Monitoring data analysis system for surgical operation

Abstract

The invention relates to the field of surgical monitoring, in particular to a monitoring data analysis system for a surgical operation. The system firstly obtains the inspiration flow velocity and tidal volume at the current moment and each historical moment, analyzes the difference between the inspiration flow velocity at each moment and the inspiration flow velocity at each previous moment, obtains the difference degree of the inspiration flow velocity at each moment, and clusters all moments to obtain the difference degree of the inspiration flow velocity at each moment. According to the difference between the tidal volume of the current moment and the historical moment in the cluster where the current moment is located, the tidal volume difference degree of the current moment is obtained, and according to the difference between the inspiration flow speed and the historical moment and the difference between the tidal volume and the difference between the inspiration flow speed and the historical moment, the difference between the inspiration flow speed and the historical moment is obtained; according to the inspiration flow rate, the tidal volume difference degree at the current moment, the stability similarity at the current moment is obtained, and the anesthesia state of the patient is evaluated according to the inspiration flow rate, the tidal volume and the stability similarity at the current moment. The anesthesia state of the patient can be accurately evaluated, and the accuracy of anesthesia monitoring of the patient is improved.

Description

Technical Field

[0001] The present invention relates to the field of surgical monitoring, and in particular to a monitoring data analysis system for surgical operations. Background Art

[0002] The surgical monitoring data analysis system is a system used to monitor and analyze the patient's physiological parameters and condition changes during surgery. Since patients who need surgery are usually in serious conditions, anesthesia is an important part of surgery. Surgery needs to be performed on patients who are fully anesthetized to ensure that the operation can be carried out safely and smoothly. Therefore, in surgery, the assessment of the patient's anesthesia status is crucial.

[0003] In related technologies, the patient's anesthetic status is usually evaluated by analyzing changes in the patient's physical indicators such as blood pressure, heart rate, respiration and body temperature. However, since the patient's physiological response is relatively slow during the anesthesia process and the changes in physical indicator data are relatively complex, the existing methods cannot accurately evaluate the patient's anesthetic status, reducing the accuracy of the patient's anesthesia monitoring. Summary of the invention

[0004] In order to solve the technical problem that the existing methods cannot accurately evaluate the anesthesia state of the patient and reduce the accuracy of the anesthesia monitoring of the patient, the purpose of the present invention is to provide a monitoring data analysis system for surgical operations. The technical solution adopted is as follows: The present invention provides a monitoring data analysis system for surgical operations, the system comprising: A data acquisition module, used for acquiring the inspiratory flow rate and tidal volume of the patient at each moment, taking the last moment as the current moment, and taking all other moments except the current moment as historical moments; A data analysis module, for taking any moment as a target moment, and obtaining the inspiratory flow rate difference degree of the target moment according to the difference between the inspiratory flow rate of other moments before the target moment and the target moment, and the change of the inspiratory flow rate of other moments before the target moment; clustering all moments according to the difference between the inspiratory flow rate of different moments to obtain multiple clustering clusters; obtaining the tidal volume difference degree of the current moment according to the difference between the historical moments in the clustering cluster where the current moment is located and the current moment; taking the preset number of historical moments closest to the current moment as the reference historical moments of the current moment, and obtaining the stationary similarity of the current moment according to the difference between the inspiratory flow rate and the tidal volume between the current moment and the reference historical moment, the difference between the inspiratory flow rate difference degree between the current moment and the reference historical moment, and the tidal volume difference degree of the current moment; an anesthesia analysis module, used to obtain the anesthesia possibility at the current moment according to the inspiratory flow rate, the tidal volume and the stationary similarity at the current moment; The anesthesia assessment module is used to assess the patient's anesthesia status based on the anesthesia possibility at the current moment.

[0005] Further, obtaining the inspiratory flow rate difference at the target time includes: All moments before the target moment are taken as the moments to be analyzed of the target moment; According to the difference of the inspiratory flow rate between the target moment and each moment to be analyzed, obtaining a first difference parameter at the target moment; The absolute value of the difference between the inspiratory flow rate at each moment and the adjacent previous moment is used as the flow rate change at each moment; and the second difference parameter at the target moment is obtained according to the difference between the flow rate change between the target moment and each moment to be analyzed; The first difference parameter and the second difference parameter are integrated and normalized to obtain the inspiratory flow rate difference at the target moment.

