Method and system for monitoring intraoperative state of anorectal patient in real time

Abstract

The invention relates to the technical field of mode recognition, in particular to a real-time monitoring method and system for the intraoperative state of an anorectal patient. The method comprises the following steps: firstly, dividing a sampling time interval according to all emotion starting points and emotion ending points, and obtaining each target segmentation time interval; in the target segmented time interval, according to the correlation between the heart rate value and the blood pressure value at the continuous sampling moments, obtaining a prominent weight of the target segmented time interval; the weight of the error function is set according to the protruding weight of each target segmented time interval, all heart rate values in each target segmented time interval are filtered, and filtering data are obtained. According to the embodiment of the invention, the highlighted weight reflects the degree that the target segment time interval cannot correspond to the emotion stage, so that the heart rate fluctuation interference caused by the emotion is inhibited, the filtering data can more accurately reflect the real physiological characteristics of the patient, and the accuracy of identifying the real physiological characteristics of the patient is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pattern recognition, and in particular to a method and system for real-time monitoring of the intraoperative status of anorectal patients. Background Art

[0002] During anorectal surgery, real-time monitoring of the patient's vital signs helps to understand the patient's condition and ensure the safety of the operation. Heart rate monitoring data can reflect the patient's physiological signs. Since heart rate monitoring data is easily interfered by noise, doctors may misjudge the patient's true physical condition during the operation, affecting the operation process and the patient's life safety. Therefore, it is necessary to denoise the heart rate monitoring data to prevent misdiagnosis and accident risks during the operation.

[0003] The existing minimum mean square adaptive filtering algorithm has been widely used in the field of biomedical signal processing. In the process of filtering heart rate monitoring data using the minimum mean square adaptive filtering algorithm, the minimum mean square adaptive filtering algorithm uses the minimum mean square error as the error function, ignoring the impact of the patient's emotions on filtering accuracy, resulting in the problem that the filtering results are difficult to accurately reflect the patient's true physiological characteristics, resulting in poor effect in real-time identification of the patient's true physiological characteristics. Summary of the invention

[0004] In order to solve the technical problem that the filtering effect of heart rate monitoring data in the prior art is poor, the purpose of the present invention is to provide a method and system for real-time monitoring of the intraoperative status of anorectal patients. The technical scheme adopted is as follows:

[0005] A method for real-time monitoring of the intraoperative status of anorectal patients, the method comprising:

[0006] Obtain the heart rate and blood pressure values ​​at each sampling time in the sampling time interval;

[0007] According to the local deviation from normal and the local difference of each heart rate value, the emotion starting degree of each heart rate value is obtained; according to the emotion starting degree of each heart rate value, each emotion starting point is obtained; according to all the emotion starting points, the sampling time interval is divided to obtain each segmented time interval to be analyzed; in the segmented time interval to be analyzed, according to the local change trend of each heart rate value, the local deviation from normal and the time distance from the emotion starting point, the emotion termination degree of the heart rate value is obtained; according to the emotion termination degree of all heart rate values ​​in the segmented time interval to be analyzed, the emotion termination point is obtained; according to all the emotion starting points and the emotion termination points, the sampling time interval is divided to obtain each target segmented time interval;

[0008] In the target segmented time interval, the prominent weight of the target segmented time interval is obtained according to the correlation between the heart rate value and the blood pressure value at continuous sampling moments; the weight of the error function is set according to the prominent weight of each target segmented time interval, and all heart rate values ​​in each target segmented time interval are filtered to obtain filtered data.

[0009] Furthermore, the method for obtaining the emotion onset degree includes:

[0010] Get all neighboring heart rate values ​​within a preset neighborhood range;

[0011] In a preset neighborhood range, according to the difference between all the neighboring heart rate values ​​on the front side of the heart rate value and the preset normal heart rate value and the deviation of the fluctuation of all the neighboring heart rate values ​​on the front side of the heart rate value from the normal fluctuation, the front side normality of the heart rate value is obtained;

[0012] In the preset neighborhood range, according to the local differences and deviations from normal of all neighboring heart rate values ​​on the rear side of the heart rate value, the rear side variation degree of the heart rate value is obtained;

[0013] The emotion onset degree of the heart rate value is obtained according to the front side normality and the rear side variation degree; the front side normality and the rear side variation degree are both positively correlated with the emotion onset degree.

[0014] Furthermore, the method for obtaining the front normality includes:

[0015] In a preset neighborhood range, according to the overall distribution of all neighboring heart rate values ​​on the front side of the heart rate value, the front overall heart rate value of the heart rate value is obtained; according to the difference between the front overall heart rate value and the preset normal heart rate value, the front heart rate abnormality of the heart rate value is obtained;

[0016] According to the volatility of all neighboring heart rate values ​​on the front side of the heart rate value, the front side overall fluctuation value of the heart rate value is obtained; according to the deviation between the front side overall fluctuation value of the heart rate value and the preset normal fluctuation value, the front side fluctuation abnormality of the heart rate value is obtained;

[0017] The front normality of the heart rate value is obtained according to the front heart rate abnormality and the front fluctuation abnormality; the front heart rate abnormality and the front fluctuation abnormality are both negatively correlated with the front normality.

[0018] Furthermore, the method for obtaining the rear side variation degree includes:

[0019] Among all neighboring heart rate values ​​behind the heart rate value, according to the deviation between each neighboring heart rate value and its previous neighboring heart rate value, a local difference index of the neighboring heart rate value is obtained;

[0020] Obtaining a rear-side overall change index according to the overall distribution of the local difference index of all neighboring heart rate values ​​on the rear side of the heart rate value;

[0021] Obtaining the degree of mutation of the heart rate value according to the difference between the local difference index of a heart rate value in a neighborhood of the heart rate value and the rear overall change index;

[0022] According to the deviation between each neighborhood heart rate value and the preset normal heart rate value, obtaining the posterior heart rate abnormality of the heart rate value;

[0023] According to the mutation degree and the posterior heart rate abnormality, the posterior variation of the heart rate value is obtained; the mutation degree and the posterior heart rate abnormality are both positively correlated with the posterior variation.

