Pulmonary function assessment method and system based on time characteristics of pulmonary electrical impedance images

Abstract

The present invention provides a method, system, device, medium and program product for evaluating lung function based on the time characteristics of pulmonary electrical impedance images, and relates to the field of intelligent medical care. The method comprises: obtaining a time-series EIT image of a subject to be tested; obtaining regional time distribution diagrams corresponding to different exhaled air volumes of the subject to be tested containing time characteristics based on the time-series EIT image; and obtaining regional time distribution values ​​corresponding to different exhaled air volumes based on the regional time distribution diagram; comparing the regional time distribution value of the subject to be tested at the first exhaled air volume with the standard value of a healthy person at the first exhaled air volume, and if the regional time distribution value of the subject to be tested is greater than the standard value, outputting the result of the respiratory muscle burst difference of the subject to be tested.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical treatment, and more specifically, to a lung function assessment method, system, device, medium and program product based on the time characteristics of lung electrical impedance images. Background Art

[0002] The respiratory function of the lungs is to continuously inhale fresh air, expel turbid air, and exchange gas between the body and the external environment to maintain human life activities. At present, pulmonary function tests mainly rely on pulmonary function measurement and symptom assessment. Pulmonary function measurement (PFT) using a spirometer is a mature lung function measurement method, and its corresponding parameters, forced expiratory volume in one second (FEV1) and forced vital capacity (FVC), can be used to diagnose and grade the severity of the disease. However, PFT can only provide overall lung function and cannot reflect local lung ventilation changes that may occur in the early stages of the disease process. Forced oscillation technique (FOT) reflects airway characteristics by measuring oscillatory airflows of different frequencies. Although FOT has the potential to detect respiratory abnormalities early, it is still limited to overall functional assessment. In addition, CT is also used to screen lung cancer, but it has radiation and is difficult to become a widespread early screening tool. Therefore, there is an urgent need for a lung function assessment method that can reflect the local lung function status and is non-invasive and radiation-free.

[0003] Electrical impedance tomography (EIT) can reflect regional pulmonary ventilation function and has been widely used in clinical pulmonary ventilation function assessment, especially suitable for early screening and detection of lung function damage. However, based on dynamic time-series EIT images, how to evaluate lung function has become an important practical problem for early screening of lung function damage using EIT. At present, some scholars have proposed methods based on the spatial characteristics of pulmonary electrical impedance images based on existing PFT parameters, including regional FEV1, FEV1 / FVC, etc. These image parameters objectively characterize the exhaled volume per unit time and reflect the explosive power of human respiratory muscles. They play a good role in evaluating human lung function. However, these indicators cannot reflect the time required to exhale a unit volume of air. These parameters based on the time characteristics of pulmonary electrical impedance images are of great significance for objectively characterizing human lung function. They are not only related to the explosive power of human respiratory muscles, but also to the endurance of respiratory muscles, and can comprehensively evaluate respiratory function. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a lung function assessment method and system based on the time characteristics of lung electrical impedance images; the method of the present invention characterizes the time characteristics required to exhale different amounts of gas in FVC by regional time distribution and its coefficient of variation based on different exhaled volumes; and then accurately reflects the time required for unit exhaled volume by comparing the regional time distribution values ​​and coefficients of variation corresponding to different exhaled volumes, objectively characterizes the explosive power of human respiratory muscles, endurance of respiratory muscles, and whether the lungs are damaged, and deeply explores the life laws hidden behind biological data to more comprehensively evaluate respiratory function.

[0005] The first aspect of the present application discloses a method for evaluating lung function based on the time characteristics of lung electrical impedance images, the method comprising:

[0006] Acquire time-series EIT images of the subject;

[0007] Based on the time series EIT image, regional time distribution diagrams corresponding to different exhaled air volumes of the subject including time characteristics are obtained; and based on the regional time distribution diagrams, regional time distribution values ​​corresponding to different exhaled air volumes are obtained;

[0008] Compare the regional time distribution value of the subject under test at the first exhaled air volume with the standard value of a healthy person at the first exhaled air volume, and if the regional time distribution value of the subject under test is greater than the standard value, output the result of the respiratory muscle burst difference of the subject under test.

