Simulation method, apparatus, device, and medium for analyzing ventricular arrhythmia

Abstract

This invention relates to the field of cardiac electrophysiological simulation technology, specifically to simulation methods, devices, equipment, and media for analyzing ventricular arrhythmias. The invention first sets the ion channel conductivity parameters of the heart under pathological conditions, then calculates the total ion current generated by the lesion based on these parameters, and simultaneously sets the myocardial tissue conductivity caused by the lesion. The cardiac geometry, initial membrane potential, and the set ion channel conductivity parameters, total ion current, and myocardial tissue conductivity are substituted into a Bidomain model to solve for the cardiac transmembrane potential and intracellular / extracellular potential. Based on these potentials, the invention analyzes whether the aforementioned lesions will induce ventricular arrhythmias. This invention has the ability to simulate a wide range of pathological scenarios, effectively reducing the limitations of existing medical diagnostic methods in analyzing the mechanisms of arrhythmia.

Description

Technical Field

[0001] This invention relates to the field of cardiac electrophysiology simulation technology, specifically to simulation methods, devices, equipment, and media for analyzing ventricular arrhythmias. Background Technology

[0002] Ventricular arrhythmias pose a threat to human life and health. By conducting clinical consultations or electrocardiograms (ECGs) on patients with ventricular arrhythmias, data on the patient's cardiac lesions can be obtained, and the mechanisms underlying ventricular arrhythmias can be analyzed based on this data. However, neither clinical consultations nor ECGs can comprehensively analyze the mechanisms underlying ventricular arrhythmias; in other words, the analyzed mechanisms cannot cover all possible ventricular arrhythmias.

[0003] In summary, the existing technologies for understanding the mechanisms of ventricular arrhythmia have limitations.

[0004] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a simulation method, apparatus, device, and medium for analyzing ventricular arrhythmias, thus resolving the issue that existing technologies reduce the assessment results of ventricular arrhythmias.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a simulation method for analyzing ventricular arrhythmias, comprising: A biventricular geometric model of the heart is constructed. Ion channel conductivity distribution parameters are set on the biventricular geometric model of the heart for normal tissue and lesion area, respectively. The ion channel conductivity distribution parameters are substituted into and the electrophysiological model of cardiac cells is solved to obtain the total ion current on the biventricular geometric model of the heart. The electrical conductivity of myocardial tissue due to lesions was set on a geometric model of the biventricular heart. Simulated cardiac membrane capacitance and heart size data are obtained. The cardiac membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current are used as parameters of the Bidomain model. By solving the Bidomain model, the cardiac transmembrane potential and intracellular / extracellular potential are solved. Based on the cardiac transmembrane potential and intracellular / extracellular potential, simulation results regarding whether ventricular arrhythmia is present are obtained.

[0007] In one implementation, a geometric model of the two ventricles of the heart is constructed, including: Acquire the patient's cardiac medical imaging data; Three-dimensional reconstruction technology was applied to the cardiac medical imaging data to obtain a geometric model of the two ventricles of the heart.

[0008] In one implementation, the ion channel conductivity distribution parameters are substituted into and the cardiac cell electrophysiological model is solved to obtain the total ion current on the biventricular geometric model of the heart, including: Obtain a cell state vector containing the conductivity distribution parameters of the ion channels; The cell state vector is divided into gating variables and ion concentration variables; The gate variables of the cardiac cell electrophysiological model were solved using the Rush-Larsen method, and the ion concentration variables of the cardiac cell electrophysiological model were solved using the explicit Euler method. Based on the obtained gating variables and ion concentration variables, the total ion current is obtained.

[0009] In one implementation, the myocardial tissue conductivity due to lesions is set on a biventricular geometric model of the heart, including: A vector field representing the direction of myocardial fibers caused by lesions is assigned to the biventricular geometric model of the heart, and this vector field is used as the electrical conductivity of myocardial tissue.

[0010] In one implementation, the cardiac membrane capacitance, the heart size data, the myocardial tissue conductivity, and the total ion current are used as parameters of a Bidomain model. The transmembrane potential and intracellular / extracellular potential of the heart are solved by solving the Bidomain model, including: The geometric model of the two ventricles of the heart is divided into several grids; The cardiac membrane capacitance, cardiac size data, myocardial tissue conductivity, and total ion current within each grid are used as known quantities in the Bidomain model. The Bidomain model is solved in parallel to solve for the cardiac transmembrane potential and intracellular / extracellular potential within each grid.

