VR-based method and system for alleviating patients' perioperative anxiety and tension

Abstract

The present invention belongs to the field of VR technology, and provides a method and system for relieving patients' anxiety and tension during the perioperative period based on VR. The method and system form an emotion sequence by calculating and obtaining the emotion information in the original EEG data, mentally classify the emotion information in the emotion sequence, mark the abnormal emotion sequence according to the mental classification result, calculate the emotional resistance of the emotion information in the abnormal emotion sequence in turn, mark the intervention point in the abnormal emotion sequence according to the emotional resistance, and control the VR device to play preset music according to the intervention point. Marking the intervention point according to the emotional resistance of the emotional information can reduce the number of marked intervention points, thereby reducing the frequency of stimulation, avoiding the adverse effects of high-frequency stimulation, and making up for the shortcoming that biofeedback training cannot psychologically adjust special types of people, and can help more patients relieve anxiety and tension during the perioperative period.

Description

Technical Field

[0001] The present invention belongs to the field of VR technology, and in particular relates to a method and system for alleviating perioperative anxiety and tension of patients based on VR. Background Art

[0002] Perioperative anxiety is a kind of nervous and uneasy emotion that patients experience during the perioperative period. It is a normal emotional and psychological reaction under stress. Perioperative anxiety is usually characterized by tension, irritability and increased activity of the autonomic nervous system, which manifests as changes in emotions, cognition and behavior. In order to reduce the anxiety of surgical patients, humanistic care in perioperative nursing has been widely used. In a reference study entitled "The effect of perioperative humanistic care combined with music therapy on alleviating anxiety in surgical patients", the method of combining humanistic care with music can effectively alleviate perioperative anxiety, but this method has certain limitations. Not all people can relax by listening to soothing music, and this method is difficult to detect people's inner psychological changes; in order to solve this problem, in the Chinese invention patent application filed on April 19, 2024, with announcement number CN117899329B, entitled "A psychological counseling device based on VR technology", biofeedback training combined with VR technology can test people's psychological changes, and relax themselves through self-regulation, but this method is not suitable for all people, especially patients who have already developed anxiety or depression and other emotional fluctuations during the perioperative period, who cannot relax themselves through self-regulation, resulting in a lengthy and ineffective training process and a waste of computer resources. In addition, the above method may produce frequent and multiple EEG stimulations, which may cause discomfort symptoms such as dizziness, headache, and limb numbness. Summary of the invention

[0003] The purpose of the present invention is to propose a method and system based on VR to relieve patients' perioperative anxiety and tension, so as to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] In order to achieve the above object, according to one aspect of the present invention, a method for relieving patient's perioperative anxiety and tension based on VR is provided, and the method for relieving patient's perioperative anxiety and tension based on VR comprises the following steps:

[0005] S100, obtaining raw EEG data, and calculating the emotional information in the raw EEG data to form an emotional set;

[0006] S200, evaluating and grading the mental state of all the emotion information in the emotion set in turn;

[0007] S300, marking abnormal emotion sequences in the emotion set according to the mental state assessment and classification results;

[0008] S400, sequentially calculating the emotional resistance of the emotional information data in the marked abnormal emotional sequence;

[0009] S500, marking intervention points in the emotion sequence according to the emotion resistance;

[0010] S600, controls the VR device to play preset music according to the intervention point.

[0011] Furthermore, the raw EEG data were acquired 30 minutes after the patients finished their dinner.

[0012] Furthermore, in S100, raw EEG data is acquired through an electroencephalogram (EEG) physiology instrument.

[0013] Furthermore, raw EEG data were obtained starting from the eighth day before the patient's surgery.

[0014] Furthermore, the total acquisition time is 60 min.

[0015] Furthermore, in S600, the VR device is any one of Huawei CV10 VR all-in-one machine, A1 VR all-in-one machine, and PICO A8110VR all-in-one machine.

[0016] Furthermore, the preset music played in S600 is any one of meditation music, classical music, and light music.

[0017] Further, in S100, the original EEG data is obtained, and the method of calculating the emotional information in the original EEG data to form an emotional set is: the original EEG data is obtained through an electroencephalometer to form brain waves, and the relaxation degree is calculated at every time Tu on the brain waves as emotional information to form an emotional set CS={CS i}, where i is the sequence number of the emotion information, i∈[1,M], and M is the number of emotion information.

[0018] Furthermore, the method for calculating the degree of relaxation is: collecting the original EEG signals generated by the brain through an electroencephalogram (EEG) physiology instrument, filtering out the noise mixed in the signals and the disturbance caused by movement, and then amplifying the useful signals, and obtaining the degree of relaxation through the NeuroSky eSense algorithm for the processed EEG signals; wherein the degree of relaxation reflects the state of relaxation, and the value range is 0 to 100. When the relaxation value is high, the brain activity will be significantly reduced, indicating that the brain is in a higher state of relaxation, see the reference patent: On February 26, 2014, a Chinese invention patent with publication number CN103606245A is entitled Fatigue Driving Detection and Warning System Based on Bluetooth EEG Headset and Android Phone.

