Noninvasive deep time domain interference electrical stimulation individualized electrode site determination method and device for treating disturbance of consciousness

Abstract

The invention provides a noninvasive deep time domain interference electrical stimulation individualized electrode site determination method and device for treating disturbance of consciousness. The method comprises the steps that a brain region segmentation model is generated according to brain structure image data of a patient; the brain region segmentation model is used for representing structures of different brain regions in the brain; a front lead field matrix is calculated according to the brain region segmentation model, the front lead field matrix is used for representing the influence of neurons at different positions on the electric signals measured by each electrode site, and each electrode site corresponds to a plurality of neurons; determining a brain region stimulation scheme according to the front lead field matrix; determining a target electrode site according to the electroencephalogram data related to the left thalamus or the right thalamus of the patient and a brain region stimulation scheme; the target electrode site is used for time domain interference electrical stimulation. According to the embodiment of the invention, the recognition accuracy of the electrode sites for electrical stimulation treatment of disturbance of consciousness can be improved, and the improvement of the treatment effect of disturbance of consciousness is facilitated.

Description

Technical Field

[0001] This application relates to the field of neural technologies, and particularly to a non-invasive deep-time-domain interference electrical stimulation individualized electrode site determination method and device for treating disorders of consciousness. Background Art

[0002] Electrical stimulation methods are an intervention means for treating brain and / or neurological disorders, such as those related to disorders of consciousness (DoC). DoC refers to a state of loss of consciousness caused by various severe brain injuries, such as the vegetative state (VS) and the minimally conscious state (MCS).

[0003] By applying electrical signals to specific brain regions, electrical stimulation can change the activities of neurons, thereby improving the patient's state of consciousness. However, this method lacks the ability to accurately localize brain regions and cannot determine the optimal electrode sites, resulting in inaccurate electrical stimulation and affecting the treatment effect. Summary of the Invention

[0004] In view of this, this application proposes a non-invasive deep-time-domain interference electrical stimulation individualized electrode site determination method and device for treating disorders of consciousness to solve the problem in the related art that the optimal electrode sites cannot be determined.

[0005] The first aspect embodiment of this application proposes a non-invasive deep-time-domain interference electrical stimulation individualized electrode site determination method for treating disorders of consciousness, including:

[0006] Generating a brain region segmentation model based on the patient's brain structure imaging data; the brain region segmentation model is used to characterize the structures of different brain regions within the brain; a plurality of pre-set electrode sites are included on the brain region segmentation model;

[0007] Calculating a forward lead field matrix based on the brain region segmentation model, the forward lead field matrix is used to characterize the influence of neurons at different positions on the electrical signals measured at each electrode site, and each electrode site corresponds to multiple neurons;

[0008] Determining a brain region stimulation plan based on the forward lead field matrix;

[0009] Determining the target electrode site based on the intervention data related to the patient's left thalamus or right thalamus and the brain region stimulation plan; the target electrode site is used for electrical stimulation.

[0010] In the embodiments of the present disclosure, a forward lead field matrix is calculated through a brain segmentation model to characterize the influence of neurons at different positions on the electrical signals measured at each electrode site, and a brain region stimulation scheme is determined according to the forward lead field matrix. A target electrode site is determined based on the intervention data related to the left thalamus or right thalamus of the patient and the brain region stimulation scheme, which can accurately identify the electrode site with the best therapeutic effect for treating disorders of consciousness, thereby helping to improve the therapeutic effect of disorders of consciousness.

[0011] In the embodiments of the present application, calculating the forward lead field matrix according to the brain segmentation model includes:

[0012] The brain segmentation model is meshed to obtain a plurality of finite element units, each finite element unit includes a plurality of nodes, and each node is used to characterize neurons at corresponding positions on the cerebral cortex;

[0013] The electric potential values of the multiple nodes in each finite element unit are calculated respectively;

[0014] The electric potential values of the multiple finite element units are combined to obtain the forward lead field matrix.

[0015] In the embodiments of the present application, combining the electric potential values corresponding to the multiple finite element units to obtain the forward lead field matrix includes:

[0016] For any one of the multiple finite element units, an admittance matrix of the finite element unit is calculated according to the electric potential values of the multiple nodes in the finite element unit; the admittance matrix is used to characterize the mutual relationship between the nodes in the finite element unit;

[0017] The admittance matrices corresponding to the multiple finite element units one by one are combined to obtain a system admittance matrix; the system admittance matrix is used to characterize the mutual relationship between all the nodes in the multiple finite element units;

[0018] For any one of the multiple finite element units in the system admittance matrix, a unit current is applied to each electrode site in the finite element unit to obtain the electric potential distribution of each node in the finite element unit;

[0019] The electric potential distributions of the multiple nodes corresponding to the multiple finite element units in the system admittance matrix are combined to obtain the forward lead field matrix.

[0020] In the embodiments of the present application, determining the brain region stimulation scheme according to the forward lead field matrix includes:

[0021] Determining the current intensity distribution and the brain region electric field intensity through the forward lead field matrix;

[0022] Determine the brain region stimulation scheme according to the electric field intensity of the brain region and the current intensity distribution.

[0023] In the embodiment of the present application, determining the current intensity distribution and the electric field intensity of the brain region through the anterior lead field matrix includes:

[0024] Calculate the anterior lead field matrix through a genetic algorithm to determine the current intensity distribution;

[0025] Calculate the anterior lead field matrix through a particle swarm optimization algorithm to determine the electric field intensity of the brain region.

