preset parameters; a set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a sampling time.

[0072] The difference is that the second discrete nonlinear air resistance model is more robust than the first discrete nonlinear air resistance model, but it requires more computation.

[0073] In this embodiment, a total of N sets of sampling data are acquired. N should be as large as possible to ensure the accuracy of the preset parameter identification.

[0074] Step 104: Based on multiple sets of sampled data and a pre-established parameter update model, solve for the preset parameters in the discrete nonlinear air resistance model to obtain the parameter values ​​of the preset parameters.

[0075] The parameter update model is a model that updates preset parameters. In this implementation, the following discrete nonlinear air resistance model is used first for explanation; the solution approach for other discrete nonlinear air resistance models is the same.

[0076] (4)

[0077] The process of solving for the preset parameters is as follows:

[0078] 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 represented by a matrix as follows:

[0079]

[0080] in, Let i represent the input observations of the i-th set of data. In this embodiment, n=4, corresponding to ; , representing n preset parameters, corresponding to . This represents the output observation of the i-th data set, which corresponds to the following in this embodiment. According to the least squares criterion, the parameter values ​​of the preset parameters are obtained when the index function is minimized, that is, when the following formula is minimized.

[0081]

[0082] This index function measures the error between the airway pressure in the sampled data and the airway pressure calculated using a discrete nonlinear air resistance model. The preset parameter values ​​are:

[0083] Based on this, the parameter update model in this embodiment is represented as follows:

[0084]

[0085] in This represents the parameter value obtained from the Nth set of sampled data. This represents the parameter value obtained from the (N-1)th set of sampled data. For correction amount, Equivalent to , Equivalent to , Let the gain matrix satisfy the following formula:

[0086]

[0087] In the above formula, the covariance matrix = covariance matrix The initial value is generally given in the following way: , For sufficiently large integers .

[0088] The initial value of the preset parameter is set to , It is a zero vector or a sufficiently small real vector. The initial value is based on this preset parameter. The parameter update model is recursively applied until the parameter values ​​are obtained. .

[0089] When selected

[0090] (10)

[0091] As a pre-defined discrete nonlinear air resistance model, it is only necessary to... The input observations are adjusted accordingly, and the... The preset parameters can be adjusted accordingly; the rest of the solution process remains the same.

[0092] Step 106: Based on the parameter values ​​and the substitution relationships between preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, solve for linear airway resistance, nonlinear airway resistance, and lung compliance.

[0093] In one embodiment, when the following discrete nonlinear air resistance model is used for solving...

[0094] (4)

[0095] The solution is obtained (include After that, the linear airway resistance, nonlinear airway resistance, lung compliance, and pressure constant are first obtained based on the following first substitution relationship:

[0096]

[0097] In one embodiment, when the following discrete nonlinear air resistance model is used for solving...

[0098] (10)

[0099] The solution is obtained (include After that, the linear airway resistance, lung compliance, and pressure constant are first obtained based on the following first substitution relationship:

[0100]

[0101] The aforementioned method for estimating airway resistance and compliance involves acquiring multiple sets of sampled data and a pre-defined discrete nonlinear airway resistance model. Each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a single sampling time. The discrete nonlinear airway resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and the discretized airway pressure, total flow rate, and tidal volume by substituting pre-defined parameters for linear airway resistance, nonlinear airway resistance, and lung compliance. Based on the multiple sets of sampled data and a pre-established parameter update model, the pre-defined parameters in the discrete nonlinear airway resistance model are solved to obtain their values. The parameter update model updates the pre-defined parameters. Based on the parameter values ​​and the substitution relationships between the pre-defined parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, linear airway resistance, nonlinear airway resistance, and lung 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 usage scenarios for different users. This approach does not consider the relationship between tidal volume and flow rate. Instead, it uses the measured airway pressure, total flow rate, and tidal volume to estimate airway resistance and compliance. It employs a pre-defined discrete nonlinear air resistance model and parameter update model to accurately estimate airway resistance and compliance.

[0102] In one embodiment, such as Figure 2 As shown, an apparatus for estimating airway resistance and compliance is proposed, the apparatus comprising:

[0103] The acquisition module 202 is used to acquire multiple sets of sampled data and acquire a preset discrete nonlinear air resistance model. A set of sampled data includes airway pressure, total flow rate and tidal volume sampled at a sampling time. The discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance and lung compliance and discretized airway pressure, total flow rate and tidal volume by replacing linear airway resistance, nonlinear airway resistance and lung compliance with preset parameters.

