Method and system for determining end-expiratory lung volume and breathing machine

Abstract

The invention provides a method and system for determining the volume of the end-expiratory lung and a breathing machine, and relates to the technical field of breathing detection.The method comprises the steps that the volume-impedance relation of the lung of a target object is obtained; and determining the end-expiratory lung volume of the target object based on the absolute impedance of the lung of the target object at the end-expiratory moment and the volume-impedance relationship. According to the method and system for determining the end-expiratory lung volume and the breathing machine, the end-expiratory lung volume can be determined, lung function evaluation, mechanical ventilation strategy guidance, prognosis improvement and the like are facilitated, and the practicability is high. And meanwhile, the device also has the advantages of no radiation, no invasion, low cost, convenience in operation, less time consumption, no requirement on special working environment, low matching requirement on patients, bedside operation, suitability for critical patients and the like.

Description

Technical Field

[0001] The present application relates to the field of respiratory detection technology, and in particular to a method and system for measuring end-expiratory lung volume, and a ventilator. Background Art

[0002] End-expiratory lung volume (EELV) refers to the volume of gas in the lungs at the end of exhalation. During natural, quiet breathing or mechanical ventilation without positive end-expiratory pressure (PEEP), EELV is equivalent to functional residual capacity (FRC). Therefore, PEEP can affect EELV during mechanical ventilation. EELV reflects the mechanical state and gas exchange capacity of the lungs and is a key parameter for assessing lung function and guiding mechanical ventilation strategies. Therefore, measuring EELV is often clinically significant. However, current methods for measuring EELV in clinical practice often suffer from one or more of the following issues: complexity, low accuracy, radiation exposure, time-consuming and high cost, high patient cooperation requirements, and limited bedside application.

[0003] It would therefore be desirable to provide a method of determining end-expiratory lung volume that overcomes the aforementioned problems and other possible problems. Summary of the Invention

[0004] One embodiment of the present application provides a method for determining end-expiratory lung volume, comprising: obtaining a volume-impedance relationship of a target subject's lungs; and determining the end-expiratory lung volume of the target subject based on the absolute impedance of the target subject's lungs at the end of expiration and the volume-impedance relationship.

[0005] In some embodiments, obtaining the volume-impedance relationship of the target subject's lungs includes: obtaining the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inspiration; determining tidal impedance change data of the target subject's lungs based on the difference between the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inspiration; obtaining tidal volume data of the target subject's lungs; and establishing the volume-impedance relationship of the target subject's lungs based on the tidal volume data and the tidal impedance change data.

[0006] In some embodiments, obtaining the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inspiration includes: collecting at least an EIT measurement electrical signal of the target subject's lungs at the end of inspiration and an EIT measurement electrical signal at the end of exhalation; and determining the absolute impedance of the target subject's lungs at the end of inspiration and the absolute impedance at the end of exhalation based on the EIT measurement electrical signal of the target subject's lungs at the end of inspiration and the EIT measurement electrical signal of the target subject at the end of exhalation, respectively, using a static EIT reconstruction algorithm.

[0007] In some embodiments, obtaining the tidal volume data of the target subject's lungs includes: monitoring the target subject's respiratory flow to obtain the tidal volume data of the target subject's lungs.

[0008] In some embodiments, obtaining the volume-impedance relationship of the target object's lungs includes: obtaining a first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} ; Among them, ΔX(t i ,t j ) represents the target object's lung at time point t i The absolute impedance and the j The difference between the absolute impedance of the target object and the target object; obtaining a second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} ; Among them, ΔY(t i ,t j ) indicates that the target objects are at time point t i Lung volume and at time point t j The difference between the lung volumes of the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Data fitting was performed to establish the volume-impedance relationship.

[0009] In some embodiments, the first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} , comprising: obtaining a first time series data set {(E(t i ),E(t j ))}i＜j且i,j∈{1,2,…,n} ; Among them, E(t i ) and E(t j ) represent the target object’s lungs at time point t i and time point t j EIT measurement electrical signal; based on the first time series data set {(E(t i ),E(t j ))} i＜j且i,j∈{1,2,…,n} Determine a second time series data set {(X(t i ),X(t j ))} i＜j且i,j∈{1,2,…,n} ; Among them, X(t i ) and X(t j ) represent the target object’s lungs at time point t i and time point t j Absolute impedance; Based on the second time series data set {(X(t i ),X(t j ))} i＜j且i,j∈{1,2,…,n} Calculate the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} , where ΔX(t i ,t j )=|X(t j )-X(t i )|.

[0010] In some embodiments, the second time difference sequence data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} , comprising: monitoring the respiratory flow of the target object to obtain a respiratory flow change curve C(t) of the target object; integrating the respiratory flow change curve over time to obtain a respiratory volume change curve V(t)=∫C(t)dt of the target object's lungs; and calculating the second time difference sequence data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} ,in,

[0011] In some embodiments, the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )}i＜j且i,j∈{1,2,…,n} Performing data fitting to establish the volume-impedance relationship Y=f(X) includes: based on the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Generate a ΔX-ΔY scatter plot; wherein the scatter points of the ΔX-ΔY scatter plot include {(ΔX(t i ),ΔY(t j ))} i＜j且i,j∈{1,2,…,n} ; Select a regression model based on the scatter distribution of the ΔX-ΔY scatter plot; Based on the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Parameters of the regression model are estimated to obtain the volume-impedance relationship.

[0012] In some embodiments, the method further comprises: determining the volume of the target subject's lung region of interest at end-expiration based on the absolute impedance of the region of interest at end-expiration and the volume-impedance relationship.

[0013] One embodiment of the present application provides a system for determining end-expiratory lung volume, comprising: an acquisition module for acquiring a volume-impedance relationship of a target subject's lungs; and a determination module for determining the end-expiratory lung volume of the target subject based on the absolute impedance of the target subject's lungs at the end of expiration and the volume-impedance relationship.

[0014] In some embodiments, the acquisition module includes: a first acquisition submodule, the first acquisition submodule is used to obtain the absolute impedance of the target object's lungs at the end of exhalation and the absolute impedance at the end of inspiration; a determination submodule, the determination submodule is used to determine the tidal impedance change data of the target object's lungs based on the difference between the absolute impedance of the target object's lungs at the end of exhalation and the absolute impedance at the end of inspiration; a second acquisition submodule, the second acquisition submodule is used to obtain tidal volume data of the target object's lungs; and an establishment submodule, the establishment submodule is used to establish the volume-impedance relationship of the target object's lungs based on the tidal volume data and the tidal impedance change data.

[0015] In some embodiments, the first acquisition submodule includes: an acquisition unit, which is used to acquire at least an EIT measurement electrical signal of the target object's lung at the end of inspiration and an EIT measurement electrical signal at the end of expiration; a determination unit, which is used to determine the absolute impedance of the target object's lung at the end of inspiration and the absolute impedance at the end of expiration based on the EIT measurement electrical signal of the target object's lung at the end of inspiration and the EIT measurement electrical signal at the end of expiration through a static EIT reconstruction algorithm.

[0016] In some embodiments, the second acquisition submodule includes: a monitoring unit, wherein the monitoring unit is configured to monitor the respiratory flow of the target object to acquire tidal volume data of the target object's lungs.

