Vital sign monitoring system and method in anesthesia process

Abstract

The invention relates to the technical field of anesthesia monitoring, in particular to a vital sign monitoring system and method in the anesthesia process. Acquiring brain wave time sequence signals of the pregnant woman and time sequence data of various physical sign parameters; in view of different fluctuation states of physical sign parameters in different anesthesia stages, the anesthesia stage corresponding to each moment needs to be judged. Then, the change fluctuation of each physical sign parameter and the historical moment data value of the same anesthesia stage is analyzed, a fluctuation index is determined, and the weight of each physical sign parameter at each moment is dynamically calculated; and calculating the surgical anesthesia risk index in combination with the sign parameter change period, the data value and the weight at the historical moment. And finally, vital sign monitoring is carried out based on the surgical anesthesia risk index at each moment. According to the method, the weights of the physical sign parameters in different anesthesia stages are dynamically allocated for parameter fusion, the anesthesia risk model is established, more accurate surgical anesthesia risk indexes are obtained, the accuracy of a monitoring result is ensured, and risk trend prediction is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of anesthesia monitoring, and particularly to a vital sign monitoring system and method during anesthesia. Background Art

[0002] In general anesthesia surgeries involving pregnant patients, anesthesia management faces multi-dimensional clinical challenges. Due to the special physiological association of the maternal-fetal dual life system, it is often necessary to construct a dual safety monitoring system to ensure the safety of the mother and fetus.

[0003] During the surgical anesthesia of pregnant women, due to the action of anesthetic drugs, the time series signals of their brain waves will change significantly, and these changes are closely related to the depth of anesthesia. At the same time, various physical sign parameters of pregnant women will also show different fluctuation characteristics with different anesthesia stages.

[0004] Traditional vital sign monitoring methods often use fixed fusion weights to perform fusion analysis on all types of physical sign parameters, so as to complete data integration for vital sign monitoring and early warning. However, due to the differences in the fluctuations of vital sign parameters in different anesthesia stages, the fixed fusion weights cannot accurately reflect these subtle changes, and often lack multi-parameter coupling analysis, resulting in inaccurate and incomplete assessment of surgical anesthesia risks, and may cause misjudgment or false early warning. Summary of the Invention

[0005] In order to solve the technical problems that the fluctuations of vital sign parameters in different anesthesia stages will cause differences, the fixed fusion weights cannot accurately reflect these subtle changes, and often lack multi-parameter coupling analysis, resulting in inaccurate and incomplete assessment of anesthesia risks, and causing misjudgment or false early warning, the purpose of the present invention is to provide a vital sign monitoring system and method during anesthesia, and the specific technical solutions adopted are as follows: Obtain the time series signal of the brain waves of a pregnant woman and the time series data of various physical sign parameters; At each moment, perform frequency domain analysis on the local data corresponding to each moment in the time series signal of the brain waves, and determine the corresponding anesthesia stage at each moment based on the data change situation in the frequency domain data; In the time series data of each physical sign parameter, analyze the change and fluctuation situation of the data value at each moment and the data value at the historical moment in the same anesthesia stage, and determine the fluctuation index of each physical sign parameter at each moment; at each moment, comprehensively compare the fluctuation indexes of all types of physical sign parameters, and thus calculate the weight of each physical sign parameter; In the time series data of various physical sign parameters, for any moment, combine the change period, data value, and the weight of each physical sign parameter of the physical sign parameters at the historical moment in the same anesthesia stage as this moment, and obtain the surgical anesthesia risk index at this moment; Vital signs are monitored according to the surgical anesthesia risk indicators at each moment.

[0006] Furthermore, the method for determining the anesthesia stage includes: In terms of time sequence, for any moment, starting from this moment, a preset number of consecutive moments are selected from historical moments to obtain the corresponding time sequence data segment of this moment in the electroencephalogram time sequence signal; Perform a fast Fourier transform on the time sequence data segment corresponding to this moment to obtain frequency domain data; Based on a preset frequency, divide the frequency domain data corresponding to this moment to obtain different frequency bands, and the frequency bands include the Alpha band, Beta band, Theta band, and Delta band; In each frequency band, analyze the change and fluctuation of the frequency amplitude to obtain the intensity characteristic value of each frequency band; In the frequency domain data corresponding to this moment, when the frequency band corresponding to the maximum intensity characteristic value is the Alpha band or Beta band, it is determined that this moment is in the anesthesia awakening stage; when the frequency band corresponding to the maximum intensity characteristic value is the Theta band, it is determined that this moment is in the anesthesia induction stage; when the frequency band corresponding to the maximum intensity characteristic value is the Delta band, it is determined that this moment is in the anesthesia maintenance stage.