[0026] In the embodiment of the present application, the target electrode sites include the first pair of target electrode sites and the second pair of target electrode sites corresponding to the left thalamus, and the third pair of target electrode sites and the fourth pair of target electrode sites corresponding to the right thalamus; after determining the target electrode sites according to the intervention data and the brain region stimulation scheme, the method further includes:

[0027] Extract features from the current electroencephalogram data of the patient to obtain multiple frequency domain features;

[0028] Calculate the frequency of the stimulation electric wave by performing weighted summation on the multiple frequency domain features; the stimulation electric wave includes a first stimulation electric wave and a second stimulation electric wave; the frequency difference between the first stimulation electric wave and the second stimulation electric wave is a preset frequency value;

[0029] Deliver the first stimulation electric wave to the first pair of target electrode sites and the third pair of target electrode sites, and deliver the second stimulation electric wave to the second pair of target electrode sites and the fourth pair of target electrode sites.

[0030] In the embodiment of the present application, the frequency of the first stimulation electric wave is 2000 Hertz (Hz), the frequency of the second stimulation electric wave is 2200 Hz, the preset frequency value is 200 Hz, and the current intensity of both the first stimulation electric wave and the second stimulation electric wave is 5 milliamperes (mA).

[0031] An embodiment of the second aspect of the present application provides a non-invasive deep time-domain interference electrical stimulation individualized electrode site determination device for treating disorders of consciousness, including:

[0032] A brain region segmentation model generation module for generating a brain region segmentation model according to the brain structure imaging data of the patient; the brain region segmentation model is used to characterize the structures of different brain regions within the brain; a plurality of electrode sites are preset on the brain region segmentation model;

[0033] Anterior lead field matrix calculation module, configured to calculate an anterior lead field matrix according to the brain region segmentation model, where the anterior lead field matrix is used to characterize the influence of neurons at different positions on the electrical signals measured at each electrode site, and each electrode site corresponds to multiple neurons;

[0034] Brain region stimulation scheme determination module, configured to determine a brain region stimulation scheme according to the anterior lead field matrix;

[0035] Target electrode site determination module, configured to determine the target electrode site according to the intervention data related to the left thalamus or right thalamus of the patient and the brain region stimulation scheme; the target electrode site is used for electrical stimulation.

[0036] An embodiment of the third aspect of the present application provides a computer device, which includes a memory and a processor. The memory and the processor are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the non-invasive deep time-domain interference electrical stimulation individualized electrode site determination method for treating disorders of consciousness described in the first aspect above.

[0037] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the non-invasive deep time-domain interference electrical stimulation individualized electrode site determination method for treating disorders of consciousness described in the first aspect above.

[0038] Additional aspects and advantages of the present application will be given in part in the following description, will become apparent in part from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. Moreover, throughout the drawings, the same reference numerals are used to represent the same components.

[0040] In the drawings:

[0041] Figure 1 A flowchart showing a method for determining an individualized electrode site for non-invasive deep time-domain interference electrical stimulation for treating disorders of consciousness provided by an embodiment of the present application;

[0042] Figure 2 A flowchart showing the process of calculating the anterior lead field matrix according to the brain region segmentation model provided by an embodiment of the present application;

[0043] Figure 3The flowchart shows how to combine the potential values of multiple finite element units to obtain the frontal lead field matrix provided by an embodiment of the present application;

[0044] Figure 4 The flowchart shows how to determine a brain region stimulation plan based on the frontal lead field matrix provided by an embodiment of the present application;

[0045] Figure 5 The flowchart shows how to determine the stimulation wave frequency based on electroencephalogram data provided by an embodiment of the present application;

[0046] Figure 6 The flowchart shows the clinical application plan of a non-invasive deep brain stimulation for the treatment of DoC provided by an embodiment of the present application;

[0047] Figure 7 The structural diagram shows a non-invasive deep time-domain interference electrostimulation individualized electrode site determination device for the treatment of disorders of consciousness provided by an embodiment of the present application;

[0048] Figure 8 The structural diagram shows a computer device provided by an embodiment of the present application;

[0049] Figure 9 The schematic diagram shows a storage medium provided by an embodiment of the present application. Detailed implementation manners

[0050] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be completely conveyed to those skilled in the art.

[0051] It should be noted that unless otherwise specified, the technical terms or scientific terms used in the present application should have the ordinary meanings understood by those skilled in the art to which the present application belongs.

[0052] The following describes the technical scenarios involved in the embodiments of the present application.