[0019] Furthermore, the algorithm for filtering out the noise mixed in the signal and the disturbance caused by the motion is one of the wavelet transform method and the independent component analysis method.

[0020] Further, in S200, the method of evaluating and grading the mental state of all the emotional information in the emotional set is: within the value range of i, traverse each element in the emotional set CS in turn, and calculate the unit mean every one minute. The calculation method is: remove the maximum and minimum values ​​in the emotional information within this minute, and calculate the average of the remaining as the unit mean. The calculated unit mean is used as the mental state index of the emotional information within this minute, and the mental state index is divided into ten relaxation levels;

[0021] More specifically, the specific way of dividing the relaxation into ten levels according to the mental state index is: relaxation level 1 (mental state index is 0-9), relaxation level 2 (mental state index is 10-19), relaxation level 3 (mental state index is 20-29), relaxation level 4 (mental state index is 30-39), relaxation level 5 (mental state index is 40-49), relaxation level 6 (mental state index is 50-59), relaxation level 7 (mental state index is 60-69), relaxation level 8 (mental state index is 70-79), relaxation level 9 (mental state index is 90-99), relaxation level 10 (mental state index is 100).

[0022] Further, in S300, according to the mental state assessment and classification results, the method of marking abnormal emotion sequences in the emotion set is: according to the emotion set CS, a relaxation change curve RL (time-emotion information) is drawn, where CS i The brain waves formed by the EEG signals obtained by the electroencephalography are i The relaxation calculated at i is the i-th time interval Tu;

[0023] Within the value range of i, all emotional information satisfying all mental state indicators less than 60 or relaxation levels less than 7 is searched on RL to form an abnormal emotional sequence RS={RS j}, where j represents the sequence number of the emotion information in the abnormal emotion sequence.

[0024] The Chinese invention patent application CN117899329B, filed on April 19, 2024, and entitled "A psychological counseling device based on VR technology", mentions the use of biofeedback training to enable patients to self-regulate their own nervous emotions. Since the biofeedback training method has little effect on people with poor self-regulation ability, and the need for frequent training will cause a large waste of computer resources, and frequent training for patients who have already experienced anxiety or depression and other emotional fluctuations during the perioperative period may cause frequent stimulation, which will have adverse effects on such people, see reference: Analysis of the clinical efficacy of biofeedback therapy combined with thioridazine in the treatment of anxious mental patients [J]. Therefore, the present invention provides the following method: in the abnormal emotion sequence that has been marked (the point where stimulation is generated in the biofeedback training), the emotional resistance corresponding to the emotional information in the abnormal emotion sequence is calculated in sequence, the intervention points that require external intervention are screened out, the number of stimulation points is reduced, and preventive measures can also be taken against the patient's emotional changes.

[0025] Further, in S400, the method of sequentially calculating the emotional resistance of the emotional information in the marked abnormal emotional sequence is as follows: for each emotional information in the abnormal emotional sequence, the point with the smallest difference from the current emotional information is retrieved on the curve RL and recorded as CS nj , retrieve all emotional information from the current emotional information forward on the curve RL, and record all those greater than the previous moment (T i -Tu) is the first information, and all the information greater than the next moment (T i +Tu) The first information of the emotional information is the second information, and all the second information greater than or equal to 60 is used as the third information to form a third emotional sequence. In the third emotional sequence, the first emotional information is used as the adjacent maximum value CS of the current emotional information. nmax , the first emotional information greater than or equal to 60 retrieved from the current emotional information on the curve RL is taken as the neighboring point of the current emotional information and is recorded as CS nqj ;

[0026] The reference factor RI of the current emotional information of the abnormal emotional sequence j CS nj The adjacent maximum value CS of the current emotional information nmax The absolute value of the difference between the point CS nj The adjacent maximum value CS of the current emotional information nmax The ratio of the time intervals between

[0027] Furthermore, the emotional resistance SR of the current emotional information of the abnormal emotional sequence j It is the product of the reference factor of the current emotion information and the time interval between the neighboring point of the current emotion information and the current emotion information.

[0028] Furthermore, in S500, the method of marking the intervention point in the abnormal emotion sequence according to the emotion resistance is as follows: let j=1, within the value range of j, all emotion information whose emotion resistance is greater than the average emotion resistance is marked as the intervention point.

[0029] The beneficial effect is: the reference factor corresponding to each emotional information in the abnormal emotional sequence above is based on the ratio between the emotional information with the smallest difference with the current emotional information and the adjacent maximum value with the current emotional information on the relaxation change curve RL. The reference factor calculated at this time can represent the maximum reference ability to adjust the current emotional information.