[0009] The time series EIT image is obtained through chest electrical impedance signal data of the subject performing FVC action time;

[0010] The method for obtaining the regional time distribution diagram of different exhaled air volumes including time characteristics includes:

[0011] Calculate and obtain the quantitative gas volume exhaled by each pixel at different exhaled gas volumes during the exhalation phase during the FVC action time, that is, the impedance change of each pixel;

[0012] Calculate and obtain the time corresponding to the impedance change of each pixel;

[0013] The regional time distribution diagram is obtained based on the time corresponding to the impedance change of each pixel and the impedance change of each pixel.

[0014] The method of calculating and obtaining the quantitative amount of gas exhaled at different exhaled volumes of each pixel during the FVC action time comprises: calculating and obtaining the maximum value of each pixel during the FVC action time; calculating and obtaining the quantitative amount of gas exhaled at different exhaled volumes by multiplying different exhaled volumes by the maximum value of each pixel; and the exhaled volume is expressed as a percentage of FVC.

[0015] The method further comprises:

[0016] Obtaining regional time distribution values ​​corresponding to at least two different exhaled air volumes based on the regional time distribution graph;

[0017] Comparing the regional time distribution value of the subject under test at the first exhaled air volume with the standard value of healthy people at the first exhaled air volume, and comparing the regional time distribution value of the subject under test at the second exhaled air volume with the standard value of healthy people at the second exhaled air volume;

[0018] If the regional time distribution values ​​of the subject at the first exhaled volume and the second exhaled volume are both greater than the corresponding standard values, the difference between the regional time distribution value of the subject at the first exhaled volume and its corresponding standard value is calculated as the first difference, and the difference between the regional time distribution value of the subject at the second exhaled volume and its corresponding standard value is calculated as the second difference. If the first difference is less than the second difference, the result of the poor respiratory muscle endurance of the subject is output, and the first exhaled volume is less than the second exhaled volume.

[0019] The method further comprises:

[0020] Calculating a coefficient of variation of the regional time distribution graph based on the regional time distribution graph;

[0021] Compare the coefficient of variation of the subject under test at the first exhaled air volume with the standard coefficient of a healthy person at the first exhaled air volume, and if the coefficient of variation of the subject under test is greater than the corresponding standard coefficient, output a result indicating that the subject under test has regional ventilation function impairment.

[0022] The method for obtaining the coefficient of variation includes any of the following:

[0023] Based on the regional time distribution map, the standard deviation and the mean of the regional time distribution map are calculated; the coefficient of variation is obtained by dividing the mean by the standard deviation; or, the coefficient of variation is calculated by a clustering method, and the clustering method includes any one or more of the following: Kmeans algorithm, DBSCAN algorithm, spectral clustering algorithm, Laplace mapping algorithm, PCA, GMM, MeanShift algorithm (mean migration), hierarchical clustering algorithm.

[0024] The method for acquiring the time-series EIT image includes:

[0025] Acquiring chest electrical impedance signal data of the subject to be tested, wherein the chest electrical impedance signal data is the chest electrical impedance signal data of the subject to be tested performing FVC action time;

[0026] Taking the end-expiratory moment of FVC as a reference, an algorithm is used to reconstruct a time-series EIT image of the time when the subject performs the FVC action, which is the time-series EIT image.

[0027] A second aspect of the present application discloses a lung function assessment system based on the time characteristics of lung electrical impedance images, including a computer program, which implements the steps of the above method when executed by a processor.

[0028] The third aspect of the present application discloses a computer device, which includes: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, it is used to execute the steps of the above method.

[0029] A fourth aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0030] A fifth aspect of the present application discloses a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0031] This application has the following beneficial effects:

[0032] 1. The present application innovatively discloses a lung function assessment method based on the time characteristics of lung electrical impedance images. The method reconstructs a time-series EIT image of a human body performing FVC action time by taking the end-expiration moment of FVC as a reference, and obtains a regional time distribution diagram containing time characteristics based on the time-series EIT image. The regional time distribution of different exhaled air volumes and their coefficient of variation obtained from the regional time distribution diagram accurately reflect the time required for unit exhaled air volume, objectively characterize the explosive power of human respiratory muscles, endurance of respiratory muscles, and whether there is any damage to the lungs, more comprehensively assess the respiratory function of the human lungs, and overcome the technical difficulty that the indicators proposed by existing scholars cannot reflect the time required for exhaling a unit volume of air.

