A system, method and apparatus for simulating a lung breathing pattern

Abstract

The application relates to the field of intelligent medical treatment, in particular to a system, a method and a device for simulating a lung breathing mode. The system comprises an electrical impedance network module for simulating the electrical impedance change of the lung; a power supply module for supplying power for the electrical impedance network module and a control module; the control module acquires breathing instruction data, judges a breathing mode based on the breathing instruction data, controls the electrical impedance network module to simulate corresponding electrical impedance change based on the breathing mode, and obtains simulated lung breathing data; wherein the breathing mode comprises a shallow breathing mode, a deep breathing mode, a pendulum breathing mode and a lesion breathing mode. The application can simulate data of different breathing modes, is used for evaluating the accuracy of an EIT system, and has good clinical application value.

Description

Technical Field

[0001] This application relates to the field of intelligent healthcare, specifically to a system, method, apparatus, and computer-readable storage medium that simulates lung breathing patterns. Background Technology

[0002] Electrical impedance tomography (EIT) is a novel medical imaging method characterized by its non-invasiveness, lack of radiation, portability, and functional imaging capabilities, attracting widespread clinical attention. It holds promising applications in areas such as lung ventilation monitoring, pulmonary perfusion assessment, and dynamic detection of brain diseases. EIT involves attaching an electrode array to the human body surface, applying a safe excitation current while simultaneously detecting the surface voltage, and finally calculating the distribution of electrical impedance changes within the body using image reconstruction algorithms. This distribution of electrical impedance changes then allows for the assessment of physiological and pathological changes within the body.

[0003] The EIT system assesses physiological and pathological changes in the human body based on changes in impedance. Therefore, the accuracy of impedance measurement in the EIT system directly affects the assessment results. Traditional EIT accuracy assessment devices use a resistor network to establish a network of resistors to evaluate or calibrate the EIT system's accuracy. However, traditional EIT calibration boards are composed of pure resistors, and therefore cannot simulate changes in the imaginary part of impedance. Furthermore, traditional calibration boards can only periodically change the overall resistance value to simulate changes in lung respiration. For example, CN114024522A discloses a resistor network, device, and method for simulating lung respiration, used to simulate continuous impedance changes in the lungs during respiration. However, it cannot represent the depth of respiration or the breathing patterns during lung diseases, thus failing to verify the accuracy of the EIT system at the algorithm level. Summary of the Invention

[0004] To address the above problems, the present invention provides a system for simulating lung breathing patterns, specifically comprising: Electrical impedance network module: used to simulate changes in the electrical impedance of the lungs; Power supply module: Used to provide power to the impedance network module and control module; Control module: acquires respiratory command data, determines the respiratory mode based on the respiratory command data, and controls the impedance network module based on the respiratory mode to simulate the corresponding impedance changes to obtain simulated lung respiratory data; wherein, the respiratory mode includes shallow breathing mode, deep breathing mode, pendulum breathing mode, and lesion breathing mode.

[0005] Optionally, the impedance network module includes a standard impedance network module and a variable impedance network module. The variable impedance network module includes N variable resistors and S variable capacitors, and the standard impedance network module includes L standard resistors. N, S, and L are natural numbers greater than 1. The standard impedance network module and the variable impedance network module are arranged in a simulated manner based on the actual lung region to obtain a simulated lung. The standard impedance network module and the variable impedance network module in the simulated lung are controlled based on the breathing pattern to obtain simulated lung breathing data.

[0006] Optionally, the impedance network module further includes an electrode contact impedance module disposed around the periphery of the simulated lung to simulate impedance changes on the skin surface. Based on the breathing pattern, the electrode contact impedance module, the standard impedance network module, and the variable impedance network module in the simulated lung are controlled to obtain simulated lung breathing data.

[0007] Optionally, when the breathing mode is shallow breathing mode, the control module controls the impedance network module to simultaneously generate periodic shallow breathing data for the simulated left and right lungs; when the breathing mode is deep breathing mode, the control module controls the impedance network module to simultaneously generate periodic deep breathing data for the simulated left and right lungs; when the breathing mode is pendulum breathing mode, the control module controls the impedance network module to simultaneously control the left and right lungs to generate breathing data after delaying the right lung data by m seconds, where m is a natural number greater than or equal to 1; when the breathing mode is lesion breathing mode, the control module controls the impedance network module to generate breathing data for the simulated left and right lungs, and the impedance value of the simulated lesion point remains unchanged.

[0008] Optionally, the periodic shallow breathing data and periodic deep breathing data are simulated by increasing the resistance and capacitance values ​​of the variable resistor and variable capacitor in the variable impedance network module; the increase in the resistance and capacitance values ​​of the periodic deep breathing data is greater than that of the periodic shallow breathing data.

[0009] Optionally, the right lung data is delayed by m seconds after the left lung data is delayed by m seconds; then, the respiratory impedance changes of the left and right lungs are simulated by increasing the resistance value of the variable resistor and the capacitance value in the variable impedance network module.

[0010] Optionally, the lesion breathing mode first determines the location of the lesion in the simulated lung, keeping the impedance value of the lesion location unchanged, and then simulates the breathing data of the right and left lungs excluding the lesion location by increasing the variable resistance value and variable capacitance value in the variable impedance network module.

