Design method and device of transcranial magnetic stimulation waveform and electronic equipment

Abstract

The invention discloses a transcranial magnetic stimulation waveform design method, a transcranial magnetic stimulation waveform design device and electronic equipment, and belongs to the technical field of medical instrument design, and compared with a traditional commercial discharge circuit, the transcranial magnetic stimulation waveform design method selects a novel discharge circuit with controllable waveform parameters, and has higher flexibility. Taking a first preset circuit parameter and a second preset circuit parameter in the waveform parameter controllable transcranial magnetic discharge circuit as optimization indexes, taking a minimum selectivity index as an optimization target, and utilizing an optimization algorithm to find out an optimal first preset circuit parameter and a corresponding second preset circuit parameter; taking a waveform corresponding to the optimal first preset circuit parameter and the corresponding second preset circuit parameter as a target design waveform; the method can improve the selectivity of transcranial magnetic stimulation, the determined target design waveform can improve the controllable transcranial magnetic stimulation effect to a large extent, and meanwhile, the potential of waveform parameter controllable transcranial magnetic stimulation is exerted to the maximum extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical device design, and more specifically, relates to a design method, device and electronic equipment for a transcranial magnetic stimulation waveform. Background Art

[0002] Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technology that is widely used in the diagnosis and treatment of neurological diseases, neurological function assessment, and exploratory research in brain science and cognitive science. This technology places a stimulation coil above the patient's head, and the coil is fed with a time-varying current provided by a discharge circuit to generate an induced magnetic field, which further induces an induced electric field in the target area of ​​the brain, thereby changing the membrane potential of nerve cells and causing neuronal depolarization, thereby achieving the desired stimulation effect.

[0003] In order to obtain a more selective stimulation effect, many studies have been devoted to enhancing the focusing of the induced electric field by optimizing the coil design, thereby improving the stimulation accuracy of the transcranial magnetic stimulation coil. However, due to the extremely complex structure of brain neurons, the benefits of improving stimulation selectivity by optimizing coil design are relatively limited. Related experiments have shown that the relevant parameters of the discharge waveform have an important influence on neuronal activation, which provides a new way to improve the selectivity of transcranial magnetic stimulation. To this end, researchers have developed a discharge circuit with controllable waveform parameters, aiming to improve the selectivity of stimulation by flexibly changing the stimulation waveform. However, since the relevant research on the mechanism of the influence of waveform parameters on stimulation selectivity still needs to be deepened, the advantages of this flexible discharge circuit are difficult to play, which limits the further improvement of transcranial magnetic stimulation selectivity and is difficult to meet the urgent need for improving the stimulation effect in practical applications. Summary of the invention

[0004] In response to the above defects or improvement needs of the prior art, the present invention provides a design method, device and electronic device for a transcranial magnetic stimulation waveform, the purpose of which is to solve the technical problems of low selectivity and poor stimulation effect of transcranial magnetic stimulation under the existing waveform.

[0005] To achieve the above object, according to one aspect of the present invention, a method for designing a transcranial magnetic stimulation waveform is provided, comprising:

[0006] S1: Taking the first preset circuit parameter and the second preset circuit parameter in the waveform parameter controllable transcranial magnetic discharge circuit as optimization indicators and minimizing the selectivity indicator as the optimization goal, using an optimization algorithm to find the optimal first preset circuit parameter and its corresponding second preset circuit parameter;

[0007] The selectivity index is obtained by inputting the waveforms corresponding to the first preset circuit parameters and the second preset circuit parameters into a multi-scale neuron model, and determining the relative activation degree of the target area compared with other areas in the sample area of ​​the neuron model; the second preset circuit parameter is calculated by inputting the first preset circuit parameter into the constraint condition equation group of the TMS discharge circuit;

[0008] S2: Taking the waveform corresponding to the optimal first preset circuit parameter and its corresponding second preset circuit parameter as the target design waveform.