[0024] Furthermore, the method for obtaining the emotion starting point includes:

[0025] The heart rate value whose emotion starting degree is greater than the preset emotion starting value is used as the emotion starting point.

[0026] Furthermore, the method for obtaining the degree of emotion termination includes:

[0027] In a preset neighborhood range, according to the change trend of all neighboring heart rate values ​​in front of the heart rate value, obtain the front side downward trend coefficient of the heart rate value;

[0028] According to the difference between each neighboring heart rate value on the rear side of the heart rate value and the preset normal heart rate value in the preset neighborhood range, obtaining the rear normal coefficient of the heart rate value;

[0029] According to the time distance between the heart rate value and the starting point of the emotion, the time distance coefficient of the heart rate value is obtained;

[0030] According to the front downward trend coefficient of the heart rate value, the rear normal coefficient, the time distance coefficient and the emotion starting degree, the emotion termination degree of the heart rate value is obtained; the front downward trend coefficient, the rear normal coefficient and the time distance coefficient are all positively correlated with the emotion termination degree; the emotion starting degree and the emotion termination degree are negatively correlated.

[0031] Furthermore, the method for obtaining the front side downward trend coefficient includes:

[0032] Calculate the difference between each neighborhood heart rate value and its previous neighborhood heart rate value to obtain the change parameter of the neighborhood heart rate value;

[0033] All the neighborhood heart rate values ​​with negative variation parameters are counted to obtain the front downward trend coefficient.

[0034] Furthermore, the method for obtaining the rear normal coefficient includes:

[0035] Among all neighboring heart rate values ​​behind the heart rate value, the absolute value of the difference between each neighboring heart rate value and the preset normal heart rate value is calculated to obtain a deviation parameter of the neighboring heart rate value from the normal state;

[0036] The mean of the deviation from normal parameters of all neighboring heart rate values ​​on the posterior side of the heart rate value is calculated to obtain the posterior normal coefficient.

[0037] Furthermore, the method for obtaining the prominence weight includes:

[0038] In the target segmented time interval, the Pearson coefficients of the heart rate value and the blood pressure value are calculated, and the Pearson coefficients are normalized to obtain the prominent weight of the target segmented time interval.

[0039] The present invention proposes a real-time monitoring system for the intraoperative status of anorectal patients, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the system implements any one of the steps of a method for real-time monitoring the intraoperative status of anorectal patients.

[0040] The present invention has the following beneficial effects:

[0041] In order to suppress the interference of heart rate fluctuations caused by emotions, the present invention needs to divide the sampling time interval according to the heart rate fluctuation characteristics corresponding to the emotions, taking into account that emotions can cause the heart rate to show staged fluctuation characteristics, and taking into account that patients often experience nervousness during surgery, as the nervousness arises, the heart rate shows a significant increase; as the nervousness persists, the heart rate data rises to a higher level and then maintains a higher level; as the nervousness eases, the heart rate shows a gradual downward trend until it returns to a normal heart rate, the emotion starting point and the emotion ending point are determined, and the sampling time interval is divided to obtain each target segmented time interval, which preliminarily reflects the emotional stage or the real physiological stage.

[0042] Considering that when patients have emotional fluctuations, heart rate values ​​and blood pressure values ​​have a strong correlation, that is, when patients are in the nervous stage, as heart rate increases, blood pressure tends to rise. Considering that in the physiological imbalance stage and the physiological balance stage, heart rate values ​​and blood pressure values ​​have a weak correlation. Considering the need to suppress the interference of heart rate fluctuations caused by emotions, the prominent weight of the target segmented time interval is obtained. The larger the prominent weight, the weaker the correlation between the heart rate value and the blood pressure value in the target segmented time interval, the less likely the target segmented time interval is to correspond to the emotional stage, and the more data that needs to be retained for filtering. The weight of the error function is set according to the prominent weight of each target segmented time interval, and all heart rate values ​​in each target segmented time interval are filtered to suppress the interference of heart rate fluctuations caused by emotions, so that the filtered data can more accurately reflect the patient's true physiological characteristics and improve the accuracy of identifying the patient's true physiological characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flow chart of a method for real-time monitoring of the intraoperative status of anorectal patients provided by one embodiment of the present invention;

[0045] Figure 2 A flow chart of a method for obtaining the onset degree of emotion provided by an embodiment of the present invention;

[0046] Figure 3 A flow chart of a method for obtaining front side normality provided by one embodiment of the present invention;

[0047] Figure 4 A flow chart of a method for obtaining a rear side variation degree provided by an embodiment of the present invention;

[0048] Figure 5 A flow chart of a method for obtaining the degree of emotional termination provided by an embodiment of the present invention;

[0049] Figure 6 A structural diagram of a real-time monitoring system for the intraoperative status of anorectal patients provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method and system for real-time monitoring of the intraoperative status of anorectal patients proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0051] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0052] The following is a detailed description of a method and system for real-time monitoring of the intraoperative status of anorectal patients provided by the present invention in conjunction with the accompanying drawings.

[0053] The present invention provides a method and system for real-time monitoring of the intraoperative status of anorectal patients. Figure 1 , which shows a flow chart of a method for real-time monitoring of the intraoperative status of anorectal patients provided by an embodiment of the present invention, the method comprising the following steps:

[0054] Step S1: Obtain the heart rate value and blood pressure value at each sampling time in the sampling time interval.

[0055] The main purpose of the present invention is to perform more reasonable filtering on the heart rate value, and to provide data reference for subsequent analysis of the possibility that the heart rate value is a true physiological characteristic by obtaining the heart rate value and blood pressure value at each sampling moment in the sampling time interval.

[0056] Specifically, anesthetic drugs are used to perform local anesthesia on anorectal patients. During the operation when the anesthesia depth of the anorectal patient reaches the requirements of anorectal surgery, the heart rate value and blood pressure value on the intelligent medical monitoring instrument are synchronously sampled according to the preset sampling frequency, that is, the heart rate value at each sampling moment in the sampling time interval is counted using the heart rate sensor equipped on the intelligent medical monitoring instrument according to the preset sampling frequency; that is, the blood pressure value at each sampling moment in the sampling time interval is counted using the blood pressure sensor equipped on the intelligent medical monitoring instrument according to the preset sampling frequency. In one embodiment of the present invention, the preset sampling frequency is 0.1 seconds each time, which can be set by the implementer according to the implementation scenario. In one embodiment of the present invention, the sampling time interval is 10 minutes, and the last sampling moment of the sampling time interval is the current moment, which can be set by the implementer according to the implementation scenario.

