Breathing machine adjusting method, device and system based on electrical impedance tomography and medium

Abstract

The invention discloses a breathing machine adjusting method, device and system based on electrical impedance tomography and a medium. The method comprises the steps that chest electrical impedance data are collected in real time through EIT equipment; based on the chest electrical impedance data, obtaining an impedance value of the target lung region image; acquiring parameters of the breathing machine by using the breathing machine; inputting the respirator parameters and the impedance value of the target lung area image into a trained mathematical model to obtain a ventilation parameter predicted value output by the trained mathematical model; and performing parameter adjustment on the breathing machine based on the ventilation parameter prediction value. By means of the method, the accuracy of parameter adjustment of the breathing machine can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of thoracic impedance tomography, and in particular to a ventilator adjustment method, device, system and medium based on electrical impedance tomography. Background Art

[0002] The role of ventilators in acute and critical respiratory support is vital, and they can mainly provide effective life support for patients with acute respiratory failure and critical illness.

[0003] At present, when using a ventilator, the ventilator parameters are adjusted mainly based on the doctor's own clinical experience and observation of the patient's physiological characteristics. However, it is difficult for the doctor to accurately grasp the ventilation conditions of each area of ​​the lungs based on experience and observation alone. Therefore, relying on the doctor's own clinical experience and observation of the patient's physiological characteristics, it is impossible to accurately adjust the ventilator parameters, which can easily lead to problems such as local underventilation or overventilation. Summary of the invention

[0004] The present application provides a ventilator adjustment method, device, system and medium based on electrical impedance imaging, which can improve the accuracy of ventilator parameter adjustment.

[0005] In a first aspect, the present application provides a ventilator adjustment method based on electrical impedance imaging, the method comprising:

[0006] Use EIT equipment to collect chest electrical impedance data in real time;

[0007] Based on the chest electrical impedance data, obtaining an impedance value of a target lung region image;

[0008] Obtaining ventilator parameters using the ventilator;

[0009] Inputting the ventilator parameters and the impedance value of the target lung region image into a trained mathematical model to obtain a ventilation parameter prediction value output by the trained mathematical model;

[0010] The ventilator is adjusted based on the predicted ventilation parameter value.

[0011] A further technical solution is that obtaining the impedance value of the target lung area image based on the chest electrical impedance data includes:

[0012] Reconstructing the chest electrical impedance data using a finite element method to generate a real-time lung ventilation distribution image;

[0013] Segmenting the lung ventilation distribution image to obtain a target lung region image;

[0014] Based on the chest electrical impedance data, an impedance value of the target lung region image is obtained.

[0015] A further technical solution is that before segmenting the lung ventilation distribution image, the method further includes:

[0016] Performing denoising on the lung ventilation distribution image to obtain a denoised lung ventilation distribution image;

[0017] The step of segmenting the lung ventilation distribution image to obtain a target lung region image includes:

[0018] The denoised lung ventilation distribution image is segmented to obtain a target lung region image.

[0019] A further technical solution is that after adjusting the parameters of the ventilator based on the predicted ventilation parameter values, the method further includes:

[0020] Determine whether the effect of the adjusted ventilator parameters on the lung ventilation status meets the expected requirements;

[0021] If the effect of the adjusted ventilator parameters on the pulmonary ventilation state does not meet the expected requirements, the ventilator parameters continue to be adjusted within the preset range of the ventilation parameter prediction value until the expected requirements are met.

[0022] A further technical solution is to use the ventilator to obtain ventilator parameters, including:

[0023] Use ventilators to collect real-time ventilation data;

[0024] Based on the real-time ventilation data, ventilator parameters are obtained.

[0025] A further technical solution is that the ventilation data includes tidal volume and airway pressure, the ventilator parameters include respiratory cycle and average airway pressure, and the ventilator parameters are obtained based on the real-time ventilation data, including:

[0026] Based on the changes in the airway pressure and / or the tidal volume, obtaining the time points of the start and end of breathing;

[0027] Based on the time points of the start and end of breathing, the breathing cycle is obtained;

[0028] Obtaining the airway pressure at all sampling points in the respiratory cycle;

[0029] The average airway pressure of all sampling points in the respiratory cycle is calculated using an integration or discrete average method to obtain the average airway pressure.