[0017] In some embodiments, the acquisition module includes: a first acquisition submodule, the first acquisition submodule is used to acquire a first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} ; Among them, ΔX(t i ,t j ) represents the target object's lung at time point t i The absolute impedance and the j The second acquisition submodule is used to obtain a second time difference sequence data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} ; Among them, ΔY(t i ,t j ) indicates that the target objects are at time point t i Lung volume and at time point t j The difference between the lung volumes of the first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Data fitting was performed to establish the volume-impedance relationship.

[0018] In some embodiments, the first acquisition submodule includes: an acquisition unit configured to acquire a first time series data set {(E(t i ),E(t j ))} i＜j且i,j∈{1,2,…,n}; Among them, E(t i ) and E(t j ) represent the target object’s lungs at time point t i and time point t j EIT measurement electrical signal; a determination unit, the determination unit is used to reconstruct the first time series data set {(E(t i ),E(t j ))} i＜j且i,j∈{1,2,…,n} Determine a second time series data set {(X(t i ),X(t j ))} i＜j且i,j∈{1,2,…,n} ; Among them, X(t i ) and X(t j ) represent the target object’s lungs at time point t i and time point t j The absolute impedance of the first calculation unit, the first calculation unit is used to calculate the absolute impedance of the second time series data set {(X(t i ),X(t j ))} i＜j且i,j∈{1,2,…,n} Calculate the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} , where ΔX(t i ,t j )=|X(t j )-X(t i )|.

[0019] In some embodiments, the second acquisition submodule includes: a monitoring unit, the monitoring unit is used to monitor the respiratory flow of the target object to obtain the respiratory flow change curve C(t) of the target object; an integration unit, the integration unit is used to integrate the respiratory flow change curve over time to obtain the respiratory volume change curve V(t)=∫C(t)dt of the target object's lungs; a second calculation unit, the second calculation unit is used to calculate the second time difference sequence data set {ΔY(t i ,t j )} i＜k且i,j∈{1,2,…,n} ,in,

[0020] In some embodiments, the establishing submodule includes: a generating unit, the generating unit being configured to generate a time difference sequence data set based on the first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,tj )} i＜j且i,j∈{1,2,…,n} Generate a ΔX-ΔY scatter plot; wherein the scatter points of the ΔX-ΔY scatter plot include {(ΔX(t i ),ΔY(t j ))} i＜j且i,j∈{1,2,…,n} ; A selection unit for selecting a regression model based on the scatter distribution of the ΔX-ΔY scatter plot; a parameter estimation unit for estimating the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Parameters of the regression model are estimated to obtain the volume-impedance relationship.

[0021] In some embodiments, the system further includes: a local end-expiratory lung volume determination module, which is used to determine the volume of the target object's lung region of interest at the end of expiration based on the absolute impedance of the region of interest at the end of expiration and the volume-impedance relationship.

[0022] One embodiment of the present application provides a ventilator, comprising a system for determining end-expiratory lung volume as described in any of the above embodiments.

[0023] The present invention provides a method, system, and ventilator for determining end-expiratory lung volume. Based on the principle that lung impedance (absolute lung impedance or static lung impedance) corresponds to lung volume, the method obtains the volume-impedance relationship of the target subject's lung (e.g., the conversion relationship between the target subject's lung impedance and lung volume). The method then determines the target subject's end-expiratory lung volume based on the target subject's absolute lung impedance at the end of expiration and the volume-impedance relationship. This method is helpful in assessing lung function, guiding mechanical ventilation strategies, and improving prognosis. Furthermore, in the present invention, the determination of end-expiratory lung volume is primarily based on the absolute lung impedance of the target subject, which can be obtained using electrical impedance tomography (EIT). Therefore, the method, system, and ventilator for determining end-expiratory lung volume provided by the present invention also have a series of advantages of EIT technology, such as being radiation-free, non-invasive, low-cost, easy to operate, time-saving, not requiring a special working environment, requiring low patient cooperation, capable of bedside operation, and suitable for critically ill patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0025] Figure 1 is a flow chart of a method for determining end-expiratory lung volume according to some embodiments of the present application;

[0026] Figure 2 is a flow chart of a method for obtaining a volume-impedance relationship of a target subject's lungs according to some embodiments of the present application;

[0027] Figure 3 is a flow chart of a method for obtaining a volume-impedance relationship of a target subject's lungs according to other embodiments of the present application;

[0028] Figure 4 is a block diagram of a system for determining end-expiratory lung volume according to some embodiments of the present application;

[0029] Figure 5 is a block diagram of a ventilator according to some embodiments of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain the present application and are not intended to limit the present application. On the contrary, the present application covers any substitutions, modifications, equivalent methods and solutions made on the spirit and scope of the present application as defined by the claims. Furthermore, in order to enable the public to have a better understanding of the present application, some specific details are described in detail in the detailed description of the present application below. Those skilled in the art can fully understand the present application without the description of these details.

[0031] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0032] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0033] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0034] End-expiratory lung volume (EELV) refers to the volume of gas in the lungs at the end of exhalation. It reflects the mechanical state of the lungs and their gas exchange capacity. When a person is breathing quietly or mechanically ventilated without positive end-expiratory pressure (PEEP), EELV is equivalent to functional residual capacity (FRC). EELV is of great significance in respiratory physiology and clinical medicine, and can be used to assess lung disease and guide mechanical ventilation strategies. For example, changes in EELV can be used to assess obstructive lung disease (such as COPD and asthma, which are manifested by increased EELV) and restrictive lung disease (such as pulmonary fibrosis and chest wall deformity, which are manifested by decreased EELV). For another example, for patients undergoing mechanical ventilation, optimal PEEP can be set based on changes in EELV to reduce ventilation-related lung injury (such as barotrauma, collapsitis, and volutrauma).

[0035] Because EELV is a key parameter for assessing lung function, guiding mechanical ventilation, and optimizing respiratory therapy, measuring (or determining) EELV is of great clinical significance, particularly in mechanical ventilation and lung disease management. Currently, EELV measurement is commonly performed clinically using gas dilution, plethysmography, and traditional imaging methods. The following is an introduction to these three methods.

[0036] The principle of the gas dilution method is to have the patient inhale an inert gas of known concentration (for example, helium or nitrogen) and calculate the end-expiratory lung volume based on the change in gas concentration. Although measuring the end-expiratory lung volume by the gas dilution method is non-invasive and simple to operate, it is relatively time-consuming and has high requirements for patient cooperation (for example, the patient needs to cooperate in breathing well). Once the patient's breathing is in an abnormal state, the measurement accuracy will be affected. Therefore, the gas dilution method is not suitable for measuring the end-expiratory lung volume in critically ill patients (for example, patients with severe pulmonary edema or uneven ventilation).