[0057] It should be noted that, in order to facilitate calculation, all indicator data involved in the calculation in the embodiment of the present invention are preprocessed to eliminate the dimension effect. The specific means of eliminating the dimension effect are technical means well known to those skilled in the art and are not limited here.

[0058] The present invention uses heart rate monitoring to reflect whether the patient's physiological condition maintains balance during the operation, so as to ensure whether the current anorectal surgery needs to adjust the medicine to ensure the safety of the operation. For example, the characteristic of physiological balance is that the heart rate is stable at a normal level, and the characteristic of physiological imbalance is that the heart rate fluctuates too much or deviates from the normal level. If the patient's physiological imbalance is affected by the side effects of anesthetic drugs during the operation, resulting in a slow heart rate, the heart rate can be increased by adjusting drugs such as atropine to ensure the safety of the operation. The heart rate fluctuations caused by emotions interfere with the judgment of the patient's true physiological characteristics. It is necessary to suppress these non-pathological heart rate fluctuations to help doctors more accurately evaluate the patient's heart function. The present invention filters all heart rate values ​​to suppress the interference of heart rate fluctuations caused by emotions, so that the filtering results can more accurately reflect the patient's true physiological characteristics.

[0059] Step S2: According to the local deviation of each heart rate value from normal and the local difference, obtain the emotion starting degree of each heart rate value; according to the emotion starting degree of each heart rate value, obtain each emotion starting point; according to all emotion starting points, divide the sampling time interval to obtain each segmented time interval to be analyzed; in the segmented time interval to be analyzed, according to the local change trend of each heart rate value, the local deviation from normal and the time distance from the emotion starting point, obtain the emotion termination degree of the heart rate value; according to the emotion termination degree of all heart rate values ​​in the segmented time interval to be analyzed, obtain the emotion termination point; according to all emotion starting points and emotion termination points, divide the sampling time interval to obtain each target segmented time interval.

[0060] In order to suppress the interference of heart rate fluctuations caused by emotions, the present invention needs to divide the sampling time interval according to the heart rate fluctuation characteristics corresponding to the emotions. Considering that emotions can cause the heart rate to show staged fluctuation characteristics, the present invention takes the surgery of anorectal patients who are prone to tension and whose anesthesia depth reaches the requirements of anorectal surgery as an example. Considering that patients often experience tension during the operation, as the tension arises, the heart rate shows a significant increase; as the tension continues, the heart rate data rises to a higher level and then maintains at a higher level; as the tension eases, the heart rate shows a gradual downward trend until it returns to a normal heart rate, the emotion starting point and the emotion ending point are determined, and the sampling time interval is divided to obtain each target segmented time interval, which preliminarily reflects the emotional stage or the real physiological stage.

[0061] In order to divide the heart rate fluctuation stages caused by emotions, it is necessary to determine the starting point of the emotional heart rate fluctuation stage. First, analyze the characteristics of heart rate fluctuation caused by the onset of emotions, and obtain the degree of emotional onset that reflects the possibility that the heart rate value is the starting point of emotional heart rate fluctuation. Figure 2, which shows a flow chart of a method for obtaining the degree of emotion onset in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining the degree of emotion onset includes:

[0062] Step S201: Obtain all neighborhood heart rate values ​​of the heart rate value in a preset neighborhood range.

[0063] In order to analyze the characteristics of the data points around the heart rate value, all neighboring heart rate values ​​of the heart rate value in a preset neighborhood range are determined based on a time series method.

[0064] In one embodiment of the present invention, a method for obtaining all neighboring heart rate values ​​of a heart rate value in a preset neighborhood range includes:

[0065] The heart rate values ​​at all sampling moments are sorted in time sequence, any heart rate value is used as the range point to be constructed, the time interval including a preset number of heart rate values ​​before the range point to be constructed, the range point to be constructed, and a preset number of heart rate values ​​after the range point to be constructed is used as the preset neighborhood range, and all heart rate values ​​in the preset neighborhood range except the range point to be constructed are used as neighborhood heart rate values. In one embodiment of the present invention, the preset number is 10, and the implementer can set it according to the time distance between emotions. When the time distance of emotions is short, the preset number can be appropriately reduced.

[0066] Step S202: in a preset neighborhood range, the front side normality of the heart rate value is obtained according to the difference between all the front side neighboring heart rate values ​​of the heart rate value and the preset normal heart rate value and the deviation of the volatility of all the front side neighboring heart rate values ​​of the heart rate value from the normal fluctuation.

[0067] In order to determine the starting point of the emotional heart rate fluctuation stage, considering that during the operation, the doctor will ensure that the patient is in the physiological balance stage as much as possible, that is, the heart rate value is stable at a normal level to ensure the safety of the patient's operation. The patient is nervous during the operation. As the nervousness begins, the heart rate value often changes from a normal level to a significant increase. By analyzing the normality of all the neighboring heart rate values ​​on the front side of the heart rate value, the front side normality of the heart rate value is obtained. The larger the front side normality, the more likely the heart rate values ​​on the front side of the heart rate value are in the physiological balance stage. Please refer to Figure 3 , which shows a flow chart of a method for obtaining the normality of the front side in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining the normality of the front side includes:

[0068] Step S211: In a preset neighborhood range, according to the overall distribution of all neighboring heart rate values ​​in front of the heart rate value, obtain the overall heart rate value in front of the heart rate value; according to the difference between the overall heart rate value in front and the preset normal heart rate value, obtain the abnormality of the heart rate in front of the heart rate value.

[0069] First, the overall heart rate value of the front side of the heart rate value is obtained. The larger the overall heart rate value of the front side, the larger the overall level of the heart rate value of the front side neighborhood. According to the difference between the overall heart rate value of the front side and the preset normal heart rate value, the abnormality of the heart rate of the front side of the heart rate value is obtained. The larger the abnormality of the heart rate of the front side of the heart rate value, the greater the degree of deviation of all the neighboring heart rate values ​​of the front side of the heart rate value from the normal heart rate value, and the less likely the front side of the heart rate value is to be in the physiological balance stage.