[0033] 2. This application innovatively uses regional time distribution diagrams containing time characteristics to obtain relevant parameters containing time characteristics, which characterize the time characteristics required to exhale different amounts of gas in FVC. It can not only reflect the explosive power of the human respiratory muscles, but also characterize the endurance of the human respiratory muscles. It conducts comprehensive evaluation from multiple dimensions, greatly improving the accuracy and depth of the analysis of human respiratory function. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 is a schematic diagram of a method flow chart provided by the first aspect of an embodiment of the present invention;

[0036] Figure 2 is a schematic flow chart of a system provided by the second aspect of an embodiment of the present invention;

[0037] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present invention;

[0038] Figure 4 The embodiment of the present invention provides a healthy person in T 25 , T 50 , T 75 , T 90 Schematic diagram of regional time distribution at ;

[0039] Figure 5 The embodiment of the present invention provides a COPD patient in T 25 , T 50 , T 75 , T 90 Schematic diagram of regional time distribution at . DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0041] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0043] Figure 1 1 is a flow chart of a method for evaluating lung function based on the time characteristics of lung electrical impedance images provided by an embodiment of the present invention. Specifically, the method comprises the following steps:

[0044] 101: Acquire time-series EIT images of a subject;

[0045] In one embodiment, the time-series EIT image is obtained through chest electrical impedance signal data of the subject performing FVC action time.

[0046] In one embodiment, the method for acquiring the time-series EIT image includes:

[0047] Obtain chest electrical impedance signal data of the subject, wherein the chest electrical impedance signal data is the chest electrical impedance signal data of the time when the subject performs the FVC action; in one embodiment, the method for obtaining the chest electrical impedance signal data includes: placing N electrodes on the chest surface of the subject, the subject performs the FVC action, and collecting the chest electrical impedance signal data of the time when the subject performs the FVC action in real time through an EIT device connected to the electrodes; N is a natural integer greater than or equal to 2; preferably, N electrodes are placed on the chest surface between the fourth and fifth ribs of the subject; the standard for the subject to perform the FVC action includes: after three cycles of calm breathing, the subject takes a deep breath, exhales quickly and forcefully, and after completing the exhalation action, takes a deep breath and resumes calm breathing. N is preferably 16.

[0048] Taking the end-expiratory moment of FVC as a reference, the GREIT algorithm is used to reconstruct the time series EIT image of the time when the subject performs the FVC action, which is the time series EIT image. The EIT image at time t is Wherein, N is the total number of pixels of the EIT image.

[0049] In one embodiment, the subject is a patient with abnormal respiratory function or lung disease, or a patient suspected of having abnormal respiratory function.

[0050] The GREIT algorithm is one of the commonly used electrical impedance reconstruction algorithms. In addition to the GREIT algorithm, other EIT algorithms can also be used, such as: traditional second-order norm weighted algorithm, sparse weighted algorithm, etc.

[0051] 102: obtaining regional time distribution diagrams corresponding to different exhaled air volumes of the subject having time characteristics based on the time series EIT images; and obtaining regional time distribution values ​​corresponding to different exhaled air volumes based on the regional time distribution diagrams;

[0052] In one embodiment, the method for obtaining the regional time distribution diagram of different exhaled air volumes including time characteristics includes:

[0053] Calculate and obtain the quantitative gas volume exhaled by each pixel at different exhaled gas volumes during the exhalation phase during the FVC action time, that is, the impedance change of each pixel;

[0054] Calculating and obtaining the time corresponding to the impedance change of each pixel; fitting the corresponding time according to the selected exhaled air volume;

[0055] The regional time distribution diagram is obtained based on the time corresponding to the impedance change of each pixel and the impedance change of each pixel.

[0056] In one embodiment, the method of calculating and obtaining the quantitative amount of gas exhaled at different exhaled volumes of each pixel during the FVC action time includes: calculating and obtaining the maximum value of each pixel during the FVC action time; calculating and obtaining the quantitative amount of gas exhaled at different exhaled volumes by multiplying different exhaled volumes by the maximum value of each pixel; and the exhaled volume is expressed as a percentage of FVC.

[0057] In one embodiment, T x Represents the regional time distribution map, with C x Coefficient of variation of the regional time profile, where x is the expired volume expressed as a percentage of FVC. x The calculation method is as follows:

[0058] First, calculate the maximum value P of each pixel during the FVC action i max , where i represents the i-th pixel;

[0059] Secondly, calculate the amount of gas x*P exhaled by each pixel during the exhalation phase i max .