[0009] In one embodiment, the constraint equations are expressed as:

[0010]

[0011] Among them, V C1 and V C2 Respectively represent the initial voltage of the two energy storage capacitors C1 and C2 in the circuit, V C1 , -V C2 、V C1 -V C2 , 0 are all four level amplitudes that this topology can generate on the coil. Taking the generation of a four-level waveform as an example, V1-V4 represent the amplitudes of each level of the four-level waveform, pw1-pw4 represent the pulse width of each level, PW represents the total pulse width of the multi-level waveform, R and L represent the coil resistance and inductance, V k ∈[V1,V4],i L Indicates the current flowing through the coil.

[0012] In one embodiment, the first preset circuit parameters include: V C1 、V C2 , V1, V2, pw1 and pw2; the second preset circuit parameters include: V3, V4, pw3 and pw4.

[0013] In one embodiment, the selectivity index is expressed as: Among them, t represents the number of model neurons in the target area, s represents the total number of model neurons in the sample area, and threshEs_target i represents the stimulation threshold of the i-th model neuron in the target area, i∈[1,t]; threshEs j Represents the stimulation threshold of the jth model neuron in the sample area, j∈[1,s].

[0014] In one of the embodiments, the optimization algorithm is a particle swarm optimization algorithm.

[0015] According to another aspect of the present invention, there is provided a device for designing a transcranial magnetic stimulation waveform, comprising:

[0016] An optimization module, for using a first preset circuit parameter and a second preset circuit parameter in a pulse parameter controllable transcranial magnetic discharge circuit as optimization indicators, minimizing a selectivity indicator as an optimization goal, and using an optimization algorithm to find the optimal first preset circuit parameter and its corresponding second preset circuit parameter;

[0017] The selectivity index is obtained by inputting the waveforms corresponding to the first preset circuit parameters and the second preset circuit parameters into a multi-scale neuron model, and determining the relative activation degree of the target area compared with other areas in the sample area of ​​the neuron model; the second preset circuit parameter is calculated by inputting the first preset circuit parameter into the constraint condition equation group of the TMS discharge circuit;

[0018] The determination module is used to use the waveform corresponding to the optimal first preset circuit parameter and the corresponding second preset circuit parameter as the target design waveform.

[0019] According to another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0021] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0022] (1) The present invention provides a method for designing a transcranial magnetic stimulation waveform, which selects a new type of discharge circuit with controllable waveform parameters. Compared with the traditional commercial discharge circuit that is limited to generating biphasic, monophasic or half-wave sinusoidal pulses, the discharge topology has greater flexibility. The first preset circuit parameter and the second preset circuit parameter in the waveform parameter controllable transcranial magnetic discharge circuit are used as optimization indicators, and the minimization of the selectivity indicator is used as the optimization target. The optimization algorithm is used to find the optimal first preset circuit parameter and its corresponding second preset circuit parameter; the waveform corresponding to the optimal first preset circuit parameter and its corresponding second preset circuit parameter is used as the target design waveform; this method can improve the selectivity of transcranial magnetic stimulation, and the target design waveform determined in this way can greatly improve the effect of controllable transcranial magnetic stimulation, while maximizing the potential of waveform parameter controllable transcranial magnetic stimulation.

[0023] (2) This scheme is based on the discharge characteristics of the topological structure of the transcranial magnetic discharge circuit with controllable waveform parameters. The equation group is established according to the constraints of the discharge waveform related parameters. This parameterization method is more targeted. While accurately reflecting the discharge characteristics of the circuit, it reduces the calculation requirements and ensures the reproducibility of the waveform.

[0024] (3) The first preset circuit parameter and the second preset circuit parameter of the present solution are selected in this way, so that the computational complexity is low and the execution efficiency of the entire algorithm can be improved.

[0025] (4) This scheme uses a multi-scale model that takes into account the real morphology of neurons to reflect the activation of neurons by transcranial magnetic stimulation. Quantifying the impact of transcranial magnetic stimulation waveform parameters on stimulation selectivity is also more practical.

