A method for localizing epileptogenic foci using SEEG electrodes and a processing device

Abstract

The present application provides a method and processing device for locating epileptogenic foci using SEEG electrodes, the method comprising: constructing an individualized digital twin brain model based on multimodal imaging data and physiological signal data; obtaining possible epileptogenic zones based on the digital twin brain model; obtaining an optimal SEEG electrode distribution scheme based on the possible epileptogenic zones; and locating the epileptogenic foci based on the optimal SEEG electrode distribution scheme. The present application locates possible epileptogenic zones by constructing a digital twin brain model, comprehensively considering the relationship between brain structural abnormalities and physiological signal abnormalities, and determining the optimal SEEG electrode distribution scheme based on the possible epileptogenic zones, so that the SEEG electrodes cover the suspicious areas more accurately, reducing the number of SEEG electrodes, reducing the cost burden on patients, and improving the effectiveness of monitoring and the efficiency of data processing.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent biomedical signal processing, and more specifically to a method and a processing device for locating epileptic foci with SEEG electrodes. Background Art

[0002] Surgical treatment for medically refractory epilepsy has been a rapidly developing functional neurosurgical technique in recent years. One of its core techniques is the accurate localization of the epileptogenic focus. While some patients with epilepsy can locate the epileptogenic focus based on typical clinical manifestations of seizures, cranial imaging such as CT, MRI, and PET, and noninvasive tests such as electroencephalography (EEG), others require invasive EEG, including cortical and deep electrode placement. Cortical electrode monitoring requires a large craniotomy with the placement of cortical electrodes on the surface of the brain, which is invasive, causes significant bleeding, and is prone to intracranial infection. Furthermore, a secondary craniotomy is required to remove the electrodes, leading to a gradual decline in their clinical use.

[0003] Stereo-electroencephalography (SEEG) technology uses stereotactic or robotic-assisted neurosurgery to precisely place deep electrodes within the brain, specifically within areas associated with the origin or propagation of epileptic seizures. SEEG not only collects EEG signals and maps functional brain regions, but also allows radiofrequency thermal coagulation of epileptogenic foci, achieving both diagnostic and therapeutic goals. SEEG represents the current state of the art in epileptogenic foci localization, offering advantages such as minimal invasiveness, minimal damage, and the ability to provide deep EEG information. However, intracranial electrodes are expensive, necessitating the rapid localization of epileptogenic foci using as few electrodes as possible. Summary of the Invention

[0004] To overcome the problem of how to locate the epileptic focus with as few intracranial electrodes as possible in the prior art, the present application provides a method and processing device for locating the epileptic focus with SEEG electrodes, which adopts the following technical solutions:

[0005] In a first aspect, the present application provides a method for locating an epileptogenic focus using a SEEG electrode, comprising:

[0006] Build a personalized digital twin brain model based on multimodal imaging data and physiological signal data;

[0007] Based on the digital twin brain model, obtaining possible epileptogenic areas;

[0008] Obtaining the optimal SEEG electrode distribution plan based on the possible epileptogenic zone;

[0009] The epileptogenic focus was located based on the optimal SEEG electrode distribution plan.

[0010] Furthermore, the construction of an individualized digital twin brain model based on multimodal imaging data and physiological signal data includes:

[0011] Standardizing and registering the multimodal imaging data, aligning the data of different modalities into the same coordinate system, and obtaining preprocessed data;

[0012] Segmenting the brain image of the preprocessed data into different anatomical structures, and reconstructing the segmented brain structure data into a three-dimensional brain structure model;

[0013] Based on the fMRI data in the preprocessed data, identifying functional connections between different brain regions and obtaining a brain functional network map;

[0014] Based on source reconstruction technology, EEG electrophysiological signals are mapped to a three-dimensional brain structure model to identify the electrical activities of different brain regions;

[0015] The three-dimensional brain structure model, brain functional network diagram and electrical activities of different brain regions are integrated to obtain a multi-dimensional digital twin brain model.

[0016] Furthermore, obtaining a possible epileptogenic zone based on the digital twin brain model includes:

[0017] Perform time-frequency analysis on the EEG signal in each time window to obtain the spectrum characteristics of different time periods and identify the frequency components related to epileptic seizures;

[0018] Construct a functional connectivity network between different brain regions, treating each brain region as a node, calculate the phase-locking value between different brain regions, use the phase-locking value between different brain regions as the weight of the edge, obtain subnetworks with abnormally high connection strength (high phase-locking value), and identify potential epileptogenic areas;

[0019] Frequency components related to epileptic seizures and potential epileptogenic areas are input into the machine learning model for training. Labeled epilepsy data is used for classification, ultimately predicting the possible epileptogenic area.

[0020] The final predicted possible epileptogenic zone location is mapped to the digital twin brain model, and based on the anatomical structure and functional connectivity, the marked area in the digital twin brain model is dynamically observed to determine the epileptogenic zone in the digital twin brain model.

[0021] Furthermore, obtaining an optimal SEEG electrode distribution plan based on the possible epileptogenic zone includes:

[0022] Based on the epileptogenic zone, determine the target brain area that needs to be monitored;

[0023] Generate multiple possible distribution paths in the digital twin brain model based on pre-set constraints and target brain regions;

[0024] Find the optimal SEEG electrode distribution combination to minimize the number of SEEG electrodes covering the epileptogenic zone.