[0053] Disorders of consciousness (DoC) refer to the state of loss of consciousness caused by various severe brain injuries, such as vegetative state (VS) and minimally conscious state (MCS). The VS state refers to the state of preserving the basic brainstem reflexes and the sleep-wake cycle, with spontaneous eye opening or eye opening in response to stimulation, but without conscious content. The MCS state refers to the emergence of clear signs of consciousness that are discontinuous and fluctuating in patients after severe brain injury. Its main feature is the behavioral activities in which fluctuations of signs of consciousness can be repeatedly detected. From the level of complexity of behavioral responses, MCS can be subdivided into two subtypes: "MCS+" and "MCS-". "MCS+" refers to the occurrence of activities such as eye movement, eye opening and closing, or limb movement following simple instructions, but still unable to complete functional communication with the outside world, or unable to use items purposefully. "MCS-" refers to the occurrence of visual tracking, pain localization, and directional voluntary movement, but unable to complete activities following simple instructions. Prolonged disorders of consciousness (pDoC) refer to disorders of consciousness in which the loss of consciousness lasts for more than 28 days. Patients may have various functional impairments, including impairments in consciousness, cognition, emotion, swallowing, speech, urination and defecation, motor function, etc. According to statistics, there are about 500,000 pDoC patients in China at present, and the number is increasing at a rate of more than 100,000 cases per year. The annual cumulative expenditure for these patients is as high as tens of billions of yuan. From the perspective of mortality, about 1 / 3 of the traumatic brain injury patients and 1 / 2 of the non-traumatic brain injury patients among DoC patients die within 1 year after onset. The latest research on the mortality of pDoC patients shows that about 29% of pDoC patients die within 1 year after onset, which all illustrate the importance of early clinical treatment of this type of disease.

[0054] The thalamus plays an important role in the treatment of DoC and is crucial in the regulation and maintenance of consciousness. It is one of the main sensory transmission stations in the brain, responsible for receiving sensory information from various parts of the body and transmitting it to the corresponding areas of the cerebral cortex. In addition, the thalamus is involved in the regulation of the wake-sleep cycle and regulates the alert state of the brain through its interaction with the brainstem. The central circuit hypothesis of the disruption of thalamocortical and cortico-cortical connections proposes that the disruption of the integrity of the connection circuit between the thalamus, frontal lobe, parietal, occipital, and temporal sensory cortices will lead to pDoC. Therefore, the thalamus is often regarded as the target area for neuroregulation of DoC in clinical practice. The neural activity of the thalamus and its connection pathways with the cortex directly affect the conscious recovery of patients.

[0055] Currently, the main clinical treatment methods for DoC include drugs and neuromodulation. Drug treatment is often used to improve the neurological function of patients. For example, dopamine receptor agonists or GABA receptor agonists are used to increase the level of consciousness. However, the efficacy of these drugs varies greatly among individuals, and the rate of arousal promotion is not high. Traditional neuromodulation techniques, such as deep brain stimulation (DBS) and spinal cord stimulation (SCS), although they can improve symptoms by stimulating specific areas of the brain, require surgery, which itself has safety risks such as bleeding and infection. Anesthesia will further affect the recovery of consciousness, and highly specialized medical personnel and facilities are required, and the cost is expensive.

[0056] To solve the above problems, the main purpose of this application is to provide a new non-invasive deep electrical stimulation method for treating DoC, in order to provide a safer and more effective method for the clinical treatment of DoC.

[0057] According to an embodiment of the present application, there is provided an embodiment of a method for determining individualized electrode sites for non-invasive deep time-domain interference electrical stimulation for treating disorders of consciousness. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0058] In this embodiment, a method for determining individualized electrode sites for non-invasive deep time-domain interference electrical stimulation for treating disorders of consciousness is provided. Figure 1 It is a flowchart of a method for determining individualized electrode sites for non-invasive deep time-domain interference electrical stimulation for treating disorders of consciousness according to an embodiment of the present application, as Figure 1 shown, this process includes the following steps:

[0059] Step S101, generating a brain segmentation model based on the brain structure imaging data of the patient.

[0060] Among them, the brain segmentation model is used to characterize the structures of different brain regions in the brain; a plurality of electrode sites are preset on the brain segmentation model.

[0061] In an embodiment of the present disclosure, the brain segmentation model can be reconstructed from the brain structure imaging data (sMRI, Structural Magnetic Resonance Imaging) of the patient through three-dimensional reconstruction technology. This brain segmentation model can be understood as a specific model after the patient's brain is segmented, such as a model composed of tetrahedrons or polyhedrons.

[0062] Step S102: Calculate the forward lead field matrix according to the brain region segmentation model.

[0063] In the embodiments of the present disclosure, the forward lead field matrix is used to characterize the influence of neurons at different positions on the electrical signals measured at each electrode site, and each electrode site corresponds to multiple neurons. An example is given to illustrate this: Neurons at each position in the brain can emit electrical signals, and the intensity of the electrical signals emitted by each neuron is the same. Electrodes at each electrode site on the scalp can capture the electrical signals of neurons inside the brain. The electrical signals captured by each electrode are actually a mixture of electrical signals emitted by multiple neurons. Therefore, each neuron has an impact on the electrical signals captured by the electrodes on the scalp. The magnitude of the impact depends on the distance and direction from the electrode, so the intensity of the electrical signals captured by each electrode will also be different.

[0064] In some specific embodiments, as Figure 2 shown, the above step S102 includes steps S1021 - S1023:

[0065] Step S1021: Mesh the brain region segmentation model to obtain multiple finite element units.

[0066] In the embodiments of the present disclosure, the brain region segmentation model can be divided into finite element units composed of tetrahedrons or other shapes (such as hexahedrons, triangles, etc.) through a mesh generation method. Each finite element unit includes multiple nodes, and each node is used to represent neurons at corresponding positions on the cerebral cortex.

[0067] In some specific embodiments, after obtaining the brain region segmentation model, since the images of the brain regions are relatively complex and need to be segmented into the brain region segmentation model, that is, segmented into smaller parts for easy meshing. The specific steps are as follows:

[0068] 1. Segmentation: In this application, the MRI image data is first segmented into the brain region segmentation model.