[0011] Optionally, the simulated lung obtained by simulating the arrangement of real lung regions is obtained by constructing a discretized thoracic cavity structure map from images of real lungs, and then setting a standard electrical impedance network module and a variable resistance network model based on the discretized thoracic cavity structure map to obtain the simulated lung.

[0012] Optionally, the discretized thoracic cavity structure map is obtained by drawing the minimum circumscribed quadrilateral of the real lung image to obtain the boundary map, and a mesh covering the boundary map is generated based on the boundary map to obtain the discretized thoracic cavity structure map.

[0013] Optionally, the simulated lung further includes simulated impedance changes. The impedance value is calculated for any grid cell in the discretized chest cavity structure diagram based on the conductivity of each tissue in the real chest cavity. The resistance or capacitance of each grid cell is set based on the impedance value to obtain the simulated lung. The resistance or capacitance of each grid cell in the simulated lung is adjusted based on the breathing pattern to obtain the impedance distribution of the simulated lung.

[0014] The purpose of this invention is to provide a method for simulating lung breathing patterns, comprising: Acquire breathing command data; The breathing pattern is determined by judging the breathing command; the breathing pattern includes one or more of the following: shallow breathing pattern, deep breathing pattern, pendulum breathing pattern, and lesion breathing pattern. Respiratory data is generated based on the described breathing pattern; Optionally, the respiratory data is generated by dynamically controlling the impedance of a human electrical impedance model, which includes a resistor, a variable resistor, and a variable capacitor to simulate the impedance values ​​of various lung tissues. The resistance value of the variable resistor and the capacitance value of the variable capacitor are dynamically adjusted according to the breathing mode to obtain respiratory data corresponding to different breathing modes.

[0015] The purpose of this invention is to provide a method for assessing EIT based on a simulated lung system, comprising: Acquire breathing command data; The breathing command data is input into the above-described method for simulating lung breathing patterns to obtain simulated breathing data; EIT functional images are obtained by performing EIT imaging based on the simulated respiratory data; The accuracy of the EIT system is evaluated based on the EIT functional image, and the evaluation result is obtained.

[0016] The purpose of this invention is to provide a device for simulating lung breathing patterns, comprising: Electrical impedance network module: used to simulate changes in the electrical impedance of the lungs; Power supply module: Used to provide power to the impedance network module and control module; Control module: acquires respiratory command data, determines the respiratory mode based on the respiratory command data, and controls the impedance network module based on the respiratory mode to simulate the corresponding impedance changes to obtain simulated lung respiratory data; wherein, the respiratory mode includes shallow breathing mode, deep breathing mode, pendulum breathing mode, and lesion breathing mode.

[0017] Optionally, the electrical impedance network module includes a standard electrical impedance network module and a variable electrical impedance network module. The variable electrical impedance network module includes a digital potentiometer and an electronically controlled variable capacitor. The standard electrical impedance network module and the variable electrical impedance network module form a simulated lung. Different modes of respiratory data are obtained by controlling the standard electrical impedance network module and the variable electrical impedance network module. Optionally, the simulated lung composed of the standard electrical impedance network module and the variable electrical impedance network module is obtained by constructing a discretized thoracic cavity structure map from real lung images, and then setting the standard electrical impedance network module and the variable electrical impedance network model based on the discretized thoracic cavity structure map. Optionally, the impedance network module further includes an electrode contact impedance module disposed around the simulated lung to simulate impedance changes on the skin surface.

[0018] The purpose of this invention is to provide a computer program product that includes a computer program or instructions, wherein the computer program or instructions are executed by a processor to implement the above-described method for simulating lung breathing patterns, or to implement the above-described method for implementing an EIT system based on simulated lung assessment.

[0019] The purpose of this invention is to provide a computer device comprising a memory, a processor, and a computer program or instructions stored in the memory, wherein the computer program or instructions are executed by the processor to implement the above-described method of simulating lung breathing patterns, or to implement the above-described method of an EIT system based on simulating lung assessment.

[0020] The purpose of this invention is to provide a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions are executed by a processor to implement the above-described method of simulating lung breathing patterns, or to implement the above-described method of an EIT system based on simulating lung assessment.

[0021] Advantages of this invention: 1. To address the inability of traditional calibration plates to simulate changes in the imaginary part of impedance, this invention constructs a realistic human body impedance model by combining resistors and capacitors, thereby simulating lung impedance and improving accuracy. This model includes electrode contact impedance, a standard impedance network, and a variable impedance network. The electrode contact impedance is used to simulate the impedance of the skin surface, while the standard and variable impedance networks are used to simulate various tissues inside the thoracic cavity. In the variable impedance network, the values ​​of each resistor and capacitor can be independently controlled, thereby creating different lung impedance distributions by controlling the resistors and capacitors.

[0022] 2. Traditional calibration boards can only periodically change the overall resistance value to simulate changes in lung respiration, failing to represent the depth of breathing or the breathing patterns during lung diseases, and cannot verify the accuracy of the EIT system at the algorithm level. This invention uses a variable impedance module to dynamically generate different breathing patterns, including shallow breathing, deep breathing, pendulum breathing, and lesion breathing. By simulating the breathing data generated by different breathing patterns, the corresponding EIT function graph is completed, and then the accuracy of the EIT system is verified based on the EIT function graph.