[0025] Furthermore, the method of finding the optimal SEEG electrode distribution combination to minimize the number of SEEG electrodes covering the epileptogenic zone includes:

[0026] Based on multiple possible distribution paths, a set of initial electrode distribution combinations is randomly generated as a population. Each distribution combination is regarded as an individual, and the objective function value of each individual is calculated. The higher the objective function, the better the individual.

[0027] According to the objective function value of the individual, a selection operator is used to select some individuals as parents. Individuals with higher objective function values are more likely to be selected.

[0028] Perform a crossover operation on the parent individuals, such as single-point crossover or multi-point crossover, to generate offspring individuals; the crossover operation simulates the gene recombination process and generates new distribution combination possibilities;

[0029] Replace some of the parent individuals with offspring individuals to form a new population, and calculate the objective function value of each individual in the new population;

[0030] When the preset maximum number of iterations is reached or the objective function value of the optimal individual in the population does not change significantly in multiple iterations, the iteration is stopped and the optimal electrode distribution combination is output.

[0031] Furthermore, the objective function min(N) in the optimal electrode distribution combination needs to satisfy C≥C min and S≥S min , where N is the number of electrodes, min(N) is the minimum number of electrodes, C is the coverage index of the electrode combination, S is the safety factor, and C min is a pre-set minimum coverage, S min It is a pre-set minimum safety factor.

[0032] Furthermore, the positioning of the epileptogenic focus based on the optimal SEEG electrode distribution scheme includes:

[0033] The space of the digital twin brain model is divided into a number of voxels, the voxels are numbered, and the spatial position of each voxel is recorded; the voxels constitute a potential source space, and each voxel is regarded as a possible source of electrical activity; each voxel is assigned a conductivity, and the conductivity is obtained based on the individual's imaging data;

[0034] Based on the brain's geometry, tissue conductivity, and the positional relationship between voxels and SEEG electrodes, the potential generated by each voxel at the electrode location is obtained to obtain a potential distribution matrix. The potential distribution matrix represents the potential contribution generated by each voxel source at all electrode locations. Each row of the potential distribution matrix corresponds to a SEEG electrode, and each column corresponds to a voxel source in brain space.

[0035] Based on the optimal SEEG electrode distribution combination, the patient's EEG signals during the ictal and interictal periods are collected and preprocessed, including filtering and artifact removal, to obtain preprocessed EEG signals;

[0036] Perform feature extraction on the preprocessed EEG signal to obtain the time domain features, frequency domain features and time-frequency features of the EEG signal;

[0037] A multi-constrained linear equation system is constructed based on time-domain features, frequency-domain features, and time-frequency features, and the regularization strategy is adjusted for the equation system. The equation system constructed based on time-domain, frequency-domain, and time-frequency features and the equation system adjusted by the regularization strategy are jointly solved to obtain an estimate of the voxel source intensity vector x. The location of the epileptogenic focus is determined based on the estimated value, and the voxel with the maximum source intensity or the voxel region is identified as the possible location of the epileptogenic focus.

[0038] In a second aspect, the present application further provides a SEEG electrode localization and treatment device for epileptogenic focus, comprising:

[0039] A digital twin brain model construction module, used to build personalized digital twin brain models based on multimodal imaging data and physiological signal data;

[0040] A possible epileptogenic zone acquisition module, configured to acquire the possible epileptogenic zone based on the digital twin brain model;

[0041] An optimal SEEG electrode distribution scheme acquisition module is used to acquire an optimal SEEG electrode distribution scheme based on the possible epileptogenic zone;

[0042] The epileptogenic focus localization module is used to locate the epileptogenic focus based on the optimal SEEG electrode distribution plan.

[0043] In a third aspect, the present application provides an electronic device, comprising:

[0044] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable storage medium is run on a computer, the computer executes the method described in the first aspect.

[0046] In a fifth aspect, the present application provides a computer program, which, when executed by a computer, is used to execute the method described in the first aspect.

[0047] In one possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged with the processor, or may be stored in whole or in part on a memory not packaged with the processor.

[0048] This application has the following beneficial effects:

[0049] 1. This application constructs an individualized digital twin brain model based on multimodal imaging data and physiological signal data. This allows the identification of possible epileptogenic zones and the localization of epileptogenic foci to be conducted without relying solely on imaging data and EEG data. By building a digital twin brain model, the physiological signal data is combined with the imaging data to further clarify the regional and dynamic changes of brain function. When abnormal rhythms in EEG signals are observed in a certain brain region, the potential connection between abnormal electrical activity and brain structure is analyzed in combination with the imaging characteristics of that region.

[0050] 2. This application obtains the possible epileptogenic zone based on the digital twin brain model, and obtains the optimal SEEG electrode distribution scheme based on the possible epileptogenic zone. In the process of obtaining the possible epileptogenic zone, this application comprehensively considers the complex relationship between brain structural abnormalities and physiological signal abnormalities, avoiding excessive attention to irrelevant areas and unnecessary examinations. By determining the optimal SEEG electrode distribution scheme based on the possible epileptogenic zone, it can ensure that the electrodes collect signals in the most critical areas. At the same time, when obtaining the optimal SEEG electrode distribution scheme, the minimization of the number of electrodes, coverage, and safety factor is used as constraint conditions, so that the SEEG electrode distribution scheme meets the minimum number of electrodes, ensures the coverage and safety of the epileptogenic zone, reduces the cost burden on patients, and improves the effectiveness of monitoring and the efficiency of data processing.