[0037] The principle of plethysmography is to have a patient sit in a sealed chamber, where their breathing causes pressure changes within the chamber. The patient's end-expiratory lung volume is then calculated using Boyle's law. Plethysmography is currently one of the gold standard methods for measuring end-expiratory lung volume in clinical practice. It is highly accurate and suitable for patients with severe airway obstruction or uneven gas distribution within the lungs. However, the required equipment is expensive and complex to maintain, and the operation is cumbersome and time-consuming. Measurement accuracy is easily affected by swallowing, coughing, or air leaks, and it requires high patient cooperation (for example, requiring the patient to breathe in a closed environment). It cannot be performed at the bedside and is not suitable for critically ill patients (for example, those undergoing mechanical ventilation or in the ICU).

[0038] The principle of traditional imaging methods is to scan the patient's lungs at the end of expiration through imaging methods such as CT or X-ray, and then perform quantitative analysis (for example, CT three-dimensional reconstruction or X-ray density analysis) of the patient's end-expiratory lung volume through the lung image. Although traditional imaging methods can intuitively display abnormalities in lung structure and local lung volume, there is a risk of radiation exposure, and bedside operations cannot be performed, and they are not suitable for critically ill patients (for example, patients undergoing mechanical ventilation or in the ICU). In addition, traditional imaging methods also include measuring end-expiratory lung volume through magnetic resonance imaging. Specifically, an MRI scan is performed on the patient's lungs while the patient holds his breath at the end of expiration, and the end-expiratory lung volume is reconstructed in three dimensions. Although MRI is radiation-free, it still has problems such as expensive equipment, long time consumption, and inability to perform bedside operations, making it unsuitable for critically ill patients (for example, patients undergoing mechanical ventilation or in the ICU).

[0039] In view of the fact that the above-mentioned methods for measuring the end-expiratory lung volume have problems such as being time-consuming, costly, inaccurate, requiring high patient cooperation, causing radiation, being unable to be performed at the bedside, and being unsuitable for critically ill patients, an embodiment of the present application provides a method for determining the end-expiratory lung volume. Based on the principle that there is a corresponding relationship between lung impedance (absolute lung impedance or static lung impedance) and lung volume, the end-expiratory lung volume of the target object is determined by obtaining the volume-impedance relationship of the target object's lungs (for example, the conversion relationship between the target object's lung impedance and lung volume), and then based on the absolute impedance of the target object's lungs at the end of expiration and the volume-impedance relationship of the target object's lungs. The method for determining the end-expiratory lung volume provided in the embodiments of the present application can determine the end-expiratory lung volume with high accuracy. At the same time, this method is mainly based on the absolute impedance of the target object's lungs, and the absolute impedance of the target object can be obtained through electrical impedance tomography (EIT) technology. Therefore, the method for determining the end-expiratory lung volume provided in the present application also has a series of advantages of EIT technology, such as no radiation, non-invasiveness, low cost, easy operation, less time consumption, no requirement for a special working environment, low patient cooperation requirements, bedside operation, and applicability to critically ill patients.

[0040] The method for determining the end-expiratory lung volume provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0041] Figure 1 Flowchart of a method for determining end-expiratory lung volume according to some embodiments of the present application. Figure 1 The method 100 of determining end-expiratory lung volume shown in Figure 4 This is achieved by the system 400 for determining end-expiratory lung volume shown in FIG.

[0042] like Figure 1 As shown, the method 100 for determining the end-expiratory lung volume provided in the embodiment of the present application may include the following steps:

[0043] Step 110, obtaining the volume-impedance relationship of the target object's lungs. Specifically, step 110 can be performed by Figure 4 The acquisition module 410 in the system 400 for determining the end-expiratory lung volume shown in FIG.

[0044] In step 110, obtaining the volume-impedance relationship of the target subject's lungs is to obtain the relationship between the target subject's lung volume and the absolute impedance of its lungs (i.e., a corresponding relationship or conversion relationship). Specifically, since the absolute impedance of the target subject's lungs at a certain moment depends specifically on the target subject's lung volume at that moment, the volume-impedance relationship of the target subject's lungs can reflect the correlation between the absolute impedance of the target subject's lungs and the target subject's lung volume. Furthermore, the volume-impedance relationship of the target subject's lungs can be represented by a specific functional relationship Y=f(X), where Y and X represent the target subject's lung volume and the absolute impedance of the target subject's lungs, respectively.

[0045] It should be noted that the absolute impedance of the target object's lungs involved in the embodiments of the present application may refer to the sum of the absolute impedance values ​​of all lung pixels in the target object's lungs, wherein the absolute impedance value of each lung pixel may reflect the impedance of the lung tissue of the lung pixel under a fixed frequency (e.g., 50kHz) alternating current. In some embodiments, the absolute impedance of the target object's lungs may be obtained by static EIT. Among them, static EIT is a technology in EIT, which reconstructs a quantitative distribution image of the absolute electrical impedance inside the target object's lungs through a specific algorithm from the electrical signal collected from the target object's lungs, that is, the absolute impedance value of each lung pixel in the target object's lungs is obtained, and then the absolute impedance of the target object's lungs can be obtained by calculating the sum of the absolute impedance values ​​of all lung pixels in the target object's lungs.

[0046] In some embodiments, the volume-impedance relationship of the target subject's lungs may represent a unit conversion relationship between the absolute impedance of the target subject's lungs and the target subject's lung volume. Specifically, the volume-impedance relationship of the target subject's lungs may reflect how many unit volumes of the target subject's lung volume correspond to one unit impedance of the absolute impedance of the target subject's lungs, that is, how many unit volumes of the target subject's lung volume are caused by each unit impedance change in the absolute impedance of the target subject's lungs.

[0047] Furthermore, since the volume-impedance relationship of the target subject's lungs can reflect how many unit volume changes in the target subject's lung volume are caused by each unit impedance change in the target subject's lungs, in some embodiments, the volume-impedance relationship of the target subject's lungs can be obtained by obtaining the corresponding relationship between the change in the target subject's lungs' absolute impedance and the change in the target subject's lung volume. More information on how to obtain the volume-impedance relationship of the target subject's lungs will be provided elsewhere in this specification (e.g., Figure 2 and Figure 3 and its related descriptions) are described in detail, and no further description is given here.

[0048] Step 120, based on the absolute impedance of the target subject's lungs at the end of expiration and the volume-impedance relationship, determines the target subject's end-expiratory lung volume. Specifically, step 120 can be performed by Figure 4 The determination module 420 in the system 400 for determining the end-expiratory lung volume shown in FIG.

[0049] In step 120, the absolute impedance of the target subject's lung at the end of expiration can be input into the volume-impedance relationship of the target subject's lung to output the target subject's lung volume at the end of expiration, i.e., the end-expiratory lung volume. Specifically, the end-expiratory lung volume of the target subject can be obtained by Y 呼气末 =f(X 呼气末 ), where X 呼气末 and Y 呼气末 They represent the absolute impedance of the target subject's lungs at the end of exhalation and the target subject's end-expiratory lung volume, respectively.

[0050] How to obtain the volume-impedance relationship of the target subject's lungs will be described in detail below with reference to the accompanying drawings.

[0051] Figure 2 4 is a flow chart of a method for obtaining the volume-impedance relationship of the lungs of a target subject according to some embodiments of the present application.

[0052] In some embodiments, step 110 may be performed using Figure 2 The method 200 for obtaining the volume-impedance relationship of the target subject's lungs is implemented as shown in FIG. Specifically, the method 200 may include the following steps:

[0053] Step 210 : Obtain the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inhalation.