[0007] Furthermore, the method for obtaining the intensity characteristic value includes: In the frequency domain data of this moment, in each frequency band, take the cumulative value of the amplitudes at all frequencies as the first intensity factor of each frequency band; In each frequency band, take the mean value of the amplitudes at all frequencies as the intensity mean value, and take the ratio of the intensity mean value of each frequency band to the intensity mean values of all frequency bands as the second intensity factor of each frequency band; Take the normalized value of the product of the first intensity factor and the second intensity factor of each frequency band as the intensity characteristic value of each frequency band.

[0008] Furthermore, the method for obtaining the fluctuation index includes: For any moment, among the historical moments before this moment in terms of time sequence, take the historical moments in the same anesthesia stage as this moment as reference moments, and form the reference data segment of this moment with the corresponding values of this moment and all reference moments in the time sequence data of each physical sign parameter; In the reference data segment of this moment for each physical sign parameter, perform adaptive segmentation based on the data distribution to obtain multiple segments, and analyze the segmentation situation of the reference data segment to determine the first fluctuation factor of each physical sign parameter at this moment; At this moment, in the reference data segment of each physical sign parameter, comprehensively analyze the fluctuations of the data values within all segments, and determine the second fluctuation factor of each physical sign parameter at this moment; The value obtained by normalizing the product of the first fluctuation factor and the second fluctuation factor of each physical sign parameter at this moment is used as the fluctuation index of each physical sign parameter at this moment.

[0009] Furthermore, the method for obtaining the first fluctuation factor includes: For any moment, in the reference data segment of each physical sign parameter at this moment, use the adaptive piecewise constant approximation method to segment the reference data segment, obtaining multiple segments and the APCA approximation value of each segment; Take the absolute value of the difference between the APCA approximation value of each segment and the mean of the APCA approximation values of all segments as the deviation factor, and take the ratio of the deviation factor to the length of each segment as the first fluctuation coefficient of each segment; Take the cumulative sum of the fluctuation coefficients of all segments as the first fluctuation factor of each physical sign parameter at this moment.

[0010] Furthermore, the method for obtaining the second fluctuation factor includes: In each segment, calculate the absolute value of the difference between each data value and the mean of all data values as the difference factor, and take the mean of the difference factors of all data values as the second fluctuation coefficient of each segment; Take the mean of the fluctuation coefficients of all segments as the second fluctuation factor of each physical sign parameter at this moment.

[0011] Furthermore, the method for obtaining the weight includes: At each moment, take the proportion of the fluctuation index of each physical sign parameter among the fluctuation indexes of all types of physical sign parameters as the weight of each physical sign parameter at each moment.

[0012] Furthermore, the method for obtaining the surgical anesthesia risk index includes: The physical sign parameters include pregnant woman's blood pressure, pregnant woman's heart rate, and fetal heart rate; For any moment, in the reference data segment of the pregnant woman's heart rate and fetal heart rate at this moment, use the AMPD algorithm to obtain the minimum AMPD value in the reference data segment, and obtain the period value of the reference data segment of the pregnant woman's heart rate and fetal heart rate corresponding to this moment based on the time delay corresponding to the minimum AMPD value; The formula model of the surgical anesthesia risk index at this moment includes: ; Among them, represents the surgical anesthesia risk index at this moment; Represents the weight of the pregnant woman's heart rate at this moment; Represents the period value of the reference data segment of the pregnant woman's heart rate at this moment; Represents the numerical value of the pregnant woman's heart rate at this moment; Represents the weight of the fetal heart rate at this moment; Represents the period value of the reference data segment of the fetal heart rate at this moment; Represents the numerical value of the fetal heart rate at this moment; Represents the weight of the pregnant woman's blood pressure at this moment; Represents the numerical value of the pregnant woman's blood pressure at this moment; Represents the natural constant; Represents the normalization function.