[0060] Then, calculate the impedance change x*P of each pixel i max The corresponding time Further, obtain the regional time distribution map

[0061] 103: Compare the regional time distribution value of the subject at the first exhaled air volume with the standard value of a healthy person at the first exhaled air volume, and if the regional time distribution value of the subject is greater than the standard value, output the result of the respiratory muscle burst difference of the subject;

[0062] In one embodiment, the regional time distribution value is obtained through a regional time distribution graph.

[0063] In one embodiment, the method further comprises:

[0064] Obtaining regional time distribution values ​​corresponding to at least two different exhaled air volumes based on the regional time distribution graph;

[0065] Comparing the regional time distribution value of the subject under test at the first exhaled air volume with the standard value of healthy people at the first exhaled air volume, and comparing the regional time distribution value of the subject under test at the second exhaled air volume with the standard value of healthy people at the second exhaled air volume;

[0066] If the regional time distribution values ​​of the subject at the first exhaled volume and the second exhaled volume are both greater than the corresponding standard values, the difference between the regional time distribution value of the subject at the first exhaled volume and its corresponding standard value is calculated as the first difference, and the difference between the regional time distribution value of the subject at the second exhaled volume and its corresponding standard value is calculated as the second difference. If the first difference is less than the second difference, the result of the poor endurance of the subject's respiratory muscles is output, and the first exhaled volume is less than the second exhaled volume. Specifically, the different time distributions corresponding to at least two different exhaled volumes of the subject are calculated, and the two different exhaled volumes are respectively recorded as x1 and x2, and the time distribution corresponding to x1 is T x1 , the time corresponding to x2 is T x2 , x1 is less than x2; when the subject is at T x1 If the regional time distribution value at T is greater than the standard value, the result of the respiratory muscle burst difference of the subject is output; if the subject is at T x1 and T x2 The regional time distribution values ​​at T x1 The difference between the regional time distribution value at T and the standard value is the first difference. x2 The difference between the regional time distribution value at and the standard value is the second difference. If the first difference is less than the second difference, the result of the poor respiratory muscle endurance of the subject is output.

[0067] When compared, the first exhaled volume and the second exhaled volume refer to the same exhalation process (exhalation phase), and each exhalation process refers to the forced exhalation process of FVC. The exhalation time constant in this area is an exponential parameter obtained by an exponential fitting exhalation curve, which represents the time required to exhale a certain amount of gas (for example, 75% of the total exhaled volume):

[0068]

[0069] In one embodiment, the method further comprises:

[0070] The coefficient of variation of the regional time distribution diagram is calculated based on the regional time distribution diagram; specifically, the mean is expressed as mean(T x ) indicates that the coefficient of variation C x Expressed in terms of coefficient of variation, mean refers to the average, which is mainly an arithmetic term, referring to the value obtained by dividing the sum of several unequal numbers by the number of these numbers; the mean of people with abnormal lung function will be different from that of healthy people, but the coefficient of variation can reflect whether there is a problem.

[0071] Compare the coefficient of variation of the subject under test at the first exhaled air volume with the standard coefficient of a healthy person at the first exhaled air volume, and if the coefficient of variation of the subject under test is greater than the corresponding standard coefficient, output a result indicating that the subject under test has regional ventilation function impairment.

[0072] In one embodiment, the method for obtaining the coefficient of variation includes any one of the following:

[0073] The standard deviation and mean of the regional time distribution diagram are calculated based on the regional time distribution diagram; the coefficient of variation is obtained by dividing the mean by the standard deviation, i.e., C x =std(T x ) / mean(T x ); or, the coefficient of variation is calculated by a clustering method, and the clustering method includes any one or more of the following: Kmeans algorithm, DBSCAN algorithm, spectral clustering algorithm, Laplace mapping algorithm, PCA, GMM, MeanShift algorithm (mean migration), hierarchical clustering algorithm. The standard deviation (foreign name: Standard Deviation, also known as: mean square error) is the square root of the arithmetic mean of the squares of the deviations of the standard values ​​of each unit of the population from their mean, represented by σ, and the standard deviation is the arithmetic square root of the variance. The standard deviation is most commonly used as a statistical distribution degree in probability statistics, and it can also reflect the degree of discreteness of a data set. Two sets of data with the same mean may not have the same standard deviation. The principle of clustering is to reflect the degree of convergence of all pixels. The closer they are, the smaller the coefficient of variation; the larger the coefficient of variation, the greater the damage to some areas of the lungs, and the greater the difference from healthy people.