[0069] 2. Registration: Then, the obtained brain region segmentation models after segmentation are registered to ensure their relative positions in space are accurate, so as to reflect the actual physical connections and interactions.

[0070] 3. Refinement: Refinement is performed after registration to ensure that the mesh of the brain region segmentation model is fine enough to capture the key features and details of the brain region segmentation model.

[0071] 4. Meshing: Then, the brain region segmentation model is meshed into finite element units composed of tetrahedrons or other shapes (such as hexahedrons, triangles, etc.). After meshing, multiple finite element units are obtained.

[0072] 5. Assign physical properties: Appropriate physical properties such as impedance, elastic modulus, thermal conductivity, etc. are assigned to each finite element unit. Different physical properties can be assigned according to actual requirements, and specific details are not limited here.

[0073] 6. Electromagnetic simulation and calculation: After completing meshing and assigning physical properties, the system will perform electromagnetic simulation operations. Electromagnetic simulation requires solving Maxwell's equations. For a static electric field, the Laplace equation shown in formula (1) can be used:

[0074]

[0075] where σ is the conductivity of the material, is the electric potential, and is the Laplace operator.

[0076] Discretization: By using the Finite Element Method (FEM), the continuous partial differential equation is discretized into small and simple subdomains (called elements). The problem is approximately solved on these elements, and then these approximate solutions are combined to obtain the solution for the entire domain. In this embodiment, through the finite element method, it is discretized into electric potentials, and then multiple electric potential values are obtained. Each electric potential value is the electric potential value at any point within the finite element unit. Finally, the multiple electric potential values are combined to obtain the frontal lead field matrix. The frontal lead field matrix is a matrix that describes the interaction between electrodes and tissues.

[0077] Step S1022, calculate the electric potential values of multiple nodes within each finite element unit respectively.

[0078] In the embodiments of the present disclosure, the electric potential of each node within each finite element unit can be calculated by defining a shape function, as follows:

[0079] Define the shape function Ni to represent the electric potential Vi at any point within the finite element unit, as shown in formula (2) below:

[0080] V≈∑ i N i V i , (2);

[0081] where V is the electric potential at any point within the finite element unit, that is, the electric potential value in the electric field distribution; N i is the shape function, which is used to interpolate the field quantities at the nodes. The shape function is an important concept in the finite element method and is used to represent the relationship between the value at any point within the element and the values at the element nodes. The shape function N i corresponds to the i-th node of the finite element unit. V iis the electric potential value at node i in the finite element. This is the electric potential value at discrete nodes and is an unknown quantity in the solution process. i: represents the index for traversing all nodes. For a given finite element, the number of nodes depends on the type of finite element (for example, a triangular element has 3 nodes, a quadrilateral element has 4 nodes, a tetrahedral element has 4 nodes, etc.).

[0082] Step S1023, combine the multiple electric potential values of the multiple finite elements to obtain the frontal lead field matrix.

[0083] In some specific embodiments, as Figure 3 shown, the above step S1023 includes steps a1 - a4:

[0084] Step a1, for any one of the multiple finite elements, calculate the admittance matrix of the finite element according to the electric potential values of multiple nodes in the finite element; the admittance matrix is used to characterize the mutual relationship between each node in the finite element.

[0085] In the embodiments of the present disclosure, the admittance matrix of each finite element can be shown by the following formula (3):

[0086]

[0087] where K e is the element admittance matrix, which is used to describe the conductance characteristics of the element. In electromagnetic field simulation, the element admittance matrix reflects the influence of the conductivity of the material and the geometric structure on the electric field distribution. ∫ Ve is the integral symbol, indicating integration over the volume V e of the finite element e. σ is the conductivity of the material, which is a scalar or matrix and describes the ability of the material to conduct current. is the gradient of the shape function N, indicating the rate of change of the shape function in space. The shape function N is used to interpolate the field quantities within the element. is the transpose of the shape function gradient. Since the shape function gradient is usually a vector, so is a row vector. is the product of the shape function gradient and the integral volume element, indicating the weighted integral carried out within the element volume.

[0088] Step a2, combine the multiple admittance matrices corresponding to the multiple finite elements one by one to obtain the system admittance matrix; the system admittance matrix is used to characterize the mutual relationship between all nodes within the multiple finite elements.

[0089] In the embodiments of the present disclosure, the system admittance matrix can be obtained by the following formula (4):

[0090] K = ∑ e K e , (4);

[0091] Wherein, K represents the system admittance matrix, representing the mutual relationship between all nodes in the entire finite element model (including multiple finite element cells). ∑ represents the summation over all cells. K e is the element admittance matrix, representing the mutual relationship between nodes within a single finite element cell. e represents the index of a single finite element cell. Each finite element cell has a unique index e for distinguishing different finite element cells.

[0092] Step a3, for any one of the multiple finite element cells in the system admittance matrix, apply a unit current to each electrode site in the finite element cell to obtain the potential distribution of each node within the finite element cell.

[0093] Step a4, combine the potential distributions of multiple nodes corresponding to multiple finite element cells in the system admittance matrix to obtain the forward lead field matrix.