[0013] Further, the vital sign monitoring according to the surgical anesthesia risk index at each moment includes: When the surgical anesthesia risk index at a certain moment is greater than or equal to the preset risk threshold, it is prompted that there is a surgical anesthesia risk at this moment for early warning; When the surgical anesthesia risk index at a certain moment is less than the preset risk threshold, it is prompted that there is no surgical anesthesia risk at this moment and no early warning is required.

[0014] A vital sign monitoring system during anesthesia includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory. When at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor, the steps of the vital sign monitoring method during anesthesia are implemented.

[0015] The present invention has the following beneficial effects: First, by obtaining the time-series signals of the pregnant woman's brain waves and the time-series data of various physical sign parameters, it is possible to comprehensively and meticulously reflect the changes in the physiological state of the pregnant woman during the anesthesia process. In different anesthesia stages, the fluctuation states of the physical sign parameters are different, so the weights will vary. Therefore, it is necessary to determine the anesthesia stage corresponding to each moment. Given that when in different anesthesia stages, the brain electrical activities of the pregnant woman usually exhibit different band changes, so at each moment, frequency-domain analysis is performed on the local data segments in the time-series signals of the brain waves, and based on the data changes in the frequency-domain data, the anesthesia stage corresponding to each moment is determined. Then, for each moment, in the time-series data of each physical sign parameter, the change fluctuations between its data value and the data value at the historical moment in the same anesthesia stage are analyzed to determine the fluctuation index. This process can quantify the fluctuation conditions of each physical sign parameter, which helps to comprehensively compare the fluctuation indexes of all types of physical sign parameters in the subsequent process and dynamically calculate the weight of each physical sign parameter at each moment; this dynamic weight allocation method is more in line with the characteristics of physiological changes in the actual anesthesia process. Further, by combining the change period, data value of the physical sign parameter at the historical moment in the same anesthesia stage as the current moment, and the weight of each physical sign parameter, the surgical anesthesia risk index is calculated. Finally, based on the surgical anesthesia risk index at each moment, vital sign monitoring is carried out. In summary, the present invention can adaptively and dynamically set the fusion weight of each physical sign parameter at each moment according to the progress stage of the anesthesia process and in combination with the physiological condition of the pregnant woman herself. Through multi-parameter fusion, a more accurate surgical anesthesia risk index is obtained for vital sign monitoring, ensuring the accuracy of the monitoring results and realizing risk trend prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of a method for monitoring vital signs during an anesthesia process provided by an embodiment of the present invention; Figure 2 It is the time-series signal of the brain waves in an anesthesia awakening stage provided by an embodiment of the present invention; Figure 3 It is the time-series signal of the brain waves in an anesthesia induction stage provided by an embodiment of the present invention; Figure 4 It is the time-series signal of the brain waves in an anesthesia maintenance stage provided by an embodiment of the present invention; Figure 5 The flowchart of a method for determining the anesthesia stage provided by an embodiment of the present invention; Figure 6 The flowchart of a method for obtaining a fluctuation index provided by an embodiment of the present invention; Figure 7 The system block diagram of a vital sign monitoring system during anesthesia provided by an embodiment of the present invention; Figure 8 The system structure schematic diagram of a vital sign monitoring system during anesthesia provided by an embodiment of the present invention. Detailed implementation manners

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features, and effects of a vital sign monitoring system and method during anesthesia proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solutions of a vital sign monitoring system and method during anesthesia provided by the present invention with reference to the accompanying drawings.

[0021] Please refer to Figure 1 , which shows the flowchart of a method for a vital sign monitoring method during anesthesia provided by an embodiment of the present invention. The method includes the following steps: Step S1: Obtain the time series signal of the electroencephalogram of the pregnant woman and the time series data of various vital sign parameters.