[0011] In one implementation, the Bidomain model is solved in parallel to determine the cardiac transmembrane potential and intracellular / extracellular potential within each grid cell, including: The Bidomain model, with known quantities substituted, is transformed into a system of nonlinear algebraic equations concerning cardiac transmembrane potential and intracellular / extracellular potential of cardiac cells. The nonlinear algebraic equations corresponding to each grid are solved in parallel to solve for the transmembrane potential of the heart and the intracellular / extracellular potential of the heart cells within each grid.

[0012] In one implementation, the geometric model of the two ventricles of the heart is divided into several meshes, including: Diseased and non-disease areas are defined on the geometric model of the two ventricles of the heart. The diseased area and the non-diseased area are each divided into several grids, and the grid density of the diseased area is greater than that of the non-diseased area.

[0013] Secondly, embodiments of the present invention also provide a simulation device for analyzing ventricular arrhythmias, wherein the device comprises the following components: An ion current simulation module is used to construct a geometric model of the two ventricles of the heart. Ion channel conductivity distribution parameters are set on the geometric model of the two ventricles of the heart, respectively, for normal tissue and lesion areas of the heart. The ion channel conductivity distribution parameters are substituted into and the electrophysiological model of the heart cells is solved to obtain the total ion current on the geometric model of the two ventricles of the heart. The conductivity simulation module is used to set the electrical conductivity of myocardial tissue due to lesions on a geometric model of the biventricular heart. The solution module is used to acquire simulated cardiac membrane capacitance and heart size data. The cardiac membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current are used as parameters of the Bidomain model. By solving the Bidomain model, the transmembrane potential and intracellular / extracellular potential of the heart are solved. Based on the transmembrane potential and intracellular / extracellular potential of the heart, simulation results regarding whether ventricular arrhythmia is obtained.

[0014] Thirdly, embodiments of the present invention also provide a terminal device, wherein the terminal device includes a memory, a processor, and a simulation program for analyzing ventricular arrhythmias stored in the memory and executable on the processor. When the processor executes the simulation program for analyzing ventricular arrhythmias, it implements the steps of the simulation method for analyzing ventricular arrhythmias described above.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a simulation program for analyzing ventricular arrhythmias. When the simulation program for analyzing ventricular arrhythmias is executed by a processor, it implements the steps of the simulation method for analyzing ventricular arrhythmias described above.

[0016] Beneficial Effects: This invention first sets the ion channel conductivity parameters of the heart under pathological conditions, and then calculates the total ion current generated by the lesion based on the ion channel conductivity parameters, while simultaneously setting the myocardial tissue conductivity caused by the lesion. The cardiac membrane potential and heart size data (heart size data represents the geometric structure of the heart), along with the set ion channel conductivity parameters, total ion current, and myocardial tissue conductivity, are substituted into a Bidomain model to solve for the cardiac transmembrane potential and intracellular / extracellular potential in the Bidomain model. Then, based on the cardiac transmembrane potential and intracellular / extracellular potential, the invention analyzes whether the aforementioned lesions will induce ventricular arrhythmias. In other words, through simulation, it is possible to simulate whether various pathological conditions will induce ventricular arrhythmias, thereby analyzing the mechanism of arrhythmia. Because all possible pathological conditions can be simulated, the ventricular arrhythmia mechanism obtained based on simulation can cover more pathological conditions, thus reducing the limitations of arrhythmia mechanism analysis. Attached Figure Description

[0017] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a simulation flowchart in an embodiment of the present invention; Figure 3 This is a schematic diagram of the geometric model of the two ventricles of the heart in an embodiment of the present invention; Figure 4 This is a schematic diagram of the simulation results at each time step in the embodiments of the present invention; Figure 5 This is a schematic diagram comparing the temporal changes of transmembrane potentials at stimulation and monitoring points under different pathological region sizes in an embodiment of the present invention. Figure 6 This is a schematic diagram comparing simulated electrocardiograms of lead V2 under different pathological region sizes in embodiments of the present invention. Figure 7 The diagram shows the structure of the simulation device for analyzing ventricular arrhythmias provided by this invention. Figure 8 This is a block diagram illustrating the internal structure of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] Research has found that ventricular arrhythmias are life-threatening. By conducting clinical interviews or electrocardiograms (ECGs) on patients with ventricular arrhythmias, data on their cardiac lesions can be obtained, and the mechanisms underlying ventricular arrhythmias can be analyzed based on this data. However, neither clinical interviews nor ECGs can comprehensively analyze the mechanisms underlying ventricular arrhythmias; in other words, the analyzed mechanisms cannot cover all possible ventricular arrhythmias.