[0051] 3. This application constructs a multi-constrained linear equation system based on time domain features, frequency domain features, and time-frequency features, and adjusts the regularization strategy for the equation system. The equation system constructed based on the time domain, frequency domain, and time-frequency features and the equation system adjusted by the regularization strategy are jointly solved to obtain an estimated value of the voxel source intensity vector x, determine the location of the epileptic focus based on the estimated value, and determine the voxel source area with the maximum intensity as the possible location of the epileptic focus. The electrode channel corresponding to the onset time of the seizure in the time domain features is often closely related to the epileptic focus. By constructing a set of equations containing time domain features, these brain areas related to the onset signal can be given priority. In the frequency domain features, for the voxel sources corresponding to the areas with abnormal energy in the frequency domain, corresponding constraints are set in the equation system to make the final voxel source intensity estimate more consistent with the frequency characteristics of the actual EEG signal, thereby improving the positioning accuracy. Based on the propagation path and frequency change pattern of the electrical activity in the time-frequency diagram, time-varying constraints are set in the equation system to more accurately track the position changes of the epileptic focus and the dynamic information of the epileptic network. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0053] Figure 2 This is a flow chart of a method for locating epileptogenic focus using SEEG electrodes according to an embodiment of the present application;

[0054] Figure 3 A flowchart for constructing a digital twin brain model according to an embodiment of the present application;

[0055] Figure 4 A flowchart for obtaining a possible epileptogenic zone according to an embodiment of the present application;

[0056] Figure 5 A flowchart for obtaining the optimal SEEG electrode distribution scheme for the embodiment of the present application;

[0057] Figure 6 A flowchart for obtaining the optimal electrode distribution combination for an embodiment of the present application;

[0058] Figure 7 This is a flowchart of epileptogenic focus localization according to an embodiment of the present application;

[0059] Figure 8 This is a flow chart of a processing device according to an embodiment of the present application;

[0060] Figure 9 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0062] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0064] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0065] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0066] Terminal devices 101, 102, and 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers, desktop computers, etc.

[0067] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0068] It should be noted that the SEEG electrode localization epileptic focus method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the SEEG electrode localization epileptic focus processing device is generally set in the server / terminal device.

[0069] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0070] Continue to refer Figure 2 , the figure shows a flow chart of a method for locating epileptogenic focus using SEEG electrodes of the present application, the method comprising the following steps:

[0071] Step 201: Build an individualized digital twin brain model based on multimodal imaging data and physiological signal data.

[0072] In a possible implementation, imaging data acquisition includes at least one of magnetic resonance imaging, functional magnetic resonance imaging, computed tomography, diffusion tensor imaging, etc. In actual scenarios, imaging data is acquired according to actual needs.

[0073] Explanation: Magnetic resonance imaging (MRI) can obtain structural brain images, functional magnetic resonance imaging (fMRI) can obtain brain functional activity data, computed tomography (CT) can obtain anatomical structure data, and diffusion tensor imaging (DTI) can obtain structural connection information of brain white matter fiber bundles;

[0074] In a possible implementation, the physiological signal data acquisition includes at least one of electroencephalogram (EEG), magnetoencephalogram (MEG), functional near-infrared spectroscopy (fNIRS), etc. In actual scenarios, the physiological signal data is acquired according to actual needs.

[0075] Explanation: Electroencephalography (MRI) records the electrophysiological activity of the brain and can be collected in different states (such as wakefulness, sleep, interictal period and ictal period) to obtain information on the characteristics of epileptic seizures and the functional state of the brain; magnetoencephalography (MEG) has a high temporal resolution and can be used to detect changes in the brain's magnetic field. Combined with electroencephalography (EEG), it can improve the accuracy of localizing epileptic sources; functional near-infrared spectroscopy (fNIRS) can measure changes in blood oxygenation in local areas of the brain and provide information about brain functional activity.

[0076] In one possible implementation, a personalized digital twin brain model is constructed based on multimodal imaging data and physiological signal data. Please refer to Figure 3 , specifically including:

[0077] Step 11: standardize and register the multimodal imaging data, align the data of different modalities into the same coordinate system, and obtain preprocessed data.

[0078] Step 12: Segment the preprocessed brain image data into different anatomical structures, and reconstruct the segmented brain structure data into a three-dimensional brain structure model. The three-dimensional brain structure model includes structures such as the brain surface, ventricles, and sulci. The three-dimensional brain structure model is then smoothed to improve visualization. The anatomical structures include gray matter, white matter, and ventricles.

[0079] Step 13: Based on the fMRI data in the preprocessed data, identify the functional connections between different brain regions and obtain a brain functional network diagram.

[0080] Step 14: Map the EEG electrophysiological signals to the three-dimensional brain structure model based on source reconstruction technology to identify the electrical activities of different brain regions.

[0081] Step 15: Fuse the three-dimensional brain structure model, the brain functional network diagram, and the electrical activities of different brain regions to obtain a multi-dimensional digital twin brain model.

[0082] In a possible implementation, the multimodal imaging data and physiological signal data include data during normal times and during attacks.