[0026] (5) The optimization algorithm described in this scheme is the particle swarm optimization algorithm, which is mature and easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a flow chart of a method for designing a transcranial magnetic stimulation waveform provided in Example 1 of the present invention;

[0028] Figure 2 A schematic diagram of the structure of a bridge-type pulse parameter-controllable transcranial magnetic discharge circuit provided in Example 1 of the present invention;

[0029] Figure 3 A schematic diagram of the distribution of stimulation thresholds of large pyramidal neurons in the fifth layer of the cerebral cortex under different stimulation waveforms provided in Example 1 of the present invention;

[0030] Figure 4 The coil current and voltage waveforms corresponding to the waveform optimization process provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0032] Example 1

[0033] like Figure 1 As shown, this embodiment provides a method for designing a transcranial magnetic stimulation waveform, including: S1: Figure 2As shown, the first preset circuit parameters and the second preset circuit parameters in the bridge waveform parameter controllable transcranial magnetic discharge circuit are used as optimization indicators, and the minimization of the selectivity index is used as the optimization goal, and the optimization algorithm is used to find the optimal first preset circuit parameters and their corresponding second preset circuit parameters; wherein the selectivity index is obtained by inputting the waveforms corresponding to the first preset circuit parameters and the second preset circuit parameters into a multi-scale neuron model, and is determined according to the relative activation degree of the target area in the sample area compared with other areas in the neuron model; the second preset circuit parameters are calculated by inputting the first preset circuit parameters into the constraint condition equation group of the TMS discharge circuit; S2: the waveform corresponding to the optimal first preset circuit parameter and its corresponding second preset circuit parameter is used as the target design waveform.

[0034] Existing transcranial magnetic stimulation discharge circuits with controllable waveform parameters are mainly bridge structures or modular variants of bridge structures. Therefore, the present invention takes into account the waveform characteristics of the bridge structure when setting the waveform parameters. This method enables waveform optimization to maximize the potential of the bridge topology, while also facilitating adaptation to other discharge circuits with controllable waveform parameters. Figure 2 Figure 1 is a schematic diagram of the structure of a bridge-type waveform parameter-controllable transcranial magnetic discharge circuit. C1 and V C2 is the initial storage voltage of the two energy storage capacitors. In this type of circuit, the capacitor usually has a large capacitance value, and the pulse width of the TMS waveform is determined by the switching device Q 11 -Q 22 The on-off timing of the coil is controlled by a timing that is much shorter than the resonant period of the energy storage capacitor and the coil inductance. Therefore, the capacitor is not fully discharged during the pulse, and the capacitor voltage can be considered to remain approximately at the initial value during the entire pulse. In this way, the coil voltage will produce a composite multi-level waveform composed of the above voltage levels generated by the topology. This assumption is made to facilitate the analysis and understanding of the stimulus law of the waveform, temporarily ignoring the impact of voltage changes.

[0035] Since the induced electric field generated by the coil is proportional to the coil voltage, the waveform of the induced electric field is consistent with the waveform of the coil voltage, but the magnitude is different. The optimization process takes the four-level coil voltage waveform as an example. The four levels can be the same or different. Based on the discharge characteristics of the selected topology, the equation group is established according to the constraints of the discharge waveform related parameters. Figure 2 Taking topology as an example, the system of equations can be expressed as:

[0036]

[0037] Among them, V C1 and V C2 Respectively represent the initial voltage of capacitors C1 and C2, V C1 , -VC2 、V C1 -V C2 , 0 is Figure 2 The topology shown varies with the switch Q 11 -Q 22 The state change can generate all four levels of amplitude on the coil L. Taking the generation of a four-level waveform as an example, V1-V4 represents the amplitude of each level of the four-level waveform, pw1-pw4 represents the pulse width of each level, PW represents the total pulse width of the multi-level waveform, R and L represent the coil resistance and inductance, V k ∈[V1,V4],i L Indicates the current flowing through the coil. For example, the total pulse width can be set to 315μs, the resistance can be set to 0.025Ω, and the inductance can be set to 16.35μH.

[0038] Although the pulse waveform can be composed of more levels, in the initial stage, it is best to minimize the complexity of the waveform while maintaining the flexibility of the waveform, so as to facilitate the subsequent analysis of the optimization results and summarize the basic laws of the waveform stimulation effect. Once some general principles are recognized, the number of levels of the waveform can be adjusted according to the potential optimization direction.

[0039] Furthermore, the first preset circuit parameters include: V C1 、V C2 , V1, V2, pw1 and pw2; the second preset circuit parameters include: V3, V4, pw3 and pw4.