[0070] In one embodiment of the present invention, in a preset neighborhood range, the mean of all neighboring heart rate values ​​on the front side of the heart rate value is calculated to obtain the overall heart rate value of the front side of the heart rate value, and the overall distribution is reflected by the mean. The absolute value of the difference between the overall heart rate value of the front side and the preset normal heart rate value is calculated to obtain the abnormality of the heart rate of the front side of the heart rate value; the difference is reflected by the absolute value of the difference. In one embodiment of the present invention, the preset normal heart rate value is 70, which can be set by the implementer according to the implementation scenario. In other embodiments of the present invention, the method for obtaining the preset normal heart rate value is: one day before the operation, the patient's heart rate value is collected according to the preset sampling frequency, and the doctor determines the time interval that can reflect the patient's normal heart rate, for example, the time interval is selected as 13:00-14:00, and the variance of all heart rate values ​​in the time interval is calculated as the preset normal heart rate value.

[0071] Step S212: Obtain the front overall fluctuation value of the heart rate value according to the volatility of all neighboring heart rate values ​​in front of the heart rate value; obtain the front fluctuation abnormality of the heart rate value according to the deviation between the front overall fluctuation value of the heart rate value and the preset normal fluctuation value.

[0072] The front overall fluctuation value of the heart rate value is obtained. The larger the front overall fluctuation value, the greater the fluctuation degree of the heart rate value in the front neighborhood of the heart rate value. Considering that the heart rate value in the physiological balance stage has a small fluctuation and is relatively stable, the front fluctuation abnormality of the heart rate value is obtained. The larger the front fluctuation abnormality, the greater the front overall fluctuation value is than the preset normal fluctuation value, and the less likely the front of the heart rate value is to be in the physiological balance stage.

[0073] In one embodiment of the present invention, the variance of all neighboring heart rate values ​​on the front side of the heart rate value is calculated to obtain the overall fluctuation value of the front side of the heart rate value. The difference between the overall fluctuation value of the front side of the heart rate value and the preset normal fluctuation value is calculated, the difference is normalized, the abnormality of the front side fluctuation of the heart rate value is obtained, and the deviation is reflected by the difference. In one embodiment of the present invention, the linear normalization method is used for normalization, and the value range is limited to between 0 and 1. In other embodiments of the present invention, the standard deviation of all neighboring heart rate values ​​on the front side of the heart rate value is calculated to obtain the overall fluctuation value of the front side of the heart rate value. In one embodiment of the present invention, the preset normal fluctuation value is 13. The implementer can set it according to the implementation scenario. In other embodiments of the present invention, the method for obtaining the preset normal heart rate value is: one day before the operation, the patient's heart rate value is collected according to the preset sampling frequency, and the doctor determines the time interval that can reflect the patient's normal heart rate, for example, the time interval is selected as 13:00-14:00, and the variance of all heart rate values ​​in the time interval is calculated as the preset normal heart rate value.

[0074] Step S213: Obtain the front normality of the heart rate value according to the front heart rate abnormality and the front fluctuation abnormality; the front heart rate abnormality and the front fluctuation abnormality are negatively correlated with the front normality.

[0075] The anterior normality comprehensively reflects the possibility that the anterior side of the heart rate value is in the physiological balance stage. The larger the value of the anterior normality is, the greater the possibility that the anterior side of the heart rate value is in the physiological balance stage.

[0076] In one embodiment of the present invention, the product of the anterior heart rate abnormality and the anterior fluctuation abnormality is calculated, and the product is negatively correlated to obtain the anterior normality of the heart rate value; in one embodiment of the present invention, the method of negative correlation mapping is: the inverse of the mean square error is used as the power of an exponential function with a natural constant as the base to obtain a negative correlation mapping result. It should be noted that in other embodiments of the present invention, other negative correlation mapping methods such as the inverse number and the reciprocal number can also be selected, which are all technical means well known to those skilled in the art and are not elaborated or limited here.

[0077] In a specific method of an embodiment of the present invention, the expression of the front normality is:

[0078] Z i =exp{-[|xq i -xo|×norm(σq i -σo)]}; where Zq i is the front normality of heart rate value i; xq i is the overall heart rate value in front of heart rate value i; xo is the preset normal heart rate value; σq iis the overall fluctuation value of the front side of the heart rate value i; σo is the preset normal fluctuation value; || is the absolute value symbol; exp{} represents the exponential function with the natural constant e as the base; norm() represents the normalization processing function; |xq i -xo| is the anterior heart rate abnormality of heart rate value i; |σq i -σo| is the anomaly of the front fluctuation of the heart rate value i; it should be noted that the norm[] normalization function is for the data set, when the maximum value is equal to the minimum value, norm(σq i -σo) takes a value of 1. It should be noted that normalization is a technical means well known to those skilled in the art, and the normalization method may be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0079] Step S203: in a preset neighborhood range, according to the local differences and deviations from the normal of all neighboring heart rate values ​​behind the heart rate value, obtain the rear-side variation degree of the heart rate value.

[0080] Considering that as tension begins, the heart rate value often changes from a normal level to a significant increase. The back-side change of the heart rate value reflected by the back-side change degree is consistent with the characteristic that the heart rate increases significantly due to emotions. Figure 4 , which shows a flow chart of a method for obtaining a rear side variation degree in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining a rear side variation degree includes:

[0081] Step S221: among all neighboring heart rate values ​​behind the heart rate value, obtain a local difference index of the neighboring heart rate value according to the deviation between each neighboring heart rate value and its previous neighboring heart rate value.

[0082] In order to understand the rising trend of each neighboring heart rate value behind the heart rate value, the local difference index of the neighboring heart rate value is obtained. The larger the local difference index, the more obvious the rising trend of the neighboring heart rate value is compared with the previous neighboring heart rate value.

[0083] In one embodiment of the present invention, among all neighboring heart rate values ​​behind the heart rate value, the difference between each neighboring heart rate value and its previous neighboring heart rate value is calculated to obtain a local difference index of the neighboring heart rate values.