[0023] 3. The distribution of traditional calibration plate impedance networks is not based on real human data, so its measurement results cannot accurately correspond to disease results. To address this, this invention constructs a thoracic cavity model by discretization when building the human body impedance model, and performs simulation circuit impedance calculation on the discretized thoracic cavity model based on the actual conductivity of each tissue in the thoracic cavity, providing more accurate measurement results for the human body impedance model to simulate lung respiration. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A schematic diagram of the system flow for simulating lung breathing patterns provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a method based on a simulated lung assessment EIT system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an EIT system based on simulated lung assessment provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a device based on a simulated lung assessment EIT system provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the electrical impedance network module provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of breathing mode switching provided in an embodiment of the present invention; Figure 7 The present invention provides a fractal segmentation based on the contours of the thoracic cavity and lungs, and an equivalent circuit of one of the square units.

[0026] Figure 8 The EIT image obtained by acquiring the EIT data of the constructed circuit and performing image reconstruction is provided in the embodiments of the present invention. Figure 9 The simulated lung provides a lung electrical impedance distribution for embodiments of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0028] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as S101, S102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0029] Figure 1 The system schematic diagram of the simulated lung breathing pattern provided in this embodiment of the invention specifically includes: Electrical impedance network module: used to simulate changes in the electrical impedance of the lungs; In one embodiment, the impedance network module includes a standard impedance network module and a variable impedance network module. The variable impedance network module includes N variable resistors and S variable capacitors, and the standard impedance network module includes L standard resistors. N, S, and L are natural numbers greater than 1. The standard impedance network module and the variable impedance network module are arranged in a simulated manner based on the actual lung region to obtain a simulated lung. The standard impedance network module and the variable impedance network module in the simulated lung are controlled based on the breathing pattern to obtain simulated lung breathing data.

[0030] In one embodiment, the impedance network module further includes an electrode contact impedance module disposed around the periphery of the simulated lung to simulate impedance changes on the skin surface. The electrode contact impedance module, the standard impedance network module, and the variable impedance network module in the simulated lung are controlled based on the breathing pattern to obtain simulated lung breathing data.

[0031] In one embodiment, the electrode contact impedance module is connected to a standard impedance network module, the standard impedance network module is connected to a variable impedance network module, the standard impedance network module surrounds the variable impedance network module, and the electrode contact impedance module is located on the outermost periphery. Figure 5 As shown.

[0032] Power supply module: Used to provide power to the impedance network module and control module; In one embodiment, the power supply module is connected to the resistor network module and the control module, and the control module is connected to the resistor network module. The control module uses instructions to control the changes in the resistance or capacitance of the resistor network module to simulate different breathing patterns and generate breathing data.

[0033] Control module: acquires respiratory command data, determines the respiratory mode based on the respiratory command data, and controls the impedance network module based on the respiratory mode to simulate the corresponding impedance changes to obtain simulated lung respiratory data; wherein, the respiratory mode includes shallow breathing mode, deep breathing mode, pendulum breathing mode, and lesion breathing mode.

[0034] In one embodiment, when the breathing mode is shallow breathing mode, the control module controls the impedance network module to simultaneously generate periodic shallow breathing data for the simulated left and right lungs; when the breathing mode is deep breathing mode, the control module controls the impedance network module to simultaneously generate periodic deep breathing data for the simulated left and right lungs; when the breathing mode is pendulum breathing mode, the control module controls the impedance network module to simultaneously generate breathing data for both lungs after delaying the right lung data by m seconds, where m is a natural number greater than or equal to 1; when the breathing mode is lesion breathing mode, the control module controls the impedance network module to generate breathing data for the simulated left and right lungs, and the impedance value of the simulated lesion point remains unchanged.

[0035] In one embodiment, the periodic shallow breathing data and periodic deep breathing data are simulated by increasing the resistance and capacitance values ​​of the variable resistor and variable capacitor in the variable impedance network module; the increase in the resistance and capacitance values ​​of the periodic deep breathing data is greater than that of the periodic shallow breathing data.

[0036] In one embodiment, the right lung data is delayed by m seconds after the left lung data is delayed by m seconds; then, the respiratory impedance changes of the left and right lungs are simulated by increasing the resistance and capacitance values ​​of the variable resistor and variable capacitor in the variable impedance network module.

[0037] In one embodiment, the lesion breathing pattern first determines the location of the lesion in the simulated lung, with the impedance value of the lesion location remaining unchanged, and then simulates the breathing data of the right and left lungs excluding the lesion location by increasing the variable resistance value and variable capacitance value in the variable impedance network module.

[0038] In one embodiment, the increase in the variable resistor and variable capacitor values ​​when simulating breathing on the left and right sides of the lungs in the pendulum breathing mode and lesion breathing mode is the same as that before the cycle of breathing.

[0039] In one embodiment, the breathing cycle includes: an inhalation period, an exhalation period, and a apnea period.