[0006] Further, the obtaining of the first difference parameter of the target time includes: The difference between the inspiratory flow rate at each time to be analyzed and the target time is used as the difference value of the inspiratory flow rate between each time to be analyzed and the target time; The average value of the inspiratory flow rate difference between all the time points to be analyzed and the target time point is normalized and used as the first difference parameter at the target time point.

[0007] Further, the obtaining of the second difference parameter of the target time includes: The difference between the flow velocity change at the target moment and each time to be analyzed is used as the difference value of the change between the target moment and each time to be analyzed; The average value of the difference in the variation between the target moment and all the moments to be analyzed is normalized to be used as the second difference parameter of the target moment.

[0008] Furthermore, obtaining a plurality of clusters includes: The absolute value of the difference between the inspiratory flow rates at any two moments is used as the distance measure between the two moments; Using the K-means clustering algorithm, and based on the distance metric between any two moments, all moments are clustered to obtain a plurality of clusters.

[0009] Furthermore, obtaining the tidal volume difference at the current moment includes: The absolute value of the difference between the tidal volume at the current moment and the tidal volume at each historical moment in the cluster where the current moment is located is used as the initial tidal volume difference parameter between the current moment and each historical moment in the cluster where the current moment is located; The average value of the initial tidal volume difference parameter between the current moment and all historical moments in the cluster where the current moment is located is normalized to obtain the tidal volume difference degree at the current moment.

[0010] Further, the obtaining of the stationary similarity at the current moment includes: The current moment or any reference historical moment is used as the moment to be measured, the inspiratory flow rate at the moment to be measured is used as the numerator, the tidal volume at the moment to be measured is used as the denominator, and the ratio is used as the characteristic index of the moment to be measured; The absolute value of the difference between the characteristic index at the current moment and each reference historical moment is used as the first difference coefficient between the current moment and each reference historical moment; Obtaining a second difference coefficient between the current moment and each reference historical moment according to the tidal volume difference at the current moment and the difference in the inspiratory flow rate difference between the current moment and each reference historical moment; The product of the first difference coefficient and the second difference coefficient is used to obtain a comprehensive difference coefficient between the current moment and each reference historical moment; The accumulated values ​​of the comprehensive difference coefficients between the current moment and all reference historical moments are normalized by negative correlation to obtain the stable similarity of the current moment.

[0011] Further, obtaining the second difference coefficient between the current moment and each reference historical moment includes: The absolute value of the difference between the inspiratory flow rate difference between the current moment and each reference historical moment is used as the flow rate change difference between the current moment and each reference historical moment; The product value of the flow rate change difference value and the tidal volume difference at the current moment is used as the second difference coefficient between the current moment and each reference historical moment.

[0012] Further, obtaining the anesthesia possibility at the current moment includes: The inspiratory flow rate and the tidal volume at the current moment are integrated and negatively correlated to obtain the possibility parameter at the current moment; The stationary similarity and the possibility parameter at the current moment are integrated and normalized to obtain the anesthesia possibility at the current moment.

[0013] Further, the assessment of the patient's anesthetic state includes: If the anesthesia possibility at the current moment is greater than the preset anesthesia threshold, the patient has completely entered the anesthesia state; otherwise, the patient has not yet completely entered the anesthesia state.

[0014] The present invention has the following beneficial effects: The present invention takes into account that the existing methods cannot accurately evaluate the patient's anesthetic state, which reduces the accuracy of the patient's anesthetic monitoring. Therefore, the inspiratory flow rate and tidal volume of the patient at each moment are first obtained. Considering that when the patient enters the anesthetic state at a certain moment, the inspiratory flow rate at this moment is smaller than that at previous moments. As the anesthetic drug gradually diffuses, the change rate of the inspiratory flow rate gradually increases. Therefore, the difference between the characteristics of the respiratory flow rate at the target moment and the previous moments can be reflected by the inspiratory flow rate difference. Subsequently, based on the difference in the inspiratory flow rate difference between the current moment and the previous moments, the stability of the inspiratory flow rate at the current moment and the previous moments can be analyzed to improve the accuracy of the patient's anesthetic state assessment. Considering that when the patient enters the anesthetic state, for moments with similar inspiratory flow rates, the tidal volumes at these moments are also relatively When the patient is not in anesthesia, the consistency of tidal volume at these moments is poor and there are certain differences. Therefore, all moments can be clustered first to obtain multiple clusters, and then the tidal volume difference can be used to reflect the difference in tidal volume between the current moment and the historical moments with similar inspiratory flow rate. Subsequently, whether the patient has entered an anesthetized state can be accurately assessed based on the tidal volume difference. Considering that when the patient enters an anesthetized state, the patient's inspiratory flow rate and tidal volume are both low, and the change characteristics of the two are relatively similar and stable. Therefore, the stable similarity can be used to reflect the similarity of the low-level stable change characteristics between the inspiratory flow rate and the tidal volume in the time period composed of the current moment and each reference historical moment, and then the obtained anesthesia possibility can be used to accurately reflect the possibility of the patient being in an anesthetized state, thereby improving the accuracy of anesthesia assessment and monitoring of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 A block diagram of a monitoring data analysis system for surgical operations provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining the stationary similarity at the current moment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] 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 specific implementation, structure, features and effects of a monitoring data analysis system for surgical operations proposed by the present invention in conjunction 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.

