Clinical acupuncture treatment scheme selection method based on brain-computer interface technology

Abstract

The invention discloses a clinical acupuncture treatment scheme selection method based on a brain-computer interface technology, and relates to the technical field of clinical acupuncture, and the method comprises the following steps: collecting historical prescriptions related to a current illness state according to the illness state of a clinical patient, and recording limiting conditions of different prescriptions; the method comprises the following steps: counting prescription acupoints of a current patient in combination with basic information of the patient, dividing prescription acupoint safety degrees of the patient according to traditional Chinese medicine specifications, including first level, second level and third level, and establishing a patient prescription database to record prescription acupoint data of the patient; the patient prescription acupoints of the current patient are pressed in sequence, brain wave fluctuation data of the patient are received through a non-invasive brain-computer interface connected with the patient, and the corresponding relation between the brain waves and the patient prescription acupoints is established. By analyzing the possible influence of the matching of different prescription acupoints in the historical prescription on the current patient, a doctor can make a more scientific and more reasonable treatment decision, and the doctor can conveniently select a specific acupuncture scheme for different patients.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical acupuncture, and more specifically, to a method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology. Background Art

[0002] Acupuncture originated in ancient China and is a treatment method developed based on the theory of meridians in traditional Chinese medicine. It is a treatment method that selects specific needles to pierce specific acupoints to achieve specific treatment effects. During the process of selecting an acupuncture treatment plan for a patient, usually a doctor selects the acupuncture treatment plan based on historical prescriptions. However, different patient individuals have unique body response information, which easily leads to the selected plan may not be the most suitable for the patient. It not only fails to effectively reduce the safety risk of the patient during acupuncture, but also is difficult to improve the patient's acupuncture experience. Different patients have a huge difference in the acupuncture tolerance for the same disease, but these factors cannot be considered in the traditional acupuncture selection method, resulting in uneven treatment effects for different patients with the same disease. And during the process of selecting an acupuncture treatment plan, there is a large degree of blindness in acupoint selection. Although traditional Chinese medicine has acupoint standard specifications, in actual application, there is a lack of comprehensive consideration of acupoint safety and patient individual sensitivity. It is difficult for doctors to accurately identify sensitive acupoints in the acupoints prescribed for the current patient's disease, and it is easy to cause risks due to improper acupoint selection, resulting in problems of low practicability and functionality.

[0003] For the problems in the related art, no effective solution has been proposed yet. Summary of the Invention

[0004] For the problems in the related art, the present invention proposes a method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology to overcome the above-mentioned technical problems existing in the existing related technologies.

[0005] To this end, the specific technical solution adopted by the present invention is as follows: A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology, the method comprising the following steps: S1. According to the condition of a clinical patient, collect historical prescriptions related to the current condition, record the limiting conditions of different prescriptions, count the acupoints used in the current patient's prescription in combination with the patient's own basic information, and divide the safety degree of the acupoints used in the patient's prescription according to traditional Chinese medicine specifications, including level 1, level 2, and level 3, and establish a patient prescription database to record the acupoint data used in the patient's prescription; S2. Press the acupoints used in the current patient's prescription in sequence, receive the patient's brain wave fluctuation data through a non-invasive brain-computer interface connected to the patient, establish the corresponding relationship between the brain waves and the acupoints used in the patient's prescription, obtain the acupoint sensitivity value of the current patient according to the reference brain wave data and stimulation sensitivity of the current patient, and determine the sensitive acupoints and non-sensitive acupoints among the acupoints used in the current patient's prescription; S3. Select an acupuncture treatment plan based on the safety of acupoints used in the current patient's prescription, sensitive acupoints, and non-sensitive acupoints, and combine the relevant historical prescriptions of the patient's current condition to comprehensively screen the treatment plan suitable for the current patient.

[0006] As a preferred embodiment, S1 includes the following steps: S11. Collect the basic information of the current clinical patient, including the patient's age, gender, and disease, and collect the historical prescription data of the current disease, including restrictive conditions and a list of acupoints used in the prescription; S12. Based on the basic information of the current clinical patient and combined with the historical prescription data, statistically analyze the data of acupoints used in the current patient's prescription. Combine the traditional Chinese medicine standard norms to classify the safety of acupoints used in the current patient's prescription. Establish a patient prescription database through MySQL and mark the acupoints used in the patient's prescription with different safety levels.