[0022] In general anesthesia surgeries involving pregnant patients, anesthesia management is crucial, which is related to the double safety of the pregnant woman and the fetus. To ensure the safety and effectiveness of the surgical process, it is necessary to monitor the vital signs of the mother in real time and simultaneously monitor the fetal heart rate. By real-time monitoring of various vital sign parameters of the pregnant woman, targeted monitoring and management of the state changes during the surgical process can be carried out, which helps doctors detect and handle potential risks in a timely manner, regulate the anesthesia process, and ensure the smooth progress of the surgery while ensuring the safety of the mother and baby.

[0023] Therefore, the acquisition and analysis of vital sign data are very important. The anesthesia process usually includes the following stages: anesthesia induction stage, anesthesia maintenance stage, and anesthesia recovery stage. Under the influence of anesthetic drugs, there will be subtle differences in physiological fluctuations at different stages. Therefore, in the embodiments of the present invention, when monitoring the vital signs during the anesthesia process, it is also necessary to determine which stage of the anesthesia process is currently in; in view of the fact that under different anesthesia stages, the electroencephalogram (EEG) activities of pregnant women usually show different band changes, so while acquiring the vital sign data, it is also necessary to acquire the EEG time series signal of the pregnant woman. It should be noted that the EEG time series data can specifically adopt the bispectral index (BIS) or the EEG entropy index.

[0024] Specifically, before the anesthesia surgery starts, place the multi-parameter monitor and the electroencephalogram device in appropriate positions. The multi-parameter monitor is used to collect the time series data of various vital sign parameters of the pregnant woman in real time, and the electroencephalogram device is used to collect the EEG time series signal of the pregnant woman in real time; in this embodiment of the present invention, the types of vital sign parameters are set as the heart rate of the pregnant woman, the blood pressure of the pregnant woman, and the fetal heart rate; the sampling intervals of the EEG time series signal and the time series data of the vital sign parameters are both set to 0.1 s. The specific time interval can be adjusted according to the implementation scenario and is not limited here. The time series data of the vital sign parameters and the EEG time series signal are collected simultaneously and aligned in time.

[0025] In the embodiments of the present invention, the acquisition and obtaining of the vital sign parameter data and the EEG data of the pregnant woman are all authorized by relevant users, and the process does not violate relevant laws and regulations and does not violate public order and good customs.

[0026] Step S2: At each moment, perform frequency domain analysis on the local data corresponding to each moment in the EEG time series signal, and determine the anesthesia stage corresponding to each moment based on the data change situation in the frequency domain data.

[0027] When in different anesthesia stages, under the influence of anesthetic drugs, the fluctuations of vital sign parameters will change slightly. At the same time, the EEG will also show different frequency characteristics. The frequency domain characteristics can provide intuitive information about the anesthesia state. For example, when in the anesthesia recovery stage, the EEG will be in the high-frequency Alpha wave (8 - 12 Hz) and Beta wave (12 - 30 Hz) intervals, as Figure 2 shown, which shows an EEG time series signal in an anesthesia recovery stage; in the anesthesia induction stage, the mid-low frequency EEG signals increase continuously, and Theta waves (4 - 8 Hz) appear, as Figure 3 shown, which shows an EEG time series signal in an anesthesia induction stage; when entering the deep anesthesia state, that is, the anesthesia maintenance stage, the Alpha wave completely disappears, and the EEG signal is mainly concentrated near the Delta wave (0.5 - 4 Hz), asFigure 4 As shown, it shows the electroencephalogram time-series signal during the anesthesia maintenance stage.

[0028] Since accurately judging the anesthesia stage helps doctors to timely detect and handle any potential anesthesia risks, and at the same time can more accurately identify the fluctuations of physical sign parameters in different anesthesia stages, which is helpful to improve the accuracy of vital sign monitoring during anesthesia. Therefore, at each moment, the local data corresponding to each moment in the electroencephalogram time-series signal is subjected to frequency-domain analysis, and based on the data changes in the frequency-domain data, the anesthesia stage corresponding to each moment is determined.

[0029] Preferably, in an embodiment of the present invention, the method for determining the anesthesia stage includes: Please refer to Figure 5 , which shows the flowchart of the method for determining the anesthesia stage in an embodiment of the present invention. The method includes the following steps: Step S201: For any moment, determine the time-series data segment corresponding to this moment in the electroencephalogram time-series signal.

