Methods, devices, systems, and media of determining operating parameters of a magnetic stimulation device

Abstract

The application discloses a method, device, system and medium for determining working parameters of a magnetic stimulation device. The method for determining working parameters of the magnetic stimulation device is applied to the magnetic stimulation device, and the method comprises the following steps: acquiring medical data of a patient, wherein the medical data is used to reflect the state of the function of the pelvic floor muscle of the patient; and determining the working parameters of the magnetic stimulation device by using the medical data. In this way, the treatment effect of the magnetic stimulation can be improved.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to a method, apparatus, system and medium for determining the operating parameters of a magnetic stimulation device. Background Technology

[0002] Pelvic floor dysfunction (PFD) refers to a series of symptoms caused by degeneration, damage, or other factors affecting the pelvic floor support tissues, leading to weakened pelvic floor support or decreased muscle function, resulting in displacement or dysfunction of pelvic organs. Treatment methods for PFD are varied, including techniques such as magnetic stimulation, electrical stimulation, and biofeedback to revitalize damaged pelvic floor muscles, increase their strength and elasticity, and restore normal pelvic floor function. However, the operating parameters of magnetic stimulation devices are currently often determined by physicians based on experience; insufficient physician experience may lead to poor treatment outcomes. Summary of the Invention

[0003] The main technical problem addressed by this application is to provide a method, device, system, and medium for determining the operating parameters of a magnetic stimulation device, which can improve the therapeutic effect of magnetic stimulation.

[0004] To address the aforementioned technical problems, the first aspect of this application provides a method for determining the operating parameters of a magnetic stimulation device, applied to the magnetic stimulation device. The method includes: acquiring medical data of a patient, wherein the medical data reflects the state of the patient's pelvic floor muscle function; and using the medical data to determine the operating parameters of the magnetic stimulation device, wherein the operating parameters of the magnetic stimulation device are used to perform magnetic stimulation on the patient.

[0005] To address the aforementioned technical problems, a second aspect of this application provides a magnetic stimulation device, which includes a memory and a processor coupled to each other. The memory is used to store program data, and the processor is used to execute the program data to implement the aforementioned method.

[0006] To address the aforementioned technical problems, a third aspect of this application provides a pelvic floor muscle rehabilitation system comprising the aforementioned magnetic stimulation device, biofeedback device, and networking device. The biofeedback device is used to acquire the patient's medical data, and the networking device is used to connect the biofeedback device and the magnetic stimulation device.

[0007] To address the aforementioned technical problems, a fourth aspect of this application provides a computer-readable storage medium storing program data, which, when executed by a processor, is used to implement the aforementioned method.

[0008] The beneficial effects of this application are as follows: Unlike the prior art, this application obtains the patient's medical data through a magnetic stimulation device. Since the medical data reflects the state of the patient's pelvic floor muscle function, the working parameters of the magnetic stimulation device determined based on this medical data take into account the state of the patient's pelvic floor muscle function. Therefore, the magnetic stimulation device can automatically determine the appropriate working parameters according to the state of the pelvic floor muscle function of different patients, which can reduce the subjective judgment factor of doctors and thus improve the therapeutic effect of magnetic stimulation. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this application, the accompanying drawings required in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0010] Figure 1 This is a flowchart illustrating an embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application;

[0011] Figure 2 This is a schematic diagram of the structure of one embodiment of a magnetic stimulation device;

[0012] Figure 3 This is a schematic diagram of another embodiment of the magnetic stimulation device;

[0013] Figure 4 yes Figure 1 A flowchart illustrating an implementation method for step S11;

[0014] Figure 5 This is a flowchart illustrating another embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application;

[0015] Figure 6 This is a schematic diagram of the resting characteristic phase and the contraction characteristic phase;

[0016] Figure 7 yes Figure 5 A flowchart illustrating an implementation method for step S22;

[0017] Figure 8 This is a flowchart illustrating yet another embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application;

[0018] Figure 9 This is a flowchart illustrating another embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application;

[0019] Figure 10 This is a schematic block diagram of the structure of an embodiment of the magnetic stimulation device of this application;

[0020] Figure 11 This is a schematic block diagram of an embodiment of the pelvic floor muscle rehabilitation system of this application;

[0021] Figure 12 This is a schematic block diagram of an embodiment of a computer-readable storage medium of this application. Detailed Implementation

[0022] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] The terms "first" and "second" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] In clinical use, biofeedback devices can collect electromyographic or pressure data for assessment, evaluating muscle damage, and then use electrical stimulation and biofeedback for treatment. Magnetic stimulators use changing magnetic fields to generate induced currents in the pelvic floor tissues, causing excitation of the surrounding pelvic floor tissues, thereby improving the coordination of pelvic floor nerve regulation and pelvic floor muscle function.