[0020] To address the aforementioned technical problems, this invention provides a simulation method, apparatus, device, and medium for analyzing ventricular arrhythmias, thus resolving the issue that existing technologies reduce the assessment results of ventricular arrhythmias.

[0021] The simulation method for analyzing ventricular arrhythmias in this embodiment can be applied to a terminal device, which can be a data processing terminal product, such as a computer. In this embodiment, as... Figure 1 As shown, the simulation method for analyzing ventricular arrhythmias specifically includes the following steps: S100, Construct a geometric model of the two ventricles of the heart, set the ion channel conductivity distribution parameters of the normal tissue and lesion area of ​​the heart on the geometric model of the two ventricles of the heart respectively, and substitute the ion channel conductivity distribution parameters into and solve the electrophysiological model of the heart cells to obtain the total ion current on the geometric model of the two ventricles of the heart. S200 sets the myocardial tissue conductivity due to lesions on a biventricular geometric model of the heart. S300: Acquire simulated cardiac membrane capacitance and heart size data. Use the cardiac membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current as parameters of the Bidomain model. Solve the Bidomain model to solve for the cardiac transmembrane potential and intracellular / extracellular potential. Based on the cardiac transmembrane potential and intracellular / extracellular potential, obtain simulation results regarding whether ventricular arrhythmia is present.

[0022] The construction of the biventricular geometric model of the heart in step S100 includes: acquiring the patient's cardiac medical imaging data; and applying three-dimensional reconstruction technology to the cardiac medical imaging data to obtain the biventricular geometric model of the heart.

[0023] That is, as Figure 2 As shown, the geometric model of the two ventricles of the heart is extracted from cardiac medical imaging data such as CT and MRI. This involves performing image segmentation processing on the cardiac medical imaging data to segment the heart image from the image, and then applying three-dimensional reconstruction technology to the heart image to obtain the geometric model of the two ventricles of the heart. The geometric model of the two ventricles of the heart is used to describe the geometric structure of the heart.

[0024] The geometric model of the two ventricles of the heart is meshed using tetrahedral unstructured meshes, meaning the model is divided into several tetrahedral unstructured meshes with overlapping areas. The meshed geometric model of the two ventricles of the heart is as follows: Figure 3 As shown.

[0025] Figure 3 The purplish-red areas represent diseased areas, and the other areas represent non-diseased areas. Figure 3 As can be seen, in this embodiment, the mesh density in the lesion area is greater than that in the non-lesion area to achieve high-precision simulation in the lesion area. The subsequent parallel simulation is a parallel simulation based on the mesh, that is, the cardiac membrane capacitance, heart size data, myocardial tissue conductivity and total ion current in each mesh are substituted into the Bidomain model. By solving the Bidomain model, the cardiac transmembrane potential and intracellular / extracellular potential of the heart in that mesh are solved. Simultaneously solving the cardiac transmembrane potential and intracellular / extracellular potential of multiple meshes constitutes parallel solution, which can improve the solution speed.

[0026] The lesions in step S100 include long QT syndrome (LQT2 type), Brugada syndrome, and symptoms of heart failure.

[0027] By rectifying potassium current in the lesion area Maximum conductivity Reduce by 30% to 50% to simulate long QT syndrome, which means if the patient's rectified potassium current is reduced. Maximum conductivity A decrease of 30% to 50% indicates that the patient has long QT syndrome. For non-lesion areas, rectified potassium current... The maximum conductivity remains within normal limits. When simulating long QT syndrome, the ion channel conductivity parameter in step S100 represents the rectified potassium current. Maximum conductivity The distribution data within the diseased and non-diseased grids; the distribution data is the rectified potassium current at each point within the grid. The magnitude of the electrical conductance.