[0094] In the above steps a3 - a4, apply a current source to the brain segmentation model, and through this current source, apply a unit current to each electrode site in the finite element cell, establish and solve the matrix equation as shown in the following formula (5):

[0095] KV = J, (5);

[0096] Wherein, V is the node potential vector in the finite element cell, and J is the current source vector.

[0097] To construct the forward lead field matrix, the following steps need to be performed for each electrode in the finite element cell: apply a unit current, apply a unit current to a certain electrode, solve to obtain the potential distribution Vi of all nodes, record the potential response, and then combine the unit current responses of all electrodes into the forward lead field matrix as follows:

[0098] L = [V 1 , V 2 , …, V n , (6);

[0099] Wherein, V i is the column vector of the node potential when a unit current is applied to the i-th electrode. Thus, the forward lead field matrix of the individualized head model is obtained through the above method.

[0100] Step S103, determine the brain region stimulation scheme according to the forward lead field matrix.

[0101] In some specific embodiments, such as Figure 4As shown, the above-mentioned step S103 includes steps S1031 - S1032:

[0102] Step S1031, determining the current intensity distribution and the electric field intensity of the brain region through the forward lead field matrix.

[0103] Among them, the forward lead field matrix can be calculated by a genetic algorithm to determine the current intensity distribution, and the forward lead field matrix can be calculated by a particle swarm optimization algorithm to determine the electric field intensity of the brain region.

[0104] Step S1032, determining the brain region stimulation scheme according to the electric field intensity of the brain region and the current intensity distribution.

[0105] In the above steps S1031 - S1032, after obtaining the forward lead field matrix, the scheme of arranging electrodes is iteratively optimized through the forward lead field matrix, as described below:

[0106] 1. Conduct single-objective search using a genetic algorithm. The genetic algorithm is used to determine the optimal current intensity distribution to maximize the electric field intensity in the target region of the brain. The genetic algorithm method includes encoding the solution as genes, applying operations such as mutation and crossover to generate new phenotypes, and iteratively refining these solutions to converge to the optimal result.

[0107] 2. Implement the Particle Swarm Optimization (PSO) algorithm to achieve multi-objective optimization. The PSO algorithm regards each solution as a particle, which moves in the decision space and is affected by its own experience and the collective experience of the group. The PSO algorithm is used to find a set of Pareto optimal solutions that balance multiple conflicting objectives, such as maximizing the electric field intensity in the target region while minimizing the electric field intensity in the non-target region, so as to improve the focusing of the electric field.

[0108] 3. Through multiple rounds of iteration, the optimization solution is achieved to calculate the scheme for guiding the intervention and stimulation of the target brain region with a stronger focusing degree, that is, the brain region stimulation scheme is obtained. Thus, the target electrode sites are more accurate.

[0109] Step S104, determining the target electrode sites according to the intervention data related to the left thalamus or right thalamus of the patient and the brain region stimulation scheme; the target electrode sites are used for electrical stimulation.

[0110] In the embodiments of the present disclosure, the target electrode sites for electrical stimulation treatment of disorders of consciousness can be determined from multiple electrode sites through the intervention data related to the left thalamus or right thalamus.

[0111] In some specific embodiments, as Figure 5 shown, after step S104, the method further includes:

[0112] Step b1, extracting features from the patient's current EEG data to obtain multiple frequency domain features.

[0113] In the disclosed embodiment, EEG data is mainly obtained through the multi-channel EEG acquisition module provided by the software. EEG data is feature extracted to obtain EEG features, which are the frequency domain expressions of EEG data in the frequency domain. Fourier transform can be used to convert EEG data into multiple frequency domain features in the frequency domain.

[0114] Step b2, calculating the frequency of the stimulation wave by weighted summing the multiple frequency domain features; the stimulation wave includes a first stimulation wave and a second stimulation wave.

[0115] In the disclosed embodiment, since the individual characteristics of the target are different, a personalized learning model can be used to select different points as required points according to the individual characteristics, and then calculate the relevant frequencies from the EEG characteristics in a weighted manner to obtain the required stimulation wave parameters. The frequency difference between the first stimulation wave and the second stimulation wave is a preset frequency value; the preset frequency value can be set to 200 Hz.

[0116] Step b3, delivering the first stimulation wave to the first pair of target electrode sites and the third pair of target electrode sites, and delivering the second stimulation wave to the second pair of target electrode sites and the fourth pair of target electrode sites.

[0117] The target electrode sites include a first pair of target electrode sites and a second pair of target electrode sites corresponding to the left thalamus, and a third pair of target electrode sites and a third pair of target electrode sites corresponding to the right thalamus.

[0118] Specifically, the two pairs of target electrode sites for the left thalamus target are: the first pair: TP8 (anode), C3 (cathode), the second pair: O2 (anode), FC3 (cathode); the two pairs of target electrode sites for the right thalamus target are: the first pair: O2 (anode), Fz (cathode), the second pair: Oz (anode), FC4 (cathode). After determining the target electrode site information, place the corresponding electrode.

[0119] In the embodiments of the present disclosure, a Temporal Interference (TI) method can be adopted. TI stimulation changes the activities of neurons in the action area through the frequency difference generated by two groups of non-interfering high-frequency alternating currents flowing through tissues, achieving the purpose of non-invasively regulating the nerve function of the stimulation area. According to a large number of previous explorations in this project, the effect of intervening in DoC patients with a 200 Hz stimulation wave frequency difference and a 5 mA stimulation intensity is relatively good. Therefore, in this application, by setting the difference between the frequency of the first stimulation wave (2000 Hz) and the frequency of the second stimulation wave (2200 Hz) to 200 Hz, and the current intensity of both pairs of electric fields to 5 mA, the treatment of DoC is achieved while ensuring safety.