[0104] The identification module 204 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 the parameter values ​​of the preset parameters. The parameter update model is the model for updating the preset parameters. Based on the parameter values ​​and the substitution relationship between the preset parameters and linear airway resistance, nonlinear airway resistance and lung compliance, the linear airway resistance, nonlinear airway resistance and lung compliance are solved.

[0105] In one embodiment, the acquisition module 202 is further configured to: construct a first nonlinear air resistance model, which expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume; and discretize and replace the data of the first linear air resistance model to obtain a discrete nonlinear air resistance model.

[0106] In one embodiment, the acquisition module 202 is specifically used to: discretize the first linear air resistance model to obtain a discretized equation; and replace the coefficient of a term in the discretized equation with a preset parameter to obtain a first substitution relationship and a discrete nonlinear air resistance model.

[0107] In one embodiment, linear airway resistance, nonlinear airway resistance, and lung compliance are solved based on parameter values ​​and a substitution relationship between preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, including: solving linear airway resistance, nonlinear airway resistance, and lung compliance based on parameter values ​​and a first substitution relationship.

[0108] In one embodiment, the acquisition module 202 further comprises: constructing a second nonlinear air resistance model, which is obtained by fitting a first nonlinear air resistance model, and the second nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume; and discretizing and replacing the relationship in the second linear air resistance model to obtain a discrete nonlinear air resistance model.

[0109] Figure 3 An internal structural diagram of a device for estimating airway resistance and compliance in one embodiment is shown. Figure 3 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 internal memory. The non-volatile storage medium stores an operating system and may also store a computer program that, when executed by the processor, enables the processor to implement a method for estimating airway resistance and compliance. The internal memory may also store a computer program that, when executed by the processor, enables the processor to implement a method for estimating airway resistance and compliance. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the device for estimating airway resistance and compliance applied thereto. 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 different component arrangements.

[0110] An apparatus 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, it performs the following steps: acquiring multiple sets of sampled data and acquiring a preset discrete nonlinear airway resistance model. Each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a sampling time. The discrete nonlinear airway resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by substituting preset parameters for linear airway resistance, nonlinear airway resistance, and lung compliance. Based on the multiple sets of sampled data and a pre-established parameter update model, the preset parameters in the discrete nonlinear airway resistance model are solved to obtain parameter values. The parameter update model is a model that updates the preset parameters. Based on the parameter values ​​and the substitution relationship between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, linear airway resistance, nonlinear airway resistance, and lung compliance are solved.

[0111] In one embodiment, before obtaining a preset discrete nonlinear air resistance model, the method further includes: constructing a first nonlinear air resistance model, which is used to express the nonlinear relationship between linear airway resistance, nonlinear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume; and discretizing and replacing the first linear air resistance model with its relationships to obtain a discrete nonlinear air resistance model.

[0112] In one embodiment, the first linear air resistance model is discretized and the relationship is replaced to obtain a discrete nonlinear air resistance model, including: discretizing the first linear air resistance model to obtain a discretized equation; replacing the coefficient of a term in the discretized equation with a preset parameter to obtain a first substitution relationship and a discrete nonlinear air resistance model.

[0113] In one embodiment, linear airway resistance, nonlinear airway resistance, and lung compliance are solved based on parameter values ​​and a substitution relationship between preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, including: solving linear airway resistance, nonlinear airway resistance, and lung compliance based on parameter values ​​and a first substitution relationship.

[0114] In one embodiment, the method further includes: constructing a second nonlinear air resistance model, which is obtained by fitting a first nonlinear air resistance model, and the second nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume; and discretizing and replacing the second linear air resistance model with data to obtain a discrete nonlinear air resistance model.

[0115] A computer-readable storage medium storing a computer program, which, when executed by a processor, performs the following steps: acquiring multiple sets of sampled data and acquiring a preset discrete nonlinear air resistance model, wherein each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a sampling time, wherein the discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by substituting preset parameters for linear airway resistance, nonlinear airway resistance, and lung compliance; based on the multiple sets of sampled data and a pre-established parameter update model, solving for the preset parameters in the discrete nonlinear air resistance model to obtain parameter values, wherein the parameter update model is a model for updating the preset parameters; and based on the parameter values ​​and the substitution relationship between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, solving for linear airway resistance, nonlinear airway resistance, and lung compliance.

[0116] In one embodiment, before obtaining a preset discrete nonlinear air resistance model, the method further includes: constructing a first nonlinear air resistance model, which is used to express the nonlinear relationship between linear airway resistance, nonlinear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume; and discretizing and replacing the first linear air resistance model with its relationships to obtain a discrete nonlinear air resistance model.