[0007] As a preferred embodiment, S12 includes the following steps: S121. For the collected historical prescription data, screen the historical prescription data based on the patient's age and gender. The specific steps are as follows: According to the patient's gender, retain the historical prescription data that conforms to the patient's gender. Statistically analyze the patient's age under the same retained prescription data. Based on the minimum treatment age and the maximum treatment age, obtain the required interval of prescription data: ; Among them, represents the minimum treatment age of the patient in the current prescription data, represents the maximum treatment age of the patient in the current prescription data. Combine the current patient's age to further screen the historical prescription data: When the patient's age , retain the current prescription data and record the acupuncture points involved in the current prescription data and the acupuncture depth; S122. According to the recorded acupuncture points and acupuncture depth, combine the location of the acupuncture points in the traditional Chinese medicine norms and the acupuncture depth to classify the safety of the data of acupoints used in the current record. The specific steps are as follows: According to the location of the acupoints and the information around the acupoints in the traditional Chinese medicine norms, assign corresponding risk coefficients to the acupoints in different locations. Based on the location of the currently recorded acupuncture points, obtain the acupoint location risk value L: ; According to the acupuncture depth of the recorded acupuncture points, compare it with the standard safe depth range of the acupoints to obtain the acupoint depth risk value D: ; S123. Based on the obtained acupoint position risk value L and acupoint depth risk value D, comprehensively determine the safety degree S of the acupoints used in the current patient's prescription: ; Among them, the safety levels of level 1, level 2, and level 3 represent safety, general, and danger respectively. Establish a patient prescription database through MySQL, establish the current patient's file, and establish files for the historical prescriptions that match the current patient in the current patient's file respectively. Record the acupoints used in the prescription in the corresponding historical prescription file. Mark the acupoints used in the patient's prescription with a safety level of 2 in the historical prescription file in blue, and mark the acupoints used in the patient's prescription with a safety level of 3 in red.

[0008] As a preferred implementation manner, the S2 includes the following steps: S21. Connect the non-invasive brain-computer interface to the patient, perform deduplication and statistics according to the types of acupoints of the current patient in the patient prescription database, press the acupoints used in the current patient's prescription in sequence, record the patient's electroencephalogram (EEG) fluctuation data, and establish the corresponding relationship between different EEG values and the acupoints used in the patient's prescription; S22. Statistically analyze the baseline EEG data of the current patient, and combine the EEG fluctuation intervals of historical patients when pressing acupoints to determine the type of the current patient, including sensitive type, normal type, and dull type. Set different acupoint sensitivity values respectively to determine the sensitive acupoints and non-sensitive acupoints of the acupoints used in the current patient's prescription.

[0009] A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 4, characterized in that the S21 includes the following steps: S211. According to the deduplicated list of acupoints used in the current patient's prescription, press the acupoints used in the patient's prescription in sequence. Each press lasts for 5 - 10 seconds, with an interval of 15 seconds, and each acupoint is pressed 3 times repeatedly. During each press, the EEG data of the current patient is recorded in real time through the brain-computer interface device. The data includes timestamp, frequency, and amplitude of the EEG; S212. Preprocess the recorded EEG data, including filtering and noise reduction. Extract characteristic parameters from the preprocessed EEG data, including power spectral density, peak amplitude, and frequency in specific frequency bands. Statistically analyze the specific frequency bands of the EEG that change in the current patient during the repeated pressing of acupoints, and associate the extracted EEG characteristic parameters with the corresponding pressed acupoint names.

[0010] As a preferred implementation manner, the S22 includes the following steps: S221. Statistically analyze the EEG specific frequency band data of historical patients when pressing the same acupoints, and statistically analyze the maximum value set of the frequency band power spectral density , minimum value set and mean value set , record the maximum value, mean value, and minimum value of the associated frequency band during 3 presses on the same acupoint of the current patient, and record them respectively as , where i represents the acupoint number; S222. Calculate the difference values of the maximum value, mean value, and minimum value of the power spectral density of the brain wave frequency band at the same acupoint between the current patient and the historical patient data, and obtain the maximum value difference , mean value difference , and minimum value difference respectively. The specific algorithm formula is: ; Among them, n represents the number of means in the historical data, represents the average value of the historical means, and based on the sensitive threshold and the insensitive threshold , determine the type of the current patient: When and , determine that the acupoint of the current patient is sensitive; When , determine that the acupoint of the current patient is insensitive; When the sensitive type and insensitive type are not satisfied, determine that the current acupoint of the patient is normal; S223. For the acupoints prescribed for the current patient, count the most common acupoint type as the type to which the current patient belongs, including sensitive type, normal type, and insensitive type. Set acupoint sensitivity values for different types of the patient respectively, and further determine the acupoints prescribed for the current patient.