[0054] In some embodiments, the last moment of inspiration and the last moment of expiration involved in the embodiments of the present application can be within the same respiratory cycle of the target object, or can be within different respiratory cycles of the target object. In some embodiments, the absolute impedance of the target object's lungs at the last moment of inspiration or the last moment of expiration can be the absolute impedance of the target object at a certain last moment of inspiration or a certain last moment of expiration, or can be the average value of the absolute impedance at the last moment of inspiration or the last moment of expiration in multiple respiratory cycles respectively. It is understandable that the respiratory cycle involved in the embodiments of the present application can refer to the time from the start of one inspiration to the start of the next inspiration of the target object, wherein the last moment of inspiration can refer to the time when inspiration ends within the respiratory cycle, and the last moment of expiration can refer to the time when expiration ends within the respiratory cycle.

[0055] In some embodiments, in step 210, at least an EIT measurement electrical signal of the target subject's lungs at one end-inspiration moment and one end-expiration moment may be collected, and then, using a static EIT reconstruction algorithm, the absolute impedance of the target subject's lungs at the end-inspiration moment and the absolute impedance of the target subject's lungs at the end-expiration moment are determined based on the EIT measurement electrical signal of the target subject's lungs at the end-inspiration moment and the EIT measurement electrical signal of the target subject's lungs at the end-expiration moment, respectively. In some embodiments, the EIT measurement electrical signal of the target subject's lungs may be collected by performing an EIT measurement on the target subject's lungs. As an example, during the EIT measurement, an electrode belt may be arranged on the target subject's chest, and an excitation signal (current or voltage) may be applied to the electrodes on the electrode belt, while measuring the response signal (voltage or current) between the remaining electrodes on the electrode belt to obtain the EIT measurement electrical signal.

[0056] In some embodiments, in order to collect the EIT measurement electrical signals of the target subject's lungs at the end of inspiration and the EIT measurement electrical signals at the end of expiration, the target subject can hold his breath after completing one inhalation and perform EIT measurement on his lungs to obtain the EIT measurement electrical signals of the target subject's lungs at the end of inspiration, and the target subject can hold his breath after completing one exhalation and perform EIT measurement on his lungs to obtain the EIT measurement electrical signals of the target subject's lungs at the end of expiration.

[0057] In some embodiments, to collect EIT measurement electrical signals from the target subject's lungs at the end of inspiration and the end of expiration, EIT measurement can be performed on the target subject's lungs over a continuous period of time to collect a curve showing the change of the EIT measurement electrical signals from the target subject's lungs over time. In some embodiments, to ensure that the EIT measurement electrical signals from the target subject's lungs at the end of inspiration and the end of expiration are collected, the continuous period of time must include at least one end of expiration and one end of inspiration of the target subject, i.e., the continuous period of time must cover at least the time between a set of adjacent end of inspiration and end of expiration of the target subject (i.e., half a respiratory cycle). In some embodiments, the EIT measurement electrical signals from the target subject's lungs at the end of inspiration and the end of expiration can be determined based on the curve showing the change of the EIT measurement electrical signals over time over a continuous period of time. Specifically, the time at which the maximum value in the curve of the EIT measurement signal over time within a continuous time period occurs is the target subject's end-inspiration moment, and the maximum value is the target subject's lung EIT measurement signal at the corresponding end-inspiration moment. The time at which the minimum value in the curve of the EIT measurement signal over time within a continuous time period occurs is the target subject's end-expiration moment, and the minimum value is the target subject's lung EIT measurement signal at the corresponding end-expiration moment. It is understood that when the target subject undergoes ventilator therapy, setting different ventilator ventilation modes can result in different curves of the EIT measurement signal over time within the target subject's lung. In some embodiments, the target subject's lung EIT measurement signal at the end-inspiration moment and the EIT measurement signal at the end-expiration moment can be, respectively, any EIT measurement signal at the end-inspiration moment and any EIT measurement signal at the end-expiration moment determined from the curve of the EIT measurement signal over time within a continuous time period, wherein the end-inspiration moment and the end-expiration moment can be within the same respiratory cycle of the target subject or in different respiratory cycles of the target subject. In some embodiments, the EIT measurement electrical signal of the target subject's lungs at the end of inspiration and the EIT measurement electrical signal at the end of expiration can be respectively the average value of multiple EIT measurement electrical signals at the end of inspiration and the average value of multiple EIT measurement electrical signals at the end of expiration determined from the time-varying curve of the EIT measurement electrical signal in a continuous time.

[0058] In some embodiments, the absolute impedance data of the target object's lung at the end of inspiration and the absolute impedance data of the target object's lung at the end of expiration can be determined respectively based on the EIT measurement electrical signal of the target object's lung at the end of inspiration and the EIT measurement electrical signal of the target object's lung at the end of expiration by an EIT static reconstruction algorithm. The absolute impedance data of the target object's lung at the end of expiration may include the absolute impedance distribution of the target object's lung, that is, the absolute impedance value of each lung pixel of the target object's lung at the end of expiration. Therefore, the absolute impedance of the target object's lung at the end of expiration can be obtained by calculating the sum of the absolute impedance values ​​of all lung pixels of the target object's lung at the end of expiration. At the same time, the absolute impedance data of the target object's lung at the end of inspiration may include the absolute impedance distribution of the target object's lung, that is, the absolute impedance value of each lung pixel of the target object's lung at the end of inspiration. Therefore, the absolute impedance of the target object's lung at the end of inspiration can be obtained by calculating the sum of the absolute impedance values ​​of all lung pixels of the target object's lung at the end of inspiration.

[0059] In some embodiments, the static EIT reconstruction algorithm may include a linear approximation algorithm, a nonlinear iterative algorithm, a machine learning driven algorithm, etc. As an exemplary implementation, a nonlinear iterative algorithm is used to process the EIT measurement electrical signal to reconstruct the absolute conductivity or impedance distribution image: first, based on the initial guess (for example, setting a uniform conductivity distribution), a forward model is established by the finite element method and the theoretical boundary voltage is calculated. Subsequently, the sensitivity matrix is ​​calculated based on the current conductivity distribution. In each round of iteration, the conductivity estimate is updated by an optimization algorithm such as Levenberg-Marquardt, combining the residual between the actual measurement data and the theoretical prediction, and regularization constraints (such as Tikhonov regularization or total variation) are introduced to suppress noise and ill-posedness. This process is repeated until the convergence condition is met, and the absolute impedance distribution image of the target area is finally obtained.

[0060] It is understandable that the absolute impedance of the target subject's lungs at the end of expiration in step 120 of method 100 may be the absolute impedance of the target subject's lungs at any one of the end of expiration moments obtained in step 210, or may be the average value of the absolute impedances of the target subject's lungs at each end of expiration moment obtained in step 210.

[0061] In step 220 , tidal impedance change data of the target subject's lungs is determined based on the difference between the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inspiration.