[0030] In terms of time sequence, for any moment, starting from this moment and tracing back, that is, selecting a preset number of consecutive moments in the historical moments to obtain the time-series data segment corresponding to this moment in the electroencephalogram time-series signal. The reason for determining the time-series data segment of each moment is to ensure that there is enough data for frequency-domain analysis while maintaining the accuracy of the analysis.

[0031] It should be noted that the preset number is set to the number of sampling moments within one minute, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0032] Step S202: Obtain the frequency-domain data of the time-series data segment corresponding to this moment.

[0033] Perform a fast Fourier transform on the time-series data segment corresponding to this moment to obtain the frequency-domain data. The frequency-domain data can reveal the intensity of different frequency components in the local data corresponding to each moment, which is an important basis for judging the anesthesia stage.

[0034] It should be noted that the fast Fourier transform is a well-known technology, and the specific process is not described in detail here.

[0035] Step S203: Divide the frequency-domain data corresponding to this moment based on a preset frequency to obtain different frequency bands.

[0036] As can be seen from the foregoing analysis, the frequency-domain characteristics of electroencephalogram signals vary in different anesthesia stages, which can be specifically divided into Alpha waves (8 - 12 Hz), Beta waves (12 - 30 Hz), Theta waves (4 - 8 Hz), Delta waves (0.5 - 4 Hz), etc. Therefore, based on this classification method, preset frequencies of 0.5 Hz, 4 Hz, 8 Hz, 12 Hz, and 30 Hz are set to divide the frequency-domain data, obtaining different frequency bands, including the Alpha band, Beta band, Theta band, and Delta band. Each band corresponds to the aforementioned Alpha wave, Beta wave, Theta wave, and Delta wave respectively.

[0037] Step S204: In each frequency band, analyze the change and fluctuation of the frequency amplitude to obtain the intensity characteristic value of each frequency band.

[0038] For any moment, in the frequency-domain data at this moment, in each frequency band, the cumulative value of the amplitudes at all frequencies is used as the first intensity factor of each frequency band. The first intensity factor reflects the total energy or intensity of the electroencephalogram signal in each frequency band, and the larger this value is, the higher the signal intensity in the frequency band, indicating that the signal intensity in the frequency band within the time-series data segment corresponding to this moment is greater.

[0039] In each frequency band, the mean value of the amplitudes at all frequencies is used as the intensity mean value. Then, calculate the sum value of the intensity mean values of all frequency bands, and use the ratio of the intensity mean value of each frequency band to the sum value of the intensity mean values of all frequency bands as the second intensity factor of each frequency band. The second intensity factor considers the relative position of the average intensity or energy of the electroencephalogram signal in each frequency band among all frequency bands. The larger this value is, the more important the frequency band occupies in the whole, that is, the more significant the feature.

[0040] Based on the foregoing analysis, the larger the first intensity factor corresponding to a certain frequency band is, the higher the signal intensity in that frequency band; the larger the second intensity factor is, the more significant the feature of that frequency band in the whole. Therefore, finally, the value obtained by normalizing the product of the first intensity factor and the second intensity factor of each frequency band is used as the intensity characteristic value of each frequency band. At this time, the larger the intensity characteristic value of a certain frequency band is, the more it can be regarded as occupying a dominant position in the frequency-domain data. Among them, normalization is a well-known technical means for those skilled in the art. The choice of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0041] Step S205: Compare the intensity characteristic values of each frequency band in the frequency-domain data of the time-series data segment at this moment to determine the anesthesia stage at this moment.

[0042] In the frequency-domain data corresponding to this moment, when the frequency band corresponding to the maximum intensity eigenvalue is the Alpha band or the Beta band, it indicates that these two bands are dominant, that is, the data intensities in these two bands are the largest in the frequency-domain data corresponding to this moment. Then, this moment can be regarded as being in the anesthesia recovery stage. Similarly, when the frequency band corresponding to the maximum intensity eigenvalue is the Theta band, it is determined that this moment is in the anesthesia induction stage. When the frequency band corresponding to the maximum intensity eigenvalue is the Delta band, it is determined that this moment is in the anesthesia maintenance stage.