[0040] Transcranial magnetic stimulation waveform optimization needs to be based on a neuron model. Since this study aims to optimize stimulation selectivity, a multiscale model that considers the real morphology of neurons was selected to reflect the activation of neurons by transcranial magnetic stimulation. This method can quantify the impact of transcranial magnetic stimulation waveform parameters on stimulation selectivity.

[0041] To optimize the selectivity of the electric field waveform, we first need to define the selectivity criteria. The stimulus selectivity refers to the relative activation of the waveform in the target area compared to other areas in the sample area. Further, selectivity is defined as the ratio of the sum of the stimulation thresholds of the neurons in the target area to the sum of the stimulation thresholds of all neurons in the sample area.

[0042] Furthermore, the selectivity index is expressed as: Among them, t represents the number of model neurons in the target area, s represents the total number of model neurons in the sample area, and threshEs_target i represents the stimulation threshold of the i-th model neuron in the target area, i∈[1,t]; threshEs jrepresents the stimulation threshold of the jth model neuron in the sample area, j∈[1,s]. The smaller f1 is, the lower the ratio of the stimulation threshold of the neurons in the target area to the stimulation threshold of other neurons in the sample area is, that is, the target area is easier to be activated than other areas, that is, the selectivity of the waveform is better. Furthermore, to facilitate comparison of results, the f1 value can be scaled proportionally.

[0043] Furthermore, the optimization algorithm can iteratively optimize the stimulus selectivity index. Optionally, in this example, a particle swarm optimization (PSO) algorithm is used for optimization. The specific implementation method is as follows: Select V C1 、V C2 , V1, V2, pw1 and pw2 are used as decision variables, and V3, V4, pw3 and pw4 are calculated according to the constraint condition equation group to obtain a complete electric field waveform; the obtained waveform is used to stimulate the neuron model, and the corresponding selectivity index f1 is calculated; f1 is used as the objective function for optimization, and the particle swarm optimization algorithm is used for iteration, the decision variables are updated to generate a new waveform, and it is iterated in the direction of minimizing f1; the waveform corresponding to f1 obtained after the algorithm converges is the optimal waveform.

[0044] Taking the large pyramidal neurons in the fifth layer of the cerebral cortex as an example, the standard setting of the stimulation intensity is the stimulation intensity that can activate 50% of the neurons in the target area. Figure 3 The distribution of stimulation thresholds of large pyramidal neurons in layer 5 of the cerebral cortex under different stimulation waveforms, where the black outline represents the stimulation target area, the dark area represents all areas that are activated at the intensity required to activate the target area, and the light gray area represents the area that cannot be activated. Figure 4 The waveforms of coil current and voltage in the corresponding waveform optimization process are shown in Figure 2. The corresponding stimulation selectivity intensity f1 is 0.8331 and 0.4494, which shows that the waveform optimization method provided in this case can significantly improve the selectivity of transcranial magnetic stimulation.

[0045] The design method of the transcranial magnetic stimulation waveform provided in this embodiment improves the selectivity of transcranial magnetic stimulation and maximizes the potential of transcranial magnetic stimulation with controllable waveform parameters. The electric field waveform parameterization method used is more targeted, while accurately reflecting the discharge characteristics of the circuit, reducing the calculation requirements and ensuring the reproducibility of the waveform. The discharge topology used has greater flexibility. The improvement in waveform flexibility expands the optimization space and allows simulation of traditional waveforms. The stimulation model used takes into account the real morphology of neurons, accurately reflects the activation of neurons by transcranial magnetic stimulation, and thus quantifies the effect of transcranial magnetic stimulation waveform parameters on stimulation selectivity, which is more practical. While obtaining the electric field waveform with the best stimulation selectivity index, relatively small coil heating can also be guaranteed, and further exploration of the transcranial magnetic stimulation mechanism is promoted. Various types of electric field waveforms with the best stimulation selectivity index help explain individual differences in transcranial magnetic stimulation results and support the development of personalized waveforms.