[0026] In clinical practice, electrical stimulation and magnetic stimulation are two complementary treatment methods. Electrical stimulation primarily targets muscles and nerves within approximately 2 cm of the electrode; while magnetic stimulation provides deeper and wider stimulation, affecting the entire pelvic floor nerves and muscles, thus being more efficient. Magnetic stimulation is also painless, non-invasive, and non-surgical, offering an alternative for patients who do not wish to undergo invasive treatment. Therefore, in clinical practice, for better treatment outcomes, doctors often use a combination of electro-magnetic stimulation. However, currently, biofeedback devices and magnetic stimulators are independent devices, and their data cannot be shared. Based on this, this application proposes a method, device, system, and medium for determining the operating parameters of a magnetic stimulation device.

[0027] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application. The method for determining the operating parameters of a magnetic stimulation device is applied to a magnetic stimulation device capable of outputting magnetic stimulation.

[0028] The method may include the following steps:

[0029] Step S11: Obtain the patient's medical data, which reflects the status of the patient's pelvic floor muscle function.

[0030] Patient medical data may include, but is not limited to, at least one of the following: patient information feature sets, examination record feature sets, and assessment feature sets. Among these, the patient information feature sets, examination record feature sets, and assessment feature sets can all reflect the state of a patient's pelvic floor muscle function. The state of pelvic floor muscle function can be divided into different levels, such as excellent, good, moderate, and poor. The state of pelvic floor muscle function can be represented by a score, for example, a score range of 0 to 100. A score greater than 90 corresponds to excellent pelvic floor muscle function, 80 to 90 corresponds to good pelvic floor muscle function, 60 to 80 corresponds to moderate pelvic floor muscle function, and less than 60 corresponds to poor pelvic floor muscle function.

[0031] The patient information feature set can include the patient's basic personal information and gynecological information. Personal information includes name, age, and contact information. Gynecological information includes parity, delivery method, fetal weight, chief complaint, and medical history. The collected patient information feature set includes not only patient information stored on the biofeedback and magnetic stimulator devices, but also information related to the patient on the network, such as the hospital information system (HIS system).

[0032] The examination record feature set can include the patient's preliminary diagnosis and specialist diagnosis. The preliminary diagnosis can be derived from a manual examination performed by the physician during the outpatient stage; this is a preliminary diagnosis, which is then supplemented by more accurate specialist examinations. Preliminary diagnosis may include, for example, the condition of the vulva, vagina, cervix, uterus, fallopian tubes, ovaries, parametrial tissues, and pelvic lining. A vaginal speculum may be used during the preliminary diagnosis. Specialist examination results may include, for example, the results of a routine vaginal discharge examination, gynecological ultrasound, six sex hormone tests, TCT (liquid-based thin-layer cytology test, also known as cervical cytology test), and HPV (human papillomavirus) test.

[0033] Patient information feature sets and examination record feature sets can be combined and summarized to obtain patient information and examination record feature sets.

[0034] The assessment feature set may include resting feature values ​​and contraction feature values, which are derived based on contraction signals. Contraction signals include resting phases (including pre-resting and post-resting phases) and contraction phases (including rapid contraction, sustained contraction, and endurance contraction phases). Resting feature values ​​correspond to the resting phase in the contraction signal, and contraction feature values ​​correspond to the contraction phase in the contraction signal. Contraction signals are generated by the patient during pelvic floor muscle contractions and can be a contraction waveform. Contraction signals can be acquired using methods such as pressure detection and electromyography (EMG). When using pressure detection, the acquisition unit may include an air balloon; when using EMG, the acquisition unit may include vaginal electrodes (electrode pads). Specific calculation methods for the assessment feature set can be found in subsequent embodiments and will not be elaborated here.

[0035] Step S12: Use medical data to determine the operating parameters of the magnetic stimulation device.

[0036] The operating parameters of a magnetic stimulation device can include, but are not limited to, at least one of the following: current magnitude, contraction time, and relaxation time. Generally, the state of a patient's pelvic floor muscle function can be determined based on medical data, and different states of pelvic floor muscle function can be matched with different operating parameters of the magnetic stimulation device, thereby providing different levels of magnetic stimulation treatment tailored to different states of pelvic floor muscle function. For example, if the pelvic floor muscle function is poor, a larger current is selected for magnetic stimulation; if the pelvic floor muscle function is good, a smaller current is selected.

[0037] In some embodiments, the operating parameters of the magnetic stimulation device include the numerical values ​​of the operating parameters of the magnetic stimulation device and / or the types of the operating parameters of the magnetic stimulation device. Specific methods can be found in subsequent embodiments, which will not be elaborated here.

[0038] In the above approach, the magnetic stimulation device acquires the patient's medical data. Since the medical data reflects the state of the patient's pelvic floor muscle function, the operating parameters of the magnetic stimulation device, determined based on this data, take into account the state of the patient's pelvic floor muscle function. Therefore, the magnetic stimulation device can automatically determine the appropriate operating parameters according to the state of the pelvic floor muscle function of different patients, which can reduce the subjective judgment factor of doctors and thus improve the therapeutic effect of magnetic stimulation.

[0039] Please see Figure 2 and Figure 3 , Figure 2 This is a schematic diagram of one embodiment of a magnetic stimulation device. Figure 3 This is a schematic diagram of another embodiment of the magnetic stimulation device.