[0040] In one embodiment, when the breathing mode is shallow breathing mode, the resistance of each variable resistor in the variable impedance network module is increased from 100 ohms to 300 ohms, and the capacitance of each variable capacitor is increased from 7 pF to 10 pF.

[0041] In one embodiment, when the breathing mode is deep breathing mode, the resistance of each variable resistor in the variable impedance network module is increased from 100 ohms to 1000 ohms, and the capacitance of each variable capacitor is increased from 7 pF to 13 pF.

[0042] In one embodiment, when the breathing mode is pendulum breathing mode, the right lung data phase lags the left lung data by 1 second, the resistance of each variable resistor in the right and left lungs is increased from 100 ohms to 300 ohms, and the capacitance of each variable capacitor is increased from 7 pF to 10 pF.

[0043] In one embodiment, when the breathing mode is the lesion breathing mode, the lesion location is obtained, and the lesion area of ​​the impedance network module is determined based on the lesion location. The resistance value of each variable resistor in the right lung and the left lung is increased from 100 ohms to 300 ohms, and the capacitance value of each variable capacitor is increased from 7 pF to 10 pF. The variable resistance or variable capacitance of the lesion area remains unchanged.

[0044] In one embodiment, the simulated lung obtained by simulating the arrangement of real lung regions is obtained by constructing a discretized thoracic cavity structure map from an image of the real lung, and then setting a standard electrical impedance network module and a variable resistance network model based on the discretized thoracic cavity structure map to obtain the simulated lung.

[0045] Optionally, the discretized thoracic cavity structure map is obtained by drawing the minimum circumscribed quadrilateral of the real lung image to obtain the boundary map, and a mesh covering the boundary map is generated based on the boundary map to obtain the discretized thoracic cavity structure map.

[0046] In one embodiment, the simulated lung further includes simulated impedance changes. The impedance value is calculated for any grid cell in the discretized chest cavity structure diagram based on the conductivity of each tissue in the real chest cavity. The resistance or capacitance of each grid cell is set based on the impedance value to obtain the simulated lung. The resistance or capacitance of each grid cell in the simulated lung is adjusted based on the breathing pattern to obtain the impedance distribution of the simulated lung.

[0047] In one embodiment, the process of constructing a discretized thoracic cavity structure map is as follows: S1. Obtain an image of human thoracic cavity tissue, calculate the minimum bounding box of the chest region, and obtain the thoracic cavity boundary map. S2. Generate a mesh covering the thoracic cavity boundary map based on any vertex of the thoracic cavity boundary map to obtain a thoracic cavity mesh map; S3. Filter the effective pixel blocks of the thoracic mesh to obtain the effective pixel block area; S4. Calculate the total area of ​​the thoracic mesh map and minimize the absolute value of the difference between the total area of ​​the effective pixel blocks and the total area of ​​the thoracic mesh map; S5. Update the vertices in the neighborhood of the current vertex to obtain the updated vertex. Repeat S2-S4 based on the updated vertex to obtain the absolute value set. S6. Based on the set of absolute values, select the vertex corresponding to the largest absolute value, and obtain the thoracic cavity mesh diagram based on the corresponding vertex. The thoracic cavity mesh diagram serves as a discretized thoracic cavity structure diagram.

[0048] In one embodiment, the effective pixel block refers to a block in which the area of ​​the chest region accounts for more than 50%. Optionally, the effective block pixel area filtering process is as follows: Obtain the grid diagram; Traverse each pixel block in the grid and calculate the intersection area between the pixels in each pixel block and the chest region; Calculate the area ratio of the intersection area in each pixel block. When the area ratio is 50%, it is marked as a valid pixel block.

[0049] In one embodiment, the method further includes resolution setting, determining the number of pixels on the horizontal axis or the number of pixels on the vertical axis based on the horizontal axis length or the vertical axis length of the region boundary map to obtain the resolution; the grid generated by the region boundary map is calculated by the horizontal axis length / vertical axis length and the resolution. Optionally, the generation starts from a vertex and calculates the side length using the horizontal axis length / vertical axis length and resolution to obtain a mesh covering the boundary map of the region.

[0050] In one embodiment, the process of calculating the impedance value for any grid cell is as follows: Obtain the mesh and its conductivity; The mesh stiffness matrix is ​​calculated based on the electrical conductivity of the mesh. The impedance between any two points in the mesh is obtained by inverting the stiffness matrix.

[0051] In one embodiment, the stiffness matrix is ​​calculated as follows: Obtain the mesh and connect any two diagonal vertices of the mesh to obtain two triangular elements; Calculate the grid electric field using grid conductivity; The electric field of the mesh is solved based on the triangular element to obtain the current and potential between the three vertices of the triangular element; The first stiffness matrix is ​​constructed based on the current and potential between the three vertices. The stiffness matrix of the triangular element is obtained by solving the first stiffness matrix using Taylor series. The mesh stiffness matrix is ​​obtained based on the stiffness matrices of the two triangular elements.

[0052] Optionally, the grid is a square grid.

[0053] In one specific embodiment, the present invention allows switching between shallow breathing mode, deep breathing mode, pendulum breathing mode, and lesion mode (pulmonary nodule breathing mode) via a calibration plate, such as... Figure 6 As shown, where: 1. Shallow breathing mode: In shallow breathing mode, the resistance of each variable resistor changes from 100 ohms to 300 ohms, and the capacitance of each variable capacitor changes from 7pF to a maximum of 10pF. The impedance switching frequency is 100Hz per second.