[0030] A further technical solution is that the trained mathematical model is trained in the following manner:

[0031] Using the ventilator to obtain training sets of ventilator parameters and chest electrical impedance data respectively;

[0032] The mathematical model to be trained is trained using the training set of the ventilator parameters and chest electrical impedance data to obtain a trained mathematical model.

[0033] In a second aspect, the present application provides a device for adjusting ventilator parameters, and the device for adjusting ventilator parameters includes a unit for executing any of the above methods.

[0034] In a third aspect, the present application provides a ventilator adjustment system, the ventilator adjustment system includes a memory and a processor, the memory is used to store program data, and the processor is used to execute the program data to implement the steps of any of the above methods.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium, which is used to store a computer program, and when the computer program is executed by a processor, it is used to implement the steps of any of the above methods.

[0036] The beneficial effect of the present application is as follows: different from the prior art, the present application utilizes EIT equipment to collect chest electrical impedance data in real time, so as to obtain the impedance value of the target lung area image based on the chest electrical impedance data, and then inputs the impedance value of the target lung area and the ventilator parameters into a trained mathematical model, utilizes the trained mathematical model to predict the ventilation parameter prediction value, and adjusts the ventilator parameters based on the ventilation parameter prediction value. Since the ventilation parameter prediction value is predicted by the trained mathematical model based on the chest electrical impedance data collected by the EIT equipment in real time, the ventilation parameter prediction value can be considered as the optimal ventilation parameter under the current respiratory state. Therefore, adjusting the ventilator parameters according to the ventilation parameter prediction value can improve the accuracy of the ventilator parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0038] Figure 1 It is a flow chart of a first embodiment of a ventilator adjustment method based on electrical impedance imaging provided by the present application;

[0039] Figure 2 It is a structural schematic diagram of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It will be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only some but not all structures related to the present application are shown in the drawings. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0041] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0042] In the field of modern medicine, ventilators play an irreplaceable and important role in rescuing and treating patients with respiratory failure. The following are the importance and clinical value of ventilators:

[0043] 1. The importance of ventilators

[0044] a) Treatment of acute respiratory failure: Ventilator is the core means of treating acute respiratory failure such as acute respiratory distress syndrome (ARDS), severe pneumonia, and acute lung injury. It can quickly restore the patient's ventilation and oxygenation function and save lives.

[0045] b) Maintaining stable vital signs: In the intensive care unit (ICU), many critically ill patients have unstable vital signs. The ventilator can provide continuous mechanical ventilation support to help maintain the patient's vital signs stable.

[0046] c) Assisted anesthesia and surgery: During general anesthesia and complex surgery, the ventilator ensures the normal functioning of the patient's respiratory function and reduces the risk of respiratory distress during anesthesia and surgery.

[0047] d) Treatment of acute exacerbation of chronic respiratory diseases: For patients with acute exacerbation of chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD) and neuromuscular diseases, ventilators can effectively reduce the respiratory burden and improve symptoms.

[0048] 2. Clinical value

[0049] a) Efficient ventilation support: The ventilator can provide sufficient and controllable airway pressure and ventilation volume to ensure that the patient has adequate oxygen supply and carbon dioxide removal.

[0050] b) Precise control of respiratory parameters: Parameters such as tidal volume, respiratory rate, inspiratory pressure and positive end-expiratory pressure (PEEP) can be accurately adjusted to meet the individual needs of different patients.

[0051] c) Stable airway management: Ensure airway patency through endotracheal intubation or tracheotomy to effectively prevent the risk of upper airway obstruction and aspiration.

[0052] d) Real-time monitoring and alarm: Modern ventilators are equipped with advanced monitoring and alarm systems that can monitor respiratory parameters in real time, detect and alarm abnormalities in a timely manner, and ensure patient safety.

[0053] e) Assisted treatment of complex cases: In complex cases that require a high level of respiratory support, the ventilator can provide continuous and reliable ventilation support, which cannot be replaced by other means of respiratory support.

[0054] At present, when using a ventilator, the ventilator parameters are adjusted mainly based on the doctor's own clinical experience and observation of the patient's physiological characteristics. However, it is difficult for the doctor to accurately grasp the ventilation conditions of each area of ​​the lungs based on experience and observation alone. Therefore, relying on the doctor's own clinical experience and observation of the patient's physiological characteristics, it is impossible to accurately adjust the ventilator parameters, which can easily lead to problems such as local underventilation or overventilation.