[0040] Unlike existing technologies, this application also proposes a magnetic stimulation device that can be connected to a biofeedback device, including two solutions: one is to connect two independent biofeedback devices and the magnetic stimulation device via a network, such as... Figure 2 As shown; another method is to integrate a biofeedback device and a magnetic stimulator to obtain a magnetic stimulation device (also known as a magneto-electric biofeedback device), such as... Figure 3 As shown.

[0041] In the first approach, the network can be provided by networked devices that connect the biofeedback device and the magnetic stimulation device, enabling data and information exchange. Examples of networked devices include routers and switches. The biofeedback device is used to acquire electromyographic data, pressure data, and output electrical stimulation. The magnetic stimulation device is used to output magnetic stimulation. Furthermore, the biofeedback device and the magnetic stimulation device can access each other via the network.

[0042] In another implementation, the interconnected network comprising at least one magnetic stimulation device and at least one biofeedback device may further include a central processing unit (CPU). The CPU can communicate with the processors of the host devices (including the biostimulation host and the magnetic stimulation host) to enable interaction with the host devices, such as sending instructions or data (patient's medical data) to the host devices, and receiving and displaying data (patient's medical data) uploaded by the host devices. The CPU can connect to multiple host devices, facilitating the management and unified data transmission of data from multiple devices.

[0043] In the second approach, a minimally sized magnetic stimulation device can be obtained by integrating a biofeedback device and a magnetic stimulator. The device includes a biostimulation module, a magnetic stimulation module, and a host module. The biostimulation module is used for acquiring electromyographic data, pressure data, and outputting electrical stimulation. The magnetic stimulation module is used for outputting magnetic stimulation. The host module controls both the biostimulation module and the magnetic stimulation module. The host module may include a memory and a processor.

[0044] Based on the two schemes mentioned above, step S11 in the above embodiments includes two methods for obtaining patient medical data.

[0045] Please see Figure 4 , Figure 4 yes Figure 1 A flowchart illustrating an implementation method for step S11.

[0046] In this embodiment, step S11 may include steps S111 and S112. The medical data includes medical data based on bioelectrical signals.

[0047] Step S111: Establish a communication connection with the hospital management information system or biofeedback device.

[0048] Step S112: Obtain the patient's medical data from the hospital management information system or biofeedback device.

[0049] In some implementations, the magnetic stimulation device establishes a communication connection with a biofeedback device and then sends an acquisition command to the biofeedback device. This acquisition command is used to acquire the patient's medical data, and the device then receives the patient's medical data returned by the biofeedback device. The magnetic stimulation device and the biofeedback device are two independent devices, but they are connected via a network, allowing the magnetic stimulation device to send acquisition commands to the biofeedback device over the network. Alternatively, the magnetic stimulation device can be an integrated device combining the biofeedback device and the magnetic stimulator, allowing the magnetic stimulation device to directly send acquisition commands to the biofeedback device without a network connection. That is, the medical data can be returned via the network or directly from the integrated magnetic stimulation device.

[0050] Correspondingly, medical data can be actively sent from the hospital management information system to the magnetic stimulation device, or the magnetic stimulation device can send an acquisition command to the hospital management information system; this is not limited here.

[0051] Please see Figures 5 to 7 , Figure 5 This is a schematic flowchart of another embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application. Figure 6 This is a schematic diagram of the resting characteristic phase and the contraction characteristic phase. Figure 7 yes Figure 5A flowchart illustrating step S22 of the implementation method.

[0052] Unlike the embodiments described above, this embodiment uses an evaluation feature set from medical data to determine the values ​​of the operating parameters of the magnetic stimulation device, corresponding to a scenario where the operating parameters of the magnetic stimulation device are automatically determined. It is understood that the type of operating parameters of the magnetic stimulation device can be determined by the doctor, and then the parameter values ​​can be determined or adjusted based on this method.

[0053] The method may include the following steps:

[0054] Step S21: Obtain the patient's medical data, which includes an assessment feature set that reflects the patient's pelvic floor muscle function status.

[0055] The evaluation feature set can be obtained by a biostimulation feedback device. Specifically, the biostimulation feedback device can determine the features corresponding to each stage of the contraction signal, thereby obtaining the evaluation feature set.

[0056] The following describes how to obtain the evaluation feature set:

[0057] (1) Determine the pre-resting characteristic value

[0058] 1.1 Determine the mean B10 and variability B11 of the pre-resting phase of the contraction signal.

[0059] Variability is used to describe the characteristics of data distribution and to explain the differences between data distributions. There are three measures of variability: range, standard deviation, and variance.

[0060] 1.2. The variability B11 in the pre-resting phase is compared with the pre-resting phase variability threshold to obtain the first confidence value BX1. The value of BX1 ranges from 0 to 100%.

[0061] Comparing the variability B11 in the pre-resting phase with the pre-resting variability threshold can be done by calculating the corresponding first confidence value based on the deviation of the variability from the pre-resting variability threshold. The greater the deviation, the lower the confidence value; the smaller the deviation, the higher the confidence value. Furthermore, the comparison process below is similar and will not be repeated here.