[0062] In step 220, the tidal impedance change data of the target subject's lungs can be obtained by calculating the difference between the absolute impedance of the target subject's lungs at the end of expiration and the absolute impedance at the end of inspiration. The tidal impedance change data of the target subject's lungs can refer to the absolute impedance change value of the target subject's lungs caused by a change in the tidal volume of the target subject's lung volume.

[0063] Step 230: Acquire tidal volume data of the target subject. It is understood that step 230 and step 210 may be performed simultaneously, or step 230 may be performed before or after step 210, and this application is not intended to limit this.

[0064] Tidal volume data refers to the volume of air inhaled or exhaled at each breath by the target subject when breathing in a resting state or under mechanical ventilation (e.g., while receiving ventilator therapy), and is equal to the difference between the target subject's lung volume at the end of exhalation and the lung volume at the end of inspiration. Since the target subject's lung volume at a given moment cannot be directly measured, it is not possible to directly calculate the difference between the target subject's lung volume at the end of exhalation and the lung volume at the end of inspiration to obtain the tidal volume data of the target subject's lungs. Therefore, in step 230, the tidal volume data of the target subject's lungs can be obtained by monitoring the target subject's respiratory flow. As an example, the target subject's respiratory flow can be monitored to obtain a respiratory flow-time curve of the target subject, and then the target subject's respiratory flow-time curve can be integrated from the end of exhalation to the end of inspiration or from the end of inspiration to the end of exhalation within the same respiratory cycle of the target subject to obtain the tidal volume data of the target subject's lungs. In some embodiments, if the target subject is using a ventilator, the tidal volume data of the target subject monitored by the ventilator can be directly obtained. In some embodiments, a flow sensor can be placed in the airflow channel of the target subject. For example, the target subject can wear a mask equipped with a flow sensor, and the flow sensor can monitor the target subject's respiratory flow and then calculate and output the tidal volume data of the target subject's lungs based on the target subject's respiratory flow.

[0065] In some embodiments, the tidal volume data of the target subject's lungs and the tidal impedance change data of the target subject's lungs may be from the same respiratory cycle or from different respiratory cycles. In some embodiments, the tidal volume data of the target subject's lungs may be an average of the tidal volume data of the target subject's lungs over multiple respiratory cycles, and / or the tidal impedance change data of the target subject's lungs may be an average of the tidal impedance change data of the target subject's lungs over multiple respiratory cycles, or tidal impedance change data calculated from an average of the absolute impedance of the target subject's lungs at multiple end-expiratory moments and an average of the absolute impedance at multiple end-inspiratory moments.

[0066] Step 240 : Establishing a volume-impedance relationship of the target subject's lungs based on the tidal volume data and the tidal impedance change data.

[0067] In some embodiments, the volume-impedance relationship of the target subject's lungs may be a linear relationship, i.e., Y=f(X)=aX. Therefore, by determining the slope (or regression coefficient) a, the volume-impedance relationship of the target subject's lungs may be obtained. Further, in step 240, a may be determined by calculating the ratio of the tidal volume data to the tidal impedance change data, thereby establishing the volume-impedance relationship of the target subject's lungs. That is, the volume-impedance relationship of the target subject's lungs may be expressed as Y=(ΔY 潮 / ΔX 潮 )·X, where ΔY 潮 represents the tidal volume data of the target subject’s lungs, ΔX 潮 Indicates tidal impedance change data of the target subject's lungs.

[0068] Figure 3 This is a flow chart of a method for obtaining the volume-impedance relationship of the lungs of a target subject according to other embodiments of the present application.

[0069] In some embodiments, step 110 may be performed using Figure 3 Specifically, the method 300 for obtaining the volume-impedance relationship of the target object's lungs may include the following steps:

[0070] Step 310: Obtain a first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} .

[0071] The first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} In the equation, ΔX(t i ,t j ) represent the target object’s lungs at time point t i The absolute impedance and the j The difference between the absolute impedances, that is, the first time difference series data set is {ΔX(t1,t2),…,ΔX(t1,t n ),…,ΔX(t n-1 ,t n )}. In some embodiments, time point t i and t j It can be in the same inhalation phase or the same exhalation phase, or in different inhalation phases or different exhalation phases. In some embodiments, the time point t iIt can be located in the inspiration phase, time point t j It is in the exhalation phase, where time point t i The inspiratory phase and time t j The exhalation phase may be within the same respiratory cycle of the target subject or within different respiratory cycles. i It can be in the exhalation phase, time point t j It is in the inspiration phase, where time point t i The exhalation phase and time point t j The inhalation phases belong to different respiratory cycles. A respiratory cycle referred to in the present invention is a complete process starting from the inhalation phase and ending at the exhalation phase.

[0072] In step 310, a first time series data set {(E(t i ),E(t j ))} i＜j且i,j∈{1,2,…,n} , where ΔX(t i ,t j ) represents the target object's lung at time point t i The absolute impedance and the j The difference between the absolute impedances, that is, the first time series data set is {(E(t1),E(t2)),…,(E(t1),E(t n )),…,(E(t n-1 ),E(t n ))}; Then, a static EIT reconstruction algorithm is used to reconstruct the first time series data set {(E(t i ),E(t j ))} i＜j且i,j∈{1,2,…,n} Determine the second time series data set {(X(t i ),X(t j ))} i＜j且i,j∈{1,2,…,n} , where X(t i ) represents the target object’s lung at time point t i The absolute impedance of the second time series data set is {(X(t1),X(t2)),…,(X(t1),X(t n )),…,(X(t n-1 ),X(t n ))}; Finally, based on the second time series data set {(X(t i ),X(t j ))} i＜j且i,j∈{1,2,…,n} Calculate the first time difference series data set {ΔX(t i ,t j)} i＜j且i,j∈{1,2,…,m} , where ΔX(t i ,t j )=|X(t j )-X(t i )|, that is, the first time difference series data set is {|X(t2)-X(t1)|,…,|X(t n )-X(t1)|,…,|X(t n )-X(t n-1 )|}.

[0073] For further details regarding how to obtain the EIT measurement electrical signals of the target subject's lung at various time points in step 310, the static EIT reconstruction algorithm, and how to determine the absolute impedance of the target subject's lung at various time points based on the EIT measurement electrical signals of the target subject's lung at various time points using the static EIT reconstruction algorithm, please refer to the relevant description of step 210 in method 200 and will not be repeated here. In some embodiments, at least one of the absolute impedances of the target subject's lung at various time points determined in step 310 is the absolute impedance of the target subject's lung at end-expiration, which serves as the absolute impedance of the target subject's lung at end-expiration for determining the target subject's end-expiratory lung volume in step 120 of method 100.

[0074] Step 320: Obtain a second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} .

[0075] In the second time difference series data set {ΔY(t i ,t k )} i＜j且i,j∈{1,2,…,n} In, ΔY(t i ,t j ) indicates that the target objects are at time point t i Lung volume and at time point t j The difference between the lung volumes of the second time difference series data set is {ΔY(t1,t2),…,ΔY(t1,t n ),…,ΔY(t n-1 ,t n )}.

