TMS heart nerve precise navigation device

Abstract

The invention provides a TMS heart nerve precise navigation device. The method is applied to the magnetic stimulation signal processing field. The device comprises a first potential optimal stimulation position acquisition module used for acquiring a plurality of first potential optimal stimulation positions of a target object; the first heartbeat interval signal acquisition module is used for acquiring a first heartbeat interval signal before the target object is stimulated; the second heartbeat interval signal acquisition module is used for acquiring a second heartbeat interval signal when the target object is stimulated; the first standard score calculation module is used for calculating a first standard score of a plurality of first potential optimal stimulation positions based on the first heartbeat interval signal and the second heartbeat interval signal; and the optimal stimulation position generation module is used for performing gradient diffusion processing on the plurality of first potential optimal stimulation positions and the first standard score by using a preset optimization algorithm model to obtain the optimal stimulation position of the target object. According to the invention, the accuracy and individuation level of optimal stimulation position selection are improved.

Description

Technical Field

[0001] The present invention relates to the field of magnetic stimulation signal processing, and in particular to a TMS cardiac nerve precision navigation device. Background Art

[0002] Transcranial magnetic stimulation (TMS) can alter autonomic nervous system function by modulating the function of the dorsolateral prefrontal cortex (dlPFC). TMS is a non-invasive neurostimulation technique that applies pulsed magnetic fields to the brain, altering the membrane potential of cortical neurons and generating induced currents. This in turn influences brain metabolism and neural electrical activity, triggering physiological and biochemical responses. Existing research generally suggests that TMS induces varying heart rate changes when applied to different locations within the relatively large dlPFC region. The location with the most pronounced change is considered the optimal stimulation location, and appropriate stimulation at this optimal location is crucial for optimal results. However, the optimal stimulation location varies from person to person, and deviations during external TMS stimulation can lead to inaccurate stimulation targets. Therefore, achieving precise targeting remains an urgent challenge. Summary of the Invention

[0003] In view of this, on the one hand, the present invention provides a TMS cardiac nerve precision navigation device, including: a first potential optimal stimulation position acquisition module, used to acquire multiple first potential optimal stimulation positions on the virtual head model of the target object; a first heartbeat interval signal acquisition module, used to acquire the first heartbeat interval signals at multiple first potential optimal stimulation positions before stimulating the target object; a second heartbeat interval signal acquisition module, used to acquire the second heartbeat interval signals generated at multiple first potential optimal stimulation positions when stimulating the target object; a first standard score calculation module, used to calculate the first standard scores of multiple first potential optimal stimulation positions based on the first heartbeat interval signal and the second heartbeat interval signal; an optimal stimulation position generation module, used to perform gradient diffusion processing on the multiple first potential optimal stimulation positions and the first standard scores using a preset optimization algorithm model to obtain the optimal stimulation position of the target object.

[0004] Optionally, the first standard score calculation module includes: a first minimum amplitude extraction submodule, which is used to extract the minimum amplitude based on multiple first heartbeat interval signals of each first potential optimal stimulation position, so as to obtain multiple first minimum amplitudes of each first potential optimal stimulation position; a second minimum amplitude extraction submodule, which is used to extract the minimum amplitude based on multiple second heartbeat interval signals of each first potential optimal stimulation position, so as to obtain multiple second minimum amplitudes of each first potential optimal stimulation position; an amplitude information calculation submodule of the first potential optimal stimulation position, which is used to calculate the average value and standard deviation based on the multiple first minimum amplitudes of each first potential optimal stimulation position, so as to obtain the first amplitude average value and amplitude standard deviation of each first potential optimal stimulation position; an amplitude average value calculation submodule of the first potential optimal stimulation position, which is used to calculate the average value based on the multiple second minimum amplitudes of each first potential optimal stimulation position, so as to obtain the second amplitude average value of each first potential optimal stimulation position; a first standard score calculation submodule, which is used to calculate the first standard score based on the first amplitude average value, amplitude standard deviation and second amplitude average value of each first potential optimal stimulation position, so as to obtain the first standard scores of multiple first potential optimal stimulation positions.

[0005] Optionally, the optimal stimulation position generation module includes: a candidate stimulation position selection submodule, which is used to select the first potential optimal stimulation position with the largest first standard score to obtain the candidate stimulation position; a gradient diffusion stimulation position generation submodule, which is used to construct a circle on the head surface of the target object based on the candidate stimulation position and a preset distance, and perform gradient diffusion to obtain multiple gradient diffusion stimulation positions, where the preset distance is the interval distance from the candidate stimulation position, which is determined based on the stimulation accuracy of the implemented stimulation; a second standard score calculation submodule, which is used to respectively calculate the second standard scores of multiple gradient diffusion stimulation positions; a candidate stimulation position update submodule, which is used to update the candidate stimulation position based on the largest second standard score; and an optimal stimulation position generation submodule, which is used to compare the candidate stimulation position with the updated candidate stimulation position to obtain the optimal stimulation position of the target object.

[0006] Optionally, the gradient diffusion stimulation position generation submodule calculates multiple gradient diffusion stimulation positions in the following manner: , , , , in, represents the gradient diffusion stimulus position, represents the candidate stimulus position coordinates, Indicates the preset distance, 、 represents a vector of equal length orthogonal to the candidate stimulus position, Pick A random value, followed by Get the intervals in sequence There are eight gradient diffusion stimulation positions in total.