[0018] 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.

[0019] The following is a detailed description of a specific solution of a monitoring data analysis system for surgical operations provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a block diagram of a monitoring data analysis system for surgical operations provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a data analysis module 102, an anesthesia analysis module 103, and an anesthesia assessment module 104.

[0021] The data acquisition module 101 is used to obtain the inspiratory flow rate and tidal volume of the patient at each moment, taking the last moment as the current moment, and taking all other moments except the current moment as historical moments.

[0022] The surgical monitoring data analysis system is an efficient tool designed specifically for hospital operating rooms. The system can be used to analyze the patient's blood pressure, heart rate, respiration, body temperature and other physical indicators, helping medical staff to monitor the anesthesia status of surgical patients in real time, effectively improving the safety and accuracy of the surgical process.

[0023] Respiratory index is a key indicator for evaluating whether a patient enters an anesthetic state, including, for example, expiratory flow rate, inspiratory flow rate, expiratory pressure, inspiratory pressure or tidal volume, etc. In an embodiment of the present invention, inspiratory flow rate and tidal volume are selected as two kinds of data for subsequent evaluation and analysis of the patient's anesthetic state. First, monitoring equipment in a hospital, such as a ventilator, is used to collect the patient's inspiratory flow rate and tidal volume at each moment, wherein the time interval for data collection is set to 1 second, that is, data is collected every 1 second. The time interval for data collection can also be set by the implementer according to the specific implementation scenario, which is not limited here. Then, the last moment is taken as the current moment, and all other moments except the current moment are taken as historical moments. Subsequently, the inspiratory flow rate and tidal volume of the current moment and the historical moment are combined to perform real-time analysis of the patient's anesthetic state.

[0024] The data analysis module 102 is used to take any moment as a target moment, and obtain the inspiratory flow rate difference of the target moment according to the difference in inspiratory flow rate between other moments before the target moment and the target moment, as well as the change in inspiratory flow rate at other moments before the target moment; cluster all moments according to the difference in inspiratory flow rate between different moments to obtain multiple clusters; obtain the tidal volume difference of the current moment according to the difference in tidal volume between the historical moments in the cluster where the current moment is located and the current moment; take a preset number of historical moments closest to the current moment as reference historical moments of the current moment, and obtain the stationary similarity of the current moment according to the difference in inspiratory flow rate and tidal volume between the current moment and the reference historical moment, the difference in the inspiratory flow rate difference between the current moment and the reference historical moment, and the tidal volume difference at the current moment.

[0025] When a patient undergoes a surgical operation, as the anesthetic is injected, the patient's physiological response slows down, and the patient's body enters a quiet dormant period. When the patient enters an anesthetized state at a certain moment, the inspiratory flow rate at that moment is smaller than that at previous moments, and as the anesthetic gradually diffuses and takes effect, the rate of change of the inspiratory flow rate gradually increases. Therefore, the embodiment of the present invention first analyzes any moment, takes any moment as the target moment, and analyzes the difference in inspiratory flow rate between other moments before the target moment and the target moment, as well as the change in inspiratory flow rate at other moments before the target moment. The obtained inspiratory flow rate difference reflects the degree of difference between the characteristics of the respiratory flow rate at the target moment and the previous moments. Subsequently, based on the difference in the inspiratory flow rate difference between the current moment and each historical moment, the stability of the inspiratory flow rate in the time period formed by the current moment and the historical moments can be analyzed to improve the accuracy of the assessment of the patient's anesthesia state.