[0083] Step 202: Obtaining a possible epileptogenic zone based on the digital twin brain model;

[0084] For the steps of obtaining possible epileptogenic zones based on the digital twin brain model, please refer to Figure 4 , specifically including:

[0085] Step 21: Perform time-frequency analysis on the EEG signal in each time window to obtain the spectrum characteristics of different time periods and identify the frequency components related to epileptic seizures.

[0086] In a possible implementation, the time-frequency analysis of the EEG signal in each time window may be performed using short-time Fourier transform or wavelet transform.

[0087] Explanation: Short-time Fourier transform is a method for analyzing signals in the time-frequency plane. It divides the signal into multiple small time windows and performs Fourier transform in each window to obtain the energy distribution of the signal at different times and frequencies. For EEG signals, epileptic seizures are usually accompanied by abnormal energy changes in a specific frequency range (such as high-frequency oscillations, HFOs). For example, in temporal lobe epilepsy, high-frequency oscillations of 80-200Hz may occur. Through short-time Fourier transform, EEG signals can be converted into time-frequency diagrams to observe the time and location of these abnormal frequencies.

[0088] Explanation: The wavelet transform adaptively analyzes the time-frequency characteristics of a signal. It decomposes the signal using wavelet functions of varying scales, employing varying temporal resolutions for different frequency components. In epilepsy research, the wavelet transform can better capture transient, non-stationary features in EEG signals, such as epileptic spikes and sharp waves. These abnormal waveforms exhibit distinct characteristics in the wavelet transformed coefficient map, helping to locate potential epileptogenic zones.

[0089] Step 22: Construct a functional connectivity network between different brain regions, treating each brain region as a node, calculate the phase locking value between different brain regions, use the phase locking value between different brain regions as the edge weight, obtain subnetworks with abnormally high connection strength (high phase locking value), and identify potential epileptogenic areas;

[0090] Step 23: Input the frequency components related to epileptic seizures and potential epileptogenic regions into a machine learning model for training, classify them using the labeled epilepsy data, and ultimately predict the possible epileptogenic regions;

[0091] Step 24: Map the final predicted possible epileptogenic zone location to the digital twin brain model, dynamically observe the marked area in the digital twin brain model based on the anatomical structure and functional connectivity, and determine the epileptogenic zone in the digital twin brain model.

[0092] Step 203: Obtain an optimal SEEG electrode distribution plan based on the possible epileptogenic zone.

[0093] Explanation: The digital twin brain model is a three-dimensional space that contains structures such as the epileptogenic zone, important blood vessels, neural tissue, and functional brain regions. Each SEEG electrode needs to be implanted from an entry point on the brain surface near the target area. Based on the potential epileptogenic zone, the goal of obtaining the optimal SEEG electrode distribution plan is to find a set of electrode distribution combinations that covers the epileptogenic zone with the least number of SEEG electrodes while avoiding damage to important structures.

[0094] To obtain the optimal SEEG electrode distribution plan based on the possible epileptogenic zone, please refer to Figure 5 , specifically including:

[0095] Step 31, based on the epileptogenic zone, determining a target brain region that requires focused monitoring, wherein the target brain region that requires focused monitoring includes the size, shape, location of the epileptogenic zone, and its relationship with surrounding important brain structures;

[0096] Step 32: Based on the preset constraints and target brain regions, multiple possible distribution paths are generated in the digital twin brain model;

[0097] The preset constraint condition is to avoid the minimum distance from important blood vessels and nerves, and to ensure that the electrode does not enter the functional area or the low functional risk area.

[0098] Step 33: Find the optimal SEEG electrode distribution combination to minimize the number of SEEG electrodes covering the epileptogenic zone.

[0099] In one possible implementation, an objective function is defined to minimize the number of electrodes. Assuming the number of electrodes is N, the objective function can be expressed as min(N). A coverage index C is defined to represent the proportion of electrodes that effectively monitor the epileptogenic zone. Ideally, C should be close to 100%. A safety factor S is defined to measure the degree to which the path avoids important blood vessels, neural tissue, and brain functional areas. The higher the value of S, the safer the path. For example, S = 1 means that all dangerous structures are completely avoided, and S = 0 means that the path directly passes through important structures. In summary, the objective function can be min(N), while satisfying C ≥ C. min (C min is a pre-set minimum coverage, such as 90%) and S≥S min (S min is a pre-set minimum safety factor, such as 80%).

[0100] In one possible embodiment, the optimal SEEG electrode distribution combination is found to minimize the number of SEEG electrodes covering the epileptogenic zone. Figure 6 , specifically including:

[0101] Step 331: Based on multiple possible distribution paths, a set of initial electrode distribution combinations is randomly generated as a population. Each distribution combination is regarded as an individual, and the objective function value of each individual is calculated. The higher the objective function, the better the individual.

[0102] Step 332: Based on the objective function values of the individuals, a selection operator is used to select some individuals as parents. Individuals with higher objective function values are more likely to be selected.

[0103] Step 333: Perform a crossover operation on the parent individuals, such as a single-point crossover or a multi-point crossover, to generate offspring individuals. The crossover operation simulates the gene recombination process to generate new distribution combination possibilities.