[0030] In a paper published on February 15, 2015, titled Verduyn P, Lavrijsen S. Which emotions last longest and why, it is discussed that emotions have a certain continuity. Emotional resistance utilizes emotional continuity. The emotional regulation capacity at the neighboring point and the actual regulation time are used to calculate the emotional regulation consumption of the current emotional information in the abnormal emotional sequence as the emotional resistance of the current emotional information. Because the reference factor represents the maximum reference capacity, the emotional resistance of the current emotional information calculated at this time is the maximum resistance. The emotional information calculated in this way can reduce the number of intervention points under reasonable circumstances when marking intervention points, thereby reducing the frequency of stimulation. Because the reference factor takes into account the self-regulation ability (i.e., emotional continuity), the intervention point marked at this time is an effective intervention point, that is, this time indicates that self-regulation cannot be completed at this time and external measures are needed to complete emotional regulation.

[0031] The above methods can accurately mark the location of the intervention point in the face of large emotional fluctuations, and can also effectively reduce the number of intervention points, thereby achieving the effect of reducing stimulation. However, when people are self-regulating, they sometimes use thought suppression. In the perioperative period, patients will feel anxious when they think of the surgical process and the results of the operation. In order to suppress this idea, they will use thought suppression to suppress these ideas and relieve anxiety. However, in the process of suppression, sometimes these ideas not only do not decrease, but are more than when they are not suppressed; sometimes, although these ideas are suppressed at the time, they will appear more afterwards. Researchers call the former the immediate enhancement effect and the latter the delayed rebound effect, see reference: The immediate enhancement effect and delayed rebound effect in thought suppression [J]. It is manifested as when a person suppresses his thoughts, the degree of relaxation will suddenly decrease instantaneously or rise slowly for a period of time and then decrease instantly. At this time, the intervention points selected by the above method will have redundant marks (because the delayed rebound effect causes the degree of relaxation to rise slowly for a period of time, resulting in a decrease in average emotional resistance) and missed marks (because the immediate enhancement effect causes the degree of relaxation to decrease instantaneously, resulting in an increase in average emotional resistance). In order to solve the above problems, the present invention adds diffusion conditions to eliminate the influence on the average emotional resistance in the following cumulative manner.

[0032] Further, in S500, the method of marking the intervention point in the abnormal emotion sequence according to the emotion resistance is: record the average emotion resistance of all emotion information in the abnormal emotion sequence as Diffdom(j);

[0033] Furthermore, within the value range of j, the emotional resistance of all emotional information in the abnormal emotional sequence is traversed to determine whether it meets the diffusion condition. If the emotional resistance of the current emotional information meets the diffusion condition, the value of Diffdom(j) is corrected by performing the corresponding cumulative calculation. If the emotional resistance of the current emotional information does not meet the diffusion condition, the Diffdom(j) at this time is still recorded as the average emotional resistance.

[0034] Specifically, the diffusion condition is: if the emotional resistance of the current emotional information is greater than the emotional resistance of the previous emotional information and the emotional resistance of the current emotional information is greater than Diffdom(j), then the emotional resistance of the current emotional information meets the diffusion condition, and the corresponding accumulation calculation is performed, and the accumulation calculation method is:

[0035] Correct Diffdom(j) to Diffdom(j) minus the average of the absolute value of the difference between the first emotional resistance and the current emotional resistance and Diffdom(j);

[0036] Preferably, the diffusion condition further includes: if the emotional resistance of the current emotional information is less than the emotional resistance of the previous emotional information and the emotional resistance of the current emotional information is less than the average emotional resistance, then the emotional resistance of the current emotional information satisfies the diffusion condition, and a corresponding cumulative calculation is performed, and the cumulative calculation method is:

[0037] Correct Diffdom(j) to the average of Diffdom(j) plus the absolute value of the difference between the first emotional resistance and the current emotional resistance and Diffdom(j);

[0038] Furthermore, all emotional information with emotional resistance greater than Diffdom(j) is marked as intervention points.

[0039] Further, in S600, the method of controlling the VR device to play soothing music according to the intervention point is: arranging all the currently marked intervention points in the time sequence of obtaining the electroencephalogram signal, generating an intervention time sequence for the time points corresponding to all the intervention points, marking the first time point as the start time, and marking the last time point as the end time;

[0040] Starting from the 7th day before surgery and ending on the 12th day after surgery, every day according to the generated intervention time sequence, the VR device was controlled to play the preset music from the start time until the VR device was controlled to stop playing the music at the end time.