[0074] The purpose of the present application is to disclose a lung function assessment system based on the time characteristics of lung electrical impedance images, including a computer program, which implements the steps of the above method when executed by a processor.

[0075] Figure 2 A pulmonary function assessment system based on the time characteristics of pulmonary electrical impedance images provided by an embodiment of the present invention comprises:

[0076] An acquisition unit 201 is used to acquire a time-series EIT image of a subject;

[0077] The first processing unit 202 is used to obtain regional time distribution diagrams corresponding to different exhaled air volumes of the subject including time characteristics based on the time series EIT image; and obtain regional time distribution values ​​corresponding to different exhaled air volumes based on the regional time distribution diagram;

[0078] The second processing unit 203 is used to compare the regional time distribution value of the subject at the first exhaled volume with the standard value of a healthy person at the first exhaled volume, and output the result of the respiratory muscle burst difference of the subject if the regional time distribution value of the subject is greater than the standard value.

[0079] Combined with Figure 4 , Attachment Figure 5 FIG. 1 is a specific embodiment of the present invention, wherein: Figure 4 The embodiment of the present invention provides a healthy person in T 25 , T 50 , T 75 , T 90 Schematic diagram of regional time distribution at ; Figure 5 The embodiment of the present invention provides a COPD patient in T 25 , T 50 , T 75 , T 90 Schematic diagram of regional time distribution at Figure 4 and attached Figure 5 In, T 25 Written as T25, T 50 Written as T50, T 75 Written as T75, T 90 Written as T90, attached Figure 4 and attached Figure 5 The horizontal and vertical axes in are both pixels, and the unit of the color scale bar is time (s). Specifically include:

[0080] This study was approved by the Ethics Committee. The subjects were a healthy person and a COPD patient, who was a patient with chronic obstructive pulmonary disease.

[0081] Healthy subjects: 51 years old, male. COPD patients: 56 years old, male.

[0082] The first step is to collect the chest impedance signal of the human body when performing FVC action. The subject wears the EIT electrode belt on the subject's chest, usually between the 4th and 5th ribs. The height of the electrode belt can be adjusted appropriately according to the actual situation. Turn on the EIT machine to collect the human chest impedance signal in real time. Instruct the patient to perform FVC breathing movements, that is, after three cycles of calm breathing, take a deep breath, followed by a quick and forceful exhalation. After completing the exhalation, take a deep breath, and then gradually resume calm breathing. Taking the end of exhalation of the FVC action as a reference, reconstruct the time-series EIT image during the entire FVC action implementation period. The EIT image at time t is Wherein, N is the total number of pixels of the EIT image. In this embodiment, the GREIT algorithm is used to reconstruct the time series EIT image, and the EIT image is discretized into an image of 32*32 pixels.

[0083] The second step is to analyze the characteristic parameters based on the temporal characteristics of the EIT image.

[0084] In order to characterize the time characteristics required to exhale different amounts of gas in FVC, an embodiment of the present invention provides a regional time distribution based on different exhaled gas volumes and its coefficient of variation.

[0085] T x Represents the regional time distribution map, with C x Coefficient of variation of the regional time profile, where x is the expired volume expressed as a percentage of FVC. x The calculation method is as follows:

[0086] First, calculate the maximum value P of each pixel during the FVC action i max , where i represents the i-th pixel;

[0087] Secondly, calculate the amount of gas x*P exhaled by each pixel during the exhalation phase i max .

[0088] Then, calculate the impedance change x*P of each pixel i max The corresponding time Further, obtain the regional time distribution map

[0089] Finally, the mean and coefficient of variation C of the regional time distribution map were calculated. x It can be calculated by dividing the mean by the standard deviation, that is, C x =std(T x) / mean(T x ), or the clustering method can be used for calculation.

[0090] In this embodiment, to obtain the result of poor respiratory muscle endurance, at least two expired air volumes are required for comparison. In this embodiment, the regional time distribution corresponding to the four expired air volumes of 25%, 50%, 75% and 90% of FVC is calculated, namely, T 25 , T 50 , T 75 and T 90 The regional temporal distribution of healthy people is as follows Figure 4 As shown in the figure, the coefficients of variation of the regional time distribution maps are C 25 =0.018, C 50 =0.023, C 75 = 0.028 and C 90 =0.034. The regional temporal distribution of COPD patients is as follows Figure 5 As shown in the figure, the coefficients of variation of the regional time distribution maps are C 25 =0.038, C 50 =0.041, C 75 =0.088 and C 90 =0.19. From the results, we can see that at T 25 , T 50 The regional time distribution values ​​of COPD patients were significantly greater than those of healthy people, and as the exhaled air volume increased, the T 75 and T 90 The regional time distribution values ​​of COPD patients are more significantly greater than those of healthy people, indicating that the respiratory muscles of COPD patients not only have poor burst, but also poor respiratory muscle endurance; moreover, compared with healthy people, the coefficient of variation of the regional time distribution of COPD patients is larger, reflecting the regional ventilation function damage of COPD patients.