[0084] Step S222: Obtain the rear side overall change index according to the overall distribution of the local difference indexes of all neighboring heart rate values ​​on the rear side of the heart rate value.

[0085] In order to reflect the overall upward trend in the rear surrounding range of the heart rate value, the rear overall change index is obtained. The larger the rear overall change index is, the more obvious the overall upward trend in the rear surrounding range of the heart rate value is.

[0086] In one embodiment of the present invention, the mean of the local difference indexes of all neighboring heart rate values ​​on the rear side of the heart rate value is calculated to obtain the rear side overall change index.

[0087] Step S223: Obtain the degree of mutation of the heart rate value according to the difference between the local difference index of the heart rate value in the next neighboring region and the overall change index of the rear side.

[0088] Considering that as tension begins, the heart rate value often changes from a normal level to a significant rise, and the next heart rate value at the starting point of the emotion shows a significant upward trend, the degree of mutation of the heart rate value is obtained. The greater the degree of mutation, the more the latter part of the heart rate value conforms to the beginning characteristics of tension.

[0089] In one embodiment of the present invention, the sum of the overall change index of the rear side and the denominator adjustment factor is calculated, and the ratio of the local difference index of the heart rate value of the next neighboring heart rate value to the sum is calculated to obtain the degree of mutation of the heart rate value, and the difference is reflected by the ratio. In other embodiments of the present invention, the absolute value of the difference between the local difference index of the heart rate value of the next neighboring heart rate value and the overall change index of the rear side is calculated to obtain the degree of mutation of the heart rate value. In one embodiment of the present invention, the denominator adjustment factor is used to ensure that the denominator is not 0. The denominator adjustment factor is a number greater than 0. This embodiment sets it to 0.01, and the implementer can set it according to the implementation scenario.

[0090] Step S224: Obtain the posterior heart rate abnormality of the heart rate value according to the deviation between each neighborhood heart rate value and the preset normal heart rate value.

[0091] The greater the abnormality of the posterior heart rate, the greater the rise in the posterior heart rate value, and the posterior heart rate value meets the beginning characteristics of tension.

[0092] In one embodiment of the present invention, among all neighboring heart rate values ​​on the rear side of the heart rate value, the difference between each neighboring heart rate value and a preset normal heart rate value is calculated, and the mean of all the differences is calculated to obtain the abnormality of the rear heart rate.

[0093] Step S225: According to the mutation degree and the posterior heart rate abnormality, the posterior variation of the heart rate value is obtained; the mutation degree and the posterior heart rate abnormality are both positively correlated with the posterior variation.

[0094] The posterior variation comprehensively reflects that the posterior side of the heart rate value meets the beginning characteristics of nervous emotion. The greater the posterior variation, the more likely the heart rate value is the starting point of the emotional heart rate fluctuation stage.

[0095] In one embodiment of the present invention, the product of the mutation degree and the posterior heart rate abnormality is calculated to obtain the posterior variability of the heart rate value. In other embodiments of the present invention, the sum of the mutation degree and the posterior heart rate abnormality is calculated to obtain the posterior variability of the heart rate value.

[0096] Step S204: Obtain the emotion onset degree of the heart rate value according to the front side normality and the rear side variation; the front side normality and the rear side variation are both positively correlated with the emotion onset degree.

[0097] The starting characteristics of the front and back sides of the heart rate value that conform to the nervous emotion are comprehensively considered to obtain the emotional starting degree of the heart rate value. The greater the emotional starting degree, the more likely the heart rate value is to be the starting point of the emotional heart rate fluctuation stage.

[0098] In one embodiment of the present invention, the product of the front side normality and the back side variation is calculated to obtain the emotion onset degree of the heart rate value.

[0099] Since the greater the emotion onset, the more likely the heart rate value is the starting point of the emotional heart rate fluctuation stage, preferably, in one embodiment of the present invention, the method for obtaining the emotion starting point includes:

[0100] The heart rate value whose emotion starting degree is greater than the preset emotion starting value is used as the emotion starting point. In one embodiment of the present invention, the preset emotion starting value is 0.56, which can be set by the implementer according to the implementation scenario.

[0101] At this point, the emotional starting point corresponding to the emotional stage has been determined.

[0102] In order to divide the heart rate fluctuation stage caused by emotions, it is also necessary to determine the end point of the emotional heart rate fluctuation stage. Considering that each emotion has a beginning and an end, each emotional starting point has a corresponding emotional ending point. According to all emotional starting points, the sampling time interval is divided to obtain each segmented time interval to be analyzed; the segmented time interval to be analyzed provides data reference for the subsequent analysis of the emotional ending point corresponding to each emotional starting point.

[0103] In one embodiment of the present invention, a method for obtaining the segmented time interval to be analyzed includes:

[0104] The sampling time corresponding to each emotion starting point is used as a dividing point, and all the dividing points are used to divide the sampling time interval to obtain each segmented time interval to be analyzed. Among them, the dividing point belongs to the next segmented time interval to be analyzed. That is, the first segmented time interval to be analyzed has no corresponding emotion starting point. It should be noted that the first emotion ending point appears after the first emotion starting point, so the first segmented time interval to be analyzed does not need to obtain the emotion ending point.

[0105] In order to divide the heart rate fluctuation stages caused by emotions, it is also necessary to analyze the characteristics of heart rate fluctuations caused by the termination of emotions, that is, as the tension is relieved, the heart rate shows a gradual downward trend until it returns to a normal heart rate, and the degree of emotional termination is obtained. Figure 5, which shows a flow chart of a method for obtaining the degree of emotional termination in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining the degree of emotional termination includes:

[0106] Step S231: in a preset neighborhood range, according to the change trend of all neighboring heart rate values ​​in front of the heart rate value, obtain the front downward trend coefficient of the heart rate value.

[0107] Considering that the heart rate before the emotional termination point shows a downward trend, the front downward trend coefficient of the heart rate value is obtained. The larger the front downward trend coefficient, the more the front heart rate of the heart rate value shows a downward trend, and the heart rate value is more likely to be the emotional termination point.