[0055] In addition, inaccurate adjustment of ventilator parameters may lead to the following possible clinical complications, in addition to easily causing local hypoventilation or hyperventilation:

[0056] 1. Barotrauma: Over-expansion of alveoli due to improper ventilator parameter settings (such as excessive tidal volume or pressure).

[0057] 2. Volume injury: Mechanical damage to lung tissue caused by excessive ventilation.

[0058] 3. Atelectasis: Partial collapse of alveoli, usually due to inadequate or uneven ventilation.

[0059] 4. Ventilator-associated pneumonia: bacterial infection caused by long-term use of ventilator.

[0060] 5. Respiratory fatigue and ventilator dependence: Improper ventilator parameter settings may cause excessive or light burden on the respiratory muscles.

[0061] 5. Hemodynamic instability: Inappropriate PEEP and tidal volume settings may affect intrathoracic pressure and affect the function of the heart and great vessels.

[0062] Therefore, in order to solve the above technical problems, the present application provides a ventilator adjustment method based on electrical impedance imaging, which can improve the accuracy of ventilator parameter adjustment and help reduce the probability of complications. Please refer to the following embodiments for details.

[0063] See also Figure 1 , Figure 1 1 is a flow chart of a first embodiment of a ventilator adjustment method based on electrical impedance imaging provided by the present application. The method comprises:

[0064] Step 110: Use the EIT device to collect chest electrical impedance data in real time.

[0065] Step 120: Obtaining an impedance value of the target lung region image based on the chest electrical impedance data.

[0066] Step 130: Obtain ventilator parameters using the ventilator.

[0067] Step 140: Input the ventilator parameters and the impedance value of the target lung region image into the trained mathematical model to obtain the ventilation parameter prediction value output by the trained mathematical model.

[0068] Step 150: Adjusting the parameters of the ventilator based on the predicted ventilation parameters.

[0069] In this way, this embodiment uses the EIT device to collect chest electrical impedance data in real time, so as to obtain the impedance value of the target lung area image based on the chest electrical impedance data, and then inputs the impedance value of the target lung area and the ventilator parameters into the trained mathematical model, uses the trained mathematical model to predict the ventilation parameter prediction value, and adjusts the parameters of the ventilator based on the ventilation parameter prediction value. Since the ventilation parameter prediction value is predicted by the trained mathematical model based on the chest electrical impedance data collected by the EIT device in real time, the ventilation parameter prediction value can be considered as the optimal ventilation parameter under the current respiratory state. Therefore, adjusting the ventilator parameters according to the ventilation parameter prediction value can improve the accuracy of the ventilator parameter adjustment.

[0070] Refer to the second embodiment of the ventilator adjustment method based on electrical impedance imaging provided in this application. The method includes:

[0071] Step 210: Use the EIT device to collect chest electrical impedance data in real time.

[0072] Step 220: Reconstruct the chest electrical impedance data using a finite element method to generate a real-time lung ventilation distribution image.

[0073] Step 230: Segment the lung ventilation distribution image to obtain a target lung region image.

[0074] Among them, image segmentation methods can be used to distinguish different areas of the lungs, such as ventilation areas, restricted areas, or front and back, left and right, up and down areas, and the impedance value changes and ventilation uniformity of each area can be calculated based on the chest electrical impedance data collected in real time by the EIT equipment.

[0075] In some embodiments, before segmenting the lung ventilation distribution image, the lung ventilation distribution image may be first denoised to obtain a denoised lung ventilation distribution image; and then the denoised lung ventilation distribution image may be segmented to obtain a target lung area image.

[0076] For step 220 and step 230, reconstructing the chest electrical impedance data using the finite element method may include geometric modeling, boundary condition setting, forward problem solving, and inverse problem solving. By repeatedly optimizing the conductivity distribution, a lung ventilation distribution image of the target lung area can be reconstructed.

[0077] The specific process may include the following:

[0078] 1) Create a geometric model of the target lung region. For example, a two-dimensional or three-dimensional mesh can be defined, where the nodes and elements of the mesh will be used to discretize the target lung region.