[0054] 2. Deep Breathing Mode: In deep breathing mode, the resistance of each variable resistor changes from 100 ohms to 1000 ohms, and the capacitance of each variable capacitor changes from 7pF to a maximum of 13pF. The impedance switching frequency is 100Hz per second.

[0055] 3. Pendulum Breathing Pattern: In pendulum breathing mode, the resistance of each variable resistor in the left and right lungs varies from 100 ohms to 300 ohms, and the capacitance of each variable capacitor varies from 7pF to a maximum of 10pF. The right lung data is adjusted to lag the left lung data by 1 second, and its impedance switching frequency is 100Hz per second.

[0056] 4. Breathing patterns at the lesion site: In the lesion-based breathing mode, the resistance of each variable resistor in the left and right lungs varies from 100 ohms to 300 ohms, and the capacitance of each variable capacitor varies from 7 pF to a maximum value of 10 pF. During respiration, the impedance data at the lesion location remains constant.

[0057] In one specific embodiment, a circuit model based on real lung impedance changes was implemented by simulating the electrical impedance changes in the lungs.

[0058] First: Construct a circuit model based on real human body data: Based on the discretization of the real human thoracic cavity using square segmentation, and taking the distribution of human thoracic cavity tissues (including the heart, lungs, etc.) as a reference, square units corresponding to various tissues are determined, such as... Figure 7 As shown, white represents standard impedance, red represents variable impedance, and blue can be either standard or variable, depending on whether the signal is simulating the heart. The core discretization steps are as follows: 1) Boundary box determination: Calculate the minimum bounding box B of the chest region.

[0059] 2) Resolution setting: Based on the horizontal axis length L of the bounding box B, determine the number of pixels r (resolution) on the horizontal axis.

[0060] 3) Mesh initialization: Starting from the bottom left vertex O of bounding box B. initial Starting from the origin, generate a side with length of A square grid G ​​covers the bounding box B.

[0061] 4) Valid block filtering: Traverse each pixel block P in grid G: 5) Calculate the intersection area |P ∩ S| of P and the chest region S.

[0062] 6) Retention Conditions: If If the area of ​​the chest region within the block is greater than 50%, then P is marked as a valid block. .

[0063] 7) Mesh optimization: 8) Objective function: Minimize the total area of ​​the effective block and the total area S of the chest region. total The absolute value of the difference η: . 9) Optimization variable: y-coordinate of the grid starting point.

[0064] 10) Optimization process: In O initial Adjust y within the neighborhood, repeat steps 3-4 to generate a new grid and calculate its η, iteratively search for the optimal starting point O that maximizes η. optimal .

[0065] 11) Final segmentation: Using the optimal starting point O optimal The final mesh G is generated using resolution r. finalThe effective block is the final square discretization result of the chest region.

[0066] Second, the circuit model is constructed based on the actual impedance distribution of the chest: By filling the square cell containing the corresponding tissue with the actual electrical conductivity characteristics of human thoracic cavity tissue, a thoracic cavity model with a realistic impedance distribution is obtained.

[0067] For any square element in the thoracic cavity model Its four vertices are labeled a, b, c, and d, and the conductivity of this unit is... To further illustrate how the square conductivity distribution can be equivalent to the resistance between the four sides, auxiliary lines are added between vertices a and c, such as... Figure 7 As shown.

[0068] According to Laplace's principle, the electric field of EIT can be expressed as follows:

[0069] in, For conductivity distribution, As coordinates, For potential distribution, It is the normal vector. The current density injected into the electrode.

[0070] Solving the Laplace equation using the variational principle yields the relationship between the potential and current of the triangular unit with vertices abc (and acd).

[0071] in, Let be the stiffness matrix, describing the relationship between the electric potential and current between the three vertices a, b, and c. Its composition is as follows: , , Let be the linear coefficients of the Taylor series expansion at position (x,y), according to It can be known that , , . Let the electric potential at the three vertices be . . The current at the three vertices, , It is determined by the applied current density Decision A e This refers to a square, where i and j are the sequence numbers of the triangular units.

[0072] The above analysis shows that the stiffness matrix Determines the electric potential and current The relationship between these elements, i.e., the impedance relationship, requires further expansion of the elements in the stiffness matrix.

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] in, Let be the area of ​​the triangular unit (vertex abc).

[0079] As can be seen from the above formula, due to the use of square discretization, the stiffness matrix elements between vertices a and c are... This is manifested in actual physics as an impedance of 0 ohms between two nodes.

[0080] Similarly, the stiffness matrix of the ACD triangular element at the vertex can be obtained. Then, through node assembly, i.e., the stiffness matrix elements between two identical nodes are the sum of the stiffness matrix element values ​​calculated by the two adjacent elements, the stiffness matrix of the entire thoracic cavity model can be obtained. The relationship between the nodal potentials of all square cell elements and the applied boundary current.

[0081] in, It is the current vector that includes the EIT boundary current excitation.