[0011] As a preferred implementation manner, the S223 includes the following steps: S2231. For sensitive type, normal type, and insensitive type patients, set acupoint sensitivity values respectively to determine the type of the acupoints prescribed for the current patient. The specific steps are: For the normal type, select the average value of the historical mean value set of the current acupoint as the acupoint sensitivity value; For the sensitive type, select the sum of the average value of the historical mean value set of the current acupoint and 2.5 times the standard deviation as the acupoint sensitivity value; For the insensitive type, select the difference between the average value of the historical mean value set of the current acupoint and 2.5 times the standard deviation as the acupoint sensitivity value; Calculate the average value of the power spectral density of the associated frequency band for 3 acupoint presses of the current patient, record the acupoints with the average value of the power spectral density of the associated frequency band greater than the acupoint sensitivity value as sensitive acupoints, and the acupoints less than the acupoint sensitivity value as non-sensitive acupoints, and mark the sensitive acupoints of the current patient in the historical prescription file in orange.

[0012] As a preferred embodiment, S3 includes the following steps: S31. Based on the current patient file in the patient prescription database, count the number of color markings of different acupoints in different historical prescription files, select the acupuncture treatment plan, and comprehensively screen the treatment plan suitable for the current patient.

[0013] As a preferred embodiment, S31 includes the following steps: S311. Count the number of acupoints marked in blue (a), the number of acupoints marked in red (b), the number of acupoints marked in orange (c), the number of acupoints marked in blue + orange (d), and the number of acupoints marked in red + orange (e) in different historical prescription files, and sort them in ascending order respectively; Prioritize selecting the historical prescription with the number of acupoints marked in red + orange ranked first as the clinical acupuncture plan for the current patient. When there are e equal numbers in the first place, count the value of d in the historical prescriptions with equal numbers, and preferentially select the historical prescription with the smallest value as the clinical acupuncture plan for the current patient. When the values of d are the same, count the value of b in the historical prescriptions with equal numbers, and so on. The priority is red + orange > blue + orange > red > orange > blue; S312. When there is no number of acupoints marked in red + orange in the historical prescription, prioritize selecting the historical prescription with the number of acupoints marked in blue + orange ranked first as the clinical acupuncture plan for the current patient, and repeat step S311 until the acupuncture plan for the current patient is obtained.

[0014] The beneficial effects of the present invention are as follows: By collecting the basic information of the patient, according to the historical prescription data of the current patient's disease, and combining the acupoint sensitivity information obtained by the brain-computer interface technology, the present invention can screen the most suitable acupuncture treatment plan for the current patient, reduce the safety risk of the patient during acupuncture, and improve the patient's acupuncture experience; By dividing the acupoint safety degree according to the Chinese medicine standard, and at the same time combining the brain-computer data to obtain the type to which the patient belongs, and dividing the sensitive acupoints in the acupoints used in the prescription of the current patient's disease, and selecting the historical prescription by comprehensively considering the acupoint safety degree and sensitivity, the present invention can effectively reduce the risk caused by improper acupoint selection during acupuncture, avoid adverse reactions caused by using unsafe acupoints, and ensure the safety of the patient during the treatment process; The present invention receives the brain wave fluctuation data of patients through a non-invasive brain-computer interface, establishes the correspondence between brain waves and the acupoints prescribed for patients, determines the patient type and obtains the acupoint sensitivity value of the patient, captures the brain wave fluctuation data using the brain-computer interface, and conducts comparative analysis in combination with historical patient data to scientifically determine the acupoint sensitivity type of the patient, providing an objective basis for the formulation of treatment plans, determining sensitive acupoints and non-sensitive acupoints, reducing the impact of acupuncture caused by differences in the sensitivity of each patient's body to acupoint stimulation, facilitating doctors to select personalized treatment plans for different patients, and enhancing practicability and functionality. The entire method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology is based on a large amount of data, including the patient's condition information, historical prescription data, brain wave data, etc. By analyzing the possible effects of different prescription acupoint combinations in historical prescriptions on the current patient, doctors can make more scientific and reasonable treatment decisions, facilitating doctors to select specific acupuncture plans for different patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 is a flowchart of a method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To further illustrate the embodiments, the present invention provides drawings. These drawings are part of the disclosure of the present invention, mainly used to illustrate the embodiments, and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0018] According to an embodiment of the present invention, a method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology is provided.