[0079] 2) Divide the geometric model into a finite number of units (such as triangles or tetrahedrons). These units form a finite element mesh.

[0080] 3) Arrange electrodes on the boundaries of the geometric model and define the locations where current is applied and voltage is measured.

[0081] 4) Apply boundary conditions, including electrode positions, current injection, and voltage measurement modes.

[0082] 5) Define an objective function for the difference between the measured voltage and the calculated voltage.

[0083] 6) Select an appropriate optimization algorithm to minimize the objective function. Commonly used optimization algorithms include gradient descent, Newton's method, conjugate gradient method, etc.

[0084] 7) Using the conductivity distribution σ obtained by the final inversion, the electrical impedance image of the target lung area is reconstructed, and by visualizing the conductivity distribution, an EIT image, i.e., a lung ventilation distribution image, is generated to display the internal structure and characteristics.

[0085] 8) Through the optimization process, the conductivity distribution σ is continuously adjusted to reduce the objective function value.

[0086] 9) Repeat the iteration until the objective function converges to an allowable error range and obtains the final conductivity distribution map.

[0087] This can help doctors understand the ventilation status of the patient's lungs more intuitively, thereby optimizing ventilator parameters, reducing the occurrence of complications, and improving treatment effects.

[0088] Step 240: Obtaining an impedance value of the target lung region image based on the chest electrical impedance data.

[0089] Step 250: Obtain ventilator parameters using the ventilator.

[0090] Wherein, step 250 may include the following:

[0091] Step 251: using a ventilator to collect real-time ventilation data;

[0092] Step 252: Obtain ventilator parameters based on the real-time ventilation data.

[0093] In some embodiments, the ventilation data includes tidal volume and airway pressure, and the ventilator parameters include respiratory cycle and mean airway pressure.

[0094] The step of obtaining ventilator parameters based on the real-time ventilation data may include the following steps:

[0095] Step 2521: Based on the changes in the airway pressure and / or the tidal volume, obtain the time points of the start and end of breathing.

[0096] Among them, tidal volume can refer to the amount of gas inhaled or exhaled per breath; airway pressure can refer to the pressure measured in the airway.

[0097] Since the airway pressure increases and the tidal volume increases at the beginning of inhalation, and the airway pressure returns to the PEEP level and the tidal volume returns to zero at the end of exhalation, the start and end time points of breathing can be obtained based on the changes in the airway pressure and / or the tidal volume.

[0098] Step 2522: Based on the time points when breathing starts and ends, obtain the breathing cycle.

[0099] The breathing cycle refers to a complete breathing process, including the time required for one inhalation and one exhalation. Specifically, the time interval between the start of two adjacent inhalations or the end of two adjacent exhalations can be measured, and the time interval can be considered as the breathing cycle.

[0100] Step 2523: Obtain the airway pressure of all sampling points in the breathing cycle.

[0101] Step 2524: Use the integration or discrete average method to calculate the average airway pressure of all sampling points in the respiratory cycle to obtain the average airway pressure.

[0102] Among them, mean airway pressure (MAP) refers to the average value of airway pressure in one respiratory cycle.

[0103] In other embodiments, the ventilation data may further include positive end-expiratory pressure, etc., and the ventilator parameters may further include peak pressure, etc.

[0104] Among them, Peak Pressure refers to the maximum value of airway pressure during a breath.

[0105] Step 260: Input the ventilator parameters and the impedance value of the target lung region image into the trained mathematical model to obtain the ventilation parameter prediction value output by the trained mathematical model.

[0106] Among them, the parameter feature vector can be established through the chest impedance data obtained by the EIT device and the real-time ventilation parameters obtained by the ventilator to represent the current respiratory state, and then the trained mathematical model can be used to output the ventilation parameter prediction value corresponding to the current respiratory state.

[0107] Step 270: Adjust the parameters of the ventilator based on the predicted ventilation parameter values.

[0108] Refer to the third embodiment of the ventilator adjustment method based on electrical impedance imaging provided by this application. The method includes:

[0109] Step 310: Use the EIT device to collect chest electrical impedance data in real time.

[0110] Step 320: Obtaining the impedance value of the target lung region image based on the chest electrical impedance data.

[0111] Step 330: Obtain ventilator parameters using the ventilator.