[0082] Then, by analyzing the stiffness matrix Find the reverse. This allows us to obtain the impedance value between any two vertices of a square within the entire thoracic cavity. These impedance values ​​can be represented in circuit terms as the potential distribution caused by the actual impedance of the entire thoracic cavity tissue.

[0083] In one specific embodiment, the device for simulating lung breathing patterns of the present invention realizes that the resistance and capacitance of the variable resistance network can be changed simultaneously, which makes up for the shortcomings of the single resistance change of the existing calibration board; secondly, the present invention provides four lung breathing patterns based on the real lung state, which can be used to judge the accuracy of the EIT device; finally, based on the import of real human lung data, the accuracy assessment of the diagnostic algorithm is more accurate.

[0084] This invention provides a method for simulating lung breathing patterns, comprising: Acquire breathing command data; The breathing pattern is determined by judging the breathing command; the breathing pattern includes one or more of the following: shallow breathing pattern, deep breathing pattern, pendulum breathing pattern, and lesion breathing pattern. Respiratory data is generated based on the described breathing pattern.

[0085] In one embodiment, the respiratory data is generated by dynamically controlling the impedance of a human electrical impedance model, which includes a resistor, a variable resistor, and a variable capacitor to simulate the impedance values ​​of various lung tissues. The resistance value of the variable resistor and the capacitance value of the variable capacitor are dynamically adjusted according to the breathing pattern to obtain respiratory data corresponding to different breathing patterns.

[0086] In one embodiment, the human body electrical impedance model includes: Electrical impedance network module: used to simulate changes in the electrical impedance of the lungs; Power supply module: Used to provide power to the impedance network module and control module; Control module: Acquires respiratory command data, determines the respiratory mode based on the respiratory command data, and controls the impedance network module to simulate the corresponding impedance changes based on the respiratory mode to obtain simulated lung respiratory data; The impedance network module includes a standard impedance network module and a variable impedance network module. The variable impedance network module includes N variable resistors and S variable capacitors, and the standard impedance network module includes L standard resistors. N, S, and L are natural numbers greater than 1. The standard impedance network module and the variable impedance network module are arranged in a simulated manner based on the actual lung region to obtain a simulated lung. The standard impedance network module and the variable impedance network module in the simulated lung are controlled based on the breathing pattern to obtain simulated lung breathing data.

[0087] Optionally, the impedance network module further includes an electrode contact impedance module disposed around the periphery of the simulated lung to simulate impedance changes on the skin surface. Based on the breathing pattern, the electrode contact impedance module, the standard impedance network module, and the variable impedance network module in the simulated lung are controlled to obtain simulated lung breathing data.

[0088] Figure 2 This invention provides a method for assessing EIT based on a simulated lung system, comprising: Acquire breathing command data; The breathing command data is input into the above-described method for simulating lung breathing patterns to obtain simulated breathing data; EIT functional images are obtained by performing EIT imaging based on the simulated respiratory data; The accuracy of the EIT system is evaluated based on the EIT functional image, and the evaluation result is obtained.

[0089] In one specific embodiment, this embodiment images the EIT system, and the result is as follows: Figure 8 As shown, it can be seen that it accurately reflects the contours of the lungs. This greatly helps in evaluating the accuracy of the EIT system. Since the conductivity distribution of the thoracic cavity is known, the impedance distribution ratio of the generated EIT image is fixed, thus allowing for the measurement of the EIT system's accuracy. The present invention also discloses a computer program product or system, including a computer program that, when executed by a processor, implements the above-described method steps for simulating lung breathing patterns.

[0090] This invention provides a device for simulating lung breathing patterns, comprising: Electrical impedance network module: used to simulate changes in the electrical impedance of the lungs; Power supply module: Used to provide power to the impedance network module and control module; Control module: acquires respiratory command data, determines the respiratory mode based on the respiratory command data, and controls the impedance network module based on the respiratory mode to simulate the corresponding impedance changes to obtain simulated lung respiratory data; wherein, the respiratory mode includes shallow breathing mode, deep breathing mode, pendulum breathing mode, and lesion breathing mode.

[0091] In one embodiment, the electrical impedance network module includes a standard electrical impedance network module and a variable electrical impedance network module. The variable electrical impedance network module includes a digital potentiometer and an electrically controlled variable capacitor. The standard electrical impedance network module and the variable electrical impedance network module form a simulated lung, and different modes of respiratory data are obtained by controlling the standard electrical impedance network module and the variable electrical impedance network module.

[0092] In one embodiment, the simulated lung composed of the standard electrical impedance network module and the variable electrical impedance network module is obtained by constructing a discretized thoracic cavity structure map from real lung images, and then setting the standard electrical impedance network module and the variable electrical impedance network model based on the discretized thoracic cavity structure map.

[0093] In one embodiment, the impedance network module further includes an electrode contact impedance module disposed around the periphery of the simulated lung to simulate impedance changes on the skin surface.

[0094] In one embodiment, the simulated lung further includes simulated impedance changes. The impedance value is calculated for any grid cell in the discretized chest cavity structure diagram based on the conductivity of each tissue in the real chest cavity. The resistance or capacitance of each grid cell is set based on the impedance value to obtain the simulated lung. The resistance or capacitance of each grid cell in the simulated lung is adjusted based on the breathing pattern to obtain the impedance distribution of the simulated lung.