[0062] 1.3 Multiply the mean of the pre-resting phase by the first confidence value to obtain the pre-resting characteristic value B1, i.e., B1 = B10 * BX1.

[0063] (2) Determine the rapid contraction characteristic value

[0064] 2.1 Determine the maximum value B20, rise time B21, and fall time B22 of the rapid contraction phase of the contraction signal.

[0065] 2.2 The rise time B21 and fall time B22 of the rapid contraction phase are compared with the rise time threshold and fall time threshold of the rapid contraction phase to obtain the first sub-confidence value BX21 and the second sub-confidence value BX22, where BX21 and BX22 are in the range of 0-100%.

[0066] 2.3 Multiply the first and second sub-confidence values ​​by their corresponding weights respectively, and add the results of the multiplications to obtain the second confidence value BX2, that is, BX2 = (BX21*a + BX22*b).

[0067] Where 'a' represents the first weight corresponding to the first sub-confidence value, and 'b' represents the second weight corresponding to the second sub-confidence value. The sum of 'a' and 'b' is 1, and the specific values ​​can be chosen according to the actual situation. In one example, 'a' = 'b' = 50%. The value range of B×2 is 0-100%.

[0068] 2.4 Multiply the maximum value of the rapid contraction phase and the second confidence value to obtain the rapid contraction characteristic value B2, that is, B2 = B20 * BX2.

[0069] (3) Determine the characteristic value of sustained contraction

[0070] 3.1 Determine the mean B30 and variability B31 of the sustained contraction phase of the contraction signal.

[0071] 3.2. The variability of the continuous contraction phase is compared with the continuous contraction variability threshold to obtain the third confidence value BX3, where the value of BX3 ranges from 0 to 100%.

[0072] 3.3 Multiply the mean of the pre-resting phase and the third confidence value to obtain the sustained contraction characteristic value B3, i.e., B3 = B30 * BX3.

[0073] (4) Determine the characteristic value of durability shrinkage

[0074] 4.1 Determine the mean B40, variability B41, and the ratio of the last 10 seconds to the first 10 seconds of the contraction signal.

[0075] 4.2. The variability B41 and the ratio of the last 10 seconds to the first 10 seconds B42 during the durability shrinkage stage are compared with the durability shrinkage variability threshold and the last 10 seconds to the first 10 seconds ratio threshold, respectively, to obtain the third sub-confidence value BX41 and the fourth sub-confidence value BX42. The ranges of BX41 and BX42 are 0-100%.

[0076] 4.3 Multiply the third and fourth sub-confidence values ​​by their corresponding weights respectively, and add the results of the multiplications to obtain the fourth confidence value BX4, that is, BX4 = (BX41*c + BX42*d).

[0077] Where c is the first weight corresponding to the third sub-confidence value, and d is the second weight corresponding to the fourth sub-confidence value. The sum of c and d is 1, and the specific values ​​can be selected according to the actual situation. In one example, c = 70% and d = 30%. The value range of BX4 is 0-100%.

[0078] 4.4 Multiply the mean value of the durability shrinkage stage by the fourth confidence value to obtain the rapid shrinkage characteristic value B4, i.e., B4 = B40 * BX4.

[0079] (5) Determine the resting characteristic value

[0080] 5.1 Determine the mean B50 and variability B51 of the post-resting phase of the contraction signal.

[0081] 5.2. The variability B51 in the post-resting phase is compared with the post-resting phase variability threshold to obtain the fifth confidence value BX5. The value of BX5 ranges from 0 to 100%.

[0082] 5.3 Multiply the mean of the post-resting phase by the first confidence value to obtain the post-resting characteristic value B5, i.e., B5 = B50 * BX5.

[0083] The thresholds (including pre-resting variability threshold, rise time threshold, fall time threshold, sustained contraction variability threshold, etc.) can be stored in a database, such as the database of a biofeedback device or server.

[0084] Step S22: Using the evaluation feature set, determine the values ​​of the operating parameters of the magnetic stimulation device, wherein the evaluation feature set includes resting feature values ​​and contraction feature values.

[0085] Among them, the resting characteristic value and the contraction characteristic value can reflect the state of the patient's pelvic floor muscle function. Mathematical operations on the resting characteristic value and the contraction characteristic value can yield the numerical values ​​of the operating parameters of the magnetic stimulation device.

[0086] In some embodiments, the shrinkage characteristic value includes at least one of rapid shrinkage characteristic value, continuous shrinkage characteristic value and durable shrinkage characteristic value, and step S22 may include sub-steps S221 to S225.

[0087] Step S221: Based on the resting characteristic value and each contraction characteristic value, determine the corresponding parameter baseline value, characteristic base number and adjustment coefficient for each contraction characteristic value.

[0088] Prior to step S221, the magnetic stimulation device pre-stores resting characteristic values ​​and the mapping relationship between each contraction characteristic value and the parameter baseline value, characteristic base number, and adjustment coefficient.