[0112] Step 340: Input the ventilator parameters and the impedance value of the target lung region image into the trained mathematical model to obtain the ventilation parameter prediction value output by the trained mathematical model.

[0113] Step 350: Adjust the parameters of the ventilator based on the predicted ventilation parameter values.

[0114] Step 360: Determine whether the effect of the adjusted ventilator parameters on the lung ventilation state meets the expected requirements.

[0115] Step 370: If the effect of the adjusted ventilator parameters on the lung ventilation state does not meet the expected requirements, continue to adjust the ventilator parameters within the preset range of the ventilation parameter prediction value until the expected requirements are met.

[0116] Among them, the ventilator parameters can be adjusted first according to the predicted values ​​of the ventilation parameters. If the effects of the adjusted ventilator parameters on the lung ventilation state do not meet the expected requirements, the predicted values ​​of the ventilation parameters can be gradually increased or decreased within the preset range, thereby making subtle adjustments to the ventilator parameters.

[0117] Among them, the expected requirements may be to maintain the lung conductivity distribution within a certain range or to minimize airway pressure fluctuations.

[0118] Since each patient has different physical fitness, after determining the predicted values ​​of ventilation parameters, the adaptive control algorithm can be used to adjust the ventilator parameters in real time based on the feedback of the EIT image (i.e., lung ventilation distribution image) on the basis of the predicted values ​​of ventilation parameters to obtain the optimal ventilation parameter values, thereby making the lung conductivity distribution meet the expected requirements. In other words, a personalized respiratory support plan can be provided according to the specific situation of each patient to optimize the treatment effect, thereby reducing the probability of complications such as barotrauma, volume trauma, atelectasis and ventilator-associated pneumonia.

[0119] The adaptive control algorithm may be an adaptive algorithm such as a recursive least squares method, a Kalman filter, etc. Thus, the model parameters may be continuously optimized by using an adaptive algorithm such as a recursive least squares method, a Kalman filter, etc.

[0120] Since the EIT device can provide real-time and dynamic lung ventilation information, this application, in combination with the use of the EIT device during ventilator treatment, can help doctors accurately adjust ventilator parameters, optimize respiratory support strategies, reduce the occurrence of complications, and improve treatment outcomes.

[0121] Among them, step 310, step 320, step 330, step 340 and step 350 have the same or similar technical solutions as the above embodiments, and are not described in detail here.

[0122] In some embodiments, the trained mathematical model mentioned in the above embodiments can be trained in the following manner:

[0123] 1) respectively using the ventilator to obtain training sets of ventilator parameters and chest electrical impedance data;

[0124] 2) Using the training set of the ventilator parameters and chest electrical impedance data, the mathematical model to be trained is trained to obtain a trained mathematical model.

[0125] Based on the above embodiments, in terms of hardware, the present application may include EIT equipment, a ventilator, a data processing unit, a display and control interface, etc. The data processing unit may be a high-performance computer or an embedded system for data processing and algorithm calculation.

[0126] In terms of software, the present application may include: a data acquisition module, an image reconstruction and processing module, an intelligent control algorithm module, and a user interface module.

[0127] Among them, the data acquisition module is used to collect EIT and ventilator data and perform synchronous processing.

[0128] The image reconstruction and processing module is used for EIT image reconstruction, noise filtering and image analysis.

[0129] The intelligent control algorithm module is used to realize adaptive control and feedback control and optimize ventilator parameters.

[0130] The user interface module is used to provide real-time data display and control interface for medical staff to view and operate.

[0131] Corresponding to the ventilator adjustment method based on electrical impedance imaging in the above embodiment, the present application also provides a ventilator parameter adjustment device, which includes a unit for implementing the technical solution of any embodiment in the present application.

[0132] The present application also provides a ventilator adjustment system, which includes a memory and a processor, wherein the memory stores a computer program; the processor is used to implement the ventilator adjustment method based on electrical impedance imaging provided by any one of the aforementioned method embodiments when executing the computer program.