[0026] Preferably, in one embodiment of the present invention, the method for obtaining the inspiratory flow velocity difference at the target moment specifically includes: First, all moments before the target moment are taken as the moments to be analyzed of the target moment. According to the difference in inspiratory flow rate between the target moment and each moment to be analyzed, the first difference parameter of the target moment is obtained. The larger the first difference parameter, the smaller the inspiratory flow rate at the target moment is compared with the inspiratory flow rate at the previous moments, which further indicates that the possibility of the patient entering the anesthesia state is greater, providing a data basis for the subsequent calculation of the inspiratory flow rate difference at the target moment.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining the first difference parameter at the target moment specifically includes: The difference between the inspiratory flow rate at each time to be analyzed and the target time is taken as the inspiratory flow rate difference value between each time to be analyzed and the target time. The larger the inspiratory flow rate difference value is, the smaller the inspiratory flow rate at the target time is compared with the inspiratory flow rate at each time to be analyzed. Then, the average value of the inspiratory flow rate difference values ​​between all the times to be analyzed and the target time can be normalized as the first difference parameter of the target time.

[0028] In one embodiment of the present invention, existing The function implements normalization, and the normalization in subsequent steps can be done using The function implements normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of values, which will not be described in detail.

[0029] As an example, in one embodiment of the present invention, the expression of the first difference parameter at the target time may be specifically, for example, as follows: in, A first difference parameter representing the target time; The target time The inspiratory flow rate at the moment to be analyzed; Indicates the inspiratory flow rate at the target moment; Indicates The difference in inspiratory flow rate between the time to be analyzed and the target time; The number of time moments to be analyzed representing the target time moment; Represents the activation function, which is used for normalization.

[0030] It should be noted that, for the existing boundary problem, when the target moment is the first moment, there is no other moment before the first moment. At this time, the first difference parameter of the first moment can be directly set to a value of 0 to solve the existing boundary problem.

[0031] Then, the absolute value of the difference between the inspiratory flow rate at each moment and the adjacent previous moment is used as the flow rate change at each moment. The flow rate change reflects the degree or speed of the change in the inspiratory flow rate at each moment. It should be noted that there is no adjacent previous moment at the first moment. At this time, the flow rate change at the first moment can be set to a value of 0, and then the second difference parameter of the target moment is obtained according to the difference in flow rate change between the target moment and each moment to be analyzed. The larger the second difference parameter is, the greater the change speed of the inspiratory flow rate at the target moment is compared with the change speed of the inspiratory flow rate at the previous moments, and then the greater the possibility that the patient enters an anesthetized state is, which provides a data basis for the subsequent calculation of the difference degree of the inspiratory flow rate at the target moment.

[0032] Preferably, in one embodiment of the present invention, the method for obtaining the second difference parameter at the target moment specifically includes: The difference in flow rate change between the target moment and each moment to be analyzed is taken as the difference in change between the target moment and each moment to be analyzed. The larger the difference in change, the larger the instantaneous change in the inspiratory flow rate at the target moment is compared with the instantaneous change in the inspiratory flow rate at each moment to be analyzed. Then, the average value of the difference in change between the target moment and all moments to be analyzed can be normalized and used as the second difference parameter of the target moment.

[0033] As an example, in one embodiment of the present invention, the expression of the second difference parameter at the target time may be specifically, for example, as follows: in, a second difference parameter representing the target moment; Indicates the change in flow rate at the target time; The target time The change of flow rate at the time to be analyzed; Indicates the target time and The difference value of the change between the moments to be analyzed; The number of time moments to be analyzed representing the target time moment; Represents the activation function, which is used for normalization.

[0034] Finally, the first difference parameter and the second difference parameter are integrated and normalized to obtain the inspiratory flow rate difference at the target moment.

[0035] In the embodiment of the present invention, the integration of the first difference parameter and the second difference parameter can be achieved by calculating the sum or product of the two, which is not limited here.

[0036] As an example, in one embodiment of the present invention, the expression of the inspiratory flow rate difference at the target time may be specifically, for example, as follows: in, Indicates the difference in inspiratory flow rate at the target moment; A first difference parameter representing the target time; a second difference parameter representing the target moment; Represents the activation function, which is used for normalization.

[0037] The inspiratory flow rate difference at each moment can be obtained by the same method as above.

[0038] When the patient enters the anesthetized state, for each moment of similar inspiratory flow rate, the tidal volume at these moments is also relatively close, while when the patient does not enter the anesthetized state, the consistency of the tidal volume at these moments is poor, and there are certain differences. Therefore, all moments can be clustered according to the differences in inspiratory flow rates between different moments to obtain multiple clusters. Subsequently, the differences in tidal volume between each historical moment and the current moment in the cluster where the current moment is located can be analyzed to achieve subsequent accurate assessment of the patient's anesthesia state at the current moment.