[0007] Optionally, the second standard score calculation submodule is specifically used to: collect the third heartbeat interval signals of the eight gradient diffusion stimulation positions before stimulation is implemented on the target object; collect the fourth heartbeat interval signals generated when stimulation is implemented on the target object at the eight gradient diffusion stimulation positions; extract the minimum amplitude based on the multiple third heartbeat interval signals of each gradient diffusion stimulation position to obtain multiple third minimum amplitudes of the eight gradient diffusion stimulation positions; extract the minimum amplitude based on the multiple fourth heartbeat interval signals of each gradient diffusion stimulation position to obtain multiple fourth minimum amplitudes of each gradient diffusion stimulation position; calculate the average value and standard deviation based on the multiple third minimum amplitudes of each gradient diffusion stimulation position to obtain the first amplitude average value and amplitude standard deviation of each gradient diffusion stimulation position; calculate the average value based on the multiple fourth minimum amplitudes of each gradient diffusion stimulation position to obtain the second amplitude average value of each gradient diffusion stimulation position; calculate the second standard score based on the first amplitude average value and amplitude standard deviation of each gradient diffusion stimulation position and the second amplitude average value of each gradient diffusion stimulation position to obtain the second standard score of the eight gradient diffusion stimulation positions.

[0008] Optionally, the optimal stimulation position generation submodule is specifically used to: determine whether the candidate stimulation position is consistent with the updated candidate stimulation position; if the candidate stimulation position is consistent with the updated candidate stimulation position, it is determined that the preset optimization algorithm model has converged, and the optimal stimulation position of the target object is the candidate stimulation position; if the candidate stimulation position is inconsistent with the updated candidate stimulation position, it is determined that the preset optimization algorithm model has not converged, and the circle of the head surface of the target object continues to be constructed based on the updated candidate stimulation position and the preset distance, and gradient diffusion is performed.

[0009] Optionally, the first potential optimal stimulation position acquisition module includes: a head feature information acquisition submodule, used to collect head feature information of the target object; a virtual head model generation submodule, used to generate a virtual head model based on the head feature information; a first potential optimal stimulation position calculation submodule, used to calculate the coordinate point position on the virtual head model based on a preset unit distance to obtain multiple first potential optimal stimulation positions of the dorsolateral prefrontal cortex, where the preset unit distance is the interval distance between each coordinate point on the virtual head model.

[0010] Optionally, the TMS cardiac nerve precision navigation device provided by the present invention also includes: a second potential optimal stimulation position acquisition module, used to acquire multiple second potential optimal stimulation positions of the supplementary motor cortex on the virtual head model of the target object; a fifth heartbeat interval signal acquisition module, used to acquire the fifth heartbeat interval signal generated when the target object is stimulated at multiple second potential optimal stimulation positions; an optimal stimulation position generation module on the supplementary motor cortex, used to use a preset optimization algorithm model to perform gradient diffusion processing on multiple second potential optimal stimulation positions and the fifth heartbeat interval signal to obtain the optimal stimulation position on the supplementary motor cortex of the target object.

[0011] Optionally, the optimal stimulation position generation module on the supplementary motor cortex includes: an amplitude average value calculation submodule of the second potential optimal stimulation position, which is used to calculate the average value based on multiple second minimum amplitudes of each second potential optimal stimulation position to obtain the amplitude average value of each second potential optimal stimulation position; and an optimal stimulation position generation submodule on the supplementary motor cortex, which is used to use a preset optimization algorithm model to perform gradient diffusion processing on the second potential optimal stimulation position with the largest amplitude average value to obtain the optimal stimulation position on the supplementary motor cortex of the target object.

[0012] The second aspect of the present invention provides a TMS cardiac nerve precision navigation device, which includes: a processor and a memory connected to the processor; wherein the memory stores instructions that can be executed by the processor, and the instructions are executed by the processor to enable the processor to perform the following process: obtaining multiple first potential optimal stimulation positions on the virtual head model of the target object; collecting first heartbeat interval signals at multiple first potential optimal stimulation positions before stimulating the target object; collecting second heartbeat interval signals generated at multiple first potential optimal stimulation positions when stimulating the target object; calculating first standard scores for multiple first potential optimal stimulation positions based on the first heartbeat interval signals and the second heartbeat interval signals; and performing gradient diffusion processing on multiple first potential optimal stimulation positions and first standard scores using a preset optimization algorithm model to obtain the optimal stimulation position of the target object.