[0041] The beneficial effects are as follows: the diffusion conditions in the above method are based on the immediate enhancement effect and the delayed rebound effect caused by thought suppression. When the immediate enhancement effect occurs, the degree of relaxation will suddenly and instantly decrease from a lower value. At this time, the calculated average emotional resistance increases. Therefore, through the above accumulation method, the excess emotional resistance caused by the sudden and instantaneous decrease in relaxation is eliminated, thereby solving the problem of excess marking when marking intervention points due to the immediate enhancement effect; similarly, when the delayed rebound effect occurs, the degree of relaxation will rise from a lower value and remain for a period of time. At this time, the calculated average emotional resistance decreases. Therefore, through the above accumulation method, the missing emotional resistance for the part where the degree of relaxation rises and remains is supplemented, thereby solving the problem of missed marking when marking intervention points due to the delayed rebound effect.

[0042] The present invention also provides a system for relieving patient perioperative anxiety and tension based on VR, the system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the following system units:

[0043] An emotion information acquisition unit, used to obtain EEG data through an EEG physiology instrument and calculate the emotion information to form an emotion set;

[0044] A mental state evaluation unit, used to calculate a mental state index for the emotional information in the emotional set and perform state classification;

[0045] An abnormal sequence marking unit, used for marking abnormal emotion sequences in the emotion set according to the mental state index;

[0046] An emotional resistance calculation unit, used to calculate the emotional resistance of all emotional information in the abnormal emotional sequence;

[0047] An intervention point marking unit, used to mark intervention points in abnormal sequences based on emotional resistance;

[0048] The VR intervention unit is used to control the VR device to play preset music according to the intervention point.

[0049] The beneficial effects of the present invention are as follows: the present invention provides a method and system based on VR to relieve patients' perioperative anxiety and tension, which can provide timely psychological intervention for patients who have large mood swings such as anxiety or depression during the perioperative period, while eliminating the phenomenon of missed marks and redundant marks caused by the immediate enhancement effect and the delayed rebound effect when marking intervention points on the EEG signal, avoiding the large occupation of computer resources caused by repeated detection and the high-frequency stimulation caused by repeated training, making up for the shortcoming that biofeedback training cannot perform psychological adjustments on special types of people, and can help more patients relieve the anxiety and tension caused by the perioperative period. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Shown is a flow chart of a VR-based method for alleviating patients’ perioperative anxiety and tension;

[0051] Figure 2 Shown is a system structure diagram for relieving patients' perioperative anxiety and tension based on VR. DETAILED DESCRIPTION

[0052] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0053] Embodiment 1: Figure 1 Shown is a flow chart of a VR-based method for alleviating patients' perioperative anxiety and stress.

[0054] Reference Figure 1 The present invention proposes a method for alleviating patients' perioperative anxiety and tension based on VR, and the method comprises the following steps:

[0055] S100, obtaining raw EEG data, and calculating the emotional information in the raw EEG data to form an emotional set;

[0056] S200, evaluating and grading the mental state of all the emotion information in the emotion set in turn;

[0057] S300, marking abnormal emotion sequences in the emotion set according to the mental state assessment and classification results;

[0058] S400, sequentially calculating the emotional resistance of the emotional information data in the marked abnormal emotional sequence;

[0059] S500, marking intervention points in the emotion sequence according to the emotion resistance;

[0060] S600, controls the VR device to play preset music according to the intervention point.

[0061] Furthermore, the raw EEG data were acquired 30 minutes after the patients finished their dinner.

[0062] Furthermore, in S100, raw EEG data is acquired through an electroencephalogram (EEG) physiology instrument.

[0063] Furthermore, raw EEG data were obtained starting from the eighth day before the patient's surgery.

[0064] Furthermore, the total acquisition time is 60 min.

[0065] Furthermore, in S600, the VR device is an A1 VR all-in-one device.

[0066] Further, in S600, the preset music type played by the device is meditation music.

[0067] Further, in S100, the original EEG data is obtained, and the method of calculating the emotional information in the original EEG data to form an emotional set is: the original EEG data is obtained through an electroencephalometer to form brain waves, and the relaxation degree is calculated at every time Tu on the brain waves as emotional information to form an emotional set CS={CS i}, where i is the serial number of the emotion information, i∈[1,M], M is the number of emotion information, Tu=1s.

[0068] Furthermore, the method for calculating the degree of relaxation is: collecting the original EEG signals generated by the brain through an electroencephalogram (EEG) physiology instrument, filtering out the noise mixed in the signals and the disturbance caused by movement, and then amplifying the useful signals, and obtaining the degree of relaxation through the NeuroSky eSense algorithm for the processed EEG signals; wherein the degree of relaxation reflects the state of relaxation, and the value range is 0 to 100. When the relaxation value is high, the brain activity will be significantly reduced, indicating that the brain is in a higher state of relaxation, see the reference patent: On February 26, 2014, a Chinese invention patent with publication number CN103606245A is entitled Fatigue Driving Detection and Warning System Based on Bluetooth EEG Headset and Android Phone.

[0069] Furthermore, the algorithm for filtering out the noise mixed in the signal and the disturbance caused by the motion is the wavelet transform method.