[0095] In one specific embodiment, the circuit simulating lung impedance changes comprises three parts: an impedance network module, a control module, and a power supply module. The impedance network module consists of an electrode contact impedance module, a standard impedance network module, and a variable impedance network. The variable impedance network module comprises a digital potentiometer and an electrically controlled variable capacitor. In this invention, the digital potentiometer is selected from Analog Devices' AD8403 chip, and the electrically controlled variable capacitor is selected from Analog Devices' MAX1474 chip. The control module writes all resistance and capacitance data to different digital potentiometers via the SPI interface within one cycle. In this embodiment, the frequency of one complete breath is 4 seconds, and the data writing frequency is 100Hz; therefore, the size of the array is 4. 100 186.

[0096] Finally, the various breathing modes of the present invention are realized. When the device is working in shallow breathing mode, the control module controls the resistance value to change from 100Ω to 300Ω and the capacitance value to change from 7pF to 10pF; both lungs start to change at the same time, and one breathing cycle is 4s.

[0097] When the device is in deep breathing mode, the control module controls the resistance value to change from 100Ω to 1000Ω and the capacitance value to change from 7pF to 13pF; both lungs begin to change simultaneously, and one breathing cycle is 4s.

[0098] The breathing mode of this invention is set up such that when the device is working in pendulum breathing mode, the control module first loads shallow breathing data, then the control module performs phase transformation on the right side data, that is, the right lung data phase is delayed by 1 second, and then the data of both lungs are changed synchronously.

[0099] When this device operates in lesion breathing mode, in this embodiment, a digital potentiometer and a variable capacitor on the left are selected as the lesion area. The control module first loads shallow breathing data, and then controls the impedance network (excluding the digital potentiometer and variable capacitor) to change. This mode can be used to test the EIT system's ability to detect small lesions.

[0100] By controlling variable resistors and variable capacitors, impedance distributions for different breathing modes can be created, such as... Figure 9 As shown.

[0101] Figure 3 The system diagram of the EIT system based on simulated lung assessment provided in this embodiment of the invention specifically includes: Acquisition Unit: Acquires respiratory command data; Simulation unit: Inputs the breathing command data into the above-described method for simulating lung breathing patterns to obtain simulated breathing data; Imaging unit: Performs EIT imaging based on the simulated respiratory data to obtain EIT functional images; Evaluation Unit: The accuracy of the EIT system is evaluated based on the EIT functional images to obtain the evaluation results.

[0102] Figure 4 An embodiment of the present invention provides a schematic diagram of a computer device, specifically including: The system includes a memory and a processor; the memory is used to store program instructions; the processor is used to invoke the program instructions when the program instructions are executed to perform any of the above-described methods of simulating lung breathing patterns or to perform the above-described methods of the simulated lung assessment EIT system.

[0103] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, performs any of the above-described methods for simulating lung breathing patterns or performs the above-described method for performing an EIT system based on simulated lung assessment.

[0104] The verification results of this verification embodiment show that assigning inherent weights to indications can improve the performance of this method compared to the default settings. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this 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 merely illustrative; for example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated; the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of this embodiment. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0105] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0106] The computer device provided by the present invention has been described in detail above. For those skilled in the art, there will be changes in the specific implementation and application scope based on the ideas of the embodiments of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A system for simulating lung breathing patterns, characterized in that, include: Electrical impedance network module: used to simulate changes in the electrical impedance of the lungs; Power supply module: Used to provide power to the impedance network module and control module; Control module: acquires respiratory command data, determines the respiratory mode based on the respiratory command data, and controls the impedance network module to simulate the corresponding impedance changes to obtain simulated lung respiratory data based on the respiratory mode; wherein, the respiratory mode includes any one or more of the following: shallow breathing mode, deep breathing mode, pendulum breathing mode, lesion breathing mode.

2. The system for simulating lung breathing patterns according to claim 1, characterized in that, The impedance network module includes a standard impedance network module and a variable impedance network module. The variable impedance network module includes N variable resistors and S variable capacitors. The standard impedance network module includes L standard resistors. N, S, and L are natural numbers greater than 1. The standard impedance network module and the variable impedance network module are arranged in a simulated manner based on the real lung area to obtain a simulated lung. The standard impedance network module and the variable impedance network module in the simulated lung are controlled based on the breathing mode to obtain simulated lung breathing data. Optionally, the impedance network module further includes an electrode contact impedance module disposed around the periphery of the simulated lung to simulate impedance changes on the skin surface. Based on the breathing pattern, the electrode contact impedance module, the standard impedance network module, and the variable impedance network module in the simulated lung are controlled to obtain simulated lung breathing data.