[0013] The present invention uses a first potential optimal stimulation position acquisition module to acquire multiple first potential optimal stimulation positions on a virtual head model of a target subject. Due to individual differences in the target subject's head features, further analysis of the first potential optimal stimulation positions is required. A first heartbeat interval signal acquisition module and a second heartbeat interval signal acquisition module accurately acquire and analyze the heartbeat interval signals before and after stimulation, respectively. A first standard score calculation module calculates a first standard score based on the difference in the heartbeat interval signals before and after stimulation. This first standard score effectively assesses the difference before and after stimulation for each first potential optimal stimulation position. The optimal stimulation position generation module then uses a preset optimization algorithm model, combined with the calculated standard score, to perform gradient diffusion processing on the first potential optimal stimulation positions. This allows for rapid search for qualified positions within a limited number of measurements, effectively reducing unnecessary measurement operations and saving significant resources. Through continuous iterative optimization, the optimal stimulation position that achieves the optimal stimulation effect can be gradually and accurately located. This embodiment significantly improves the accuracy and personalization of optimal stimulation position selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 This is a structural diagram of a first TMS cardiac nerve precision navigation device according to an embodiment of the present invention; Figure 2 is a point map on the virtual head model in an embodiment of the present invention; Figure 3 4 is a structural diagram of the second TMS cardiac nerve precise navigation device in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0017] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0018] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components; wireless connections or wired connections. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0019] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0020] like Figure 1 As shown, an embodiment of the present invention provides a TMS cardiac nerve precision navigation device 100, which can be applied to various electronic devices, specifically including: The first potential optimal stimulation position acquisition module 101 is configured to acquire a plurality of first potential optimal stimulation positions on the virtual head model of the target object.

[0021] Based on the head data of the target object, a three-dimensional virtual head model of the target object is constructed. The three-dimensional head virtual model can provide more comprehensive and personalized head information and can find potential optimal stimulation positions in a wider range.

[0022] The first heartbeat interval signal acquisition module 102 is configured to acquire first heartbeat interval signals at a plurality of first potential optimal stimulation positions before stimulating the target object.

[0023] The first heartbeat interval signal acquisition module 102 needs to collect the EEG signal before the target subject is stimulated. Specifically, an electrode is connected to each of the target subject's hands and feet, including a ground electrode. The EEG signal of the target subject before stimulation is obtained by the electrodes is then acquired. The EEG signal is processed and analyzed in real time to accurately identify the position of each R wave (the R wave is the first positive peak in the QRS complex of the electrocardiogram, representing the potential change of right ventricular depolarization and marking the beginning of ventricular depolarization) and calculate the time interval between adjacent R waves. The calculated R-R interval signal is recorded in real time to obtain a detailed first heartbeat interval signal. The first heartbeat interval signal collected before stimulation is used as the baseline data. After the target subject is stimulated, the changes in the heartbeat interval signal before and after stimulation are continuously compared.

[0024] The second heartbeat interval signal acquisition module 103 is configured to acquire second heartbeat interval signals generated at a plurality of first potential optimal stimulation positions when stimulating the target object.

[0025] When the second heartbeat interval signal acquisition module 103 collects EEG signals during stimulation of the target subject, stimulation is applied to multiple first potential optimal stimulation locations using the beater of the TMS device. The beater is equipped with a four-point locator for projection into the three-dimensional virtual head model space. A computer interface then visually guides the beater to sequentially align with the first potential optimal stimulation locations to deliver, for example, 10 Hz magnetic stimulation for 5 seconds. The EEG signals from each first potential optimal stimulation location are then collected in real time. The collected EEG signals during stimulation are then processed and analyzed in real time, accurately identifying the location of each R wave and calculating the time interval between adjacent R waves. The calculated R-R interval signals are recorded in real time to obtain a second heartbeat interval signal for each first potential optimal stimulation location.

[0026] The first standard score calculation module 104 is configured to calculate first standard scores of a plurality of first potential optimal stimulation positions based on the first heartbeat interval signal and the second heartbeat interval signal.

[0027] The first standard score calculation module 104 calculates the second heartbeat interval signal and the first heartbeat interval signal of each first potential optimal stimulation position to obtain a first standard score for each first potential optimal stimulation position.

[0028] The optimal stimulation position generating module 105 is configured to perform gradient diffusion processing on a plurality of first potential optimal stimulation positions and a first standard score using a preset optimization algorithm model to obtain an optimal stimulation position for the target object.

[0029] The optimal stimulation position generation module 105 stores a preset optimization algorithm model, which uses a Bayesian optimization algorithm to provide an effective search strategy with a limited number of measurements, thereby reducing unnecessary measurements and saving resources. In the gradient diffusion process, the Bayesian optimization algorithm treats each first potential optimal stimulation position as a point carrying unique information. Based on the first standard scores carried by these points, the algorithm continuously searches for locations in space that meet specific conditions. Through continuous iterative optimization, it can gradually find the optimal stimulation position that achieves the best stimulation effect.

[0030] This embodiment uses a first potential optimal stimulation location acquisition module to acquire multiple first potential optimal stimulation locations on a virtual head model of a target subject. Due to individual differences in target subject head features, further analysis of the first potential optimal stimulation locations is required. The first and second heartbeat interval signal acquisition modules accurately acquire and analyze the heartbeat interval signals before and after stimulation, respectively. A first standard score calculation module calculates a first standard score based on the difference in the heartbeat interval signals before and after stimulation. This first standard score effectively assesses the difference before and after stimulation for each first potential optimal stimulation location. The optimal stimulation location generation module then uses a preset optimization algorithm model, combined with the calculated standard scores, to perform gradient diffusion processing on the first potential optimal stimulation locations. This allows for rapid search for eligible locations within a limited number of measurements, effectively reducing unnecessary measurement operations and saving significant resources. Through continuous iterative optimization, the optimal stimulation location that achieves the optimal stimulation effect can be gradually and precisely located. This embodiment significantly improves the accuracy and personalization of optimal stimulation location selection.