[0089] The feature base and adjustment coefficient are used to calculate the parameter adjustment value, which is then used to adjust the parameter baseline value. The adjustment coefficient is used to convert the numerical values ​​in the feature dimension to the numerical values ​​in the operating parameter dimension of the magnetic stimulation device. For example, if the unit of the feature dimension value is uV, while the unit of the operating parameter of the magnetic stimulation device is current magnitude, and the unit of the corresponding value is mA, and the unit of the corresponding value is seconds when the operating parameter of the magnetic stimulation device is contraction time or relaxation time, then a dimension conversion is required.

[0090] Step S222: Determine the relative contraction eigenvalue between each contraction eigenvalue and resting eigenvalue.

[0091] Specifically, the mean of the pre-resting eigenvalue and the post-resting eigenvalue can be calculated, and then the difference between each contraction eigenvalue and the mean of the pre-resting and post-resting eigenvalues ​​can be taken to obtain the corresponding relative contraction eigenvalue. Alternatively, the difference between each contraction eigenvalue and either the pre-resting or post-resting eigenvalue can be taken directly to obtain the corresponding relative contraction eigenvalue.

[0092] Step S223: Determine the characteristic deviation value between the relative characteristic shrinkage value and the characteristic base corresponding to each shrinkage characteristic value.

[0093] Specifically, the characteristic deviation value can be obtained by subtracting the relative characteristic shrinkage value corresponding to each shrinkage characteristic value from the corresponding characteristic base.

[0094] Step S224: The product of the characteristic deviation value and the adjustment coefficient corresponding to each shrinkage characteristic value is used as the parameter adjustment value corresponding to each shrinkage characteristic value.

[0095] Step S225: Calculate the parameter baseline value and parameter adjustment value corresponding to each contraction characteristic value to obtain the numerical values ​​of the working parameters of the magnetic stimulation device.

[0096] In some implementations, when the contraction characteristic value includes only one of the rapid contraction characteristic value, sustained contraction characteristic value, and durable contraction characteristic value, taking the sustained contraction characteristic value as an example, the values ​​of the operating parameters of the magnetic stimulation device are calculated using the following formula:

[0097] C=C1+((B2-(B1+B5) / 2)-T1)*X1,

[0098] Where C represents the operating parameters of the magnetic stimulation device, C1, T1, and X1 represent the baseline value, feature base, and adjustment coefficient corresponding to the rapid contraction feature value, respectively, B1 represents the pre-resting feature value, B5 represents the post-resting feature value, and B2 represents the rapid contraction feature value.

[0099] In other embodiments, when the contraction characteristic value includes multiple of the rapid contraction characteristic value, sustained contraction characteristic value, and durable contraction characteristic value, such as including all of them, the values ​​of the operating parameters of the magnetic stimulation device are calculated using the following formula:

[0100] C10=C1+((B2-(B1+B5) / 2)-T1)*X1,

[0101] C20=C2+((B2-(B1+B5) / 2)-T2)*X2,

[0102] C30=C3+((B2-(B1+B5) / 2)-T3)*X3,

[0103] C=C10*α1+C20*α2+C30*α3.

[0104] Wherein, C represents the numerical values ​​of the operating parameters of the magnetic stimulation device; C10 is the first initial value corresponding to the rapid contraction characteristic value, C1, T1, and X1 are the parameter baseline value, characteristic base, and adjustment coefficient corresponding to the rapid contraction characteristic value, respectively, and α1 is the first weight corresponding to the rapid contraction characteristic value; C20 is the second initial value corresponding to the sustained contraction characteristic value, C2, T2, and X2 are the parameter baseline value, characteristic base, and adjustment coefficient corresponding to the sustained contraction characteristic value, respectively, and α2 is the second weight corresponding to the sustained contraction characteristic value; C30 is the third initial value corresponding to the durable contraction characteristic value, C3, T3, and X3 are the parameter baseline value, characteristic base, and adjustment coefficient corresponding to the fast durable contraction characteristic value, respectively, and α3 is the third weight value corresponding to the durable contraction characteristic value; B1 is the pre-resting characteristic value, B5 is the post-resting characteristic value, and B2 is the rapid contraction characteristic value.

[0105] Among them, α1, α2, and α3 can be set according to the actual situation, and there is no limitation here.

[0106] The parameter determination method provided in this embodiment can determine the operating parameters of at least one magnetic stimulation device.

[0107] In some implementations, step S221 may further include determining the minimum parameter CMin and the maximum parameter CMax corresponding to the resting characteristic value and the contraction characteristic value. In this case, step S225 may further include:

[0108] The values ​​of the operating parameters of the magnetic stimulation device are compared with the maximum and minimum values ​​of the parameters, respectively.

[0109] If the value of the operating parameter of the magnetic stimulation device is greater than the maximum value, the maximum value is selected as the operating parameter value; otherwise, the current operating parameter value is maintained. If the value of the operating parameter is less than the minimum value, the minimum value is selected as the operating parameter value; otherwise, the current operating parameter value is maintained. This ensures that the magnetic stimulation parameter value is not too high, leading to safety issues, or too low, rendering it ineffective for treatment.

[0110] Among them, the magnetic stimulation device pre-selection can store resting characteristic values, contraction characteristic values, and the mapping relationship between CMin and CMax.