[0133] See also Figure 2 , Figure 2 The structure of an embodiment of a computer-readable storage medium provided by the present application is shown in FIG. 7 . The computer-readable storage medium 70 is used to store a computer program 71. When the computer program 71 is executed by a processor, it is used to implement the following method steps:

[0134] Use EIT equipment to collect chest electrical impedance data in real time;

[0135] Based on the chest electrical impedance data, obtaining an impedance value of a target lung region image;

[0136] Obtaining ventilator parameters using the ventilator;

[0137] Inputting the ventilator parameters and the impedance value of the target lung region image into a trained mathematical model to obtain a ventilation parameter prediction value output by the trained mathematical model;

[0138] The ventilator is adjusted based on the predicted ventilation parameter value.

[0139] It can be understood that when the computer program 71 is executed by the processor, it is also used to implement the technical solution of any embodiment of the present application.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only illustrative, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as two units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on two network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0142] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0143] If the integrated units in the above other embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0144] The above are only implementation methods of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A ventilator adjustment method based on electrical impedance imaging, characterized in that: The ventilator adjustment system includes the ventilator and the EIT device, and the method includes: Using the EIT device to collect chest electrical impedance data in real time; Based on the chest electrical impedance data, obtaining an impedance value of a target lung region image; Obtaining ventilator parameters using the ventilator; Inputting the ventilator parameters and the impedance value of the target lung region image into a trained mathematical model to obtain a ventilation parameter prediction value output by the trained mathematical model; The ventilator is adjusted based on the predicted ventilation parameter value.

2. The ventilator adjustment method based on electrical impedance imaging according to claim 1, characterized in that: The step of obtaining the impedance value of the target lung region image based on the chest electrical impedance data includes: Reconstructing the chest electrical impedance data using a finite element method to generate a real-time lung ventilation distribution image; Segmenting the lung ventilation distribution image to obtain a target lung region image; Based on the chest electrical impedance data, an impedance value of the target lung region image is obtained.

3. The ventilator adjustment method based on electrical impedance imaging according to claim 2, characterized in that: Before segmenting the lung ventilation distribution image, the method further includes: Performing denoising on the lung ventilation distribution image to obtain a denoised lung ventilation distribution image; The step of segmenting the lung ventilation distribution image to obtain a target lung region image includes: The denoised lung ventilation distribution image is segmented to obtain a target lung region image.

4. The ventilator adjustment method based on electrical impedance imaging according to claim 1, characterized in that: After adjusting the parameters of the ventilator based on the predicted ventilation parameter values, the method further includes: Determine whether the effect of the adjusted ventilator parameters on the lung ventilation status meets the expected requirements; If the effect of the adjusted ventilator parameters on the pulmonary ventilation state does not meet the expected requirements, the ventilator parameters continue to be adjusted within the preset range of the ventilation parameter prediction value until the expected requirements are met.

5. The ventilator adjustment method based on electrical impedance imaging according to claim 1, characterized in that: The ventilator is used to obtain ventilator parameters, including: Using the ventilator to collect real-time ventilation data; Based on the real-time ventilation data, ventilator parameters are obtained.

6. The ventilator adjustment method based on electrical impedance imaging according to claim 5, characterized in that: The ventilation data includes tidal volume and airway pressure, the ventilator parameters include respiratory cycle and average airway pressure, and the ventilator parameters are obtained based on the real-time ventilation data, including: Based on the changes in the airway pressure and / or the tidal volume, obtaining the time points of the start and end of breathing; Based on the time points of the start and end of breathing, the breathing cycle is obtained; Obtaining the airway pressure at all sampling points in the respiratory cycle; The average airway pressure of all sampling points in the respiratory cycle is calculated using an integration or discrete average method to obtain the average airway pressure.

7. The ventilator adjustment method based on electrical impedance imaging according to claim 1, characterized in that: The trained mathematical model is trained in the following manner: Using the ventilator to obtain training sets of ventilator parameters and chest electrical impedance data respectively; The mathematical model to be trained is trained using the training set of the ventilator parameters and chest electrical impedance data to obtain a trained mathematical model.

8. A device for adjusting ventilator parameters, characterized in that: The device for adjusting ventilator parameters comprises a unit for executing the method according to any one of claims 1-7.

9. A ventilator adjustment system, characterized in that: The ventilator adjustment system includes a memory and a processor, the memory is used to store program data, and the processor is used to execute the program data to implement the ventilator adjustment method based on electrical impedance imaging as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program data, and when the program data is executed by the processor, it is used to implement the ventilator adjustment method based on electrical impedance imaging as described in any one of claims 1-7.

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