[0019] Now, the present invention will be further described in conjunction with the drawings and specific embodiments: Example 1: As Figure 1 shown, a method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to an embodiment of the present invention includes the following steps: S1. According to the condition of the clinical patient, collect the historical prescriptions related to the current condition, record the restrictive conditions of different prescriptions, combine the patient's own basic information to count the acupoints used in the patient's current prescription, and classify the safety degree of the acupoints used in the patient's prescription according to traditional Chinese medicine norms, including level 1, level 2, and level 3. Establish a patient prescription database to record the data of the acupoints used in the patient's prescription. S11. Collect the basic information of the current clinical patient, including the patient's age, gender, and disease, and collect the historical prescription data of the current disease, including restrictive conditions and a list of acupoints used in the prescription. S12. Based on the basic information of the current clinical patient, combine the historical prescription data to count the data of the acupoints used in the patient's current prescription. Combine the traditional Chinese medicine norms and standards to classify the safety degree of the acupoints used in the patient's current prescription. Establish a patient prescription database through MySQL and mark the acupoints used in the patient's prescription with different safety degrees.

[0020] It should be noted that the traditional Chinese medicine norms and standards include "Acupuncture Science", acupuncture operation guidelines, etc. S121. For the collected historical prescription data, based on the patient's age and gender, screen the historical prescription data. The specific steps are as follows: According to the patient's gender, retain the historical prescription data that matches the patient's gender, count the ages of the patients under the same retained prescription data, and obtain the required interval of prescription data based on the minimum treatment age and the maximum treatment age: ; Among them, represents the minimum treatment age of the patients in the current prescription data, represents the maximum treatment age of the patients in the current prescription data. Combine the current patient's age to further screen the historical prescription data: When the patient's age , retain the current prescription data and record the acupuncture points involved in the current prescription data and the acupuncture depth. S122. According to the recorded acupuncture points and acupuncture depth, combine the location of the acupuncture points in traditional Chinese medicine norms and the acupuncture depth, and classify the safety degree of the data of the acupoints used in the current record. The specific steps are as follows: According to the location of the acupoints and the information around the acupoints in traditional Chinese medicine norms, assign corresponding risk coefficients to the acupoints in different locations, and obtain the acupoint location risk value L based on the location of the currently recorded acupuncture points: ; According to the acupuncture depth of the recorded acupuncture points, compare it with the standard safety depth range of the acupoints to obtain the acupoint depth risk value D: ; It should be noted that in the dangerous value of acupoint position, each recorded acupuncture point is compared with the acupoint position information in traditional Chinese medicine norms to determine whether the acupoint is in a dangerous area. Dangerous areas include the chest near the heart and lungs, the neck near large blood vessels and nerves, etc. According to the currently recorded acupuncture points, the dangerous value L of acupoint position is obtained. Combining the position data of different acupoints and traditional Chinese medicine norms, the safety ranges of different acupoints are divided. For example, there are many organs in the abdomen, such as the liver, gallbladder, spleen, pancreas, intestines, stomach, kidneys, and bladder. If the acupuncture is too deep, it is easy to cause damage or bleeding to the above organs. The safe acupuncture range of Zhongwan acupoint in the upper abdomen is 1 to 1.5 inches straight insertion, and the acupuncture range of Liangmen acupoint in the upper abdomen is 0.8 to 1.2 inches straight insertion, etc. Combining traditional Chinese medicine norms, the standard safety ranges of all acupoints are divided.

[0021] S123. According to the obtained dangerous value L of acupoint position and the dangerous value D of acupoint depth, comprehensively determine the safety degree S of the acupoints used in the current patient's prescription: ; Among them, the safety degree levels 1, 2, and 3 represent safe, general, and dangerous respectively. A patient prescription database is established through MySQL, a file of the current patient is established, and historical prescriptions that match the current patient are respectively established into files in the file of the current patient. The acupoints used in the prescription are recorded inside the corresponding historical prescription file. The acupoints used in the patient prescriptions with a safety degree of level 2 in the historical prescription file are marked in blue, and the acupoints used in the patient prescriptions with a safety degree of level 3 are marked in red.