[0070] Further, in S200, the method for evaluating and grading the mental state of all emotional information in the emotion set in turn is: within the value range of i, traverse each element in the emotion set CS in turn, and calculate the unit mean every minute. The calculation method is: remove the maximum and minimum values ​​in the emotional information within this minute, and average the remaining values ​​as the unit mean. The calculated unit mean is used as the mental state indicator of the emotional information within this minute, and is divided into ten relaxation levels according to the mental state indicators.

[0071] More specifically, the specific way of dividing the relaxation into ten levels according to the mental state index is: relaxation level 1 (mental state index is 0-9), relaxation level 2 (mental state index is 10-19), relaxation level 3 (mental state index is 20-29), relaxation level 4 (mental state index is 30-39), relaxation level 5 (mental state index is 40-49), relaxation level 6 (mental state index is 50-59), relaxation level 7 (mental state index is 60-69), relaxation level 8 (mental state index is 70-79), relaxation level 9 (mental state index is 90-99), relaxation level 10 (mental state index is 100).

[0072] Further, in S300, the method of marking abnormal emotion sequences in the emotion set according to the mental state assessment and classification results is as follows: according to the emotion set CS, a relaxation change curve RL (time-emotion information) is drawn, wherein CS i The brain waves formed by the EEG signals obtained by the electroencephalography are i The relaxation calculated at i is the i-th time interval Tu;

[0073] Within the value range of i, all emotional information satisfying all mental state indicators less than 60 or relaxation levels less than 7 is searched on RL to form an abnormal emotional sequence RS={RS j}, where j represents the sequence number of the emotion information in the abnormal emotion sequence.

[0074] Further, in S400, the method of sequentially calculating the emotional resistance of the emotional information in the marked abnormal emotional sequence is as follows: for each emotional information in the abnormal emotional sequence, the point with the smallest difference from the current emotional information is retrieved on the curve RL and recorded as CS nj , retrieve all emotional information from the current emotional information forward on the curve RL, and record all those greater than the previous moment (T i -Tu) is the first information, and all the information greater than the next moment (T i +Tu) The first information of the emotional information is the second information, and all the second information greater than or equal to 60 is used as the third information to form a third emotional sequence. In the third emotional sequence, the first emotional information is used as the adjacent maximum value CS of the current emotional information. nmax , the first emotional information greater than or equal to 60 retrieved from the current emotional information on the curve RL is taken as the neighboring point of the current emotional information and is recorded as CS nqj ;

[0075] The reference factor RI of the current emotional information of the abnormal emotional sequence j For: Click CS nj The adjacent maximum value CS of the current emotional information nmax The absolute value of the difference between the point CS nj The adjacent maximum value CS of the current emotional information nmax The ratio of the time intervals between

[0076] Furthermore, the emotional resistance SR of the current emotional information of the abnormal emotional sequence j is the product of the reference factor of the current emotion information and the time interval between the neighboring point of the current emotion information and the current emotion information;

[0077] The reference factor RI j The equivalent meaning is:

[0078] RI j =|CS nj -CS nmax | / T nj

[0079] Among them, CS nj For the curve RL from CS j Forward retrieval and CS j The point with the smallest difference, CSnmax For the curve RL from CS j Search forward for the first one that meets the condition: 60≤CS j+1 ≤CS j And 60≤CS j-1 ≤CS j The point is the adjacent maximum value, T nj CS nj With point CS nmax The time interval between.

[0080] The emotional resistance SR j The equivalent expression is:

[0081] SR j =RI j ×T Rj

[0082] Among them, RI j CS for emotional information j The reference factor, T Rj For the curve RL from point CS j The first emotional information point that satisfies the mental state index greater than or equal to 60 is the neighboring point CS. nqj With CS j time interval.

[0083] Furthermore, in S500, the method of marking the intervention point in the abnormal emotion sequence according to the emotion resistance is as follows: let j=1, within the value range of j, all emotion information whose emotion resistance is greater than the average emotion resistance is marked as the intervention point.

[0084] Further, in S600, the method for controlling the VR device to play the preset music according to the intervention point is: arranging all the currently marked intervention points according to the time sequence of obtaining the EEG signal, generating an intervention time sequence for the time points corresponding to all the intervention points, marking the first time point as the start time, and marking the last time point as the end time;

[0085] Starting from the 7th day before surgery and ending on the 12th day after surgery, every day according to the generated intervention time sequence, the VR device was controlled to play the preset music from the start time until the VR device was controlled to stop playing the music at the end time.

[0086] Embodiment 2: Embodiment 2 replaces the method of marking intervention points in the emotion sequence based on Embodiment 1, specifically: the average emotion resistance of all emotion information in the abnormal emotion sequence is recorded as Diffdom(j);

[0087] Furthermore, within the value range of j, the emotional resistance of all emotional information in the abnormal emotional sequence is traversed to determine whether it meets the diffusion condition. If the emotional resistance of the current emotional information meets the diffusion condition, the value of Diffdom(j) is corrected by performing the corresponding cumulative calculation. If the emotional resistance of the current emotional information does not meet the diffusion condition, the Diffdom(j) at this time is still recorded as the average emotional resistance.