[0076] In step 320, the respiratory flow of the target subject may be monitored to obtain a respiratory flow time curve C(t) of the target subject; then, the respiratory flow time curve is integrated over time to obtain a respiratory volume time curve V(t)=∫C(t)dt of the target subject's lungs; since the change in the target subject's lung volume at two time points may be equal to the change in the amount of air inhaled or exhaled by the target subject at the two time points, the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} ,in, That is, the second time difference series data set is

[0077] For more description on how to monitor the respiratory flow of the target object in step 320 to obtain the respiratory flow change curve C(t) of the target object, reference can be made to the relevant description of step 240 in method 200 , which will not be repeated here.

[0078] Step 330: For the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Data fitting was performed to establish the volume-impedance relationship.

[0079] In step 330, based on the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,b} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Generate a ΔX-ΔY scatter plot; wherein the scatter points of the ΔX-ΔY scatter plot include {(ΔX(t i ),ΔY(t j ))} i＜j且i,j∈{1,2,…,n} , that is, the scattered points include (ΔX(t1,t2), ΔY(t1,t2)), ..., (ΔX(t1,t n ), ΔY(t1,t n ))、……、(ΔX(t n-1 ,t n ), ΔY(t n-1 ,t n )); then select the regression model based on the scatter distribution of the ΔX-ΔY scatter plot; finally, based on the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n}and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Parameters of the regression model were estimated to obtain the volume-impedance relationship Y = f(X).

[0080] As an example description, if the scatter distribution of the ΔX-ΔY scatter plot is approximately a straight line distribution, a linear regression model (e.g., Y=mX+n) can be selected, and then the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t k )} i＜j且i,j∈{1,2,…,n} The parameters (m, n) of the linear regression model are estimated to obtain the volume-impedance relationship of the target subject's lungs.

[0081] As another exemplary description, if the scatter distribution of the ΔX-ΔY scatter plot presents a curve trend, for example, a curve form of an exponential function, a logarithmic function, a power function, or a polynomial function, a corresponding nonlinear regression model can be selected according to the specific curve form, for example, an exponential regression model (for example, Y = k·e cX ), logarithmic regression model (for example, Y = g·ln(X) + h), power function regression model (Y = d·X j ), polynomial regression model (e.g., Y = a n X n +a n-1 X n -1+…+a1X+a0), and then a nonlinear optimization method (e.g., gradient descent, maximum likelihood estimation, nonlinear least squares method, etc.) is used based on the first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} The parameters of the selected regression model (e.g., k and c for the exponential regression model, g and h for the logarithmic regression model, d and j for the power function regression model, a for the polynomial regression model) are calculated. n 、a n-1 , ..., a1 and a0) are estimated to obtain the volume-impedance relationship of the target subject's lungs.

[0082] As another exemplary description, if the scatter distribution of the ΔX-ΔY scatter plot is chaotic, complex, and has no obvious pattern, a non-parametric regression model (e.g., a local weighted regression model, a kernel regression model, etc.), a tree model (e.g., a random forest, XGBoost, etc.), a neural network model, a support vector regression model, a Gaussian process regression model, a mixed density network model, etc. can be selected, and the corresponding method (or algorithm) can be used based on the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} The parameters of the selected model are estimated (or optimized) to obtain the volume-impedance relationship of the target subject's lungs.

[0083] In some embodiments, the optimal hyperparameters (e.g., bandwidth, smoothing parameter, etc.) of a non-parametric regression model can be selected by minimizing predictions through cross-validation. In some embodiments, the tree structure parameters (e.g., maximum depth, number of leaf nodes, etc.) and regularization parameters (e.g., learning rate, subsampling ratio, etc.) of a tree model can be adjusted through grid search or Bayesian optimization combined with cross-validation. In some embodiments, the hyperparameters (e.g., learning rate, number of hidden layers, number of neurons, etc.) of a neural network model can be selected through hyperparameter optimization algorithms (e.g., Bayesian optimization, grid search) combined with cross-validation, and the model parameters can be optimized through backpropagation and gradient descent. In some embodiments, the kernel function parameters and regularization parameters of a support vector regression model can be selected through grid search and cross-validation. In some embodiments, the covariance function hyperparameters (e.g., length scale and variance of the RBF kernel) of a Gaussian process regression model can be estimated through maximum likelihood estimation or Markov chain Monte Carlo (MCMC) sampling. In some embodiments, the mixture distribution parameters (e.g., mean, variance, mixture weights, etc.) of a mixture density network model can be optimized by maximizing the log-likelihood function and gradient descent.

[0084] In some embodiments, the method 100 for determining the end-expiratory lung volume provided in the embodiments of the present application may also include determining the volume of the target object's lung region of interest at the end of expiration based on the absolute impedance and volume-impedance relationship of the target object's lung region of interest at the end of expiration. Specifically, the absolute impedance of the target object's lung region of interest at the end of expiration can be input into the volume-impedance relationship of the target object's lung to output the volume of the region of interest at the end of expiration. Wherein. The absolute impedance of the target object's lung region of interest at the end of expiration can be obtained by calculating the sum of the absolute impedance values ​​of all lung pixels in the target object's lung region of interest. By determining the volume of the region of interest at the end of expiration, it can help identify the collapse, over-expansion or gas retention of local lung tissue, which is conducive to achieving precise respiratory support, evaluating treatment effects, and improving prognosis.

[0085] Figure 4 1 is a block diagram of a system for determining end-expiratory lung volume according to some embodiments of the present application.

[0086] like Figure 4 As shown, an embodiment of the present application further provides a system 400 for determining end-expiratory lung volume. Specifically, system 400 may include an acquisition module 410 and a determination module 420. Acquisition module 410 is configured to acquire the volume-impedance relationship of the target subject's lungs; and determination module 420 is configured to determine the target subject's end-expiratory lung volume based on the target subject's absolute impedance and the volume-impedance relationship at the end of expiration.

[0087] In some embodiments, the acquisition module 410 may include a first acquisition submodule, a determination submodule, a second acquisition submodule, and an establishment submodule. The first acquisition submodule is configured to acquire the absolute impedance of the target subject's lungs at the end of expiration and the absolute impedance at the end of inspiration; the determination submodule is configured to determine tidal impedance change data of the target subject's lungs based on the difference between the absolute impedance of the target subject's lungs at the end of expiration and the absolute impedance at the end of inspiration; the second acquisition submodule is configured to acquire tidal volume data of the target subject's lungs; and the establishment submodule is configured to establish a volume-impedance relationship of the target subject's lungs based on the tidal volume data and the tidal impedance change data.

[0088] In some embodiments, the first acquisition submodule may include an acquisition unit and a determination unit. The acquisition unit is configured to acquire at least an EIT measurement electrical signal of the target subject's lung at the end of inspiration and an EIT measurement electrical signal at the end of expiration; and the determination unit is configured to determine, using a static EIT reconstruction algorithm, the absolute impedance of the target subject's lung at the end of inspiration and the absolute impedance at the end of expiration based on the EIT measurement electrical signal of the target subject's lung at the end of inspiration and the EIT measurement electrical signal of the target subject's lung at the end of expiration.

[0089] In some embodiments, the second acquisition submodule may include a monitoring unit configured to monitor the respiratory flow of the target subject to acquire tidal volume data of the target subject's lungs.