[0031] In some optional implementations of this embodiment, the first potential optimal stimulation position acquisition module 101 specifically includes: The head feature information collection submodule 1011 is used to collect the head feature information of the target object.

[0032] The head feature information acquisition submodule 1011 uses a four-point locator worn on the subject's head in conjunction with a dedicated camera to collect the subject's head feature information. Alternatively, a depth camera can be used to directly identify the patient's head surface features and, combined with the simulated space created by the four-point locator, create a virtual head model.

[0033] The virtual head model generating submodule 1012 is configured to generate a virtual head model based on head feature information.

[0034] The virtual head model generation submodule 1012 is used to guide the user to click on four reference points (nasion, tragus, central point of the top of the head and external occipital protuberance) of the patient's head using a positioning pen on the computer interface, and input these reference points as references into the simulation space to form a virtual head model.

[0035] The first potential optimal stimulation position calculation submodule 1013 is used to calculate the coordinate point positions on the virtual head model based on a preset unit distance to obtain multiple first potential optimal stimulation positions of the dorsolateral prefrontal cortex. The preset unit distance is the distance between each coordinate point on the virtual head model.

[0036] In this embodiment, the various submodules of the first potential optimal stimulation position acquisition module 101 can be mapped to the simulation space of the present invention using the widely used "International 10-10 System". The preset unit distance is defined in the system (the distance from the frontal pole midpoint Fpz to the nasion and the distance from the occipital point Oz to the external occipital protuberance each account for 10% of the total length of this line, and the remaining points are separated by 10% of the total length of this line). The coordinate position of each point is calculated on the virtual head model according to the system definition. The preset unit distance is the value of each point occupying 10% of the total length of the line. Figure 2 As shown, C3 is the location of the supplementary motor area at the population level. Four potential supplementary motor area locations are mapped around C3 (the midpoints of the lines connecting C3 with FC3, C5, CP3, and C1, respectively). Including C3, a total of five locations are identified for finding the stimulation point that can elicit the most pronounced electromyographic changes in the contralateral thenar muscle, serving as the threshold determination site. F3 is the location of the left dorsolateral prefrontal cortex at the population level. AF3, F1, F5, and FC3 are the four locations closest to F3, serving as the potential optimal stimulation locations that could elicit the most pronounced heart rate changes. The above example focuses on stimulation on the left side; a similar symmetrical mapping can be applied to the right side. This example calculates the potential optimal stimulation locations for the target subject. For example, the five first potential optimal stimulation locations on the left side of the dorsolateral prefrontal cortex are AF3, F1, F3, F5, and FC3. Alternatively, the target subject can be fitted with an "International 10-10 System" cap and then located using a positioning stylus.

[0037] In this embodiment, the head feature information collection submodule collects the head feature information of the target object, the virtual head model generation submodule generates a virtual head model based on the head feature information, and the first potential optimal stimulation position calculation submodule calculates multiple first potential optimal stimulation positions of the dorsolateral prefrontal cortex based on a preset unit distance. Since the traditional positioning of the stimulation position on the left side of the dorsolateral prefrontal cortex is not suitable for individualization, the first potential optimal stimulation position calculation submodule determines it as the first potential optimal stimulation position, providing a basis for subsequent further analysis to determine a more accurate and effective individualized stimulation position.

[0038] In some optional implementations of this embodiment, the first standard score calculation module 104 specifically includes: The first minimum amplitude extraction submodule 1041 is configured to extract the minimum amplitude based on the multiple first heartbeat interval signals of each first potential optimal stimulation position, and obtain multiple first minimum amplitudes of each first potential optimal stimulation position.

[0039] When collecting the first heartbeat interval signal, the first minimum amplitude extraction submodule 1041 needs to collect the signal multiple times, for example, three times at a time, and select the minimum amplitude (the trough of the signal) of each first heartbeat interval signal, which is recorded as T0. Since the peaks and troughs in the first heartbeat interval signal reflect the heart rate regulation caused by breathing, limiting it to the trough can effectively eliminate the influence of breathing and maximize the space for detecting heart rate deceleration.

[0040] The second minimum amplitude extraction submodule 1042 is configured to extract the minimum amplitude based on the multiple second heartbeat interval signals of each first potential optimal stimulation position, and obtain multiple second minimum amplitudes of each first potential optimal stimulation position.

[0041] When collecting the second heartbeat interval signal, the second minimum amplitude extraction submodule 1042 needs to use the beat to implement, for example, three stimulations on each first potential optimal stimulation position (such as AF3, F1, F3, F5, FC3), with an interval of 30 seconds each time, and then implement stimulation on the next first potential optimal stimulation position. At this time, the second heartbeat interval signal is obtained in real time, and then the minimum amplitude of each second heartbeat interval signal is selected. In this way, one first potential optimal stimulation position corresponds to three second minimum amplitudes, which are recorded as T1, T2, and T3 respectively.

[0042] The amplitude information calculation submodule 1043 of the first potential optimal stimulation position is used to calculate the average value and standard deviation based on multiple first minimum amplitudes of each first potential optimal stimulation position, and obtain the first amplitude average value and amplitude standard deviation of each first potential optimal stimulation position.