[0022] It should be noted that by comprehensively integrating the acupoint data related to the prescriptions that meet the requirements in the historical prescription data involved in the current patient's condition, obtaining the dangerous degree of acupoint position based on the position of the acupoints, and obtaining the dangerous value of acupoint depth according to the acupuncture depth of the acupoints in different prescriptions combined with the standard safety range, it can assist in the subsequent selection of the clinical acupuncture plan for the patient in combination with brain-computer data.

[0023] Example 2: S2. Press the acupoints used in the current patient's prescription in sequence. Through the non-invasive brain-computer interface connected to the patient, receive the patient's brain wave fluctuation data, establish the correspondence between the brain waves and the acupoints used in the patient's prescription, and obtain the acupoint sensitivity value of the current patient according to the baseline brain wave data and stimulation sensitivity of the current patient, and determine the sensitive acupoints and non-sensitive acupoints among the acupoints used in the current patient's prescription; S21. Connect the non-invasive brain-computer interface to the patient. According to the types of acupoints of the current patient in the patient prescription database, perform duplicate removal statistics, press the acupoints used in the current patient's prescription in sequence, record the patient's brain wave fluctuation data, and establish the correspondence between different brain wave values and the acupoints used in the patient's prescription; It should be noted that the non-invasive brain-computer interface includes dry electrodes and wet electrodes, which are placed on the forehead and scalp areas of the patient to capture the EEG signal frequency band of the current patient, and press the acupoints prescribed for the current patient in sequence. The pressing technique used is the point-pressing method, and the thumb, index finger or middle finger is used to continuously, gently and forcefully press at the corresponding acupoints, relax after pressing for a few seconds, and then continue to press. It is appropriate when the patient feels soreness and distension locally.

[0024] S211. According to the list of acupoints prescribed for the current patient after deduplication, press the acupoints prescribed for the patient in sequence. Each press lasts for 5 - 10 seconds, with an interval of 15 seconds. Each acupoint is pressed 3 times repeatedly. During each press, the EEG data of the current patient is recorded in real time through the brain-computer interface device. The data includes time stamps, the frequency and amplitude of the EEG. S212. Preprocess the recorded EEG data, including filtering and noise reduction. Extract characteristic parameters from the preprocessed EEG data, including the power spectral density, peak amplitude and frequency of specific frequency bands. Statistically analyze the specific frequency bands of the EEG that change in the current patient during the repeated acupoint pressing process, and associate the extracted EEG characteristic parameters with the corresponding acupoint names.

[0025] It should be noted that the specific frequency bands include α, β, θ, δ frequency bands, etc. Obtain the EEG data of the patient in the initial state and record it as the reference EEG data. Statistically analyze the EEG frequency bands that change during all 3 acupoint pressing processes, and retain the EEG frequency band with the largest change range as the associated frequency band for the current pressed acupoint, and record the characteristic parameters of the associated frequency band.

[0026] S22. Statistically analyze the reference EEG data of the current patient, and combine the EEG fluctuation ranges of historical patients during acupoint pressing to determine the type of the current patient, including sensitive type, normal type, and dull type. Set different acupoint sensitivity values respectively to determine the sensitive acupoints and non-sensitive acupoints of the acupoints prescribed for the current patient. S221. Statistically analyze the EEG specific frequency band data of historical patients during acupoint pressing at the same acupoints, and statistically analyze the maximum value set of the frequency band power spectral density , the minimum value set and the mean value set , record the maximum value, mean value and minimum value of the associated frequency band during 3 presses of the same acupoints for the current patient, and record them as respectively, where i represents the acupoint number; S222. Calculate the difference values of the maximum value, mean value and minimum value of the EEG frequency band power spectral density between the current patient and historical patients at the same acupoints, and obtain the maximum value difference respectively. The specific algorithm formula is as follows: ; where n represents the number of means in the historical data, represents the average of the historical means, and based on the sensitivity threshold and the dullness threshold , the type of the current patient is determined as follows: When and , it is determined that the acupoint of the current patient is sensitive; When , it is determined that the acupoint of the current patient is dull; When the sensitive type and the dull type are not satisfied, it is determined that the current acupoint of the patient is normal; It should be noted that the sensitivity threshold and the dullness threshold need to be obtained through statistical analysis of the maximum value, mean value, and minimum value of a specific frequency band in the historical data respectively. By statistically analyzing the average value and standard deviation of the maximum value set, mean value set, and minimum value set respectively, the sensitivity threshold is set as the sum of the average value of each set and 1.5 times the standard deviation, and the dullness threshold is set as the difference between the average value of each set and 1.5 times the standard deviation. When the current acupoint of the patient is sensitive, it means that the maximum value, mean value, and minimum value of the specific frequency band when pressing this acupoint of the current patient are all significantly higher than the historical data, indicating that the patient has a strong response to acupoint pressing.