[0104] Step 334: Replace some of the parent individuals with the offspring individuals to form a new population, and calculate the objective function value of each individual in the new population;

[0105] Step 335 : When the preset maximum number of iterations is reached or the objective function value of the optimal individual in the population does not change significantly in multiple iterations, the iteration is stopped and the optimal electrode distribution combination is output.

[0106] Step 204: localize the epileptogenic focus based on the optimal SEEG electrode distribution scheme.

[0107] The optimal SEEG electrode distribution scheme is used to locate the epileptogenic focus. Please refer to Figure 7 , specifically including:

[0108] Step 41: Divide the space of the digital twin brain model into a number of voxels, number the voxels, and record the spatial position of each voxel; the voxels constitute a potential source space, and each voxel is regarded as a possible source of electrical activity;

[0109] Step 42 , assigning a conductivity to each voxel, wherein the conductivity is obtained based on imaging data of the individual;

[0110] In step 43, based on the brain's geometry, tissue conductivity, and the positional relationship between voxels and SEEG electrodes, the potential generated by each voxel at the electrode location is obtained to create a potential distribution matrix. This potential distribution matrix represents the potential contribution generated by each voxel source at all electrode locations. Each row of the potential distribution matrix corresponds to a SEEG electrode, and each column corresponds to a voxel source in the brain space. Assuming M electrodes are used to record EEG signals, the matrix has M rows. Assuming the brain is divided into N voxel sources, the matrix has N columns. The potential distribution matrix is a mapping relationship that describes the potential transfer from the source space within the brain (voxel source) to the external measurement space (electrode location). It reflects how the conductive properties of brain tissue, the geometric positional relationship between the voxel source and the electrode, and the electrical activity characteristics of the voxel source jointly determine the potential measured at the electrode. For example, if a voxel source is close to an electrode and the brain tissue conductivity between them is high, the value of the element in the potential distribution matrix column corresponding to that voxel source in the row corresponding to that electrode may be larger, indicating that this voxel source contributes more to the potential of that electrode.

[0111] Step 44, based on the optimal SEEG electrode distribution combination, collect the patient's EEG signals during the ictal and interictal periods, and preprocess the EEG signals, including filtering the EEG signals and removing artifacts, to obtain preprocessed EEG signals;

[0112] Step 45, performing feature extraction on the preprocessed EEG signal to obtain time domain features, frequency domain features, and time-frequency features of the EEG signal;

[0113] Step 46: construct a multi-constrained linear equation system based on the time domain features, frequency domain features, and time-frequency features, and adjust the regularization strategy for the equation system. Jointly solve the equation system constructed based on the time domain, frequency domain, and time-frequency features and the equation system adjusted by the regularization strategy to obtain an estimated value of the voxel source intensity vector x. Determine the location of the epileptic focus based on the estimated value, and determine the voxel with the maximum source intensity or the voxel area as the possible location of the epileptic focus.

[0114] Construction of multi-constrained linear equations:

[0115] In a possible implementation, a time domain equation is constructed: based on the time domain characteristics of the onset time and the time and position of the abnormal waveform, a constraint condition is constructed to construct the time domain equation A. t x=y t , where A t is the potential distribution matrix constructed based on time domain characteristics, y t It is the part of the actual measured electrode potential vector related to the time domain characteristics (for example, mainly the electrode potential at the onset of the seizure). The time domain equation mainly constrains the potential contribution of the voxel source at the onset of the seizure, so that the result of the inverse solution can preferentially match the time domain signal characteristics at the onset of the seizure; x is the voxel source intensity vector.

[0116] In one possible implementation, construct the frequency domain characteristic equation: Incorporate the frequency domain characteristics into the equation system, then add equation A f x=y f , where A f is the potential distribution matrix constructed based on frequency domain features (for example, the matrix adjusted according to the propagation characteristics of different frequency electrical activities in the brain), y f is the actual measured electrode potential vector portion related to the frequency domain characteristics (such as the potential corresponding to the energy of a specific frequency band), and x is the voxel source intensity vector.

[0117] In a possible implementation, based on the dynamic change of time-frequency characteristics, an equation that changes with time is constructed. Let A tf (t) is the potential distribution matrix constructed in time based on time-frequency characteristics, y tf (t) is the actual measured electrode potential vector part related to time and time-frequency characteristics. Then for different time points t1, t2, ..., t n , construct the equation system A tf (t i )x=y tf (t i), i = 1, 2, ..., n. The dynamic equation constrains the potential contribution of the voxel source throughout the entire seizure process, so that it can match the dynamic changes of the EEG signal in time and frequency.

[0118] Regularization strategy adjustment:

[0119] In one possible implementation, the regularization strategy is adjusted based on temporal features. Voxel sources around the seizure onset electrode are given a lower weight in the regularization process due to their importance in localizing the epileptogenic focus. For example, in Tikhonov regularization, the regularization parameter is set to λ, and the regularization weight of voxel sources in key suspected areas is set to λ1 (λ>λ1). This allows greater flexibility in fitting the voxel sources to the temporal signal of seizure onset during the inverse solution process.

[0120] In one possible implementation, the regularization parameter is adjusted based on the energy distribution of the frequency domain features. If the energy distribution of a certain frequency band (such as high-frequency oscillations) is concentrated in a few regions, the regularization parameter is reduced for the voxel sources corresponding to these regions. For example, the regularization parameter λ is adjusted based on the proportion p of the high-frequency oscillation energy to the total energy. The adjusted regularization parameter is set to λ2 = λ(1-θp) (θ is the adjustment coefficient). This allows the inverse solution to focus more on adjusting the voxel source intensity in these frequency domain energy anomalies.