[0043] For example, the amplitude information calculation submodule 1043 of the first potential optimal stimulation position is used to calculate the average value based on the three first minimum amplitudes T0. and standard deviation ,The standard deviation is used for normalization in order to reduce the ,differences in stimulation effects caused by individual differences and ,different stimulation times at different locations.

[0044] The amplitude average value calculation submodule 1044 of the first potential optimal stimulation position is used to calculate the average value based on multiple second minimum amplitudes of each first potential optimal stimulation position to obtain the second amplitude average value of each first potential optimal stimulation position.

[0045] For example, the amplitude average calculation submodule 1044 of the first potential optimal stimulation position calculates the average value based on the three second minimum amplitudes T1, T2, and T3 of each first potential optimal stimulation position. , .

[0046] The first standard score calculation submodule 1045 is used to calculate the first standard score based on the first amplitude average value, amplitude standard deviation, and second amplitude average value of each first potential optimal stimulation position, to obtain the first standard scores of multiple first potential optimal stimulation positions.

[0047] For example, the first standard score calculation submodule 1045 may calculate the first standard scores of the plurality of first potential optimal stimulation positions in the following manner: , in, Represents the first standard score for each first potential optimal stimulation position.

[0048] For example, the first standard scores of the five first potential best stimulation positions can be obtained .

[0049] This embodiment uses the first minimum amplitude extraction submodule to repeatedly collect the first heartbeat interval signal and select the minimum amplitude, effectively eliminating the influence of breathing on heart rate regulation and maximizing the scope for detecting heart rate deceleration. When the first minimum amplitude extraction submodule collects the second heartbeat interval signal, multiple stimulations are performed at each first potential optimal stimulation location and the corresponding minimum amplitude is obtained, ensuring data comprehensiveness and accuracy. Based on this, the amplitude information calculation submodule for the first potential optimal stimulation location calculates the mean and standard deviation of the first minimum amplitude for each first potential optimal stimulation location, and the amplitude average calculation submodule for the first potential optimal stimulation location calculates the mean of the second minimum amplitude. Using standard deviation normalization can reduce differences in stimulation effect caused by individual differences and different stimulation times at different locations. Finally, the first standard score calculation submodule combines the first amplitude average, amplitude standard deviation, and second amplitude average to calculate the first standard score. This comprehensive consideration of real-time heartbeat interval data before and during stimulation allows for a more accurate assessment of the stimulation effect at each first potential optimal stimulation location.

[0050] In some optional implementations of this embodiment, the optimal stimulation position generation module 105 specifically includes: The candidate stimulation position selection submodule 1051 is configured to select a first potential optimal stimulation position having the largest first standard score to obtain a candidate stimulation position.

[0051] The candidate stimulation position selection submodule 1051 is used to select the candidate stimulation position with the largest first standard score. The first potential optimal stimulation position of is set as the candidate stimulation position, denoted as The first potential optimal stimulation position with the largest first standard score means that, excluding respiratory interference, this position has the most significant effect on regulating the heart rate of the target subject and the best stimulation effect.

[0052] The gradient diffusion stimulation position generation submodule 1052 is used to construct a circle on the head surface of the target object based on the candidate stimulation positions and the preset distance, and perform gradient diffusion to obtain multiple gradient diffusion stimulation positions. The preset distance is the interval distance from the candidate stimulation position and is determined based on the stimulation accuracy of the implemented stimulation.

[0053] The theoretical assumption of the preset optimization algorithm model is that there is only one position on the head of the target object with the maximum first standard score. , and the first standard score of the surrounding location points The target stimulation accuracy is 0.5 cm (limited by the TMS device's stimulation accuracy), which is the distance between each stimulation location and the location with the highest first standard score. The preset optimization algorithm model uses this theory to perform gradient diffusion and find the gradient diffusion stimulation location.

[0054] For example, the gradient diffusion stimulation position generation submodule 1052 may calculate the multiple gradient diffusion stimulation positions in the following manner: , , , , in, represents the gradient diffusion stimulus position, represents the central angle of a circle, and represents the candidate stimulus position coordinates, Indicates the preset distance, represents the spherical radius of the head, 、 represents a vector of equal length orthogonal to the candidate stimulus position, Pick A random value, followed by Get the intervals in sequence There are eight gradient diffusion stimulation positions in total.

[0055] The second standard score calculation submodule 1053 is configured to calculate the second standard scores of the plurality of gradient diffusion stimulation positions respectively.

[0056] The second standard score calculation submodule 1053 calculates the second standard score in the same manner as the first standard score calculation module 104 calculates the first standard score. For example, the standard scores of the eight gradient diffusion stimulation positions obtained above, i.e., the second standard scores, are: .

[0057] The candidate stimulation position updating submodule 1054 is configured to update the candidate stimulation position based on the maximum second standard score.

[0058] Exemplarily, the candidate stimulation position updating submodule 1054 is configured to take the gradient diffusion stimulation position with the largest second standard score among the eight as a new candidate stimulation position, that is, , represents the updated candidate stimulus position coordinates, Indicates the maximum second standard score.

[0059] The optimal stimulation position generating submodule 1055 is configured to compare the candidate stimulation positions with the updated candidate stimulation positions to obtain the optimal stimulation position for the target object.

[0060] The optimal stimulation position generation submodule 1055 is used to generate the candidate stimulation positions and the updated candidate stimulus position Compare and determine whether the preset optimization algorithm model has found the best stimulation position.