[0120] In this application, the target electrode site information of the thalamus that needs to be intervened for the patient is determined through the multi-modal brain imaging data of the target, the electroencephalogram data collected by the multi-channel electroencephalogram acquisition board built into the software, and the intervention information of the thalamus brain region. Then, the stimulation wave parameters are determined in combination with previous clinical experience, and the stimulation wave is transmitted to the corresponding electrode position for electrical stimulation, thereby providing a new method for non-invasive deep brain electrical stimulation treatment for DoC patients.

[0121] The embodiments of the present disclosure also provide a clinical application plan for the individualized electrode site determination method of non-invasive deep temporal interference electrical stimulation for treating disorders of consciousness using this application, which is specifically as follows:

[0122] According to a large number of previous clinical trial results, expert consensus, and guidelines, such as Figure 6 shown, the second embodiment of this application provides a clinical application plan for non-invasive deep brain electrical stimulation treatment of DoC, aiming to provide more convenient and effective guidance for the future clinical application of non-invasive deep brain electrical stimulation. This plan has been applied to the clinical treatment of DoC and achieved preliminary results. In this embodiment, we take one of the patients as an example to specifically introduce this plan.

[0123] The patient in this case is a 40-year-old male who was admitted to the hospital due to sudden loss of consciousness during a basketball game. After diagnosis by the doctor, it was mainly hypoxic-ischemic encephalopathy. Since the onset of the disease, the patient has been in a coma for more than a month. The bilateral pupils are equal in size and round, with a diameter of 3.0 mm, and the light reflex is sensitive. The patient is unable to speak and is uncooperative during the physical examination. The patient is in a tracheostomy state, with a nasoenteral tube in place, and is incontinent of urine and feces. After various drug and physical symptomatic treatments, the effect is not good. Therefore, it is planned to use our non-invasive deep brain stimulation for intervention. After the doctor confirmed that the patient is a DoC patient and suitable for intervention according to the inclusion and exclusion criteria, the family members agreed and voluntarily signed the informed consent form. Before the intervention, a baseline assessment was performed on the patient, including general demographics, clinical scales, electroencephalogram, imaging, or laboratory tests, etc. Among them, the Coma Recovery Scale-Revised (CRS-R) was selected as the clinical scale. It is the current standard clinical scale for examining and evaluating pDoC, which can distinguish VS from MCS. The improvement of consciousness from VS to MCS- or MCS+ is of great significance for prognosis and clinical intervention determination. Therefore, the CRS-R can be used as the main valid index for prognosis assessment. One course of intervention consists of 15 sessions, with each session stimulating the left or right thalamus as the target for 20 minutes each. For the specific stimulation protocol parameters during the intervention, please refer to Figure 2 . After the intervention, the patient was evaluated for effectiveness and safety during follow-up. The CRS-R results showed that the patient's score increased from 4 points at baseline to 12 points, indicating a change from VS at baseline to MCS+, and the conscious state improved significantly. The patient's behavioral performance also confirmed this, gradually recovering from the previous state of coma to the current state of being able to turn the head independently, grasp, and make sounds. In addition, the electroencephalogram data results also showed that the entropy, absolute power, relative power, functional connectivity, and brain network characteristics of the electroencephalogram signals in each frequency band and brain region that can reflect the patient's conscious state were significantly improved after the intervention, indicating an overall improvement in brain function, which means that the patient's conscious state has had a positive change.

[0124] Corresponding to the implementation manner of the above method for determining individualized electrode sites for non-invasive deep-time-domain interference electrical stimulation for treating disorders of consciousness, the embodiment of the present application also provides a device for determining individualized electrode sites for non-invasive deep-time-domain interference electrical stimulation for treating disorders of consciousness, which is used to execute the above Figures 1 to 6 any one of the embodiments shown for the method for determining individualized electrode sites for non-invasive deep-time-domain interference electrical stimulation for treating disorders of consciousness. As Figure 7 shown, the device for determining individualized electrode sites for non-invasive deep-time-domain interference electrical stimulation for treating disorders of consciousness includes:

[0125] A brain region segmentation model generation module, which is used to generate a brain region segmentation model based on the brain structure imaging data of the patient; the brain region segmentation model is used to characterize the structures of different brain regions in the brain; a plurality of electrode sites are preset on the brain region segmentation model;

[0126] Anterior lead field matrix calculation module, configured to calculate an anterior lead field matrix according to the brain region segmentation model, where the anterior lead field matrix is used to characterize the influence of neurons at different positions on the electrical signals measured at each electrode site, and each electrode site corresponds to multiple neurons;

[0127] Brain region stimulation scheme determination module, configured to determine a brain region stimulation scheme according to the anterior lead field matrix;

[0128] Target electrode site determination module, configured to determine the target electrode site according to the intervention data related to the left thalamus or right thalamus of the patient and the brain region stimulation scheme; the target electrode site is used for electrical stimulation.