[0088] Specifically, the diffusion condition is: if the emotional resistance of the current emotional information is greater than the emotional resistance of the previous emotional information and the emotional resistance of the current emotional information is greater than Diffdom(j), then the emotional resistance of the current emotional information meets the diffusion condition, and the corresponding accumulation calculation is performed, and the accumulation calculation method is:

[0089] Correct Diffdom(j) to Diffdom(j) minus the average of the absolute value of the difference between the first emotional resistance and the current emotional resistance and Diffdom(j);

[0090] Preferably, the diffusion condition further includes: if the emotional resistance of the current emotional information is less than the emotional resistance of the previous emotional information and the emotional resistance of the current emotional information is less than the average emotional resistance, then the emotional resistance of the current emotional information satisfies the diffusion condition, and a corresponding cumulative calculation is performed, and the cumulative calculation method is:

[0091] Correct Diffdom(j) to the average of Diffdom(j) plus the absolute value of the difference between the first emotional resistance and the current emotional resistance and Diffdom(j);

[0092] Furthermore, all emotional information with emotional resistance greater than Diffdom(j) is marked as intervention points. The equivalent meaning of the diffusion condition is expressed as:

[0093] When SR j >SR j-1 And SR j >SR jmean hour:

[0094]

[0095] When SR j ≤SR j-1 And SR j ≤SR jmean hour

[0096]

[0097] Among them, j is a variable, is the average emotional resistance of all emotional information in all abnormal emotional sequences.

[0098] The beneficial effects of the present invention are as follows: the present invention provides a method and system based on VR to relieve patients' perioperative anxiety and tension, which can provide timely psychological intervention for patients who have large mood swings such as anxiety or depression during the perioperative period, while eliminating the phenomenon of missed marks and redundant marks caused by the immediate enhancement effect and the delayed rebound effect when marking intervention points on the EEG signal, avoiding the large occupation of computer resources caused by repeated detection and the high-frequency stimulation caused by repeated training, making up for the shortcoming that biofeedback training cannot perform psychological adjustments on special types of people, and can help more patients relieve the anxiety and tension caused by the perioperative period.

[0099] Furthermore, the present invention is further verified by the following comparative experiments:

[0100] One hundred surgical patients were randomly divided into an experimental group and a control group, with 50 patients in each group. The anxiety and surgical fear levels of the patients were recorded before and after the intervention, and the psychological changes of the surgical patients were observed and analyzed.

[0101] Furthermore, the experimental group performed VR intervention on the patients using the method of Example 2 of the present invention.

[0102] Furthermore, the control group performed VR intervention on patients using the method in Example 1 of a Chinese invention patent application entitled "A psychological counseling device based on VR technology" with publication number CN117899329B applied on April 19, 2024.

[0103] Furthermore, the surgical patients participating in the experiment were selected from patients admitted from March 20, 2024 to April 20, 2024.

[0104] Furthermore, the criteria for surgical patients participating in the experiment were: (1) no severe cognitive or psychiatric disorders; (2) age range of 24 to 65 years old, able to cooperate with the experimental procedures; (3) no obvious hearing or visual impairment, able to perceive and appreciate music normally; (4) no symptoms of music allergy or adverse reactions to music intervention.

[0105] Furthermore, the preparation measures for each subject before the experiment included: (1) assisting the patient to complete relevant examinations before the operation; (2) understanding the patient's basic condition and medical history in detail; (3) explaining the surgery-related knowledge and precautions to the patient; and (4) cleaning the skin and helping the patient regulate his or her emotions to avoid excessive tension and anxiety.

[0106] Furthermore, the experiment was started 30 minutes after the patients finished their dinner.

[0107] Furthermore, the anxiety of the patients before and after the intervention was scored using the self-rating anxiety scale (SAS), where the total score of SAS ranges from 0 to 100 (with a cutoff value of 50 points), and the higher the score, the more severe the patient's anxiety.

[0108] Furthermore, the surgical fear questionnaire (SFQ) was used to assess the fear of surgery in patients before and after the intervention. The score ranged from 0 to 100, and the higher the score, the higher the fear of surgery.

[0109] SPSS 20.0 statistical software was used for data processing and analysis. The measurement data were in accordance with the normal distribution and were expressed as mean ± standard deviation. The independent sample t test was used for inter-group comparison; the paired sample t test was used for intra-group comparison; the technical data were expressed as number of cases (percentage) [n(%)], and the inter-group comparison was performed. Test; p < 0.05 was considered statistically significant.