[0061] In this embodiment, the candidate stimulation position selection submodule first selects the position with the largest first standard score as the candidate stimulation position, because it has a significant effect on heart rate regulation and the optimal stimulation effect. The gradient diffusion stimulation position generation submodule then constructs a circle based on the candidate stimulation positions and performs gradient diffusion to obtain multiple gradient diffusion stimulation positions. Using the theoretical assumption of fractional gradient descent and combining it with stimulation accuracy to determine the diffusion range, it can systematically search for a more optimal position on the virtual head model. The second standard score calculation submodule calculates the second standard score for each gradient diffusion stimulation position, and the candidate stimulation position update submodule then updates the candidate stimulation positions. This allows the optimal stimulation position generation submodule, through continuous iterative comparison, to more accurately find the stimulation position that produces the best heart rate regulation effect on the target subject, thereby improving the accuracy and effectiveness of the stimulation therapy.

[0062] Furthermore, the second standard score calculation submodule 1053 is specifically configured to: The third heartbeat interval signals of eight gradient diffusion stimulation positions of the target subject are collected before stimulation is performed.

[0063] The fourth heartbeat interval signal generated when the target object is stimulated at eight gradient diffusion stimulation positions is collected.

[0064] The minimum amplitude is extracted based on the multiple third heartbeat interval signals at each gradient diffusion stimulation position, thereby obtaining multiple third minimum amplitudes at eight gradient diffusion stimulation positions.

[0065] The minimum amplitude is extracted based on the multiple fourth heartbeat interval signals at each gradient diffusion stimulation position to obtain multiple fourth minimum amplitudes at each gradient diffusion stimulation position.

[0066] The average value and standard deviation are calculated based on the multiple third minimum amplitudes at each gradient diffusion stimulation position to obtain the first amplitude average value and amplitude standard deviation at each gradient diffusion stimulation position.

[0067] An average value is calculated based on multiple fourth minimum amplitudes at each gradient diffusion stimulation position to obtain a second amplitude average value at each gradient diffusion stimulation position.

[0068] The second standard score is calculated based on the first amplitude average value, amplitude standard deviation, and second amplitude average value of each gradient diffusion stimulation position to obtain the second standard scores of the eight gradient diffusion stimulation positions.

[0069] The second standard score calculation submodule 1053 in this embodiment is a module that uses the first minimum amplitude extraction submodule 1041, the second minimum amplitude extraction submodule 1042, the amplitude information calculation submodule 1043 of the first potential optimal stimulation position, the amplitude average calculation submodule 1044 of the first potential optimal stimulation position, and the first standard score calculation submodule 1045 in the first standard score calculation module 104 to calculate the standard scores of the eight gradient diffusion stimulation positions. The specific steps are not repeated here.

[0070] The second standard score calculation submodule of this embodiment collects the target subject's heartbeat interval signals before and after stimulation, extracts the minimum amplitude, and then calculates the standard deviation and average to ultimately determine the second standard score. This provides key data support for subsequently determining the optimal stimulation position for the target subject, helping to more accurately utilize the preset optimization algorithm model to find the location that most effectively stimulates the target subject.

[0071] Furthermore, the optimal stimulation position generation submodule 1055 is specifically configured to: Determine whether the candidate stimulus position is consistent with the updated candidate stimulus position; If the candidate stimulation position is consistent with the updated candidate stimulation position, it is determined that the preset optimization algorithm model has converged, and the optimal stimulation position of the target object is the candidate stimulation position; Determine whether the candidate stimulation position S1 is the same as the updated candidate stimulation position S2. If they are the same, S1=S2, it means that the preset optimization algorithm model has converged and it has found the optimal stimulation position on the target object, such as the dorsolateral prefrontal cortex. If they are different, S1≠S2, it means that the preset optimization algorithm model has not converged. Return to step S5 according to the updated candidate stimulation position and continue gradient diffusion until S1=S2 to obtain the optimal stimulation position.

[0072] If the candidate stimulation position is inconsistent with the updated candidate stimulation position, it is determined that the preset optimization algorithm model has not converged, and the circle of the target object's head surface is constructed based on the updated candidate stimulation position and the preset distance, and gradient diffusion is performed.

[0073] The optimal stimulation position generation submodule of this embodiment determines the optimal stimulation position by comparing candidate stimulation positions with updated candidate stimulation positions. This effectively determines whether the preset optimization algorithm model has converged. If converged, the optimal stimulation position for the dorsolateral prefrontal cortex is directly determined. If not, the gradient diffusion iteration continues until the optimal position is found, improving the accuracy and reliability of stimulation position determination.

[0074] like Figure 3 As shown, an embodiment of the present invention provides a TMS cardiac nerve precision navigation device, further comprising: The second potential optimal stimulation position acquisition module 106 is configured to acquire a plurality of second potential optimal stimulation positions of the supplementary motor cortex on the virtual head model of the target object.

[0075] The second potential optimal stimulation position acquisition module 106 calculates the potential optimal stimulation positions of the contralateral thenar muscle supplementary motor area of ​​the target object, such as FC3, C5, C3, CP3, and C1, in the same manner as the first potential optimal stimulation position acquisition module 101 .