[0108] In one embodiment of the present invention, a method for obtaining the front side downward trend coefficient includes:

[0109] The difference between each neighborhood heart rate value and its previous neighborhood heart rate value is calculated to obtain the change parameter of the neighborhood heart rate value; all neighborhood heart rate values ​​with negative change parameters are counted to obtain the front side downward trend coefficient.

[0110] Step S232: Obtain the rear normal coefficient of the heart rate value according to the difference between each neighboring heart rate value on the rear side of the heart rate value and the preset normal heart rate value.

[0111] Taking into account that the back side of the emotion termination point is a normal heart rate, the back side normal coefficient of the heart rate value is obtained. The larger the back side normal coefficient is, the more the back side heart rate of the heart rate value is a normal heart rate, and the heart rate value is more likely to be the emotion termination point.

[0112] In one embodiment of the present invention, a method for obtaining a rear normal coefficient includes:

[0113] Among all the neighboring heart rate values ​​on the back side of the heart rate value, calculate the absolute value of the difference between each neighboring heart rate value and the preset normal heart rate value to obtain the deviation parameter of the neighboring heart rate value from normal; calculate the mean of the deviation parameters of all the neighboring heart rate values ​​on the back side of the heart rate value to obtain the back side normal coefficient.

[0114] Step S233: Obtain the time distance coefficient of the heart rate value according to the time distance between the heart rate value and the starting point of the emotion.

[0115] Considering that patients' emotions generally last for at least a period of time, the larger the time distance coefficient is, the longer the emotion lasts and the more reasonable the distance between the emotion and the starting point is.

[0116] In one embodiment of the present invention, the Euclidean distance between the heart rate value and the sampling moment corresponding to the starting point of the emotion is calculated. If the Euclidean distance is greater than the preset time interval, the time distance coefficient of the heart rate value is set to 1; if the Euclidean distance is not greater than the preset time interval, the time distance coefficient of the heart rate value is set to 0. In one embodiment of the present invention, the preset time interval is 23, and the implementer can set it according to the duration of the patient's emotion. If the duration is short, the preset time interval can be appropriately reduced. In one embodiment of the present invention, the normalization method used is: normalization is performed using the norm normalization function, and the numerical range is limited to between 0 and 1. In other embodiments of the present invention, the Euclidean distance between the heart rate value and the sampling moment corresponding to the starting point of the emotion is used as the time distance coefficient of the heart rate value.

[0117] Step S234: Obtain the emotion termination degree of the heart rate value according to the front downward trend coefficient, the rear normal coefficient, the time distance coefficient and the emotion starting degree of the heart rate value; the front downward trend coefficient, the rear normal coefficient and the time distance coefficient are all positively correlated with the emotion termination degree; the emotion starting degree and the emotion termination degree are negatively correlated.

[0118] At the same time, the possibility that the emotion onset degree reflects the heart rate value is the emotion onset point is considered. Considering that the emotion starts, the heart rate value often changes from the normal level to a significant rise; as the emotion approaches the end, the heart rate shows a gradual downward trend until it returns to the normal heart rate and the emotion ends. It can be obtained that the greater the emotion onset degree, the less likely it is to be the emotion end point, and the emotion end degree of the heart rate value is obtained. The emotion end degree comprehensively reflects the possibility that the heart rate value is the emotion end point. The larger the value, the more likely the heart rate value is the emotion end point.

[0119] In one embodiment of the present invention, the product of the front side downward trend coefficient, the rear side normal coefficient and the time distance coefficient is calculated; the sum of the emotion onset degree and the denominator adjustment factor is calculated to obtain the denominator value; the ratio of the product and the denominator value is calculated to obtain the emotion termination degree of the heart rate value. In one embodiment of the present invention, the denominator adjustment factor is used to ensure that the denominator is not 0. The denominator adjustment factor is a number greater than 0. This embodiment sets it to 0.001, and the implementer can set it according to the implementation scenario.

[0120] Considering that the greater the degree of emotional termination, the more likely the heart rate value is the emotional termination point, the emotional termination point is obtained according to the emotional termination degree of all heart rate values ​​in the segmented time interval to be analyzed. In one embodiment of the present invention, the method for obtaining the emotional termination point includes:

[0121] In the segmented time interval to be analyzed, the emotional termination degree of all heart rate values ​​will be calculated, and the heart rate value corresponding to the maximum emotional termination degree will be used as the emotional termination point.

[0122] At this point, the emotional end point corresponding to the emotional stage has been determined.

[0123] In order to suppress the interference of heart rate fluctuations caused by emotions, it is necessary to preliminarily divide the emotional stages, divide the sampling time interval according to all emotional starting points and emotional ending points, and obtain each target segmented time interval; the target segmented time interval can preliminarily reflect the emotional stage or the real physiological stage.

[0124] In one embodiment of the present invention, a method for obtaining a target segmented time interval includes:

[0125] The sampling time corresponding to each emotion starting point and emotion ending point is used as the emotion division point, and the sampling time interval is divided using all emotion division points to obtain each target segmented time interval. That is, the target segmented time interval corresponding to each emotion starting point to the next emotion ending point preliminarily reflects the emotion stage, and the target segmented time interval that is not an emotion stage preliminarily reflects the real physiological stage.

[0126] Considering that both the physiological imbalance stage and the emotional stage have excessive heart rate fluctuations or deviations from normal levels, the physiological imbalance stage will cause misjudgment of the emotional stage. It is also necessary to further consider the correlation between heart rate and stress to distinguish the target segmented time intervals corresponding to the emotional stage.

[0127] Step S3: In the target segmented time interval, according to the correlation between the heart rate value and the blood pressure value at the continuous sampling moment, the prominent weight of the target segmented time interval is obtained; according to the prominent weight of each target segmented time interval, the weight of the error function is set, and all the heart rate values ​​in each target segmented time interval are filtered to obtain filtered data.