[0043] Step S3: In the time-series data of each vital sign parameter, analyze the change and fluctuation of the data value at each moment compared with the data value at the historical moment in the same anesthesia stage, and determine the fluctuation index of each vital sign parameter at each moment. At each moment, comprehensively compare the fluctuation indexes of all types of vital sign parameters, so as to calculate the weight of each vital sign parameter.

[0044] During the anesthesia of pregnant women, it is necessary to monitor the vital signs during the anesthesia process by combining multiple vital signs. Since the degree of change in vital sign data is different in different stages of the anesthesia process, fixed data fusion parameters will judge the physiological fluctuations caused by normal surgical procedures as abnormal, resulting in inaccurate anesthesia monitoring results. Therefore, in order to ensure that the contribution degree of different vital sign data to the monitoring results can accurately reflect the data fluctuations and enable their full fusion, it is necessary to analyze the change and fluctuation of the data at the moments in the same anesthesia stage, obtain the fluctuation index of each vital sign parameter, and then quantify the weight of each vital sign parameter.

[0045] Vital sign data often has a certain regularity, but there are various unexpected factors during the anesthesia surgery process, which will cause abnormal fluctuations in vital sign data. By analyzing the change and fluctuation of the data value at each moment compared with the data value at the historical moment in the same anesthesia stage in the time-series data of each vital sign parameter, the fluctuation characteristics of different data that may be caused by different stages of anesthesia can be captured in a timely manner, and the fluctuation index of each vital sign parameter at each moment can be determined.

[0046] Preferably, in an embodiment of the present invention, the method for obtaining the fluctuation index includes: Please refer to Figure 6 , which shows the method flowchart of the method for obtaining the fluctuation index in an embodiment of the present invention. The method includes the following steps: Step S301: For any moment, perform a backtracking in time sequence to determine the reference data segment of this moment.

[0047] For any given moment, among the historical moments prior to this moment in time sequence, the historical moments that are in the same anesthesia stage as this moment are used as reference moments. The numerical values corresponding to this moment and all reference moments in the time series data of each physical sign parameter form the reference data segment for this moment. By determining the reference data segment, it is possible to only consider data from the same anesthesia stage in subsequent calculations, thereby excluding the interference of different anesthesia stages on the analysis of the fluctuations in physical sign parameter data and making the analysis results more accurate.

[0048] Step S302: In the reference data segment of each physical sign parameter at this moment, perform adaptive segmentation based on the data distribution to obtain multiple segments, and analyze the segmentation situation of the reference data segment to determine the first fluctuation factor of each physical sign parameter at this moment.

[0049] For any given moment, in the reference data segment of each physical sign parameter at this moment, use the adaptive piecewise constant approximation method to segment the reference data segment, obtaining multiple segments and the APCA approximation value of each segment. The adaptive piecewise constant approximation method can better capture the local characteristics of the data and make the data in each segment have relatively similar change characteristics, facilitating a more accurate and detailed analysis of the fluctuations in physical sign parameters.

[0050] Take the absolute value of the difference between the APCA approximation value of each segment and the mean of the APCA approximation values of all segments as the deviation factor. The deviation factor measures the difference between the APCA approximation value of each segment and the mean of the APCA approximation values of all segments. This difference reflects the degree of fluctuation of the data in each segment relative to the average level of the overall data, and the larger the value, the greater the fluctuation.

[0051] Take the ratio of the deviation factor to the length of each segment as the first fluctuation coefficient of each segment. The first fluctuation coefficient reflects the degree of data fluctuation per unit length, making the comparison of fluctuation coefficients for segments of different lengths more fair, and the larger the value, the more prominent the data fluctuation within a certain segment.

[0052] Finally, take the sum of the fluctuation coefficients of all segments as the first fluctuation factor of each physical sign parameter at this moment. The first fluctuation factor comprehensively reflects the overall fluctuation of the data within the entire reference data segment. Through summation, the local fluctuation situation can be integrated into a global fluctuation index, facilitating subsequent analysis and decision-making; the larger the first fluctuation factor, the more obvious the fluctuation characteristics of the physical sign parameter at this moment.

[0053] It should be noted that the adaptive piecewise constant approximation method is a well-known technology, and the specific process will not be elaborated here.