[0076] The fifth heartbeat interval signal acquisition module 107 is configured to acquire a fifth heartbeat interval signal generated when stimulation is applied to the target object at a plurality of second potential optimal stimulation positions.

[0077] For example, three stimulations are respectively performed on five second potential optimal stimulation positions, FC3, C5, C3, CP3, and C1, and the second potential optimal stimulation position acquisition module 106 collects three fifth heartbeat interval signals of each position.

[0078] The optimal stimulation position generating module 108 on the supplementary motor cortex is used to perform gradient diffusion processing on multiple second potential optimal stimulation positions and the fifth heartbeat interval signal using a preset optimization algorithm model to obtain the optimal stimulation position on the supplementary motor cortex of the target object.

[0079] Specifically, the optimal stimulation position generation module 108 on the supplementary motor cortex includes: The amplitude average value calculation submodule 1081 of the second potential optimal stimulation position is used to calculate the average value based on multiple second minimum amplitudes of each second potential optimal stimulation position to obtain the amplitude average value of each second potential optimal stimulation position.

[0080] Illustratively, the amplitude average calculation submodule 1081 of the second potential optimal stimulation position is used to calculate the minimum amplitude average of the trough signals obtained at the five second potential optimal stimulation positions FC3, C5, C3, CP3, and C1, and obtain the amplitude averages of the five second potential optimal stimulation positions.

[0081] The optimal stimulation position generating submodule 1082 on the supplementary motor cortex is used to perform gradient diffusion processing on the second potential optimal stimulation position with the largest average amplitude value using a preset optimization algorithm model to obtain the optimal stimulation position on the supplementary motor cortex of the target object.

[0082] The optimal stimulation position generating submodule 1082 on the supplementary motor cortex is used to find the optimal stimulation position on the supplementary motor cortex of the target object in the same manner as the gradient diffusion stimulation position generating submodule 1052 .

[0083] In this embodiment, a second potential optimal stimulation position acquisition module is used to acquire multiple second potential optimal stimulation positions of the supplementary motor cortex, and a fifth heartbeat interval signal acquisition module is used to acquire the fifth heartbeat interval signal during the corresponding stimulation. The amplitude average value calculation submodule of the second potential optimal stimulation position in the optimal stimulation position generation module on the supplementary motor cortex calculates the average value based on the second minimum amplitude of each position. The optimal stimulation position generation submodule on the supplementary motor cortex then uses a preset optimization algorithm to perform gradient diffusion processing on the position with the largest amplitude average value, so as to personalize and accurately determine the optimal stimulation position on the supplementary motor cortex of the target object.

[0084] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0088] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A TMS cardiac nerve precision navigation device, characterized in that: include: A first potential optimal stimulation position acquisition module is used to acquire a plurality of first potential optimal stimulation positions on the virtual head model of the target object; a first heartbeat interval signal acquisition module, configured to acquire first heartbeat interval signals at a plurality of first potential optimal stimulation positions before stimulating the target object; a second heartbeat interval signal acquisition module, configured to acquire second heartbeat interval signals generated at a plurality of first potential optimal stimulation positions when stimulating the target object; a first standard score calculation module, configured to calculate first standard scores of the plurality of first potential optimal stimulation positions based on the first heartbeat interval signal and the second heartbeat interval signal; The optimal stimulation position generating module is used to perform gradient diffusion processing on the plurality of first potential optimal stimulation positions and the first standard scores using a preset optimization algorithm model to obtain the optimal stimulation position of the target object.

2. The device according to claim 1, characterized in that The first standard score calculation module includes: a first minimum amplitude extraction submodule, configured to extract a minimum amplitude based on a plurality of first heartbeat interval signals at each of the first potential optimal stimulation positions, to obtain a plurality of first minimum amplitudes at each of the first potential optimal stimulation positions; a second minimum amplitude extraction submodule, configured to extract a minimum amplitude based on a plurality of second heartbeat interval signals at each of the first potential optimal stimulation positions, to obtain a plurality of second minimum amplitudes at each of the first potential optimal stimulation positions; A submodule for calculating amplitude information of a first potential optimal stimulation position, configured to calculate an average value and a standard deviation based on a plurality of first minimum amplitudes of each of the first potential optimal stimulation positions, to obtain a first amplitude average value and an amplitude standard deviation of each of the first potential optimal stimulation positions; a first potential optimal stimulation position amplitude average calculation submodule, configured to calculate an average value based on multiple second minimum amplitudes of each first potential optimal stimulation position to obtain a second amplitude average value of each first potential optimal stimulation position; The first standard score calculation submodule is used to calculate the first standard score based on the first amplitude average value, amplitude standard deviation, and second amplitude average value of each of the first potential optimal stimulation positions, to obtain the first standard scores of multiple first potential optimal stimulation positions.

3. The device according to claim 1, characterized in that The optimal stimulation position generation module includes: a candidate stimulation position selection submodule, configured to select the first potential optimal stimulation position having the largest first standard score to obtain a candidate stimulation position; a gradient diffusion stimulation position generation submodule, configured to construct a circle on the head surface of the target subject based on the candidate stimulation positions and a preset distance, and perform gradient diffusion to obtain a plurality of gradient diffusion stimulation positions, wherein the preset distance is a distance between the candidate stimulation positions and the preset distance, determined based on the stimulation accuracy of the implemented stimulation; a second standard score calculation submodule, configured to respectively calculate the second standard scores of the plurality of gradient diffusion stimulation positions; a candidate stimulation position updating submodule, configured to update the candidate stimulation position based on a maximum second standard score; The optimal stimulation position generating submodule is used to compare the candidate stimulation position with the updated candidate stimulation position to obtain the optimal stimulation position of the target object.