[0028] By using sodium current in the lesion area Maximum conductivity Reduce sodium current in non-lesion areas by 60% to 80% Maximum conductivity Maintaining normal values ​​is intended to mimic Brugada syndrome, meaning that if a patient experiences sodium overload... Maximum conductivity A decrease of 60% to 80% indicates the presence of Brugada syndrome. When simulating Brugada syndrome, the ion channel conductivity parameter in step S100 is the sodium current. Maximum conductivity Data distribution within the lesion area grid and the non-lesion area grid.

[0029] By simultaneously downregulating the transient outward potassium current in the lesion area and inward rectified potassium current The electrical conductance is increased, and the activity of the sodium-calcium exchanger (NCX) is upregulated, while maintaining [the function] in non-lesion areas. and And NCX activity remains unchanged to simulate heart failure symptoms, that is, if the patient's transient outward potassium current remains unchanged. Conductivity and inward rectified potassium current If the conductance of all ions decreases, it indicates that the patient is experiencing symptoms of heart failure. When simulating heart failure symptoms, the ion channel conductance parameter in step S100 is the instantaneous outward potassium current. Conductivity and inward rectified potassium current The conductivity distribution data within and between the lesion region grids. The sodium-calcium exchanger (NCX) is a charge-coupled ion exchanger on the cell membrane.

[0030] The grid is internal to the previous time step (using...) The ion channel conductivity distribution parameters (representing the previous time step) are input into the cardiac cell electrophysiological model to solve for the total ion current at the current time step. This represents the total ion current at the current time step. The cardiac cell electrophysiological model used is the Ten Tusscher 2006 cell model.

[0031] Step S100, which involves substituting the ion channel conductivity distribution parameters into and solving the cardiac cell electrophysiological model to obtain the total ion current on the biventricular geometric model of the heart, includes: obtaining a cell state vector containing the ion channel conductivity distribution parameters; dividing the cell state vector into a gating variable and an ion concentration variable; solving the gating variable of the cardiac cell electrophysiological model using the Rush-Larsen method and solving the ion concentration variable of the cardiac cell electrophysiological model using the explicit Euler method; and obtaining the total ion current based on the solved gating variable and the ion concentration variable.

[0032] That is, the cell state vector This includes simulated conductivity distribution data for each ion channel (conductivity simulation distribution data is a gating variable) and ion concentration (ion concentration is an ion concentration variable). The Rush-Larsen method is used based on the previous time step (using...). The current time step (using the simulated conductivity distribution data of each ion channel representing the previous time step) is obtained by solving the problem. The conductivity simulation distribution data of each ion channel at the current time step is used; the ion concentration at the current time step is obtained by solving the ion concentration at the previous time step using the explicit Euler method. Based on the ion concentration at the current time step and the conductivity simulation distribution data of each ion channel at the current time step, the total ion current at the current time step is obtained. This represents the total ion current at the current time step. The calculation of the total ion current at the current time step is based on existing technology. Step S200, setting the myocardial tissue conductivity caused by the lesion on the biventricular geometric model of the heart, includes: assigning a vector field representing the direction of myocardial fibers caused by the lesion to the biventricular geometric model of the heart, and using the vector field as the myocardial tissue conductivity.

[0033] In other words, based on cardiac anatomy data, each cell in the grid of diseased and non-diseased areas is assigned a vector field representing the direction of myocardial fibers. . Used to define intracellular and extracellular conductivity, myocardial tissue conductivity includes the intracellular and extracellular conductivity of each cell within the grid, using tensors. Each element represents the intracellular conductivity of each cell within the grid, expressed in tensors. Each element represents the extracellular conductivity of each cell within the grid. This embodiment uses the Laplace-Dirichlet Rule Based Method to determine the distribution of the myocardial fiber vector field.

[0034] The heart size data in step S300 is the ratio of the heart's model surface area to its volume (using...). The surface area of ​​the pericardium (i.e., the surface area of ​​the cardiac membrane) is the total area of ​​the fibrous and serous layers covering the outer surface of the heart.