[0129] Optionally, the anterior lead field matrix calculation module is further configured to grid the brain region segmentation model to obtain a plurality of finite element units, each finite element unit includes a plurality of nodes, and each node is used to characterize neurons at corresponding positions on the cerebral cortex; calculate the potential values of the plurality of nodes in each finite element unit respectively; and combine the potential values of the plurality of finite element units to obtain the anterior lead field matrix.

[0130] Optionally, the anterior lead field matrix calculation module is further configured to, for any one of the plurality of finite element units, calculate the admittance matrix of the finite element unit according to the potential values of the plurality of nodes in the finite element unit; the admittance matrix is used to characterize the mutual relationship between the nodes in the finite element unit; combine the admittance matrices corresponding to the plurality of finite element units one by one to obtain a system admittance matrix; the system admittance matrix is used to characterize the mutual relationship between all the nodes in the plurality of finite element units; for any one of the plurality of finite element units in the system admittance matrix, apply a unit current to each electrode site in the finite element unit to obtain the potential distribution of each node in the finite element unit; and combine the potential distributions of the plurality of nodes corresponding to the plurality of finite element units in the system admittance matrix to obtain the anterior lead field matrix.

[0131] Optionally, the brain region stimulation scheme determination module is further configured to determine the current intensity distribution and the brain region electric field intensity through the anterior lead field matrix; and determine the brain region stimulation scheme according to the brain region electric field intensity and the current intensity distribution.

[0132] Optionally, the brain region stimulation scheme determination module is further configured to calculate the anterior lead field matrix through a genetic algorithm to determine the current intensity distribution; and calculate the anterior lead field matrix through a particle swarm optimization algorithm to determine the brain region electric field intensity.

[0133] Optionally, the device further includes: an electrical stimulation module, configured to extract features from the current electroencephalogram data of the patient to obtain a plurality of frequency-domain features; calculate the frequency of the stimulation electric wave by performing weighted summation on the plurality of frequency-domain features; the stimulation electric wave includes a first stimulation electric wave and a second stimulation electric wave; the frequency difference between the first stimulation electric wave and the second stimulation electric wave is a preset frequency value; deliver the first stimulation electric wave to the first pair of target electrode sites and the third pair of target electrode sites, and deliver the second stimulation electric wave to the second pair of target electrode sites and the fourth pair of target electrode sites.

[0134] Optionally, the frequency of the first stimulation electric wave is 2000 Hertz (Hz), the frequency of the second stimulation electric wave is 2200 Hz, the preset frequency value is 200 Hz, and the current intensities of the first stimulation electric wave and the second stimulation electric wave are both 5 milliamperes (mA).

[0135] The non-invasive deep-time-domain interference electrical stimulation individualized electrode site determination device for treating disorders of consciousness provided in the above embodiments of the present application and the non-invasive deep-time-domain interference electrical stimulation individualized electrode site determination method for treating disorders of consciousness provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0136] The embodiments of the present application also provide a computer device to execute the above non-invasive deep-time-domain interference electrical stimulation individualized electrode site determination method for treating disorders of consciousness. Please refer to Figure 8 , which shows a schematic diagram of a computer device provided in some embodiments of the present application. As Figure 8 shown, the computer device 8 includes: a processor 800, a memory 801, a bus 802, and a communication interface 803. The processor 800, the communication interface 803, and the memory 801 are connected through the bus 802; a computer program that can run on the processor 800 is stored in the memory 801, and when the processor 800 runs the computer program, it executes the non-invasive deep-time-domain interference electrical stimulation individualized electrode site determination method provided in any of the embodiments schematically described above in the present application. Figures 1 to 6

[0137] Among them, the memory 801 may include a high-speed random access memory (Random Access Memory, RAM), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 803 (which can be wired or wireless), a communication connection is realized between the system network element and at least one other network element, and the Internet, a wide area network, a local area network, a metropolitan area network, etc. can be used.

[0138] The bus 802 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 801 is used to store programs. After receiving an execution instruction, the processor 800 executes the program. The foregoing Figures 1 to 6 The method for determining individualized electrode sites of non-invasive deep time-domain interference electrical stimulation for treating disorders of consciousness disclosed in any of the foregoing embodiments can be applied to the processor 800 or implemented by the processor 800.

[0139] The processor 800 may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor 800 or instructions in software form. The above-mentioned processor 800 can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 801, and the processor 800 reads the information in the memory 801 and combines its hardware to complete the steps of the above method.

[0140] The computer device provided by the embodiments of the present application and the method for determining individualized electrode sites of non-invasive deep time-domain interference electrical stimulation for treating disorders of consciousness provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by them.

[0141] The embodiments of the present application also provide a computer-readable storage medium corresponding to the method for determining individualized electrode sites of non-invasive deep time-domain interference electrical stimulation for treating disorders of consciousness provided in the foregoing embodiments. Please refer to Figure 9, which shows that the computer-readable storage medium is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the non-invasive deep-time domain interference electrical stimulation individualized electrode site determination method for treating disorders of consciousness provided in any of the foregoing embodiments.

[0142] It should be noted that examples of the computer-readable storage medium may further include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other optical and magnetic storage media, which will not be elaborated here one by one.

[0143] The computer-readable storage medium provided in the above embodiments of the present application and the non-invasive deep-time domain interference electrical stimulation individualized electrode site determination method for treating disorders of consciousness provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run, or implemented by the application programs stored therein.