[0110] The anxiety scores (SAS) before and after the intervention are shown in Table 1; the fear scores (SFQ) before and after the intervention are shown in Table 2:

[0111] Table 1 SAS scores of the two groups before and after intervention

[0112]

[0113] Analysis: Before the intervention, the SAS values ​​of the experimental group and the control group were close, indicating that the anxiety levels of the patients were similar and had no statistical significance (p>0.05); after the intervention, the SAS values ​​of the experimental group and the control group were both lower than those before the intervention, and the difference between the SAS value of the experimental group after the intervention and the SAS value before the intervention was greater than the difference between the SAS value of the control group after the intervention and the SAS value before the intervention, and the SAS value of the experimental group after the intervention was smaller than that of the control group after the intervention, and had statistical significance (p<0.05), indicating that the method provided by the present invention can help patients relieve anxiety better, and has better effect than the control group.

[0114] Table 2 SFQ scores of the two groups before and after intervention

[0115]

[0116] Analysis: Before the intervention, the SFQ values ​​of the experimental group and the control group were close, indicating that the patients' fear of surgery was similar and had no statistical significance (p>0.05); after the intervention, the SFQ values ​​of the experimental group and the control group were lower than those before the intervention, and the difference between the SFQ value of the experimental group after the intervention and the SFQ value before the intervention was greater than the difference between the SFQ value of the control group after the intervention and the SFQ value before the intervention, and the SAS value of the experimental group after the intervention was smaller than the SFQ value of the control group after the intervention, and had statistical significance (p<0.05), indicating that the method provided by the present invention can help patients better alleviate their fear of surgery, and has better effect than the control group.

[0117] In addition, the present invention also provides an embodiment of a system for relieving patient perioperative anxiety and tension based on VR, such as Figure 2 The figure shows a structure diagram of a system for relieving patients' perioperative anxiety and tension based on VR according to the present invention. The system for relieving patients' perioperative anxiety and tension based on VR in this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned embodiment of the system for relieving patients' perioperative anxiety and tension based on VR are implemented.

[0118] The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system:

[0119] An emotion information acquisition unit, used to obtain EEG data through an EEG physiology instrument and calculate the emotion information to form an emotion set;

[0120] A mental state evaluation unit, used to calculate a mental state index for the emotional information in the emotional set and perform state classification;

[0121] An abnormal sequence marking unit, used for marking abnormal emotion sequences in the emotion set according to the mental state index;

[0122] An emotional resistance calculation unit, used to calculate the emotional resistance of all emotional information in the abnormal emotional sequence;

[0123] An intervention point marking unit, used to mark intervention points in abnormal sequences based on emotional resistance;

[0124] The VR intervention unit is used to control the VR device to play preset music according to the intervention point.

[0125] The VR-based system for relieving patients' perioperative anxiety and tension can be run on computing devices such as desktop computers, notebooks, PDAs, and cloud servers. The VR-based system for relieving patients' perioperative anxiety and tension, the executable system may include, but is not limited to, processors and memories. Those skilled in the art will understand that the examples are merely examples of VR-based systems for relieving patients' perioperative anxiety and tension, and do not constitute a limitation on VR-based systems for relieving patients' perioperative anxiety and tension, and may include more or fewer components than the examples, or a combination of certain components, or different components, for example, the VR-based system for relieving patients' perioperative anxiety and tension may also include input and output devices, network access devices, buses, and the like.

[0126] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the system operation system for relieving patient perioperative anxiety and tension based on VR, and uses various interfaces and lines to connect the various parts of the entire system operation system for relieving patient perioperative anxiety and tension based on VR.

[0127] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the VR-based system for relieving patient perioperative anxiety and tension by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0128] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

Claims

1. A method for relieving patients' perioperative anxiety and tension based on VR, characterized in that: The method comprises the following steps: S100, obtaining raw EEG data, and calculating the emotional information in the raw EEG data to form an emotional set; S200, evaluating and grading the mental state of all the emotion information in the emotion set in turn; S300, marking abnormal emotion sequences in the emotion set according to the mental state assessment and classification results; S400, sequentially calculating the emotional resistance of the emotional information data in the marked abnormal emotional sequence; S500, marking intervention points in the emotional sequence based on emotional resistance; S600, controls the VR device to play preset music according to the intervention point; In S300, the method of marking abnormal emotion sequences in the emotion set according to the mental state assessment and classification results is: According to the emotion set CS, the relaxation change curve RL is drawn. The relaxation change curve RL is the time-emotion information change curve, where CS i The brain waves formed by the EEG signals obtained by the electroencephalography are i The relaxation calculated at i is the i-th time interval Tu; Within the value range of i, all emotional information satisfying all mental state indicators less than 60 or relaxation levels less than 7 is searched on RL to form an abnormal emotional sequence RS={RS j }, where j represents the sequence number of the emotion information in the abnormal emotion sequence; In S400, the method of sequentially calculating the emotional resistance of the emotional information data in the marked abnormal emotional sequence is: For each piece of emotional information in the abnormal emotional sequence, the point on the curve RL that is retrieved from the current emotional information and has the smallest difference with the current emotional information is recorded as CS nj On the curve RL, retrieve all emotional information from the current emotional information forward, with all those greater than the previous moment (T i -Tu) emotional information is the first information, and all the information greater than the next moment (T i +Tu) The first information of the emotional information is the second information, and all the second information greater than or equal to 60 is used as the third information to form a third emotional sequence. In the third emotional sequence, the first emotional information is used as the adjacent maximum value CS of the current emotional information. nmax , the first emotional information greater than or equal to 60 retrieved from the current emotional information on the curve RL is taken as the neighboring point of the current emotional information and is recorded as CS nqj ; Record the reference factor RI of the current emotional information of the abnormal emotional sequence j For, click CS nj The adjacent maximum value CS of the current emotional information nmax The absolute value of the difference between the point CS nj The adjacent maximum value CS of the current emotional information nmax The emotional resistance SR of the current emotional information j is the reference factor RI of the current emotional information j The product of the time interval between the neighboring point of the current emotion information and the current emotion information; In S500, the method of marking the intervention point in the abnormal emotion sequence according to the emotion resistance is as follows: let j=1, within the value range of j, all emotion information whose emotion resistance is greater than the average emotion resistance is marked as the intervention point.