[0035] use The cardiac membrane capacitance in step S300 has already been calculated above. , , , ,and , Since it is already known, therefore... , , , , , Substituting this into the Bidomain model, we can solve for... , , The values ​​of these three at the current time step, among which Represents the transmembrane potential of the heart, using The intracellular potential representing the heart, used The extracellular potential representing the heart, the intracellular / extracellular potential of the heart in step S300 includes and The Bidomain model is as follows:

[0036] This represents the duration of each time step.

[0037] In step S300, the cardiac membrane capacitance, the heart size data, the myocardial tissue conductivity, and the total ion current are used as parameters of the Bidomain model. By solving the Bidomain model, the cardiac transmembrane potential and the intracellular / extracellular potential of the heart (the intracellular / extracellular potential of the heart includes the intracellular potential of the heart) are solved. and extracellular potential of the heart The process includes: setting diseased and non-diseased regions on the biventricular geometric model of the heart; dividing the diseased and non-diseased regions into several grids, with the grid density of the diseased regions being greater than that of the non-diseased regions; using the cardiac membrane capacitance, cardiac size data, myocardial tissue conductivity, and total ion current within each grid as known quantities in the Bidomain model; converting the Bidomain model with the known quantities into a system of nonlinear algebraic equations concerning the cardiac transmembrane potential and the intracellular / extracellular potential of cardiac cells; and solving the system of nonlinear algebraic equations corresponding to each grid in parallel to solve for the cardiac transmembrane potential and the intracellular / extracellular potential of cardiac cells within each grid.

[0038] This embodiment loads a Bidomain model onto a supercomputing platform to solve the Bidomain model, thereby simulating the transmembrane potential and intracellular / extracellular potential of the heart on the supercomputing platform. Specifically, multiple parallel processes are initiated on the supercomputing platform. The main process reads the heart membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current for each grid cell, and assigns this data to the corresponding computational processes. Each computational process simultaneously calculates the transmembrane potential and intracellular / extracellular potential of its corresponding grid cell, achieving parallel solution. This parallel solution approach effectively reduces the demand for computing resources and fully utilizes the parallel capabilities of the high-performance computing platform, thereby achieving high-precision, large-scale, and rapid simulation of ventricular arrhythmias, providing a powerful and practical tool for disease mechanism research and clinical treatment strategy optimization.

[0039] This embodiment will substitute... , , , , , The Bidomain model was converted using the Newton method to reflect cardiac transmembrane potentials (including...) ) and intracellular / external potentials of cardiac cells (including and A system of nonlinear algebraic equations, namely: ,in, Represents the residual vector, which is substituted into... , , , , , The subsequent Bidomain model is discretized to obtain the residual vector. for Unknown elements in include , , Therefore, by solving It can be solved , , The values ​​of these three.

[0040] Solve The steps include initialization, Newton linearization, updating the solution, and checking the termination condition. Initialization: Let the number of Newton iterations be... ,right Assign an initial estimate If it is the first time step, set it according to the initial conditions; otherwise, set it to... The solution value at the previous time step; Newton linearization: using the Newton method, ... In the current solution Linearization of the vicinity transforms it into a system of linear equations, which is... ,in yes The Jacobian matrix, the unknowns to be solved in this system of equations. It represents A Newton iteration increment; update solution: solve for Then, based on the residual function Perform a linear search and determine the linear search step size. and according to right Update the estimated value; check the termination condition: Let ,examine Has it converged? If it has converged, then it means that the solution has been obtained. , , The value at the current time step, and save it. , , If the value at the current time step has not converged, then adjust... The estimated value is based on the adjusted value. The estimated value is subjected to Newton linearization and the solution is updated until... The value converges.

[0041] In this embodiment, for each grid corresponding to... The parallelized Krylov subspace method is used to solve each Krylov subspace methods include the Generalized Minimal Residual (GMRES) method.

[0042] This embodiment is for acceleration. In To achieve convergence, an additive Schwarz preprocessor is constructed. Each computational process independently solves a local subproblem for its corresponding mesh (including overlapping elements). ( Representative by the first Each cell within a grid , , (a matrix composed of subscripts) Representing the One grid. Global preprocessing sub-grid. ( Representing the individual units on the geometric model of the two ventricles of the heart , , The matrix is ​​composed of the superposition of these local preprocessors:

[0043] in, From the global vector to the 1st Constraint operators for overlapping grids, This represents an extension operator for zero-overlapping grids. This preprocessor effectively improves the condition number of linear systems and significantly reduces the number of iterations.