[0111] Please see Figure 8 , Figure 8 This is a flowchart illustrating another embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application. Unlike the above embodiments, the medical data in this embodiment further includes patient information and a feature set of examination records. The magnetic stimulation device is used to match the type of operating parameters of the magnetic stimulation device based on the patient information, the feature set of examination records, and the evaluation feature set, corresponding to a scenario where the type of operating parameters of the magnetic stimulation device is automatically determined.

[0112] The method includes the following steps:

[0113] Step S31: Obtain the patient's medical data, wherein the medical data includes at least one of the patient information feature set, examination record feature set, and assessment feature set.

[0114] Step S32: Match the type of operating parameters of the magnetic stimulation device based on at least one feature set contained in the medical data.

[0115] Prior to step S31, the magnetic stimulation device may pre-store the correspondence between each magnetic stimulation scheme and the threshold of at least one feature set. This allows for the matching of at least one feature set with the threshold corresponding to each magnetic stimulation scheme, and the successfully matched scheme is taken as the target magnetic stimulation scheme. Each magnetic stimulation scheme includes the category of the operating parameters of the magnetic stimulation device, and may also include the original values ​​of the operating parameters of the magnetic stimulation device.

[0116] In some implementations, medical data includes patient information feature sets, examination record feature sets, and evaluation feature sets. The magnetic stimulation device can then match the type of its operating parameters based on these feature sets. Specifically, the patient's information feature sets, examination record feature sets, and evaluation feature sets can be matched against corresponding feature set thresholds in a database. The magnetic stimulation protocol corresponding to the three successfully matched feature set thresholds is then used as the target magnetic stimulation protocol. Feature set thresholds can be text or numbers. When the feature set includes text, text can be directly matched. Feature set thresholds can be set according to specific circumstances.

[0117] Please see Figure 9 , Figure 9 This is a flowchart illustrating another embodiment of the method for determining the operating parameters of a magnetic stimulation device according to this application.

[0118] Unlike the embodiments described above, in this embodiment, the magnetic stimulation device can not only automatically determine the type of its operating parameters, but also automatically determine their numerical values. Furthermore, a safety verification method is provided to check for the presence of metal in the human body, thus preventing the simultaneous use of magnetism and electricity and effectively ensuring the safety of both the patient and the device.

[0119] The method may include the following steps:

[0120] Step S41: Obtain the patient's medical data, which includes patient information and examination record feature set and assessment feature set. The assessment feature set is used to reflect the status of the patient's pelvic floor muscle function.

[0121] Step S42: Based on patient information and examination record feature sets and evaluation feature sets, match the type of operating parameters of the magnetic stimulation device.

[0122] Step S43: Using the evaluation feature set, determine the values ​​of the operating parameters of the magnetic stimulation device, wherein the evaluation feature set includes resting feature values ​​and contraction feature values.

[0123] For an explanation of steps S41 to S43 above, please refer to the corresponding positions in the above embodiments, which will not be repeated here.

[0124] Step S44: Determine if there is metal in the human body.

[0125] If there is no metal in the body, proceed to step S45; otherwise, pause the magnetic stimulation of the patient.

[0126] Metals may include, but are not limited to: copper, silver, iron, lithium, etc.

[0127] In some implementations, it can also be determined whether the vaginal electrode is in contact with the human body. The vaginal electrode can be made of metal. If the vaginal electrode is in contact with the human body, step S45 is executed; otherwise, magnetic stimulation of the patient is paused.

[0128] The biofeedback device can automatically identify the product information (including type and serial number) of the vaginal electrode, or the magnetic stimulation device can automatically identify the product information (including type and serial number) of the coil. The vaginal electrode is used to conduct electricity for electromyography assessment and magnetic stimulation therapy, while the coil is used to conduct electricity for magnetic stimulation therapy. The type and serial number of the vaginal electrode and coil can be set by the manufacturer and are not limited here.

[0129] In some implementations, chips may be embedded in the vaginal electrodes and coils, and the processors in the bio-stimulation feedback device and magnetic stimulation device can communicate with the embedded chips to obtain corresponding product information.

[0130] In some implementations, the processors in biofeedback devices and magnetic stimulation devices can record product information of vaginal electrodes or coils in use, which can then be directly read later.

[0131] The biofeedback device can send product information about the vaginal electrode in use as medical data to the magnetic stimulation device. Upon receiving this information, the magnetic stimulation device can determine that the vaginal electrode is in contact with the body (i.e., there is metal inside the body) and thus pause magnetic stimulation. If it does not receive this information, it can determine that the vaginal electrode is not in contact with the body (i.e., there is no metal inside the body), and magnetic stimulation can proceed. Similarly, the magnetic stimulation device can send product information about the coil in use as medical data to the biofeedback device. Upon receiving this information, the biofeedback device can determine that the coil is in contact with the body and thus pause electrical stimulation or electromyography (EMG) assessment. If it does not receive this information, it can determine that the coil is not in contact with the body, and electrical stimulation or EMG assessment can proceed.

[0132] Step S45: Control the magnetic stimulation device to output magnetic stimulation according to the working parameters.