[0091] Figure 3 A computer device is provided in an embodiment of the present invention, and the device comprises: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, it is used to execute the steps of the above method.

[0092] The embodiment of the present invention further discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0093] The embodiment of the present invention further discloses a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0094] definition:

[0095] FEV1, or forced expiratory volume in one second, is the maximum volume of air exhaled in one second after maximum inhalation and rapid exhalation. The normal value for an adult is 3.65 L. It is the most commonly used indicator in clinical practice, with good repeatability and strong force dependence. Both restrictive and obstructive disorders can lead to its decline.

[0096] FEV1 / FVC stands for forced expiratory volume in one second / forced vital capacity, which is the ratio of FEV1 to FVC. The normal range is about 83%. A decrease in its value is common in obstructive disorders, and >90% indicates the presence of restrictive disorders.

[0097] In mild airflow obstruction, the patient can fully exhale, and FVC may be basically normal or only slightly decreased, but the expiratory flow slows down, FEV1 decreases, and FEV1 / FVC also decreases accordingly; as the obstruction worsens, both FEV1 and FEVl / FVC decrease further; if the obstruction is severe, the patient often finds it difficult to complete full exhalation, and FVC also decreases significantly, and FEV1 / FVC may increase instead. Therefore, FEV1 / FVC can reflect the presence of airflow obstruction, but it cannot accurately reflect the degree of obstruction.

[0098] FEV1 is the volume of air exhaled in one second after the deepest breath. FEV1% measurement is a common indicator for judging asthma and COPD. Asthma mainly causes expiratory dyspnea, so FEV1% measurement will be lower or significantly lower. Clinically, the ratio of FEV1 / forced vital capacity FVC (rate in one second) is often used for judgment, and the normal value is 83%: obstructive or mixed type is slightly reduced to significantly reduced; restrictive type is normal or slightly increased.

[0099] The severity of COPD (chronic obstructive pulmonary disease) can be graded based on FEV1 / FVC, FEV1 as a percentage of predicted value, and symptoms:

[0100] Grade I (mild): FEV1 / FVC <70%, FEV1 as a percentage of predicted value >80%; avoid risk factors: influenza vaccination; use short-acting bronchodilators as needed

[0101] Grade II (moderate): FEV1 / FVC <70%, SFEV1 50%-80% of the predicted value, regular use of one or more long-acting bronchodilators, rehabilitation therapy on the basis of the previous level of treatment

[0102] Grade II (severe): FEV1 / FVC <70%, 30% SFEV1 accounts for <50% of the predicted value, and inhaled glucocorticoids can be used in addition to the previous level of treatment with repeated acute attacks.

[0103] IV (extremely severe): FEV1 / FVC <70%, FEV1 as a percentage of the predicted value <30%, or FEV1 <50% accompanied by chronic respiratory failure. On the basis of superior treatment, if there is respiratory failure, long-term oxygen therapy, and surgical treatment may be considered.

[0104] Electrical impedance tomography (EIT) means that when a certain current or voltage is added to the surface of the human body, different impedance distributions in the body will measure different voltages or currents on the body surface. Therefore, electrical impedance tomography technology is actually: injecting a known voltage into a specific part of the human body to measure the current caused on the body surface, or injecting a known current to measure the voltage caused on the body surface, using the measured current and voltage values, according to a certain reconstruction algorithm, calculate the impedance distribution of each tissue and organ in the human body under the action of the electric field, and use a computer to generate tomographic images. Specifically, if the surface boundary voltage data is measured based on the excitation current in this application, the lung perfusion EIT image is reconstructed according to the existing reconstruction algorithm.

[0105] The validation results of this validation example show that assigning intrinsic weights to indications can moderately improve the performance of the method relative to the default setting.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0108] 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 multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in each embodiment of the present invention 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.