[0039] Preferably, in one embodiment of the present invention, the method for obtaining multiple clusters specifically includes: The absolute value of the difference in inspiratory flow rate between any two moments is used as the distance metric between any two moments, and then the K-means clustering algorithm is used to cluster all moments based on the distance metric between any two moments to obtain multiple clusters, wherein the number of clusters can be determined using the existing elbow method. In other embodiments of the present invention, other clustering algorithms based on distance metrics can also be used to perform clustering operations, which is not limited here.

[0040] From the above analysis, it can be seen that when the patient has not entered the anesthetized state, there are certain differences in the tidal volumes at each moment with similar inspiratory flow rates. Therefore, the difference in tidal volume between the historical moments in the cluster where the current moment is located and the current moment can be analyzed. The obtained tidal volume difference reflects the degree of difference in tidal volume between the current moment and the historical moments with similar inspiratory flow rates. The greater the tidal volume difference, the greater the difference in tidal volume between the current moment and the historical moments with similar inspiratory flow rates, and the less likely the patient is to be in an anesthetized state. Conversely, the closer the tidal volume at the current moment with similar inspiratory flow rates is to the tidal volume at each historical moment, the more likely the patient is to be in an anesthetized state. Subsequently, whether the patient is in an anesthetized state at the current moment can be accurately evaluated based on the tidal volume difference.

[0041] Preferably, in one embodiment of the present invention, the method for obtaining the tidal volume difference at the current moment specifically includes: The absolute value of the difference between the tidal volume at the current moment and the tidal volume at each historical moment in the cluster where the current moment is located is used as the initial tidal volume difference parameter between the current moment and each historical moment in the cluster where the current moment is located. The larger the initial tidal volume difference parameter, the greater the tidal volume difference between the current moment and each historical moment with similar inspiratory flow rate. Then, the average value of the initial tidal volume difference parameter between the current moment and all historical moments in the cluster where the current moment is located can be normalized to obtain the tidal volume difference degree at the current moment.

[0042] As an example, in one embodiment of the present invention, the expression of the tidal volume difference at the current moment may be specifically, for example, as follows: in, Indicates the tidal volume difference at the current moment; Indicates the tidal volume at the current moment; Indicates the cluster at the current moment. Tidal volume at a historical moment; Indicates the number of historical moments in the cluster where the current moment is located; Represents the activation function, which is used for normalization.

[0043] When the patient enters the anesthetized state, the patient's brain consciousness is in a fuzzy state, and the sensitivity of the patient's body decreases. Therefore, the patient's inspiratory flow rate and tidal volume are both low, and the change characteristics of the inspiratory flow rate and tidal volume are relatively similar and stable. Therefore, the embodiment of the present invention first uses the preset number of historical moments closest to the current moment as the reference historical moment of the current moment, and analyzes the difference in inspiratory flow rate and tidal volume between the current moment and the reference historical moment, the difference in the degree of difference in inspiratory flow rate between the current moment and the reference historical moment, and the degree of difference in tidal volume at the current moment. The obtained stable similarity is reflected in the similarity of the low-level stable change characteristics between the inspiratory flow rate and the tidal volume in the time period composed of the current moment and each reference historical moment. The greater the stable similarity, the more likely the patient is in the anesthetized state at the current moment. Subsequently, based on the stable similarity at the current moment, the possibility of the patient being in the anesthetized state can be accurately calculated and analyzed. Among them, the preset number is set to 5, and the specific value of the preset number can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0044] Preferably, in one embodiment of the present invention, the method for obtaining the stationary similarity at the current moment specifically includes: See also Figure 2 , which shows a flow chart of a method for obtaining the stationary similarity at the current moment provided by an embodiment of the present invention.

[0045] Step S201: The current moment or any reference historical moment is used as the moment to be measured, the inspiratory flow rate at the moment to be measured is used as the numerator, the tidal volume at the moment to be measured is used as the denominator, the ratio is used as the characteristic index of the moment to be measured, and the absolute value of the difference between the characteristic indexes of the current moment and each reference historical moment is used as the first difference coefficient between the current moment and each reference historical moment.