[0046] Example 2

[0047] This embodiment provides a design device for a transcranial magnetic stimulation waveform, including: an optimization module and a determination module. The optimization module is used to use the first preset circuit parameter and the second preset circuit parameter in the waveform parameter controllable transcranial magnetic discharge circuit as optimization indicators, and minimize the selectivity index as the optimization target, and use the optimization algorithm to find the optimal first preset circuit parameter and its corresponding second preset circuit parameter. The selectivity index is obtained by inputting the waveform corresponding to the first preset circuit parameter and the second preset circuit parameter into a multi-scale neuron model, and determining it according to the relative activation degree of the target area in the sample area compared with other areas in the neuron model; the second preset circuit parameter is calculated by inputting the first preset circuit parameter into the constraint condition equation group of the TMS discharge circuit. The determination module is used to use the waveform corresponding to the optimal first preset circuit parameter and its corresponding second preset circuit parameter as the target design waveform.

[0048] Example 3

[0049] This embodiment provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0050] Example 4

[0051] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0052] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for designing a transcranial magnetic stimulation waveform, characterized in that: include: S1: Taking the first preset circuit parameter and the second preset circuit parameter in the waveform parameter controllable transcranial magnetic stimulation discharge circuit as optimization indicators and minimizing the selectivity indicator as the optimization goal, using an optimization algorithm to find the optimal first preset circuit parameter and its corresponding second preset circuit parameter; The selectivity index is obtained by inputting the waveforms corresponding to the first preset circuit parameter and the second preset circuit parameter into a multi-scale neuron model, and determining the relative activation degree of the target area compared with other areas in the sample area of ​​the neuron model; the second preset circuit parameter is calculated by inputting the first preset circuit parameter into the constraint condition equation group of the TMS discharge circuit; S2: Taking the waveform corresponding to the optimal first preset circuit parameter and its corresponding second preset circuit parameter as the target design waveform.

2. The method for designing a transcranial magnetic stimulation waveform according to claim 1, wherein: The constraint equations are expressed as: Among them, V C1 and V C2 Respectively represent the initial voltage of the two energy storage capacitors C1 and C2 in the circuit, V C1 , -V C2 、V C1 -V C2 , 0 are all four level amplitudes that this topology can generate on the coil. Taking the generation of a four-level waveform as an example, V1-V4 represent the amplitudes of each level of the four-level waveform, pw1-pw4 represent the pulse width of each level, PW represents the total pulse width of the multi-level waveform, R and L represent the coil resistance and inductance, V k ∈[V1,V4],i L Indicates the current flowing through the coil.

3. The method for designing a transcranial magnetic stimulation waveform according to claim 2, wherein: The first preset circuit parameters include: V C1 、V C2 , V1, V2, pw1 and pw2; The second preset circuit parameters include: V3, V4, pw3 and pw4.

4. The method for designing a transcranial magnetic stimulation waveform according to claim 1, wherein: The selectivity index is expressed as: Among them, t represents the number of model neurons in the target area, s represents the total number of model neurons in the sample area, and threshEs_target i represents the stimulation threshold of the i-th model neuron in the target area, i∈[1,t]; threshEs j Represents the stimulation threshold of the jth model neuron in the sample area, j∈[1,s].

5. The method for designing a transcranial magnetic stimulation waveform according to any one of claims 1 to 4, characterized in that: The optimization algorithm is a particle swarm optimization algorithm.

6. A device for designing a transcranial magnetic stimulation waveform, characterized in that: include: An optimization module, for using a first preset circuit parameter and a second preset circuit parameter in a waveform parameter controllable transcranial magnetic discharge circuit as optimization indicators, minimizing a selectivity indicator as an optimization goal, and using an optimization algorithm to find the optimal first preset circuit parameter and its corresponding second preset circuit parameter; The selectivity index is obtained by inputting the waveforms corresponding to the first preset circuit parameters and the second preset circuit parameters into a multi-scale neuron model, and determining the relative activation degree of the target area compared with other areas in the sample area of ​​the neuron model; the second preset circuit parameter is calculated by inputting the first preset circuit parameter into the constraint condition equation group of the TMS discharge circuit; The determination module is used to use the waveform corresponding to the optimal first preset circuit parameter and the corresponding second preset circuit parameter as the target design waveform.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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