[0121] In one possible implementation, a dynamic regularization strategy is employed based on the dynamic nature of time-frequency features. During the propagation of electrical activity, the regularization parameter is dynamically adjusted based on frequency changes and propagation direction. For example, at the beginning of the propagation of electrical activity, a smaller regularization parameter is used for voxels surrounding the starting region to better capture the source location. As the activity propagates, the regularization parameter is gradually increased to stabilize the solution and avoid overfitting.

[0122] In one possible implementation, the system of equations constructed based on the time domain, frequency domain, and time-frequency features and the system of equations adjusted by the regularization strategy are jointly solved, specifically as follows:

[0123] Combine the equations based on time domain, frequency domain and time-frequency features into a large linear equation system. Assume that the equation system based on time domain features is A t x=y t , the frequency domain characteristic equations are A f x=y f , the equation group of time-frequency characteristics is A tf (t i )x=y tf (t i )(i=1,2,...,n), they can be combined into a new system of equations Ax=y; where A is the equation of At 、A f and A tf (t i ) is a matrix formed by row concatenation, y is a matrix formed by y t 、y f and y tf (t i ) is a vector concatenated by rows, x is the voxel source intensity vector, and its dimension depends on the number of voxel source divisions in the brain model.

[0124] Taking Tikhonov regularization as an example, construct the objective function J(x) = ||Ax-y|| 2 +λ||x|| 2 , λ is the regularization parameter. The first term of the objective function is to minimize the difference between the calculated electrode potential (obtained by forward calculation based on the voxel source intensity x) and the actual measured electrode potential y. The second term is a regularization constraint on the voxel source intensity vector x, preventing the elements of x from being too large or changing too drastically.

[0125] Perform singular value decomposition on matrix A A=UΣV T , where U is an m×m orthogonal matrix (m is the number of rows of A), and its column vectors are called left singular vectors; Σ is an m×n diagonal matrix (n is the number of columns of A), and the elements on the diagonal are called singular values, and are arranged in descending order; V is an n×n orthogonal matrix, and its column vectors are called right singular vectors; for any matrix A, A T A and AA T are symmetric positive semidefinite matrices whose eigenvalues are the squares of the singular values in Σ, and their corresponding eigenvectors are the column vectors of V and U respectively.

[0126] Substituting x=Vz into the objective function J(x), we get J(z)=||A(Vz)-y|| 2 +λ||Vz|| 2 , because A=UΣV T , we get J(z)=||UΣz-y|| 2 +λ||Vz|| 2 , because U is an orthogonal matrix, so ||UΣz-y|| 2 =||Σz-U T y|| 2 , and ||Vz|| 2 =z T z (because V is an orthogonal matrix), the objective function becomes J(z)=||Σz-U T y|| 2 +λz T z, expand||Σz-U T y|| 2Get (Σz-U T y) T (Σz-U T y)=z T Σ 2 z-2z T ΣU T y+y T UU T y, since U is an orthogonal matrix UU T =I, so y T UU T y=y T y, the objective function further becomes J(z)=z T Σ 2 z-2z T ΣU T y+y T y+λz T z, take the derivative of J(z) with respect to z and set the reciprocal to 0, and we can get: Arrange to get (Σ 2 +λI)z=ΣU T y, let (Σ 2 +λI)=diag(σ1 2 +λ,σ2 2 +λ,...,σ n 2 +λ)ΣU T y=[v1,v2,...,v n ] T Then the element z of z i Available through After solving for z, the voxel source intensity vector x is solved by x=Vz.

[0127] Explanation: The initial guess of voxel source intensity is constructed through the time domain characteristics, frequency domain characteristics, and time-frequency characteristics of the EEG signal, specifically:

[0128] Based on the seizure onset time and electrode channel information from the time-domain features, the brain region corresponding to the electrode channel where the seizure onset signal first appears is identified as the suspected epileptogenic focus. In the initial stages of the inverse solution, a higher initial source intensity is given to the voxels near this region. For example, assume that the initial source intensity of the voxels within a radius of around the seizure onset electrode is set to x0, while the initial source intensity of the voxels in other regions is set to x1 (x0 > x1).

[0129] Based on the power spectral density (PSD) analysis of the frequency domain characteristics, an abnormal increase in high-frequency oscillation energy was found in the frequency domain signals of the electrode channels corresponding to the suspected epileptogenic focus area, which improved the initial intensity estimate of the voxel source in this area; for example, based on the initial intensity x0, the high-frequency oscillation energy was increased proportionally according to the relative size, such as the increase coefficient k (k is related to the proportion of high-frequency oscillation energy).

[0130] Time-frequency features are used to capture the energy increase or change pattern of electrical activity at specific frequencies at the onset of a seizure, as well as the propagation of this change across different brain regions. As electrical activity propagates from an initially suspected region in a specific direction, the initial intensity is dynamically adjusted based on the frequency changes during the propagation process. For example, as the frequency decays along the propagation path, the initial guess of the voxel source intensity decreases according to a specific decay function.