[0027] S223. For the acupoints prescribed for the current patient, count the most frequent acupoint type as the type to which the current patient belongs, including sensitive type, normal type, and dull type. Set acupoint sensitivity values for different types to which the patient belongs respectively, and further determine the acupoints prescribed for the current patient; S2231. For sensitive type, normal type, and dull type patients, set acupoint sensitivity values respectively to determine the type of acupoints prescribed for the current patient. The specific steps are as follows: For the normal type, select the average value of the historical mean set of the current acupoint as the acupoint sensitivity value; For the sensitive type, select the sum of the average value of the historical mean set of the current acupoint and 2.5 times the standard deviation as the acupoint sensitivity value; For the dull type, select the difference between the average value of the historical mean set of the current acupoint and 2.5 times the standard deviation as the acupoint sensitivity value; Calculate the average value of the power spectral density of the associated frequency band for 3 acupoint presses of the current patient. Record the acupoints with the average value of the power spectral density of the associated frequency band greater than the acupoint sensitivity value as sensitive acupoints, and those less than the acupoint sensitivity value as non-sensitive acupoints. Orange mark the sensitive acupoints of the current patient in the historical prescription file.

[0028] It should be noted that for sensitive patients, since the patients have a high sensitivity to acupoints, by setting a high acupoint sensitivity value, the truly stimulating acupoints of the current patient can be found. For insensitive patients, since the patients have a low sensitivity to acupoints, by reducing the acupoint sensitivity value, the truly stimulating acupoints of the current patient can be found.

[0029] S3. According to the acupoint safety, sensitive acupoints, and non-sensitive acupoints in the acupoints prescribed for the current patient, combined with the relevant historical prescriptions of the current patient's condition, select the acupuncture treatment plan, and comprehensively screen the treatment plan suitable for the current patient; S31. Based on the current patient's file in the patient prescription database, count the number of color markings of different acupoints prescribed in different historical prescription files, select the acupuncture treatment plan, and comprehensively screen the treatment plan suitable for the current patient; S311. Count the number of acupoints marked in blue a, the number of acupoints marked in red b, the number of acupoints marked in orange c, the number of acupoints marked in blue + orange d, and the number of acupoints marked in red + orange e in different historical prescription files, and sort them in ascending order respectively; Prioritize selecting the historical prescription with the largest number of red + orange acupoint markings as the current patient's clinical acupuncture plan. When there are the same number e at the top of the ranking, count the value of d in the historical prescriptions with the same number, and preferentially select the historical prescription with the smallest value as the current patient's clinical acupuncture plan. When the values of d are the same, count the value of b in the historical prescriptions with the same number, and so on. The priority is red + orange > blue + orange > red > orange > blue; S312. When there is no red + orange acupoint marking number in the historical prescription, prioritize selecting the historical prescription with the largest number of blue + orange acupoint markings as the current patient's clinical acupuncture plan, and repeat step S311 until the acupuncture plan for the current patient is obtained.

[0030] It should be noted that by color-marking and combining acupoints in different historical prescriptions, it is convenient to discover the suitability and risk of different historical prescriptions for the current patient, thus facilitating doctors to select appropriate clinical acupuncture treatment plans.

[0031] In summary, the present invention uses a non-invasive brain-computer interface to receive the brain wave fluctuation data of patients, establishes the correspondence between brain waves and the acupoints prescribed for patients, determines the patient type and obtains the acupoint sensitivity value of the patient, uses the brain-computer interface to capture the brain wave fluctuation data, combines the historical patient data for comparative analysis, scientifically determines the sensitive acupoint type of the patient, provides an objective basis for the formulation of the treatment plan, determines the sensitive acupoints and non-sensitive acupoints, reduces the impact of acupuncture caused by the difference in the sensitivity of each patient's body to acupoint stimulation, facilitates doctors to select personalized treatment plans for different patients, and enhances the practicability and functionality.