[0044] Calculate , , After the value at the current time step, then based on the value at the current time step. Continue simulations based on the Bidomain model to obtain... , , The value at the next time step is saved until all time steps of the simulation have been completed. , , Values ​​at all time steps, and based on , , Assess the patient for the possibility of developing arrhythmias.

[0045] The simulation method of the present invention is illustrated below with specific examples: 1. Import human cardiac CT image data into geometric reconstruction software to generate accurate reconstructions, such as... Figure 2 The three-dimensional geometric model of the heart is shown, and an unstructured tetrahedral mesh is generated using mesh generation software. The mesh file is then exported for subsequent simulation calculations.

[0046] 2. Set the initial and boundary conditions for the simulation: At the initial moment, the entire heart is in a resting state with a uniform distribution of transmembrane potential; the outer surface of the heart is set as an electrically insulating boundary condition.

[0047] 3. To establish the anisotropy of myocardial fiber conduction, circular lesion regions with diameters of approximately 10 mm, 20 mm, 30 mm, and 40 mm were defined in the subepicardial region of the right ventricular outflow tract (RVOT) to simulate different degrees of pathological matrix. Within these lesion regions, the transient outward potassium current from the Ten Tusscher 2006 model (…) was applied. The maximum conductivity of ) The value was set to 30 times the normal value to simulate a specific pathological environment for ion channels. The simulation was executed in a parallel computing environment with 1024 processes set and the total simulation duration set to ensure that complete arrhythmia events could be captured.

[0048] 4. Start parallel computing. After each time step is completed, output the cardiac electrophysiological state at that moment, including transmembrane potential, spatial distribution of ion concentration, and intracellular / external potential of cardiac cells. Figure 4 The simulation demonstrates several typical moments in the appearance and propagation of abnormal electrical waves due to the influence of the pathological matrix within a single simulation cycle.

[0049] In cardiac electrophysiology, "reentry" is the core mechanism of tachyarrhythmias (such as ventricular tachycardia). It refers to the phenomenon where the cardiac electrical excitation wave, during propagation, does not disappear as normally, but instead, due to factors such as local conduction block or uneven refractory period, repeatedly excites the heart along a closed loop within the myocardial tissue, thereby driving rapid and ineffective cardiac contractions. To systematically evaluate the influence of pathological matrix size on the occurrence of reentrant arrhythmias, this invention conducted comparative simulation experiments on lesion regions of four different diameters. The experimental results are shown in Table 1. From Table 1, it can be observed that... Under conditions of significantly increased conductance, smaller lesions of 10 mm and 20 mm failed to induce any reentry activity; however, when the lesion diameter increased to 30 mm, four reentry events were successfully induced; and in a lesion of 40 mm, persistent reentry arrhythmias were observed. These results indicate that the spatial size of the pathological matrix is ​​a key factor determining whether reentry can form and be maintained.

[0050] Table 1

[0051] Figure 5 The temporal changes in transmembrane potentials at the stimulation and monitoring points are shown. It can be observed that as the lesion size increases, the action potential morphology at the stimulation point changes significantly, with some depolarization events becoming more frequent and the likelihood of reentry increasing. Simultaneously, the results of a simulated electrocardiogram (pECG) in lead V2 are presented, as shown below. Figure 6 As shown, from Figure 6 As can be seen, the ST segment showed progressive elevation, which was attributed to a more significant and persistent transmembrane voltage gradient generated by the expanded pathological stroma region. Finally, through visualization analysis of the final reentrant wave dissipation process in the 40 mm lesion case, the dynamic process of wavefront collision, merging, and eventual dissipation was clearly demonstrated, revealing the complete electrophysiological mechanism of reentrant activity from triggering to spontaneous termination.

[0052] In summary, this invention, through efficient domain decomposition and massively parallel solvers, enables the computational task to be distributed across thousands of computing cores to work collaboratively, achieving rapid simulation of the long-scale electrophysiological behavior of the whole heart at high spatiotemporal resolution. This reduces simulation tasks that previously required days or even weeks to hours, making rapid clinical-grade assessment possible.