[0133] Specifically, the magnetic stimulation device can be configured with its operating parameters, and then the device can be controlled to output magnetic stimulation according to the operating parameters.

[0134] Please see Figure 10 , Figure 10 This is a schematic block diagram of an embodiment of the magnetic stimulation device of this application.

[0135] The magnetic stimulation device 100 may include a memory 110 and a processor 120 coupled to each other. The memory 110 is used to store program data, and the processor 120 is used to execute the program data to implement the steps in any of the above method embodiments.

[0136] The processor 120 is used to acquire the patient's medical data, which reflects the state of the patient's pelvic floor muscle function; and to determine the operating parameters of the magnetic stimulation device using the medical data.

[0137] In some embodiments, the magnetic stimulation device is a magnetic stimulator, or a magnetic stimulation device is an integrated device of a magnetic stimulator and a biofeedback device.

[0138] Specifically, processor 120 controls itself and memory 110 to implement the steps in any of the above method embodiments. Processor 120 may also be referred to as a CPU (Central Processing Unit). Processor 120 may be an integrated circuit chip with signal processing capabilities. Processor 120 may also be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. A general-purpose processor may be a microprocessor or any conventional processor. Furthermore, processor 120 may be implemented by multiple integrated circuit chips.

[0139] In some implementations, the medical data includes an assessment feature set that reflects the state of a patient's pelvic floor muscle function. Using the medical data, the operating parameters of the magnetic stimulation device are determined, including: using the assessment feature set to determine the numerical values ​​of the operating parameters of the magnetic stimulation device, wherein the assessment feature set includes resting feature values ​​and contraction feature values.

[0140] In some embodiments, the contraction characteristic value includes at least one of rapid contraction characteristic value, sustained contraction characteristic value, and durable contraction characteristic value. Using an evaluation feature set, the numerical values ​​of the operating parameters of the magnetic stimulation device are determined, including: based on the resting characteristic value and each contraction characteristic value, determining a corresponding parameter baseline value, characteristic base, and adjustment coefficient for each contraction characteristic value; determining a relative contraction characteristic value between each contraction characteristic value and the resting characteristic value; determining a characteristic deviation value between the relative characteristic contraction value and the characteristic base corresponding to each contraction characteristic value; using the product of the characteristic deviation value and the adjustment coefficient corresponding to each contraction characteristic value as the parameter adjustment value corresponding to each contraction characteristic value; and calculating the parameter baseline value and the parameter adjustment value corresponding to each contraction characteristic value to obtain the numerical values ​​of the operating parameters of the magnetic stimulation device.

[0141] In some implementations, determining the values ​​of the operating parameters of the magnetic stimulation device using an evaluation feature set further includes: determining the minimum and maximum values ​​of the parameters corresponding to the resting feature value and the contraction feature value; after calculating the parameter baseline value and parameter adjustment value corresponding to each contraction feature value to obtain the values ​​of the operating parameters of the magnetic stimulation device, the method further includes: if the value of the operating parameter of the magnetic stimulation device is greater than the maximum value of the parameter, then selecting the maximum value of the parameter as the value of the operating parameter of the magnetic stimulation device; if the value of the operating parameter of the magnetic stimulation device is less than the minimum value of the parameter, then selecting the minimum value of the parameter as the value of the operating parameter of the magnetic stimulation device.

[0142] In some implementations, the medical data includes at least one of a patient information feature set, an examination record feature set, and an assessment feature set, wherein the assessment feature set is used to reflect the state of the patient's pelvic floor muscle function. Using the medical data, the operating parameters of the magnetic stimulation device are determined, including: matching the type of operating parameters of the magnetic stimulation device based on at least one feature set contained in the medical data.

[0143] In some implementations, after determining the operating parameters of the magnetic stimulation device using medical data, the method further includes: determining whether there is metal in the human body; if there is no metal in the human body, controlling the magnetic stimulation device to output magnetic stimulation according to the operating parameters.

[0144] In some implementations, acquiring patient medical data includes: establishing a communication connection with a hospital management information system or a biofeedback device; and acquiring patient medical data from the hospital management information system or the biofeedback device.

[0145] The above approach acquires the patient's medical data through a magnetic stimulation device. Since the medical data reflects the patient's pelvic floor muscle function, the operating parameters of the magnetic stimulation device, determined based on this data, take into account the patient's pelvic floor muscle function. Thus, magnetic stimulation can automatically determine appropriate operating parameters based on the pelvic floor muscle function of different patients, reducing the subjective judgment factor of doctors and improving the therapeutic effect of magnetic stimulation.

[0146] Please see Figure 11 , Figure 11 This is a schematic block diagram of an embodiment of the pelvic floor muscle rehabilitation system of this application.

[0147] In this embodiment, the pelvic floor muscle rehabilitation system 200 includes a magnetic stimulation device 210, a biofeedback device 220, and a network device 230 as described in any of the above embodiments. The biofeedback device 220 is used to acquire the patient's medical data, and the network device 230 is used to connect the biofeedback device 220 and the magnetic stimulation device 210. The network device 230 may be, for example, a router or a switch.