[0128] Considering that when patients have emotional fluctuations, heart rate values ​​and blood pressure values ​​have a strong correlation, that is, when patients are in the nervous stage, as heart rate increases, blood pressure tends to rise. Considering that in the physiological imbalance stage and the physiological balance stage, heart rate values ​​and blood pressure values ​​have a weak correlation. Considering the need to suppress the interference of heart rate fluctuations caused by emotions, the prominent weight of the target segmented time interval is obtained. The larger the prominent weight, the weaker the correlation between the heart rate value and the blood pressure value in the target segmented time interval, the less likely the target segmented time interval is to correspond to the emotional stage, and the more data that needs to be retained for filtering. The weight of the error function is set according to the prominent weight of each target segmented time interval, and all heart rate values ​​in each target segmented time interval are filtered to suppress the interference of heart rate fluctuations caused by emotions, so that the filtered data can more accurately reflect the patient's true physiological characteristics and improve the accuracy of identifying the patient's true physiological characteristics.

[0129] Preferably, in one embodiment of the present invention, the method for obtaining the prominence weight includes:

[0130] In the target segmented time interval, the Pearson coefficient of the heart rate value and the blood pressure value is calculated, and the Pearson coefficient is normalized to obtain the prominent weight of the target segmented time interval. It should be noted that the Pearson correlation coefficient is a technical means well known to those skilled in the art and will not be described in detail here. In one embodiment of the present invention, the normalization method is: normalization is performed using a linear normalization method to limit the value range to between 0 and 1.

[0131] In order to achieve a better filtering effect, the weight of the error function is set according to the prominent weight of each target segmented time interval, and all heart rate values ​​in each target segmented time interval are filtered to obtain filtered data. In one embodiment of the present invention, the method for obtaining filtered data includes:

[0132] According to the time sequence, the sampling moments in the target segmented time interval are counted in turn to obtain the heart rate segmented time series data of the target segmented time interval; in the process of filtering the heart rate segmented time series data of the target segmented time interval using the minimum mean square adaptive filtering algorithm, after each error function is obtained, the weight of the error function is set using the prominent weight, that is, the product of the prominent weight and the error function is calculated to obtain the updated error function, and the heart rate segmented time series filtering data of the target segmented time interval is obtained, and all the heart rate segmented time series filtering data are used as filtering data. It should be noted that the minimum mean square adaptive filtering algorithm is a technical means well known to those skilled in the art. Here, it is briefly described that the minimum mean square adaptive filtering algorithm is a widely used adaptive filtering technology. In the minimum mean square adaptive filtering algorithm, the error function is a core concept, and the error function is used to measure the difference between the algorithm output and the expected output.

[0133] In order to emphasize the importance of certain heart rate segmented time series filtering data, weights are introduced into the error function. The weight of the error function is set by the prominent weight of each target segmented time interval, and the prominent weight reflects the importance of the heart rate value corresponding to the target segmented time interval. For the heart rate segmented time series filtering data that need to be retained, a larger weight can be assigned; for the less important heart rate segmented time series filtering data, a smaller weight is assigned. In this way, the filter will pay more attention to the heart rate segmented time series filtering data that are assigned high weights during the iteration process, thereby suppressing the interference of heart rate fluctuations caused by emotions to a certain extent, so that the filtered data can more accurately reflect the patient's true physiological characteristics.

[0134] In order to identify the patient's true physiological characteristics, in an embodiment of the present invention, the filtered data can be input into a trained neural network model through a neural network to output the patient's true physiological characteristics recognition result, that is, the patient's true physiological characteristics recognition result is physiological balance or physiological imbalance. The neural network model can select a convolutional neural network model for performing classification tasks. The specific neural network model training process is a technical means well known to those skilled in the art and will not be described in detail here. Furthermore, when the patient's true physiological characteristics recognition result is physiological imbalance, the intelligent medical monitoring instrument is driven to alarm, reminding the doctor to pay attention to the patient's physiological condition in time.

[0135] The present invention also proposes a real-time monitoring system for the intraoperative status of anorectal patients, please refer to Figure 6 , which shows a structural diagram of a real-time monitoring system for the intraoperative status of anorectal patients provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a sampling time interval division module 102, and a filtering module 103.

[0136] The data acquisition module 101 is used to acquire the heart rate value and blood pressure value at each sampling time in the sampling time interval.

[0137] The sampling time interval division module 102 is used to obtain the emotion starting degree of each heart rate value according to the local deviation from normal and the local difference of each heart rate value; obtain each emotion starting point according to the emotion starting degree of each heart rate value; divide the sampling time interval according to all emotion starting points to obtain each segmented time interval to be analyzed; in the segmented time interval to be analyzed, obtain the emotion termination degree of the heart rate value according to the local change trend of each heart rate value, the local deviation from normal and the time distance from the emotion starting point; obtain the emotion termination point according to the emotion termination degree of all heart rate values ​​in the segmented time interval to be analyzed; divide the sampling time interval according to all emotion starting points and emotion termination points to obtain each target segmented time interval.

[0138] The filtering module 103 is used to obtain the prominent weight of the target segmented time interval according to the correlation between the heart rate value and the blood pressure value at continuous sampling moments in the target segmented time interval; set the weight of the error function according to the prominent weight of each target segmented time interval, filter all heart rate values ​​in each target segmented time interval, and obtain filtered data.

[0139] In summary, the embodiments of the present invention provide a method and system for real-time monitoring of the intraoperative status of anorectal patients. First, the sampling time interval is divided according to all emotional starting points and emotional ending points to obtain each target segmented time interval; in the target segmented time interval, the prominent weight of the target segmented time interval is obtained according to the correlation between the heart rate value and the blood pressure value at the continuous sampling moment; the weight of the error function is set according to the prominent weight of each target segmented time interval, and all heart rate values ​​in each target segmented time interval are filtered to obtain filtered data. In the embodiments of the present invention, the prominent weight reflects the degree to which the target segmented time interval cannot correspond to the emotional stage, and all heart rate values ​​in each target segmented time interval are filtered to suppress the interference of heart rate fluctuations caused by emotions, so that the filtered data can more accurately reflect the true physiological characteristics of the patient.