[0110] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0111] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0112] The above is a detailed introduction to a computer device provided by the present invention. For a person skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. Pulmonary function assessment method based on the temporal characteristics of pulmonary electrical impedance images, It is characterized in that The method comprises: Acquire a time-series EIT image of the subject; the time-series EIT image is obtained by chest electrical impedance signal data of the subject performing FVC action time; Calculate and obtain the quantitative amount of gas exhaled by each pixel at different exhaled air volumes in the exhalation phase during the FVC action time, that is, the impedance change of each pixel; calculate and obtain the time corresponding to the impedance change of each pixel; obtain a regional time distribution diagram based on the time corresponding to the impedance change of each pixel and the impedance change of each pixel; and obtain the regional time distribution values ​​corresponding to different exhaled air volumes based on the regional time distribution diagram; the calculation and obtaining of the quantitative amount of gas exhaled by each pixel at different exhaled air volumes in the exhalation phase during the FVC action time includes: calculating and obtaining the maximum value of each pixel during the FVC action time; calculating and obtaining the quantitative amount of gas exhaled at different exhaled air volumes by multiplying different exhaled air volumes by the maximum value of each pixel; Compare the regional time distribution value of the subject under test at the first exhaled air volume with the standard value of a healthy person at the first exhaled air volume. If the regional time distribution value of the subject under test is greater than the standard value, output the result of the respiratory muscle burst difference of the subject under test.

2. The method for evaluating lung function based on the time characteristics of lung electrical impedance images according to claim 1, It is characterized in that The expired volume is expressed as a percentage of FVC.

3. The method for evaluating lung function based on the time characteristics of lung electrical impedance images according to claim 1, It is characterized in that The method further comprises: Obtaining regional time distribution values ​​corresponding to at least two different exhaled air volumes based on the regional time distribution graph; Comparing the regional time distribution value of the subject under test at the first exhaled air volume with the standard value of healthy people at the first exhaled air volume, and comparing the regional time distribution value of the subject under test at the second exhaled air volume with the standard value of healthy people at the second exhaled air volume; If the regional time distribution values ​​of the subject at the first exhaled volume and the second exhaled volume are both greater than the corresponding standard values, the difference between the regional time distribution value of the subject at the first exhaled volume and its corresponding standard value is calculated as the first difference, and the difference between the regional time distribution value of the subject at the second exhaled volume and its corresponding standard value is calculated as the second difference. If the first difference is less than the second difference, the result of the poor respiratory muscle endurance of the subject is output, and the first exhaled volume is less than the second exhaled volume.

4. The method for evaluating lung function based on the time characteristics of lung electrical impedance images according to claim 1, It is characterized in that The method further comprises: Calculating a coefficient of variation of the regional time distribution graph based on the regional time distribution graph; Compare the coefficient of variation of the subject under test at the first exhaled air volume with the standard coefficient of a healthy person at the first exhaled air volume, and if the coefficient of variation of the subject under test is greater than the corresponding standard coefficient, output a result indicating that the subject under test has regional ventilation function impairment.

5. The method for evaluating lung function based on the time characteristics of lung electrical impedance images according to claim 4, It is characterized in that The method for obtaining the coefficient of variation includes any of the following: Based on the regional time distribution map, the standard deviation and the mean of the regional time distribution map are calculated; the coefficient of variation is obtained by dividing the mean by the standard deviation; or, the coefficient of variation is calculated by a clustering method, and the clustering method includes any one or more of the following: Kmeans algorithm, DBSCAN algorithm, spectral clustering algorithm, Laplace mapping algorithm, PCA, GMM, MeanShift algorithm, hierarchical clustering algorithm.

6. The method for evaluating lung function based on the time characteristics of lung electrical impedance images according to any one of claims 1 to 5, It is characterized in that The method for acquiring the time-series EIT image includes: Acquiring chest electrical impedance signal data of the subject to be tested, wherein the chest electrical impedance signal data is chest electrical impedance signal data of the subject to be tested performing FVC action time; Taking the end-expiratory moment of FVC as a reference, an algorithm is used to reconstruct a time-series EIT image of the time when the subject performs the FVC action, which is the time-series EIT image.

7. A system for evaluating lung function based on the temporal characteristics of electrical impedance images of the lungs, including a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer device, It is characterized in that The device comprises: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, it is used to execute the steps of the method described in any one of claims 1 to 6.

9. A computer-readable storage medium, It is characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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