[0090] In some embodiments, the acquisition module may include a first acquisition submodule, a second acquisition submodule, and an establishment submodule. The first acquisition submodule is used to acquire a first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} ; Among them, ΔX(t i ,t j ) represents the target object’s lung at time point t i The absolute impedance and the j The second acquisition submodule is used to obtain the second time difference sequence data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,b} ; Among them, ΔY(t i ,t j ) indicates that the target objects are at time point t i Lung volume and at time point t j The difference between the lung volumes; establish a submodule for the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Data fitting was performed to establish the volume-impedance relationship.

[0091] In some embodiments, the first acquisition submodule may include an acquisition unit, a determination unit, and a first calculation unit. The acquisition unit is configured to acquire a first time series data set {(E(t i ),E(t j ))} i＜j且i,j∈{1,2,…,n} ; Among them, E(t i ) and E(t j ) represent the target object’s lungs at time point t i and time point t j EIT measurement electrical signal; determining unit for reconstructing the first time series data set {(E(t i ),E(t j ))} i＜j且i,j∈{1,2,…,n} Determine the second time series data set {(X(t i ),X(tj ))} i＜j且i,j∈{1,2,…,n} ; Among them, X(t i ) and X(t j ) represent the target object’s lungs at time point t i and time point t j The absolute impedance of the first calculation unit is used based on the second time series data set {(X(t i ),X(t j ))} i＜j且i,j∈{1,2,…,n} Calculate the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} , where ΔX(t i ,t j )=|X(t j )-X(t i )|.

[0092] In some embodiments, the second acquisition submodule may include a monitoring unit, an integration unit, and a second calculation unit. The monitoring unit is used to monitor the respiratory flow of the target object to obtain the respiratory flow change curve C(t) of the target object; the integration unit is used to integrate the respiratory flow change curve over time to obtain the respiratory volume change curve V(t)=∫C(t)dt of the target object's lungs; the second calculation unit is used to calculate the second time difference sequence data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} ,in,

[0093] In some embodiments, the establishment submodule may include a generation unit, a selection unit, and a parameter estimation unit. The generation unit is configured to generate a parameter based on the first time difference sequence data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i＜j且i,j∈{1,2,…,n} Generate a ΔX-ΔY scatter plot; wherein the scatter points of the ΔX-ΔY scatter plot include {(ΔX(t i ),ΔY(t j ))} i＜j且i,j∈{1,2,…,n} The selection unit is used to select a regression model based on the scatter distribution of the ΔX-ΔY scatter plot; the parameter estimation unit is used to estimate the regression model based on the first time difference series data set {ΔX(t i ,t j )} i＜j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )}i＜j且i,j∈{1,2,…,n} Parameters of the regression model were estimated to obtain the volume-impedance relationship.

[0094] In some embodiments, the system 400 may further include a local end-expiratory lung volume determination module, which is used to determine the volume of the target subject's lung region of interest at the end of expiration based on the absolute impedance and volume-impedance relationship of the region of interest at the end of expiration.

[0095] It should be noted that the above description of the system 400 for determining end-expiratory lung volume and its modules is for convenience only and does not limit this specification to the scope of the embodiments. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or form subsystems connected to other modules without departing from the principles. In some embodiments, Figure 4 The acquisition module 410 and determination module 420 disclosed in the disclosure may be different modules within a system, or a single module may implement the functions of two or more of the aforementioned modules. For example, the modules may share a storage module, or each module may have its own storage module. Such variations are within the scope of protection of this specification.

[0096] Figure 5 is a block diagram of a ventilator according to some embodiments of the present application.

[0097] like Figure 5 As shown, the embodiment of the present application further provides a ventilator 500, which includes a system 400 for determining end-expiratory lung volume. The ventilator 500 provided in the embodiment of the present application not only has the conventional functions of a ventilator, such as realizing mechanical ventilation, monitoring parameters such as airway pressure, tidal volume, and oxygen concentration, but also has the function of monitoring the patient's end-expiratory lung volume realized by the system 400 for determining end-expiratory lung volume. By monitoring the patient's end-expiratory lung volume, it is helpful to assess lung function, guide mechanical ventilation strategies, improve prognosis, etc. At the same time, monitoring the patient's end-expiratory lung volume through the ventilator 500 also has a series of advantages such as no radiation, non-invasiveness, low cost, easy operation, less time consumption, no requirement for special working environment, low requirement for patient cooperation, bedside operation, and applicability to critically ill patients.

[0098] The beneficial effects that may be brought about by the embodiments of the present application include but are not limited to: (1) The method and system for determining the end-expiratory lung volume provided in the present application can determine the end-expiratory lung volume, which is helpful in evaluating lung function, guiding mechanical ventilation strategies, evaluating treatment effects, and improving prognosis; (2) The method and system for determining the end-expiratory lung volume provided in the embodiments of the present application not only has high accuracy, but also has a series of advantages such as no radiation, no invasiveness, low cost, easy operation, less time consumption, no requirement for special working environment, low requirement for patient cooperation, bedside operation, and applicability to critically ill patients.

[0099] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

[0100] The basic concepts have been described above. It will be apparent to those skilled in the art that the detailed disclosure above is merely illustrative and does not limit this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and revisions to this application. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this application.

[0101] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] The embodiments of the present application are described with reference to flowcharts and / or block diagrams of methods, terminal devices (systems) or computer program products provided according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] In addition, when terms such as "first", "second", and "third" are used in the specification of this application to describe various features, these terms are only used to distinguish these features and cannot be understood as indicating or implying the relationship between the features, the relative importance, or implicitly indicating the number of features indicated.

[0106] At the same time, this application uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this application does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the present application may be appropriately combined.

[0107] Similarly, it should be noted that, in order to simplify the presentation of this application and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not mean that the subject matter of this application requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single embodiment disclosed above.

[0108] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other variations may also fall within the scope of this application. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this application may be considered consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly introduced and described in this application.

Claims

1. A method for determining end-expiratory lung volume, characterized in that: include: Obtaining a volume-impedance relationship of the target subject's lungs; The end-expiratory lung volume of the target subject is determined based on the absolute impedance of the target subject's lung at the end of expiration and the volume-impedance relationship.

2. The method according to claim 1, characterized in that The obtaining of the volume-impedance relationship of the target subject's lungs includes: Obtaining the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inspiration; determining tidal impedance change data of the target subject's lungs based on a difference between the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inspiration; obtaining tidal volume data of the target subject's lungs; A volume-impedance relationship of the target subject's lungs is established based on the tidal volume data and the tidal impedance change data.

3. The method according to claim 2, characterized in that The obtaining of the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inhalation includes: At least collecting an EIT measurement electrical signal of the target subject's lung at the end of inspiration and an EIT measurement electrical signal at the end of expiration; The absolute impedance of the target subject's lungs at the end of inspiration and the absolute impedance at the end of expiration are determined based on the EIT measurement electrical signal of the target subject's lungs at the end of inspiration and the EIT measurement electrical signal at the end of expiration, respectively, using a static EIT reconstruction algorithm.

4. The method according to claim 2, characterized in that The step of obtaining tidal volume data of the target subject's lungs includes: The respiratory flow of the target subject is monitored to obtain tidal volume data of the target subject's lungs.