[0144] It should be noted that:

[0145] In the specification provided here, a large number of specific details are described. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this specification.

[0146] Similarly, it should be understood that, in order to streamline the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting the following schematic: that the claimed present application requires more features than those expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, where each claim stands on its own as a separate embodiment of the present application.

[0147] In addition, those skilled in the art can understand that, although some of the embodiments described herein include certain features included in other embodiments but not other features, the combination of the features of different embodiments means that it is within the scope of the present application and forms different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0148] As described above, it is only the preferred specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for determining individualized electrode sites for non-invasive deep temporal interferometric electrical stimulation for the treatment of consciousness disorders, characterized in that: The method comprises: Generate a brain region segmentation model according to the patient's brain structure imaging data; the brain region segmentation model is used to characterize the structure of different brain regions in the brain; the brain region segmentation model includes a plurality of pre-set electrode sites; Calculating a pre-lead field matrix according to the brain region segmentation model, wherein the pre-lead field matrix is ​​used to characterize the influence of neurons at different positions on the electrical signals measured at each electrode site, and each electrode site corresponds to a plurality of neurons; Determining a brain region stimulation scheme according to the preceding lead field matrix; The target electrode site is determined according to the intervention data related to the left thalamus or the right thalamus of the patient and the brain area stimulation program; the target electrode site is used for electrical stimulation.

2. The method according to claim 1, characterized in that: Calculating the front lead field matrix according to the brain region segmentation model includes: Meshing the brain region segmentation model to obtain a plurality of finite element units, each finite element unit including a plurality of nodes, each node being used to represent a neuron at a corresponding position on the cerebral cortex; Calculating the potential values ​​of multiple nodes in each finite element unit respectively; The multiple potential values ​​of the multiple finite element units are combined to obtain the leading field matrix.

3. The method according to claim 2, characterized in that Combining multiple potential values ​​corresponding to the multiple finite element units to obtain the leading field matrix includes: For any one of the plurality of finite element units, an admittance matrix of the finite element unit is calculated according to potential values ​​of a plurality of nodes in the finite element unit; the admittance matrix is ​​used to characterize the mutual relationship between the nodes in the finite element unit; Combining multiple admittance matrices corresponding to the multiple finite element units one by one to obtain a system admittance matrix; the system admittance matrix is ​​used to characterize the mutual relationship between all nodes in the multiple finite element units; For any one of the multiple finite element units in the system admittance matrix, a unit current is applied to each electrode site in the finite element unit to obtain a potential distribution of each node in the finite element unit; The potential distributions of multiple nodes corresponding to multiple finite element units in the system admittance matrix are combined to obtain the leading-lead field matrix.

4. The method according to claim 1 or 2, characterized in that: Determining a brain region stimulation scheme according to the preceding lead field matrix includes: Determine the current intensity distribution and the electric field intensity of the brain region by using the lead field matrix; The brain region stimulation scheme is determined according to the electric field strength of the brain region and the current intensity distribution.

5. The method according to claim 4, characterized in that Determining the current intensity distribution and the electric field intensity of the brain region by the front lead field matrix includes: Calculating the leading field matrix by genetic algorithm to determine the current intensity distribution; The lead field matrix is ​​calculated by particle swarm optimization algorithm to determine the electric field strength of the brain area.

6. The method according to claim 1 or 2, characterized in that: The target electrode sites include a first pair of target electrode sites and a second pair of target electrode sites corresponding to the left thalamus, and a third pair of target electrode sites and a third pair of target electrode sites corresponding to the right thalamus; After determining the target electrode site according to the intervention data and the brain region stimulation scheme, the method further includes: Extract features from the patient's current EEG data to obtain multiple frequency domain features; The frequency of the stimulation wave is calculated by weighted summing the multiple frequency domain features; the stimulation wave includes a first stimulation wave and a second stimulation wave; the frequency difference between the first stimulation wave and the second stimulation wave is a preset frequency value; The first stimulation waves are delivered to the first pair of target electrode sites and the third pair of target electrode sites, and the second stimulation waves are delivered to the second pair of target electrode sites and the fourth pair of target electrode sites.

7. The method according to claim 6, characterized in that The frequency of the first stimulation wave is 2000 Hz, the frequency of the second stimulation wave is 2200 Hz, the preset frequency value is 200 Hz, and the current intensity of the first stimulation wave and the second stimulation wave is 5 mA.

8. A device for determining individualized electrode sites for non-invasive deep temporal interferometric electrical stimulation for the treatment of consciousness disorders, characterized in that: The device comprises: A brain region segmentation model generation module, used to generate a brain region segmentation model according to the patient's brain structure imaging data; the brain region segmentation model is used to characterize the structure of different brain regions in the brain; the brain region segmentation model includes a plurality of pre-set electrode sites; A leading-lead field matrix calculation module, used to calculate the leading-lead field matrix according to the brain region segmentation model, wherein the leading-lead field matrix is ​​used to characterize the influence of neurons at different positions on the electrical signals measured at each electrode site, and each electrode site corresponds to a plurality of neurons; A brain region stimulation scheme determination module, used to determine a brain region stimulation scheme according to the preceding lead field matrix; The target electrode site determination module is used to determine the target electrode site based on the intervention data related to the left thalamus or the right thalamus of the patient and the brain area stimulation program; the target electrode site is used for electrical stimulation.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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