4. The device according to claim 3, characterized in that The gradient diffusion stimulation position generation submodule calculates the multiple gradient diffusion stimulation positions in the following manner: , , , , in, represents the gradient diffusion stimulus position, represents the candidate stimulus position coordinates, Indicates the preset distance, 、 represents a vector of equal length orthogonal to the candidate stimulus position, Pick A random value, followed by Get the intervals in sequence There are eight gradient diffusion stimulation positions in total.

5. The device according to claim 4, characterized in that The second standard score calculation submodule is specifically used to: collecting third heartbeat interval signals of the eight gradient diffusion stimulation positions of the target object before stimulation is performed; collecting a fourth heartbeat interval signal generated when stimulating the target object at the eight gradient diffusion stimulation positions; extracting a minimum amplitude based on a plurality of third heartbeat interval signals at each of the gradient diffusion stimulation positions, to obtain a plurality of third minimum amplitudes at the eight gradient diffusion stimulation positions; extracting minimum amplitudes based on a plurality of fourth heartbeat interval signals at each of the gradient diffusion stimulation positions, to obtain a plurality of fourth minimum amplitudes at each of the gradient diffusion stimulation positions; Calculating the average value and standard deviation of each of the plurality of third minimum amplitudes at each gradient diffusion stimulation position to obtain the first amplitude average value and amplitude standard deviation at each gradient diffusion stimulation position; Calculating an average value based on multiple fourth minimum amplitudes at each gradient diffusion stimulation position to obtain a second amplitude average value at each gradient diffusion stimulation position; The second standard score is calculated based on the first amplitude average value, the amplitude standard deviation, and the second amplitude average value of each gradient diffusion stimulation position to obtain the second standard scores of the eight gradient diffusion stimulation positions.

6. The device according to claim 3, characterized in that The optimal stimulation position generation submodule is specifically used for: determining whether the candidate stimulation position is consistent with the updated candidate stimulation position; If the candidate stimulation position is consistent with the updated candidate stimulation position, it is determined that the preset optimization algorithm model has converged, and the optimal stimulation position of the target object is the candidate stimulation position; If the candidate stimulation position is inconsistent with the updated candidate stimulation position, it is determined that the preset optimization algorithm model has not converged, and a circle on the head surface of the target object is continuously constructed based on the updated candidate stimulation position and the preset distance, and gradient diffusion is performed.

7. The device according to claim 1, characterized in that The first potential optimal stimulation position acquisition module includes: A head feature information collection submodule is used to collect the head feature information of the target object; A virtual head model generating submodule, configured to generate the virtual head model based on the head feature information; The first potential optimal stimulation position calculation submodule is configured to calculate the positions of coordinate points on the virtual head model based on a preset unit distance to obtain multiple first potential optimal stimulation positions of the dorsolateral prefrontal cortex, where the preset unit distance is the spacing between the coordinate points on the virtual head model.

8. The device according to any one of claims 1 to 7, characterized in that Also includes: a second potential optimal stimulation position acquisition module, configured to acquire a plurality of second potential optimal stimulation positions of the supplementary motor cortex on the virtual head model of the target object; a fifth heartbeat interval signal acquisition module, configured to acquire a fifth heartbeat interval signal generated when stimulating the target object at a plurality of the second potential optimal stimulation positions; The optimal stimulation position generation module on the supplementary motor cortex is used to use a preset optimization algorithm model to perform gradient diffusion processing on multiple second potential optimal stimulation positions and the fifth heartbeat interval signal to obtain the optimal stimulation position on the supplementary motor cortex of the target object.

9. The device according to claim 8, characterized in that The optimal stimulation position generation module on the supplementary motor cortex includes: a second potential optimal stimulation position amplitude average calculation submodule, configured to calculate an average value based on a plurality of second minimum amplitudes at each of the second potential optimal stimulation positions to obtain an amplitude average value at each of the second potential optimal stimulation positions; The optimal stimulation position generation submodule on the supplementary motor cortex is used to use a preset optimization algorithm model to perform gradient diffusion processing on the second potential optimal stimulation position with the largest average amplitude value to obtain the optimal stimulation position on the supplementary motor cortex of the target object.

10. A TMS cardiac nerve precision navigation device, characterized in that: include: A processor and a memory connected to the processor; wherein the memory stores instructions executable by the processor, and the instructions are executed by the processor to cause the processor to perform the following process: obtaining a plurality of first potential optimal stimulation positions on the virtual head model of the target subject; collecting first heartbeat interval signals at a plurality of first potential optimal stimulation positions before stimulating the target object; collecting second heartbeat interval signals generated at a plurality of first potential optimal stimulation positions when stimulating the target object; calculating first standard scores for a plurality of first potential optimal stimulation positions based on the first heartbeat interval signal and the second heartbeat interval signal; A preset optimization algorithm model is used to perform gradient diffusion processing on the plurality of first potential optimal stimulation positions and the first standard scores to obtain the optimal stimulation position for the target object.

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