[0046] First, the current moment or any reference historical moment is analyzed, and the current moment or any reference historical moment is used as the moment to be measured, and the inspiratory flow rate at the moment to be measured is used as the numerator, the tidal volume at the moment to be measured is used as the denominator, and the ratio is used as the characteristic index of the moment to be measured. The characteristic index can reflect the proportional characteristics between the inspiratory flow rate and the tidal volume at the moment to be measured. The characteristic index of the current moment and each reference historical moment can be obtained by the same method as above. The smaller the difference in the characteristic index between the current moment and each reference historical moment, the more similar the change trends of the inspiratory flow rate and the tidal volume are in the time period formed by the current moment and each reference historical moment. Therefore, the absolute value of the difference between the characteristic index between the current moment and each reference historical moment can be used as the first difference coefficient between the current moment and each reference historical moment. The smaller the first difference coefficient, the smaller the difference in the characteristic index between the current moment and the reference historical moment, which provides a data basis for the subsequent calculation and analysis of the stationary similarity of the current moment.

[0047] As an example, in one embodiment of the present invention, the expression of the first difference coefficient between the current moment and each reference historical moment may be specifically, for example, as follows: in, Indicates the current time and The first coefficient of difference between the reference historical moments; Indicates the inspiratory flow rate at the current moment; Indicates the tidal volume at the current moment; Indicates the characteristic index of the current moment; Indicates The inspiratory flow rate at a reference historical moment; Indicates Tidal volume at a reference historical moment; Indicates A characteristic indicator of a reference historical moment.

[0048] Step S202: According to the tidal volume difference at the current moment and the difference in the inspiratory flow rate difference between the current moment and each reference historical moment, a second difference coefficient between the current moment and each reference historical moment is obtained, and the first difference coefficient and the second difference coefficient are combined to obtain a comprehensive difference coefficient between the current moment and each reference historical moment.

[0049] Preferably, in one embodiment of the present invention, the method for obtaining the second difference coefficient between the current moment and each reference historical moment specifically includes: The absolute value of the difference in the inspiratory flow rate difference between the current moment and each reference historical moment is used as the flow rate change difference between the current moment and each reference historical moment; the product of the flow rate change difference and the tidal volume difference at the current moment is used as the second difference coefficient between the current moment and each reference historical moment.

[0050] As an example, in one embodiment of the present invention, the expression of the second difference coefficient between the current moment and each reference historical moment may be specifically, for example, as follows: in, Indicates the current time and The second coefficient of difference between the reference historical moments; Indicates the tidal volume difference at the current moment; Indicates the difference in inspiratory flow rate at the current moment; Indicates The difference of inspiratory flow rate at a reference historical moment; Indicates the current time and The flow velocity change difference between the reference historical moments.

[0051] Among them, when When it is larger, it means that this may be the initial stage of anesthesia. Although the patient's inspiratory flow rate has changed, the change in inspiratory flow rate has not reached a stable state. The smaller the difference, the more stable the inspiratory flow rate of the patient tends to be, indicating that the patient is more likely to be fully anesthetized at this time, and the difference in tidal volume at the current moment Weight it.

[0052] Then, the product of the first difference coefficient and the second difference coefficient is used to obtain the comprehensive difference coefficient between the current moment and each reference historical moment. The smaller the comprehensive difference coefficient between the current moment and each reference historical moment, the more similar and stable the change characteristics of the inspiratory flow rate and tidal volume are.

[0053] Step S203: performing negative correlation normalization processing on the accumulated values ​​of the comprehensive difference coefficients between the current moment and all reference historical moments to obtain the stable similarity of the current moment.

[0054] From the above analysis, it can be seen that the smaller the comprehensive difference coefficient between the current moment and each reference historical moment, the smaller the comprehensive difference coefficient between the current moment and each reference historical moment, indicating that in the time period formed by the current moment and each reference historical moment, the inspiratory flow rate and tidal volume are in a low and stable changing state, and the change trend is similar, which further indicates that the patient is more likely to be in an anesthetized state at the current moment. Therefore, the cumulative value of the comprehensive difference coefficient between the current moment and all reference historical moments can be normalized by negative correlation to obtain the stable similarity of the current moment.

[0055] In one embodiment of the present invention, the natural constant A negative exponential function with α as the base realizes the normalization of negative correlation.

[0056] As an example, in one embodiment of the present invention, the expression of the stationary similarity at the current moment may be specifically, for example, as follows: in, Indicates the stable similarity at the current moment; Indicates the current time and The first coefficient of difference between the reference historical moments; Indicates the current time and The second coefficient of difference between the reference historical moments; Indicates the current time and The comprehensive difference coefficient between the reference historical moments; represents the number of all reference historical moments; Expressed as a natural constant An exponential function with base .