[0032] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology, characterized in that, The method includes the following steps: S1. According to the condition of the clinical patient, collect the historical prescriptions related to the current condition, record the restrictive conditions of different prescriptions, combine the patient's own basic information to count the acupoints used in the current patient's prescription, divide the safety degree of the acupoints used in the patient's prescription according to traditional Chinese medicine norms, including level 1, level 2, and level 3, and establish a patient prescription database to record the data of the acupoints used in the patient's prescription; S2. Press the acupoints used in the current patient's prescription one by one, receive the patient's brain wave fluctuation data through a non-invasive brain-computer interface connected to the patient, establish the corresponding relationship between the brain waves and the acupoints used in the patient's prescription, obtain the acupoint sensitivity value of the current patient according to the reference brain wave data and stimulation sensitivity of the current patient, and determine the sensitive acupoints and non-sensitive acupoints among the acupoints used in the current patient's prescription; S3. According to the safety degree of the acupoints used in the current patient's prescription, as well as the sensitive acupoints and non-sensitive acupoints, combine the historical prescriptions related to the current patient's condition, select the acupuncture treatment plan, and comprehensively screen the treatment plan suitable for the current patient.

2. The method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 1, characterized in that The S1 includes the following steps: S11. Collect the basic information of the current clinical patient, including the patient's age, gender, and disease, and collect the historical prescription data of the current disease, including restrictive conditions and a list of acupoints used in the prescription; S12. Based on the basic information of the current clinical patient, combine the historical prescription data, count the data of the acupoints used in the current patient's prescription, combine the traditional Chinese medicine standard norms, divide the safety degree of the acupoints used in the current patient's prescription, and establish a patient prescription database through MySQL to mark the acupoints used in the patient's prescription with different safety degrees.

3. A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 2, characterized in that, The S12 includes the following steps: S121. For the collected historical prescription data, based on the patient's age and gender, screen the historical prescription data. The specific steps are as follows: According to the patient's gender, retain the historical prescription data that conforms to the patient's gender, count the patient's age under the retained same prescription data, and obtain the required interval of the prescription data based on the minimum treatment age and the maximum treatment age; ； Among them, represents the minimum treatment age of the patient in the current prescription data, represents the maximum treatment age of the patient in the current prescription data, combined with the current patient age further screen the historical prescription data: When the patient's age keep the current prescription data and record the acupuncture points and acupuncture depths involved in the current prescription data; S122. According to the recorded acupuncture points and the acupuncture depth, combine the location of the acupuncture points and the acupuncture depth in the traditional Chinese medicine norms, and divide the safety degree of the data of the acupoints used in the current record of acupuncture points combined with the acupuncture depth. The specific steps are as follows: According to the location of the acupoints and the information around the acupoints in the traditional Chinese medicine norms, assign corresponding risk coefficients to the acupoints in different positions, and obtain the acupoint location risk value L based on the location of the currently recorded acupuncture points; ； According to the acupuncture depth of the recorded acupuncture points, compare it with the standard safe depth range of the acupoints, and obtain the acupoint depth risk value D; ； S123. According to the obtained acupoint location risk value L and acupoint depth risk value D, comprehensively determine the safety degree S of the acupoints used in the current patient's prescription; ； Among them, safety levels 1, 2, and 3 represent safe, general, and dangerous respectively. A patient prescription database is established through MySQL, a current patient file is established, and historical prescriptions that match the current patient are respectively established into files in the current patient file. The acupoints used in the prescription are recorded inside the corresponding historical prescription file. The acupoints used in the patient prescriptions with a safety level of 2 in the historical prescription file are marked in blue, and the acupoints used in the patient prescriptions with a safety level of 3 are marked in red.

4. A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 1, characterized in that The S2 includes the following steps: S21. Connect the non-invasive brain-computer interface to the patient, perform deduplication statistics according to the types of acupoints of the current patient in the patient prescription database, press the acupoints used in the patient prescription of the current patient in sequence, record the brain wave fluctuation data of the patient, and establish the corresponding relationship between different brain wave values and the acupoints used in the patient prescription. S22. Statistically analyze the baseline brain wave data of the current patient, and combine the brain wave fluctuation intervals of historical patients when pressing acupoints to determine the type of the current patient, including sensitive type, normal type, and dull type. Set different acupoint sensitivity values respectively to determine the sensitive acupoints and non-sensitive acupoints of the acupoints used in the prescription of the current patient.