[0053] The operator splitting algorithm employed in this invention decouples complex multi-scale problems into more easily solvable subproblems, and combines implicit time integration with high-performance preprocessing techniques to ensure that even when simulating ion channel lesions (such as... Even under extreme pathological conditions such as abnormally high levels of certain substances, the solution process remains robust and reliable.

[0054] The high efficiency and parallel processing capabilities of this invention enable the systematic study of the effects of different pathological factors (such as lesion size, location, and degree of ion channel abnormality) on arrhythmias. The comparative experiments shown in Table 1 clearly reveal the quantitative relationship between pathological matrix size and reentry risk, providing a powerful tool that traditional methods cannot match for a deeper understanding of disease mechanisms and the evaluation of treatment strategies.

[0055] This embodiment also provides a simulation device for analyzing ventricular arrhythmias, such as... Figure 7 As shown, the device comprises the following components: Ion current simulation module 01 is used to construct a geometric model of the two ventricles of the heart. Ion channel conductivity distribution parameters are set on the geometric model of the two ventricles of the heart, respectively, for normal tissue and lesion area of ​​the heart. The ion channel conductivity distribution parameters are substituted into and the electrophysiological model of the heart cells is solved to obtain the total ion current on the geometric model of the two ventricles of the heart. The conductivity simulation module 02 is used to set the myocardial tissue conductivity caused by lesions on a biventricular geometric model of the heart. The solver module 03 is used to acquire simulated cardiac membrane capacitance and heart size data. The cardiac membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current are used as parameters of the Bidomain model. By solving the Bidomain model, the transmembrane potential and intracellular / extracellular potential of the heart are solved. Based on the transmembrane potential and intracellular / extracellular potential of the heart, simulation results regarding whether ventricular arrhythmia is obtained.

[0056] Based on the above embodiments, the present invention also provides a terminal device, the principle block diagram of which can be as follows: Figure 8As shown, the terminal device includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a simulation method for analyzing ventricular arrhythmias. The display screen can be an LCD screen or an e-ink screen.

[0057] Those skilled in the art will understand that Figure 8 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0058] In one embodiment, a terminal device is provided, comprising a memory, a processor, and a simulation program for analyzing ventricular arrhythmias stored in the memory and executable on the processor. When the processor executes the simulation program for analyzing ventricular arrhythmias, it implements the following operation instructions: A biventricular geometric model of the heart is constructed. Ion channel conductivity distribution parameters are set on the biventricular geometric model of the heart for normal tissue and lesion area, respectively. The ion channel conductivity distribution parameters are substituted into and the electrophysiological model of cardiac cells is solved to obtain the total ion current on the biventricular geometric model of the heart. The electrical conductivity of myocardial tissue due to lesions was set on a geometric model of the biventricular heart. Simulated cardiac membrane capacitance and heart size data are obtained. The cardiac membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current are used as parameters of the Bidomain model. By solving the Bidomain model, the cardiac transmembrane potential and intracellular / extracellular potential are solved. Based on the cardiac transmembrane potential and intracellular / extracellular potential, simulation results regarding whether ventricular arrhythmia is present are obtained.

[0059] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation method for analyzing ventricular arrhythmias, characterized in that, include: A biventricular geometric model of the heart is constructed. Ion channel conductivity distribution parameters are set on the biventricular geometric model of the heart for normal tissue and lesion area, respectively. The ion channel conductivity distribution parameters are substituted into and the electrophysiological model of cardiac cells is solved to obtain the total ion current on the biventricular geometric model of the heart. The electrical conductivity of myocardial tissue due to lesions was set on a geometric model of the biventricular heart. Simulated cardiac membrane capacitance and heart size data are obtained. The cardiac membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current are used as parameters of the Bidomain model. By solving the Bidomain model, the cardiac transmembrane potential and intracellular / extracellular potential are solved. Based on the cardiac transmembrane potential and intracellular / extracellular potential, simulation results regarding whether ventricular arrhythmia is present are obtained.

2. The simulation method for analyzing ventricular arrhythmias as described in claim 1, characterized in that, Constructing a geometric model of the two ventricles of the heart, including: Acquire the patient's cardiac medical imaging data; Three-dimensional reconstruction technology was applied to the cardiac medical imaging data to obtain a geometric model of the two ventricles of the heart.