[0131] The goal of this application through inverse solution is to infer the actual intensity of each voxel source inside the brain based on the actual measured electrode potential (the patient's EEG signal recorded by the SEEG electrode). By combining the actual measured electrode potential with the potential distribution matrix obtained by forward calculation, mathematical methods (such as constructing a system of linear equations and solving them) can be used to estimate the intensity of each voxel source, and then find the possible location of the epileptic focus (usually the voxel or voxel area with the largest source intensity). The voxel source intensity is mapped to the digital twin model of the brain to intuitively observe the location of the epileptic focus.

[0132] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0133] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0134] Continue to refer Figure 8 The SEEG electrode localization and treatment device of this embodiment includes:

[0135] A digital twin brain model construction module 801 is used to construct an individualized digital twin brain model based on multimodal imaging data and physiological signal data;

[0136] A possible epileptogenic zone acquisition module 802 is configured to acquire a possible epileptogenic zone based on the digital twin brain model;

[0137] An optimal SEEG electrode distribution scheme acquisition module 803 is configured to acquire an optimal SEEG electrode distribution scheme based on the possible epileptogenic zone;

[0138] The epileptogenic focus localization module 804 is configured to localize the epileptogenic focus based on the optimal SEEG electrode distribution scheme.

[0139] To solve the above technical problems, the present application also provides a computer device. Figure 9 , Figure 9 This is a basic structural block diagram of the computer device in this embodiment.

[0140] The computer device 9 includes a memory 9a, a processor 9b, and a network interface 9c that are interconnected through a system bus. It should be noted that the figure only shows a computer device 9 having components 9a-9c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0141] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0142] The memory 9a includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 9a may be an internal storage unit of the computer device 9, such as the hard disk or internal memory of the computer device 9. In other embodiments, the memory 9a may also be an external storage device of the computer device 9, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 9. Of course, the memory 9a may also include both the internal storage unit of the computer device 9 and its external storage device. In this embodiment, the memory 9a is generally used to store the operating system and various application software installed on the computer device 9, such as the program code of the SEEG electrode localization method for epileptogenic focus. In addition, the memory 9a can also be used to temporarily store various types of data that have been output or are to be output.

[0143] In some embodiments, the processor 9b can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 9b is generally used to control the overall operation of the computer device 9. In this embodiment, the processor 9b is used to execute program code stored in the memory 9a or process data, such as executing the program code for the SEEG electrode localization method.

[0144] The network interface 9c may include a wireless network interface or a wired network interface. The network interface 9c is generally used to establish a communication connection between the computer device 9 and other electronic devices.

[0145] The present application also provides another embodiment, namely, providing a non-volatile computer-readable storage medium, which stores a program of a method for locating an epileptic focus with a SEEG electrode, and the program can be executed by at least one processor to enable the at least one processor to perform the steps of the method for locating an epileptic focus with a SEEG electrode as described above.

[0146] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0147] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A method for locating epileptogenic focus using SEEG electrodes, characterized in that: include: Build a personalized digital twin brain model based on multimodal imaging data and physiological signal data; Based on the digital twin brain model, obtaining possible epileptogenic areas; Obtaining the optimal SEEG electrode distribution plan based on the possible epileptogenic zone; The method of obtaining the optimal SEEG electrode distribution scheme based on the possible epileptogenic zone includes: determining the target brain area that needs to be monitored based on the epileptogenic zone; generating multiple possible distribution paths in the digital twin brain model based on preset constraints and the target brain area; finding the optimal SEEG electrode distribution combination to minimize the number of SEEG electrodes covering the epileptogenic zone; finding the optimal SEEG electrode distribution combination to minimize the number of SEEG electrodes covering the epileptogenic zone includes: randomly generating a set of initial electrode distribution combinations as a population based on multiple possible distribution paths, treating each distribution combination as an individual, and calculating the objective function value of each individual. The higher the objective function, the better the individual; according to the objective function value of the individual, a selection operator is used to select some individuals as parents, and individuals with high objective function values are more likely to be selected; a crossover operation is performed on the parent individuals, such as single-point crossover or multi-point crossover, to generate offspring individuals; the crossover operation simulates the gene recombination process to generate new distribution combination possibilities; some parent individuals are replaced with offspring individuals to form a new population, and the objective function value of each individual in the new population is calculated; when the preset maximum number of iterations is reached or the objective function value of the best individual in the population does not change significantly in multiple iterations, the iteration is stopped and the optimal electrode distribution group is output; the objective function in the optimal electrode distribution combination , need to satisfy both and , where N is the number of electrodes, To minimize the number of electrodes, C is the coverage index of the electrode combination, S is the safety factor, is a pre-set minimum coverage, is a pre-set minimum safety factor; The epileptogenic focus was located based on the optimal SEEG electrode distribution plan.

2. The SEEG electrode localization method according to claim 1, characterized in that: The construction of an individualized digital twin brain model based on multimodal imaging data and physiological signal data includes: Standardizing and registering the multimodal imaging data, aligning the data of different modalities into the same coordinate system, and obtaining preprocessed data; Segmenting the brain image of the preprocessed data into different anatomical structures, and reconstructing the segmented brain structure data into a three-dimensional brain structure model; Based on the fMRI data in the preprocessed data, identifying functional connections between different brain regions and obtaining a brain functional network map; Based on source reconstruction technology, EEG electrophysiological signals are mapped to a three-dimensional brain structure model to identify the electrical activities of different brain regions; The three-dimensional brain structure model, brain functional network diagram and electrical activities of different brain regions are integrated to obtain a multi-dimensional digital twin brain model.