[0057] At this point, the stable similarity at the current moment is obtained.

[0058] The anesthesia analysis module 103 is used to obtain the anesthesia possibility at the current moment according to the inspiratory flow rate, tidal volume and smooth similarity at the current moment.

[0059] The greater the steady similarity at the current moment, the greater the possibility that the patient is in an anesthetized state at the current moment. When the patient is in an anesthetized state, the sensitivity of the patient's body decreases, the consciousness becomes blurred, and the inspiratory flow rate and tidal volume are both low. Therefore, the inspiratory flow rate, tidal volume and steady similarity at the current moment can be analyzed. The obtained anesthesia possibility can accurately reflect the possibility that the patient is in an anesthetized state, thereby improving the accuracy of subsequent assessment of the patient's anesthesia state.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining the possibility of anesthesia at the current moment specifically includes: The inspiratory flow rate and tidal volume at the current moment are integrated and negatively correlated to obtain the possibility parameter at the current moment. The larger the possibility parameter, the more likely the patient is in an anesthetized state at the current moment. Then, the stationary similarity and possibility parameter at the current moment are integrated and normalized to obtain the anesthesia possibility at the current moment.

[0061] In the embodiment of the present invention, the integration of the inspiratory flow rate and the tidal volume at the current moment can be achieved by calculating the sum or product of the two, which is not limited here.

[0062] In the embodiment of the present invention, the integration of the stationary similarity and the possibility parameter can be achieved by calculating the sum or product of the two, which is not limited here.

[0063] As an example, in one embodiment of the present invention, the expression of the anesthesia possibility at the current moment may be specifically, for example, as follows: in, Indicates the possibility of anesthesia at the current moment; Indicates the stable similarity at the current moment; Indicates the inspiratory flow rate at the current moment; Indicates the tidal volume at the current moment; represents the possibility parameter at the current moment; Represents the activation function, which is used for normalization.

[0064] At this point, the anesthesia possibility at the current moment is obtained. Subsequently, based on the anesthesia possibility, the patient's anesthesia state at the current moment can be accurately evaluated to improve the accuracy of the surgical monitoring system.

[0065] The anesthesia assessment module 104 is used to assess the anesthesia status of the patient based on the anesthesia possibility at the current moment.

[0066] The greater the anesthesia possibility at the current moment, the more likely the patient is in an anesthetized state at the current moment. Therefore, the anesthesia state of the patient can be evaluated based on the anesthesia possibility at the current moment.

[0067] Preferably, in one embodiment of the present invention, the method for evaluating the anesthetic state of a patient specifically comprises: If the possibility of anesthesia at the current moment is greater than the preset anesthesia threshold, the patient is completely in an anesthesia state. At this time, the monitoring system can remind medical staff through prompt lights and other means that the patient has entered the anesthesia state. Otherwise, the patient has not completely entered the anesthesia state. At this time, it is necessary to continue to monitor or monitor the patient's anesthesia state. Among them, the preset anesthesia threshold is set to 0.8. The specific value of the preset anesthesia threshold can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0068] It should be noted that the sequence of the above embodiments of the present invention is only for description 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.