5. A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 4, characterized in that, The S21 includes the following steps: S211. According to the list of acupoints used in the current patient's prescription after deduplication, press the acupoints used in the patient's prescription in sequence. Each press lasts for 5 - 10 seconds, with an interval of 15 seconds. Each acupoint is pressed 3 times repeatedly. During each press, the brain wave data of the current patient is recorded in real time through the brain-computer interface device. The data includes time stamp, frequency, and amplitude of the brain wave. S212. Preprocess the recorded brain wave data, including filtering and noise reduction. Extract characteristic parameters from the preprocessed brain wave data, including power spectral density, peak amplitude, and frequency in a specific frequency band. Statistically analyze the specific frequency band of the brain wave that changes in the current patient during the repeated acupoint pressing process, and associate the extracted brain wave characteristic parameters with the corresponding acupoint names.

6. The method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 5, characterized in that The S22 includes the following steps: S221. Statistically analyze the data of specific frequency bands of electroencephalograms of historical patients under acupoint pressing at the same acupoints, and statistically analyze the set of maximum values, the set of minimum values, and the set of mean values . Record the maximum value, mean value, and minimum value of the associated frequency band during the 3 presses of the same acupoints of the current patient, and record them as respectively, where i represents the acupoint number. S222. Calculate the difference values of the maximum value, mean value, and minimum value of the power spectral density of the brain wave frequency band at the same acupoints between the current patient and the historical patient data, and obtain the maximum value difference value respectively , the mean value difference value , and the minimum value difference value. The specific algorithm formula is as follows: ； Among them, n represents the number of means in historical data, represents the average of historical means, based on a sensitivity threshold and a sluggish threshold , to determine the type of the current patient: When and it is determined that the acupoint of the current patient is sensitive; When it is determined that the acupoints of the current patient are dull; When the conditions for the sensitive type and the dull type are not met, it is determined that the current acupoint of the patient is normal. S223. For the acupoints used in the prescription of the current patient, statistically analyze the most common acupoint type as the type of the current patient, including sensitive type, normal type, and dull type. Set acupoint sensitivity values respectively for different types of the patient to further determine the acupoints used in the prescription of the current patient.

7. A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 6, characterized in that, The S22'3 includes the following steps: S2231. For sensitive type, normal type, and dull type patients, set acupoint sensitivity values respectively to determine the type of acupoints used in the prescription of the current patient. The specific steps are as follows: For the normal type, select the average value of the set of historical means of the current acupoint as the acupoint sensitivity value. For the sensitive type, select the sum of the average value of the set of historical means of the current acupoint and 2.5 times the standard deviation as the acupoint sensitivity value. For the dull type, select the difference between the average value of the set of historical means of the current acupoint and 2.5 times the standard deviation as the acupoint sensitivity value. Calculate the average power spectral density of the associated frequency bands for 3 acupoint compressions of the current patient. Record the acupoints with the average power spectral density of the associated frequency bands greater than the acupoint sensitivity value as sensitive acupoints, and the acupoints less than the acupoint sensitivity value as non-sensitive acupoints. Orange-mark the sensitive acupoints of the current patient in the historical prescription file.

8. A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 1, characterized in that The S3 includes the following steps: S31. Based on the current patient file in the patient prescription database, count the number of color markings of different acupoints used in different historical prescription files, select the acupuncture treatment plan, and comprehensively screen the treatment plan suitable for the current patient.

9. A method for selecting a clinical acupuncture treatment plan based on brain-computer interface technology according to claim 8, characterized in that, The S31 includes the following steps: S311. Count the number of acupoints marked in blue a, the number of acupoints marked in red b, the number of acupoints marked in orange c, the number of acupoints marked in blue + orange d, and the number of acupoints marked in red + orange e in different historical prescription files, and sort them in ascending order respectively; Prioritize selecting the historical prescription with the number of acupoints marked in red + orange ranked first as the clinical acupuncture plan for the current patient. When there are e numbers that are the same in the first place, count the value of d in the historical prescriptions with the same number, and preferentially select the historical prescription with the smallest value as the clinical acupuncture plan for the current patient. When the values of d are the same, count the value of b in the historical prescriptions with the same number, and so on. The priority is red + orange > blue + orange > red > orange > blue; S312. When there is no number of acupoints marked in red + orange in the historical prescription, prioritize selecting the historical prescription with the number of acupoints marked in blue + orange ranked first as the clinical acupuncture plan for the current patient, and repeat step S311 until the acupuncture plan for the current patient is obtained.

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