5. The method according to claim 1, characterized in that The obtaining of the volume-impedance relationship of the target subject's lungs includes: Obtain the first time difference sequence data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} ; Among them, ΔX(t i ,t j ) represents the target object's lung at time point t i The absolute impedance and the j The difference between the absolute impedances of Obtain a second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} ; Among them, ΔY(t i ,t j ) indicates that the target objects are at time point t i Lung volume and at time point t j The difference between the lung volumes For the first time difference series data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} Data fitting was performed to establish the volume-impedance relationship.

6. The method according to claim 5, characterized in that The first time difference sequence data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} ,include: Acquire a first time series data set {(E(t i ),E(t j ))} i<j且i,j∈{1,2,…,n} ; Among them, E(t i ) and E(t j ) represent the target object’s lungs at time point t i and time point t j The EIT measures electrical signals; Based on the first time series data set {(E(t i ),E(t j ))} i<j且i,j∈{1,2,...,n} Determine a second time series data set {(X(t i ),X(t j ))} i<j且i,j∈{1,2,…,n} ; Among them, X(t i ) and X(t j ) represent the target object’s lungs at time point t i and time point t j The absolute impedance of Based on the second time series dataset {(X(t i ),X(t j ))} i<j且i,j∈{1,2,…,n} Calculate the first time difference series data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,...,n} , where ΔX(t i ,t j )=|X(t j )-X(t i )|.

7. The method according to claim 5, characterized in that The second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} ,include: monitoring the respiratory flow of the target object to obtain a respiratory flow change curve C(t) of the target object; Integrating the respiratory flow change curve over time to obtain a respiratory volume change curve of the target subject's lungs: V(t)=∫C(t)dt; The second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} ,in, 8. The method according to claim 5, characterized in that The first time difference series data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} Performing data fitting to establish the volume-impedance relationship Y=f(X) includes: Based on the first time difference series data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} Generate a ΔX-ΔY scatter plot; wherein the scatter points of the ΔX-ΔY scatter plot include {(ΔX(t i ),ΔY(t j ))} i<j且i,j∈{1,2,…,n} ; Selecting a regression model based on the scatter distribution of the ΔX-ΔY scatter plot; Based on the first time difference series data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} Parameters of the regression model are estimated to obtain the volume-impedance relationship.

9. The method according to claim 1, characterized in that The method further comprises: The volume of the target object's lung region of interest at the end of expiration is determined based on the absolute impedance of the region of interest at the end of expiration and the volume-impedance relationship.

10. A system for determining end-expiratory lung volume, characterized in that: include: an acquisition module, the acquisition module being used to acquire a volume-impedance relationship of the target subject's lungs; A determination module is configured to determine an end-expiratory lung volume of the target subject based on an absolute impedance of the target subject's lungs at the end of expiration and the volume-impedance relationship.

11. The system according to claim 10, wherein: The acquisition module includes: a first acquisition submodule, configured to acquire the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inhalation; a determination submodule, configured to determine tidal impedance change data of the target subject's lungs based on a difference between the absolute impedance of the target subject's lungs at the end of exhalation and the absolute impedance at the end of inspiration; a second acquisition submodule, the second acquisition submodule being used to acquire tidal volume data of the target subject's lungs; An establishing submodule is configured to establish a volume-impedance relationship of the target subject's lungs based on the tidal volume data and the tidal impedance change data.

12. The system according to claim 11, wherein: The first acquisition submodule includes: an acquisition unit, configured to acquire at least an EIT measurement electrical signal of the target subject's lung at an end-of-inhalation moment and an EIT measurement electrical signal at an end-of-expiration moment; A determination unit is used to determine the absolute impedance of the target object's lung at the end of inspiration and the absolute impedance at the end of expiration based on the EIT measurement electrical signal of the target object's lung at the end of inspiration and the EIT measurement electrical signal at the end of expiration, respectively, through a static EIT reconstruction algorithm.

13. The system according to claim 11, wherein: The second acquisition submodule includes: A monitoring unit is used to monitor the respiratory flow of the target object to obtain tidal volume data of the target object's lungs.

14. The system according to claim 10, wherein: The acquisition module includes: The first acquisition submodule is used to acquire a first time difference sequence data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} ; Among them, ΔX(t i ,t j ) represents the target object's lung at time point t i The absolute impedance and the j The difference between the absolute impedances of The second acquisition submodule is used to acquire a second time difference sequence data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} ; Among them, ΔY(t i ,t j ) indicates that the target objects are at time point t i Lung volume and at time point t j The difference between the lung volumes Establish a submodule, the establishment submodule is used to i ,t j )} i<j且i,j∈{1,2,...,n} and the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,...,n} Data fitting was performed to establish the volume-impedance relationship.

15. The system according to claim 14, wherein: The first acquisition submodule includes: An acquisition unit is configured to acquire a first time series data set {(E(t i ),E(t j ))} i<j且i,j∈{1,2,…,n} ; Among them, E(t i ) and E(t j ) represent the target object’s lungs at time point t i and time point t j The EIT measures electrical signals; A determining unit configured to reconstruct the first time series data set {(E(t i ),E(t j ))} i<j且i,j∈{1,2,…,n} Determine a second time series data set {(X(t i ),X(t j ))} i<j且i,j∈{1,2,…,n} ; Among them, X(t i ) and X(t j ) represent the target object’s lungs at time point t i and time point t j The absolute impedance of A first computing unit is configured to calculate the time series data set {(X(t i ),X(t j ))} i<j且i,j∈{1,2,…,n} Calculate the first time difference series data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} , where ΔX(t i ,t j )=|X(t j )-X(t i )|.

16. The system according to claim 14, wherein: The second acquisition submodule includes: a monitoring unit, configured to monitor the respiratory flow of a target subject to obtain a respiratory flow variation curve C(t) of the target subject; An integration unit, configured to perform temporal integration on the respiratory flow change curve to obtain a respiratory volume change curve V(t)=∫C(t)dt of the target subject's lungs; The second calculation unit is used to calculate the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,...,n} ,in, 17. The system according to claim 14, wherein: The establishment submodule includes: A generating unit configured to generate a time difference sequence data set based on the first time difference sequence data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} Generate a ΔX-ΔY scatter plot; wherein the scatter points of the ΔX-ΔY scatter plot include {(ΔX(t i ),ΔY(t j ))} i<j且i,j∈{1,2,…,n} ; a selection unit configured to select a regression model based on the scatter distribution of the ΔX-ΔY scatter plot; A parameter estimation unit is configured to estimate the time difference between the first time difference series data set {ΔX(t i ,t j )} i<j且i,j∈{1,2,…,n} and the second time difference series data set {ΔY(t i ,t j )} i<j且i,j∈{1,2,…,n} Parameters of the regression model are estimated to obtain the volume-impedance relationship.

18. The system according to claim 10, wherein: The system further comprises: A local end-expiratory lung volume determination module is used to determine the volume of the target subject's lung region of interest at the end of expiration based on the absolute impedance of the region of interest at the end of expiration and the volume-impedance relationship.

19. A ventilator, characterized in that: A system for determining end-expiratory lung volume comprising the system according to any one of claims 10 to 18.

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