[0140] It should be noted that: the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, a real-time monitoring system for the intraoperative status of anorectal patients provided in the above embodiment and a real-time monitoring method for the intraoperative status of anorectal patients are of the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0141] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0142] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for real-time monitoring of the intraoperative status of anorectal patients, characterized in that: The method comprises: Obtain the heart rate and blood pressure values ​​at each sampling time in the sampling time interval; According to the local deviation from normal and the local difference of each heart rate value, the emotion starting degree of each heart rate value is obtained; according to the emotion starting degree of each heart rate value, each emotion starting point is obtained; according to all the emotion starting points, the sampling time interval is divided to obtain each segmented time interval to be analyzed; in the segmented time interval to be analyzed, according to the local change trend of each heart rate value, the local deviation from normal and the time distance from the emotion starting point, the emotion termination degree of the heart rate value is obtained; according to the emotion termination degree of all heart rate values ​​in the segmented time interval to be analyzed, the emotion termination point is obtained; according to all the emotion starting points and the emotion termination points, the sampling time interval is divided to obtain each target segmented time interval; In the target segmented time interval, the prominent weight of the target segmented time interval is obtained according to the correlation between the heart rate value and the blood pressure value at continuous sampling moments; the weight of the error function is set according to the prominent weight of each target segmented time interval, and all heart rate values ​​in each target segmented time interval are filtered to obtain filtered data.

2. According to claim 1, a method for real-time monitoring of the intraoperative status of anorectal patients is characterized by: The method for obtaining the emotion onset degree comprises: Get all neighboring heart rate values ​​within a preset neighborhood range; In a preset neighborhood range, according to the difference between all the neighboring heart rate values ​​on the front side of the heart rate value and the preset normal heart rate value and the deviation of the fluctuation of all the neighboring heart rate values ​​on the front side of the heart rate value from the normal fluctuation, the front side normality of the heart rate value is obtained; In the preset neighborhood range, according to the local differences and deviations from normal of all neighboring heart rate values ​​on the rear side of the heart rate value, the rear side variation degree of the heart rate value is obtained; The emotion onset degree of the heart rate value is obtained according to the front side normality and the rear side variation degree; the front side normality and the rear side variation degree are both positively correlated with the emotion onset degree.

3. According to claim 2, a method for real-time monitoring of the intraoperative status of anorectal patients is characterized by: The method for obtaining the front side normality includes: In a preset neighborhood range, according to the overall distribution of all neighboring heart rate values ​​on the front side of the heart rate value, the front overall heart rate value of the heart rate value is obtained; according to the difference between the front overall heart rate value and the preset normal heart rate value, the front heart rate abnormality of the heart rate value is obtained; According to the volatility of all neighboring heart rate values ​​on the front side of the heart rate value, the front side overall fluctuation value of the heart rate value is obtained; according to the deviation between the front side overall fluctuation value of the heart rate value and the preset normal fluctuation value, the front side fluctuation abnormality of the heart rate value is obtained; The front normality of the heart rate value is obtained according to the front heart rate abnormality and the front fluctuation abnormality; the front heart rate abnormality and the front fluctuation abnormality are both negatively correlated with the front normality.

4. A method for real-time monitoring of the intraoperative status of anorectal patients according to claim 3, characterized in that: The method for obtaining the rear side variation degree includes: Among all neighboring heart rate values ​​behind the heart rate value, according to the deviation between each neighboring heart rate value and its previous neighboring heart rate value, a local difference index of the neighboring heart rate value is obtained; Obtaining a rear-side overall change index according to the overall distribution of the local difference index of all neighboring heart rate values ​​on the rear side of the heart rate value; Obtaining the degree of mutation of the heart rate value according to the difference between the local difference index of a heart rate value in a neighborhood of the heart rate value and the rear overall change index; According to the deviation between each neighborhood heart rate value and the preset normal heart rate value, obtaining the posterior heart rate abnormality of the heart rate value; According to the mutation degree and the posterior heart rate abnormality, the posterior variation of the heart rate value is obtained; the mutation degree and the posterior heart rate abnormality are both positively correlated with the posterior variation.

5. A method for real-time monitoring of the intraoperative status of anorectal patients according to claim 1, characterized in that: The method for obtaining the emotion starting point comprises: The heart rate value whose emotion starting degree is greater than the preset emotion starting value is used as the emotion starting point.

6. A method for real-time monitoring of the intraoperative status of anorectal patients according to claim 2, characterized in that: The method for obtaining the degree of emotion termination includes: In a preset neighborhood range, according to the change trend of all neighboring heart rate values ​​in front of the heart rate value, obtain the front side downward trend coefficient of the heart rate value; According to the difference between each neighboring heart rate value on the rear side of the heart rate value and the preset normal heart rate value in the preset neighborhood range, obtaining the rear normal coefficient of the heart rate value; According to the time distance between the heart rate value and the starting point of the emotion, the time distance coefficient of the heart rate value is obtained; According to the front downward trend coefficient of the heart rate value, the rear normal coefficient, the time distance coefficient and the emotion starting degree, the emotion termination degree of the heart rate value is obtained; the front downward trend coefficient, the rear normal coefficient and the time distance coefficient are all positively correlated with the emotion termination degree; the emotion starting degree and the emotion termination degree are negatively correlated.

7. A method for real-time monitoring of the intraoperative status of anorectal patients according to claim 6, characterized in that: The method for obtaining the front side downward trend coefficient includes: Calculate the difference between each neighborhood heart rate value and its previous neighborhood heart rate value to obtain the change parameter of the neighborhood heart rate value; All the neighborhood heart rate values ​​with negative variation parameters are counted to obtain the front downward trend coefficient.

8. A method for real-time monitoring of the intraoperative status of anorectal patients according to claim 6, characterized in that: The method for obtaining the rear normal coefficient includes: Among all neighboring heart rate values ​​behind the heart rate value, the absolute value of the difference between each neighboring heart rate value and the preset normal heart rate value is calculated to obtain a deviation parameter of the neighboring heart rate value from the normal state; The mean of the deviation from normal parameters of all neighboring heart rate values ​​on the posterior side of the heart rate value is calculated to obtain the posterior normal coefficient.

9. A method for real-time monitoring of the intraoperative status of anorectal patients according to claim 1, characterized in that: The method for obtaining the prominence weight includes: In the target segmented time interval, the Pearson coefficients of the heart rate value and the blood pressure value are calculated, and the Pearson coefficients are normalized to obtain the prominent weight of the target segmented time interval.

10. A real-time monitoring system for the intraoperative status of anorectal patients, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a method for real-time monitoring of the intraoperative status of anorectal patients as described in any one of claims 1 to 9 are implemented.

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