[0148] In some implementations, the networking device 230 is also used to connect the hospital management information system (not shown) to at least one of the magnetic stimulation device 210 and the biofeedback device 220.

[0149] Please see Figure 12 , Figure 12 This is a schematic block diagram of an embodiment of a computer-readable storage medium of this application.

[0150] The computer-readable storage medium 300 stores program data 310, which, when executed by a processor, is used to implement the steps in any of the above method embodiments.

[0151] The computer-readable storage medium 300 can be a medium that can store computer programs, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk. It can also be a server that stores the computer program, which can send the stored computer program to other devices for execution or run the stored computer program itself.

[0152] The above approach uses a magnetic stimulation device to acquire the patient's medical data. Since the medical data reflects the state of the patient's pelvic floor muscle function, the operating parameters of the magnetic stimulation device, determined based on this data, take into account the state of the patient's pelvic floor muscle function. Thus, the magnetic stimulation device can automatically generate appropriate operating parameters according to the state of the pelvic floor muscle function of different patients, which can reduce the subjective judgment factor of doctors and improve the therapeutic effect of magnetic stimulation.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0154] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0155] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0156] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0157] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for determining the operating parameters of a magnetic stimulation device, characterized in that, The method, applied to a magnetic stimulation device, includes: Acquire the patient's medical data, wherein the medical data is used to reflect the status of the patient's pelvic floor muscle function; Using the medical data, the operating parameters of the magnetic stimulation device are determined; The medical data includes an assessment feature set reflecting the state of a patient's pelvic floor muscle function. The assessment feature set includes resting feature values ​​and contraction feature values, with the contraction feature values ​​including at least one of rapid contraction feature values, sustained contraction feature values, and endurance contraction feature values. Determining the operating parameters of the magnetic stimulation device using the medical data includes: Based on the resting characteristic value and each of the contraction characteristic values, a corresponding parameter baseline value, characteristic base number, and adjustment coefficient are determined for each of the contraction characteristic values; Determine the relative contraction characteristic value between each of the contraction characteristic values ​​and the resting characteristic value; Determine the characteristic deviation value between the relative characteristic shrinkage value and the characteristic base value corresponding to each of the described shrinkage characteristic values; The product of the characteristic deviation value and the adjustment coefficient corresponding to each of the contraction characteristic values ​​is used as the parameter adjustment value corresponding to each of the contraction characteristic values; The operating parameters of the magnetic stimulation device are obtained by calculating the parameter baseline value and the parameter adjustment value corresponding to each of the contraction characteristic values.

2. The method according to claim 1, characterized in that, The step of determining the values ​​of the operating parameters of the magnetic stimulation device using the evaluation feature set further includes: Determine the minimum and maximum values ​​of the parameters corresponding to the resting characteristic value and the contraction characteristic value; After calculating the parameter baseline value and parameter adjustment value corresponding to each of the contraction characteristic values ​​to obtain the numerical values ​​of the operating parameters of the magnetic stimulation device, the method further includes: If the value of the operating parameter of the magnetic stimulation device is greater than the maximum value of the parameter, then the maximum value of the parameter is selected as the value of the operating parameter of the magnetic stimulation device. If the value of the operating parameter of the magnetic stimulation device is less than the minimum value of the parameter, then the minimum value of the parameter is selected as the value of the operating parameter of the magnetic stimulation device.

3. The method according to claim 1, characterized in that, The medical data includes at least one from the patient information feature set, the examination record feature set, and the evaluation feature set. The step of using the medical data to determine the operating parameters of the magnetic stimulation device includes: Based on at least one feature set contained in the medical data, the type of operating parameters of the magnetic stimulation device is matched.

4. The method according to claim 1, characterized in that, After determining the operating parameters of the magnetic stimulation device using the medical data, the process further includes: Determining whether there are metals in the human body; If there is no metal in the human body, the magnetic stimulation device is controlled to output magnetic stimulation according to the operating parameters.

5. The method according to claim 1, characterized in that, The acquisition of the patient's medical data includes: Establish a communication connection with the hospital management information system or biofeedback device; The patient's medical data is obtained from the hospital management information system or the biofeedback device.

6. A magnetic stimulation device, characterized in that, The magnetic stimulation device includes a memory and a processor coupled to each other, the memory being used to store program data, and the processor being used to execute the program data to implement the method as described in any one of claims 1-5.

7. The magnetic stimulation device according to claim 6, characterized in that, The magnetic stimulation device is a magnetic stimulator, or it is an integrated device of a magnetic stimulator and a biofeedback device.

8. A pelvic floor muscle rehabilitation system, characterized in that, The device includes the magnetic stimulation device, biofeedback device, and networking device as described in claim 7, wherein the biofeedback device is used to acquire the patient's medical data, and the networking device is used to connect the biofeedback device and the magnetic stimulation device.

9. The pelvic floor muscle rehabilitation system according to claim 8, characterized in that, The networked device is also used to connect the hospital management information system to at least one of the magnetic stimulation device and the biofeedback device.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program data, which, when executed by a processor, is used to implement the method as described in any one of claims 1-5.

Images