3. The system for simulating lung breathing patterns according to claim 2, characterized in that, When the breathing mode is shallow breathing mode, the control module controls the impedance network module to simultaneously generate periodic shallow breathing data for the simulated left and right lungs; when the breathing mode is deep breathing mode, the control module controls the impedance network module to simultaneously generate periodic deep breathing data for the simulated left and right lungs; when the breathing mode is pendulum breathing mode, the control module controls the impedance network module to generate breathing data for both lungs simultaneously after delaying the left lung data by m seconds, where m is a natural number greater than or equal to 1; when the breathing mode is lesion breathing mode, the control module controls the impedance network module to generate breathing data for the simulated left and right lungs, while the impedance value of the simulated lesion remains unchanged; Optionally, the periodic shallow breathing data and periodic deep breathing data are simulated by increasing the resistance and capacitance values ​​of the variable resistor and variable capacitor in the variable impedance network module; the increase in the resistance and capacitance values ​​of the periodic deep breathing data is greater than that of the periodic shallow breathing data. Optionally, the right lung data is delayed by m seconds after the left lung data is delayed by m seconds; then the respiratory impedance changes of the left and right lungs are simulated by increasing the resistance value of the variable resistor and the capacitance value in the variable impedance network module. Optionally, the lesion breathing mode first determines the location of the lesion in the simulated lung, keeping the impedance value of the lesion location unchanged, and then simulates the breathing data of the right and left lungs excluding the lesion location by increasing the variable resistance value and variable capacitance value in the variable impedance network module.

4. The system for simulating lung breathing patterns according to claim 2, characterized in that, The simulated lungs obtained by simulating the arrangement of real lung regions are obtained by constructing a discretized thoracic cavity structure map from images of real lungs, and then setting up a standard electrical impedance network module and a variable resistance network model based on the discretized thoracic cavity structure map to obtain the simulated lungs. Optionally, the discretized thoracic cavity structure map is obtained by drawing the minimum circumscribed quadrilateral of a real lung image to obtain a boundary map, and a mesh covering the boundary map is generated based on the boundary map to obtain the discretized thoracic cavity structure map. Optionally, the simulated lung further includes simulated impedance changes. The impedance value is calculated for any grid cell in the discretized chest cavity structure diagram based on the conductivity of each tissue in the real chest cavity. The resistance or capacitance of each grid cell is set based on the impedance value to obtain the simulated lung. The resistance or capacitance of each grid cell in the simulated lung is adjusted based on the breathing pattern to obtain the impedance distribution of the simulated lung.

5. A method for simulating lung breathing patterns, characterized in that, include: Acquire breathing command data; The breathing pattern is determined by the breathing command. Breathing patterns include one or more of the following: shallow breathing pattern, deep breathing pattern, pendulum breathing pattern, and lesion breathing pattern; Respiratory data is generated based on the described breathing pattern; Optionally, the respiratory data is generated by dynamically controlling the impedance of a human electrical impedance model, which includes a resistor, a variable resistor, and a variable capacitor to simulate the impedance values ​​of various lung tissues. The resistance value of the variable resistor and the capacitance value of the variable capacitor are dynamically adjusted according to the breathing mode to obtain respiratory data corresponding to different breathing modes.

6. A method for assessing EIT based on a simulated lung system, characterized in that, include: Acquire breathing command data; The breathing command data is input into the system for simulating lung breathing patterns according to any one of claims 1-4 to obtain simulated breathing data; EIT functional images are obtained by performing EIT imaging based on the simulated respiratory data; The accuracy of the EIT system is evaluated based on the EIT functional image, and the evaluation result is obtained.

7. A device for simulating lung breathing patterns, characterized in that, include: Electrical impedance network module: used to simulate changes in the electrical impedance of the lungs; Power supply module: Used to provide power to the impedance network module and control module; Control module: acquires respiratory command data, determines the respiratory mode based on the respiratory command data, and controls the impedance network module to simulate the corresponding impedance changes based on the respiratory mode to obtain simulated lung respiratory data; wherein, the respiratory mode includes shallow breathing mode, deep breathing mode, pendulum breathing mode, and lesion breathing mode; Optionally, the electrical impedance network module includes a standard electrical impedance network module and a variable electrical impedance network module. The variable electrical impedance network module includes a digital potentiometer and an electronically controlled variable capacitor. The standard electrical impedance network module and the variable electrical impedance network module form a simulated lung. Different modes of respiratory data are obtained by controlling the standard electrical impedance network module and the variable electrical impedance network module. Optionally, the simulated lung composed of the standard electrical impedance network module and the variable electrical impedance network module is obtained by constructing a discretized thoracic cavity structure map from real lung images, and then setting the standard electrical impedance network module and the variable electrical impedance network model based on the discretized thoracic cavity structure map. Optionally, the impedance network module further includes an electrode contact impedance module disposed around the simulated lung to simulate impedance changes on the skin surface.

8. A computer program product comprising a computer program or instructions, characterized in that, The computer program or instructions are executed by a processor to implement the method of simulating lung breathing patterns as described in claim 5, or to implement the method of the EIT system based on simulated lung assessment as described in claim 6.

9. A computer device comprising a memory, a processor, and a computer program or instructions stored in the memory, characterized in that, The computer program or instructions are executed by a processor to implement the method of simulating lung breathing patterns as described in claim 5, or to implement the method of the EIT system based on simulated lung assessment as described in claim 6.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instructions are executed by a processor to implement the method of simulating lung breathing patterns as described in claim 5, or to implement the method of the EIT system based on simulated lung assessment as described in claim 6.

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