2. The method for relieving patient perioperative anxiety and tension based on VR according to claim 1, characterized in that: In S100, the original EEG data is obtained, and the method for calculating the emotional information in the original EEG data to form an emotional set is as follows: the original EEG data is obtained through an electroencephalometer to form brain waves, and the relaxation degree is calculated at every time Tu on the brain waves as emotional information to form an emotional set CS={CS i }, where i is the sequence number of the emotion information, i∈[1,M], and M is the number of emotion information.

3. The method for relieving patient perioperative anxiety and tension based on VR according to claim 2, characterized in that: In S200, the method for evaluating and grading the mental state of all emotional information in the emotion set in turn is: within the value range of i, traverse each element in the emotion set CS in turn, and calculate the unit mean every minute. The calculation method is: remove the maximum and minimum values ​​in the emotional information within this minute, and average the remaining values ​​as the unit mean. The calculated unit mean is used as the mental state indicator of the emotional information within this minute, and is divided into ten relaxation levels according to the mental state indicators.

4. The method for relieving patient perioperative anxiety and tension based on VR according to claim 3, characterized in that: Replace the method of marking intervention points in abnormal emotion sequences based on emotion resistance with: The average emotional resistance of all emotional information in the abnormal emotional sequence is recorded as Diffdom(j); Within the value range of j, the emotional resistance of all emotional information in the abnormal emotional sequence is traversed to determine whether it meets the diffusion condition. If the emotional resistance of the current emotional information meets the diffusion condition, the value of Diffdom(j) is corrected by performing the corresponding cumulative calculation. If the emotional resistance of the current emotional information does not meet the diffusion condition, the Diffdom(j) at this time is still recorded as the average emotional resistance. Specifically, the diffusion condition is: if the emotional resistance of the current emotional information is greater than the emotional resistance of the previous emotional information and the emotional resistance of the current emotional information is greater than Diffdom(j), then the emotional resistance of the current emotional information meets the diffusion condition, and the corresponding accumulation calculation is performed, and the accumulation calculation method is: Correct Diffdom(j) to Diffdom(j) minus the average of the absolute value of the difference between the first emotional resistance and the current emotional resistance and Diffdom(j); The diffusion condition also includes: if the emotional resistance of the current emotional information is less than the emotional resistance of the previous emotional information and the emotional resistance of the current emotional information is less than the average emotional resistance, then the emotional resistance of the current emotional information meets the diffusion condition, and the corresponding cumulative calculation is performed, and the cumulative calculation method is: Correct Diffdom(j) to the average of Diffdom(j) plus the absolute value of the difference between the first emotional resistance and the current emotional resistance and Diffdom(j); All emotional information with emotional resistance greater than Diffdom(j) is marked as intervention points.

5. The method for relieving patient perioperative anxiety and tension based on VR according to claim 4, characterized in that: In S600, the method for controlling the VR device to play the preset music according to the intervention point is: arranging all the currently marked intervention points in the time sequence of obtaining the EEG signal, generating an intervention time sequence for the time points corresponding to all the intervention points, marking the first time point as the start time, and marking the last time point as the end time; Starting from the 7th day before surgery and ending on the 12th day after surgery, every day according to the generated intervention time sequence, the VR device was controlled to play the preset music from the start time until the VR device was controlled to stop playing the music at the end time.

6. A VR-based system for alleviating patients' perioperative anxiety and tension, characterized by: The VR-based system for alleviating patients' perioperative anxiety and tension includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the VR-based method for alleviating patients' perioperative anxiety and tension as described in any one of claims 1 to 5 are implemented.

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