[0069] 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 monitoring data analysis system for surgical operations, characterized in that: The system comprises: A data acquisition module, used for acquiring the inspiratory flow rate and tidal volume of the patient at each moment, taking the last moment as the current moment, and taking all other moments except the current moment as historical moments; A data analysis module, for taking any moment as a target moment, and obtaining the inspiratory flow rate difference degree of the target moment according to the difference between the inspiratory flow rate of other moments before the target moment and the target moment, and the change of the inspiratory flow rate of other moments before the target moment; clustering all moments according to the difference between the inspiratory flow rate of different moments to obtain multiple clustering clusters; obtaining the tidal volume difference degree of the current moment according to the difference between the historical moments in the clustering cluster where the current moment is located and the current moment; taking the preset number of historical moments closest to the current moment as the reference historical moments of the current moment, and obtaining the stationary similarity of the current moment according to the difference between the inspiratory flow rate and the tidal volume between the current moment and the reference historical moment, the difference between the inspiratory flow rate difference degree between the current moment and the reference historical moment, and the tidal volume difference degree of the current moment; an anesthesia analysis module, used to obtain the anesthesia possibility at the current moment according to the inspiratory flow rate, the tidal volume and the stationary similarity at the current moment; an anesthesia assessment module, used to assess the patient's anesthesia state based on the anesthesia possibility at the current moment; The step of obtaining the stable similarity at the current moment includes: The current moment or any reference historical moment is used as the moment to be measured, the inspiratory flow rate at the moment to be measured is used as the numerator, the tidal volume at the moment to be measured is used as the denominator, and the ratio is used as the characteristic index of the moment to be measured; The absolute value of the difference between the characteristic index at the current moment and each reference historical moment is used as the first difference coefficient between the current moment and each reference historical moment; Obtaining a second difference coefficient between the current moment and each reference historical moment according to the tidal volume difference at the current moment and the difference in the inspiratory flow rate difference between the current moment and each reference historical moment; The product of the first difference coefficient and the second difference coefficient is used to obtain a comprehensive difference coefficient between the current moment and each reference historical moment; Perform negative correlation normalization processing on the accumulated value of the comprehensive difference coefficient between the current moment and all reference historical moments to obtain the stable similarity of the current moment; The obtaining of the second difference coefficient between the current moment and each reference historical moment comprises: The absolute value of the difference between the inspiratory flow rate difference between the current moment and each reference historical moment is used as the flow rate change difference between the current moment and each reference historical moment; The product value of the flow rate change difference value and the tidal volume difference at the current moment is used as the second difference coefficient between the current moment and each reference historical moment.

2. A monitoring data analysis system for surgical operations according to claim 1, characterized in that: The obtaining of the inspiratory flow rate difference at the target time comprises: All moments before the target moment are taken as the moments to be analyzed of the target moment; According to the difference of the inspiratory flow rate between the target moment and each moment to be analyzed, obtaining a first difference parameter at the target moment; The absolute value of the difference between the inspiratory flow rate at each moment and the adjacent previous moment is used as the flow rate change at each moment; and the second difference parameter at the target moment is obtained according to the difference between the flow rate change between the target moment and each moment to be analyzed; The first difference parameter and the second difference parameter are integrated and normalized to obtain the inspiratory flow rate difference at the target moment.

3. A monitoring data analysis system for surgical operations according to claim 2, characterized in that: The first difference parameter of the target time is obtained including: The difference between the inspiratory flow rate at each time to be analyzed and the target time is used as the difference value of the inspiratory flow rate between each time to be analyzed and the target time; The average value of the inspiratory flow rate difference between all the time points to be analyzed and the target time point is normalized and used as the first difference parameter at the target time point.

4. A monitoring data analysis system for surgical operations according to claim 2, characterized in that: The second difference parameter of obtaining the target time comprises: The difference between the flow velocity change at the target moment and each time to be analyzed is used as the difference value of the change between the target moment and each time to be analyzed; The average value of the difference in the variation between the target moment and all the moments to be analyzed is normalized to be used as the second difference parameter of the target moment.

5. A monitoring data analysis system for surgical operations according to claim 1, characterized in that: The obtaining of multiple clusters comprises: The absolute value of the difference between the inspiratory flow rates at any two moments is used as the distance measure between the two moments; Using the K-means clustering algorithm, and based on the distance metric between any two moments, all moments are clustered to obtain a plurality of clusters.

6. A monitoring data analysis system for surgical operations according to claim 1, characterized in that: The obtaining of the tidal volume difference at the current moment includes: The absolute value of the difference between the tidal volume at the current moment and the tidal volume at each historical moment in the cluster where the current moment is located is used as the initial tidal volume difference parameter between the current moment and each historical moment in the cluster where the current moment is located; The average value of the initial tidal volume difference parameter between the current moment and all historical moments in the cluster where the current moment is located is normalized to obtain the tidal volume difference degree at the current moment.

7. A monitoring data analysis system for surgical operations according to claim 1, characterized in that: The method of obtaining the anesthesia possibility at the current moment includes: The inspiratory flow rate and the tidal volume at the current moment are integrated and negatively correlated to obtain the possibility parameter at the current moment; The stationary similarity and the possibility parameter at the current moment are integrated and normalized to obtain the anesthesia possibility at the current moment.

8. A monitoring data analysis system for surgical operations according to claim 1, characterized in that: The assessment of the patient's anesthetic status includes: If the anesthesia possibility at the current moment is greater than the preset anesthesia threshold, the patient has completely entered the anesthesia state; otherwise, the patient has not yet completely entered the anesthesia state.

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