3. The SEEG electrode localization method according to claim 1, characterized in that: The obtaining of possible epileptogenic areas based on the digital twin brain model includes: Perform time-frequency analysis on the EEG signal in each time window to obtain the spectrum characteristics of different time periods and identify the frequency components related to epileptic seizures; Construct a functional connectivity network between different brain regions, treating each brain region as a node, calculate the phase-locking value between different brain regions, use the phase-locking value between different brain regions as the weight of the edge, obtain subnetworks with abnormally high connection strength (high phase-locking value), and identify potential epileptogenic areas; Frequency components related to epileptic seizures and potential epileptogenic areas are input into the machine learning model for training. Labeled epilepsy data is used for classification, ultimately predicting the possible epileptogenic area. The final predicted possible epileptogenic zone location is mapped to the digital twin brain model, and based on the anatomical structure and functional connectivity, the marked area in the digital twin brain model is dynamically observed to determine the epileptogenic zone in the digital twin brain model.

4. The method for locating epileptogenic focus using SEEG electrodes according to claim 1, characterized in that: The method of locating the epileptogenic focus based on the optimal SEEG electrode distribution scheme includes: The space of the digital twin brain model is divided into a number of voxels, the voxels are numbered, and the spatial position of each voxel is recorded; the voxels constitute a potential source space, and each voxel is regarded as a possible source of electrical activity; each voxel is assigned a conductivity, and the conductivity is obtained based on the individual's imaging data; Based on the brain's geometry, tissue conductivity, and the positional relationship between voxels and SEEG electrodes, the potential generated by each voxel at the electrode location is obtained to obtain a potential distribution matrix. The potential distribution matrix represents the potential contribution generated by each voxel source at all electrode locations. Each row of the potential distribution matrix corresponds to a SEEG electrode, and each column corresponds to a voxel source in brain space. Based on the optimal SEEG electrode distribution combination, the patient's EEG signals during the ictal and interictal periods are collected and preprocessed, including filtering and artifact removal, to obtain preprocessed EEG signals; Perform feature extraction on the preprocessed EEG signal to obtain the time domain features, frequency domain features and time-frequency features of the EEG signal; A multi-constrained linear equation system is constructed based on time-domain features, frequency-domain features, and time-frequency features, and the regularization strategy is adjusted for the equation system. The equation system constructed based on time-domain, frequency-domain, and time-frequency features and the equation system adjusted by the regularization strategy are jointly solved to obtain an estimate of the voxel source intensity vector x. The location of the epileptogenic focus is determined based on the estimated value, and the voxel with the maximum source intensity or the voxel region is identified as the possible location of the epileptogenic focus.

5. A SEEG electrode localization epileptic focus processing device, used to implement the SEEG electrode localization epileptic focus method of claims 1-4, characterized in that: include: A digital twin brain model construction module, used to build personalized digital twin brain models based on multimodal imaging data and physiological signal data; A possible epileptogenic zone acquisition module, configured to acquire the possible epileptogenic zone based on the digital twin brain model; An optimal SEEG electrode distribution scheme acquisition module is used to acquire an optimal SEEG electrode distribution scheme based on the possible epileptogenic zone; The method of obtaining the optimal SEEG electrode distribution scheme based on the possible epileptogenic zone includes: determining the target brain area that needs to be monitored based on the epileptogenic zone; generating multiple possible distribution paths in the digital twin brain model based on preset constraints and the target brain area; finding the optimal SEEG electrode distribution combination to minimize the number of SEEG electrodes covering the epileptogenic zone; finding the optimal SEEG electrode distribution combination to minimize the number of SEEG electrodes covering the epileptogenic zone includes: randomly generating a set of initial electrode distribution combinations as a population based on multiple possible distribution paths, treating each distribution combination as an individual, and calculating the objective function value of each individual. The higher the objective function, the better the individual; according to the objective function value of the individual, a selection operator is used to select some individuals as parents, and individuals with high objective function values are more likely to be selected; a crossover operation is performed on the parent individuals, such as single-point crossover or multi-point crossover, to generate offspring individuals; the crossover operation simulates the gene recombination process to generate new distribution combination possibilities; some parent individuals are replaced with offspring individuals to form a new population, and the objective function value of each individual in the new population is calculated; when the preset maximum number of iterations is reached or the objective function value of the best individual in the population does not change significantly in multiple iterations, the iteration is stopped and the optimal electrode distribution group is output; the objective function in the optimal electrode distribution combination , need to satisfy both and , where N is the number of electrodes, To minimize the number of electrodes, C is the coverage index of the electrode combination, S is the safety factor, is a pre-set minimum coverage, is a pre-set minimum safety factor; The epileptogenic focus localization module is used to locate the epileptogenic focus based on the optimal SEEG electrode distribution plan.

6. An electronic device, characterized in that: include: one or more processors; Memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the steps of the SEEG electrode localization epileptic focus method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed on a computer, enables the computer to execute the steps of the method for locating the epileptogenic focus with a SEEG electrode as claimed in any one of claims 1 to 4.

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