3. The simulation method for analyzing ventricular arrhythmias as described in claim 1, characterized in that, By substituting the ion channel conductivity distribution parameters into and solving the cardiac cell electrophysiological model, the total ion current on the biventricular geometric model of the heart is obtained, including: Obtain a cell state vector containing the conductivity distribution parameters of the ion channels; The cell state vector is divided into gating variables and ion concentration variables; The gate variables of the cardiac cell electrophysiological model were solved using the Rush-Larsen method, and the ion concentration variables of the cardiac cell electrophysiological model were solved using the explicit Euler method. Based on the obtained gating variables and ion concentration variables, the total ion current is obtained.

4. The simulation method for analyzing ventricular arrhythmias as described in claim 1, characterized in that, The electrical conductivity of myocardial tissue due to lesions is set on a biventricular geometric model of the heart, including: A vector field representing the direction of myocardial fibers caused by lesions is assigned to the biventricular geometric model of the heart, and this vector field is used as the electrical conductivity of myocardial tissue.

5. The simulation method for analyzing ventricular arrhythmias as described in claim 1, characterized in that, Using the cardiac membrane capacitance, the heart size data, the myocardial tissue conductivity, and the total ion current as parameters of a Bidomain model, the transmembrane potential and intracellular / extracellular potential of the heart are solved by solving the Bidomain model, including: The geometric model of the two ventricles of the heart is divided into several grids; The cardiac membrane capacitance, cardiac size data, myocardial tissue conductivity, and total ion current within each grid are used as known quantities in the Bidomain model. The Bidomain model is solved in parallel to solve for the cardiac transmembrane potential and intracellular / external cardiac potential within each grid. The intracellular / external cardiac potential includes intracardiac potential and extracellular potential.

6. The simulation method for analyzing ventricular arrhythmias as described in claim 5, characterized in that, The Bidomain model is solved in parallel to determine the cardiac transmembrane potential and intracellular / extracellular potential within each grid cell, including: The Bidomain model, with known quantities substituted, is transformed into a system of nonlinear algebraic equations concerning cardiac transmembrane potential and intracellular / extracellular potential of cardiac cells. The nonlinear algebraic equations corresponding to each grid are solved in parallel to solve for the transmembrane potential of the heart and the intracellular / extracellular potential of the heart cells within each grid.

7. The simulation method for analyzing ventricular arrhythmias as described in claim 5, characterized in that, The geometric model of the two ventricles of the heart is divided into several meshes, including: Diseased and non-disease areas are defined on the geometric model of the two ventricles of the heart. The diseased area and the non-diseased area are each divided into several grids, and the grid density of the diseased area is greater than that of the non-diseased area.

8. A simulation device for analyzing ventricular arrhythmias, characterized in that, The device comprises the following components: An ion current simulation module is used to construct a geometric model of the two ventricles of the heart. Ion channel conductivity distribution parameters are set on the geometric model of the two ventricles of the heart, respectively, for normal tissue and lesion areas of the heart. The ion channel conductivity distribution parameters are substituted into and the electrophysiological model of the heart cells is solved to obtain the total ion current on the geometric model of the two ventricles of the heart. The conductivity simulation module is used to set the electrical conductivity of myocardial tissue due to lesions on a geometric model of the biventricular heart. The solution module is used to acquire simulated cardiac membrane capacitance and heart size data. The cardiac membrane capacitance, heart size data, myocardial tissue conductivity, and total ion current are used as parameters of the Bidomain model. By solving the Bidomain model, the transmembrane potential and intracellular / extracellular potential of the heart are solved. Based on the transmembrane potential and intracellular / extracellular potential of the heart, simulation results regarding whether ventricular arrhythmia is obtained.

9. A terminal device, characterized in that, The terminal device includes a memory, a processor, and a simulation program for analyzing ventricular arrhythmias stored in the memory and executable on the processor. When the processor executes the simulation program for analyzing ventricular arrhythmias, it implements the steps of the simulation method for analyzing ventricular arrhythmias as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a simulation program for analyzing ventricular arrhythmias, which, when executed by a processor, implements the steps of the simulation method for analyzing ventricular arrhythmias as described in any one of claims 1-7.

Images