[0117] In one embodiment, the first linear air resistance model is discretized and the relationship is replaced to obtain a discrete nonlinear air resistance model, including: discretizing the first linear air resistance model to obtain a discretized equation; replacing the coefficient of a term in the discretized equation with a preset parameter to obtain a first substitution relationship and a discrete nonlinear air resistance model.

[0118] In one embodiment, linear airway resistance, nonlinear airway resistance, and lung compliance are solved based on parameter values ​​and a substitution relationship between preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, including: solving linear airway resistance, nonlinear airway resistance, and lung compliance based on parameter values ​​and a first substitution relationship.

[0119] In one embodiment, the method further includes: constructing a second nonlinear air resistance model, which is obtained by fitting a first nonlinear air resistance model, and the second nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume; and discretizing and replacing the second linear air resistance model with data to obtain a discrete nonlinear air resistance model.

[0120] It should be noted that the above-described methods, apparatus, devices, and computer-readable storage media for estimating airway resistance and compliance belong to the same general inventive concept, and the contents of the embodiments of methods, apparatus, devices, and computer-readable storage media for estimating airway resistance and compliance are applicable to each other.

[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, 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 various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), 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 in any way. For the sake of brevity, 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.

[0123] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for estimating airway resistance and compliance, characterized in that, The method includes: Multiple sets of sampling data are acquired, and a preset discrete nonlinear air resistance model is also acquired. Each set of sampling data includes airway pressure, total flow rate, and tidal volume sampled at a sampling time. The discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by replacing linear airway resistance, nonlinear airway resistance, and lung compliance with preset parameters. Based on the multiple sets of sampled data, and based on the 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. The parameter update model is a model for updating the preset parameters. Based on the parameter values ​​and the substitution relationships between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance, the linear airway resistance, nonlinear airway resistance, and lung compliance are solved. Before obtaining the preset discrete nonlinear air resistance model, the process further includes: A first nonlinear airway resistance model is constructed to express the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance with airway pressure, total flow rate, and tidal volume. The first nonlinear air resistance model is discretized and its relationships are replaced to obtain the discrete nonlinear air resistance model. The discrete nonlinear air resistance model includes: in, The preset parameters are... This represents the pressure constant, which is also one of the preset parameters. The airway pressure, For the total flow rate, The tidal volume; in, ; Linear airway resistance is expressed as The unit is Nonlinear airway resistance is expressed as The unit is Lung compliance is expressed as The unit is .

2. The method according to claim 1, characterized in that, The step of discretizing and replacing the data in the first nonlinear air resistance model to obtain the discrete nonlinear air resistance model includes: The first nonlinear air resistance model is discretized to obtain the discretized equation; The coefficient of one term in the discretized equation is replaced with a preset parameter to obtain the first substitution relationship and the discrete nonlinear air resistance model.

3. The method according to claim 2, characterized in that, The process of solving for linear airway resistance, nonlinear airway resistance, and lung compliance based on the parameter values ​​and the substitution relationships between the preset parameters and linear airway resistance, nonlinear airway resistance, and lung compliance includes: Based on the parameter values ​​and the first substitution relationship, linear airway resistance, nonlinear airway resistance, and lung compliance are obtained.

4. The method according to claim 1, characterized in that, The method further includes: A second nonlinear air resistance model is constructed, which is obtained by fitting the first nonlinear air resistance model. The second nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance and lung compliance and airway pressure, total flow rate and tidal volume. The second nonlinear air resistance model is discretized and its relationships are replaced to obtain the discrete nonlinear air resistance model.

5. The method according to claim 4, characterized in that, The discrete nonlinear air resistance model includes: in, The preset parameters are... This represents the pressure constant, which is also one of the preset parameters. The airway pressure, For the total flow rate, The tidal volume is mentioned.

6. A device for estimating airway resistance and compliance, characterized in that, The method of any one of claims 1-5 is applied to the apparatus, the apparatus comprising: The acquisition module is used to acquire multiple sets of sampled data and to acquire a preset discrete nonlinear air resistance model. Each set of sampled data includes airway pressure, total flow rate, and tidal volume sampled at a sampling time. The discrete nonlinear air resistance model expresses the nonlinear relationship between linear airway resistance, nonlinear airway resistance, and lung compliance and discretized airway pressure, total flow rate, and tidal volume by replacing linear airway resistance, nonlinear airway resistance, and lung compliance with preset parameters. The identification module is used to solve for 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 the 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 linear airway resistance, nonlinear airway resistance, and lung compliance, the module solves for linear airway resistance, nonlinear airway resistance, and lung compliance.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the steps of the method as described in any one of claims 1 to 5.

8. An apparatus for estimating airway resistance and compliance, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 5.

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