[0054] Step S303: At this moment, in the reference data segment of each physical sign parameter, comprehensively analyze the fluctuation of data values within all sub - segments, and determine the second fluctuation factor of each physical sign parameter at this moment.

[0055] In each sub - segment, calculate the absolute value of the difference between each data value and the mean of all data values as the difference factor. The difference factor can be used to reflect the degree of dispersion between data. The larger the difference factor, the higher the degree of deviation between data and the greater the degree of dispersion.

[0056] Take the mean of the difference factors of all data values as the second fluctuation coefficient of each sub - segment. The second fluctuation coefficient reflects the overall fluctuation degree of data within each sub - segment. The larger the value, the more dispersed the data and the greater the fluctuation; conversely, the smaller the value, the more concentrated the data and the smaller the fluctuation.

[0057] Finally, take the mean of the fluctuation coefficients of all sub - segments as the second fluctuation factor of each physical sign parameter at this moment. The second fluctuation factor integrates the discrete fluctuation degree of data in all sub - segments. The larger the value, the greater the volatility of the data of the physical sign parameter at this moment.

[0058] Step S304: Under each physical sign parameter, fuse the first fluctuation factor and the second fluctuation factor at this moment to obtain the fluctuation index of each physical sign parameter at this moment.

[0059] Based on the foregoing analysis, the larger the first fluctuation factor, the more obvious the fluctuation characteristics of the physical sign parameter at this moment; the second fluctuation factor integrates the discrete fluctuation degree of data in all sub - segments, and the larger the value, the greater the volatility of the data of the physical sign parameter at this moment. Therefore, take the value obtained by normalizing the product of the first fluctuation factor and the second fluctuation factor of each physical sign parameter at this moment as the fluctuation index of each physical sign parameter at this moment. At this time, the larger the fluctuation index, the more obvious the fluctuation of the physical sign parameter at this moment, and then the higher the possibility of abnormality. Among them, normalization is a well - known technical means to those skilled in the art. The choice of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0060] During the anesthesia process, the state of the pregnant woman is comprehensively reflected by various physical sign parameters, and each physical sign parameter reflects the physiological state of the pregnant woman from a different perspective. By comprehensively comparing the fluctuation indexes of all types of physical sign parameters, the overall state of the patient can be evaluated more comprehensively and accurately, thus avoiding the one-sidedness that may be brought by single-parameter evaluation. The importance of different physical sign parameters in evaluating the patient's state is different. Some parameters may be more sensitive and can reflect the physiological changes of the pregnant woman earlier; while some parameters may be more stable but also have important reference value. Therefore, by calculating the weight of each physical sign parameter, reasonable weight allocation can be carried out according to its importance and volatility in actual application. This can not only improve the accuracy of the evaluation but also make the evaluation result more in line with the clinical reality.

[0061] Preferably, in one embodiment of the present invention, the method for obtaining the weight includes: At each moment, the proportion of the fluctuation index of each physical sign parameter in the fluctuation indexes of all types of physical sign parameters is used as the weight of each physical sign parameter at each moment. At this time, the greater the weight of a certain physical sign parameter, the greater the degree of fluctuation of this physical sign parameter during the operation, so the attention should be increased, and thus it should have a higher proportion in the final fusion process.

[0062] Step S4: In the time-series data of multiple physical sign parameters, for any moment, combining the change period, data value of the physical sign parameters at the historical moment in the same anesthesia stage as this moment, and the weight of each physical sign parameter, the surgical anesthesia risk index at this moment is obtained.

[0063] In the general anesthesia surgery involving pregnant patients, the anesthesia process will go through multiple stages, and different anesthesia stages may lead to different fluctuation characteristics of the physical sign data. In the anesthesia induction stage, general anesthesia drugs (such as propofol, remifentanil) can cause vasodilation and myocardial depression, easily leading to hypotension and bradycardia. The fluctuations of blood pressure and heart rate are often greater than those of the fetal heart rate. In the anesthesia maintenance stage, the physiological reactions of the pregnant woman gradually become stable, and the state of the fetus is affected by the state of the pregnant woman, and there may be certain fluctuations; in the anesthesia recovery stage: pain or stress reaction may cause a sudden increase in blood pressure and an increase in heart rate, and there are often large fluctuations in the vital signs of the mother and the fetal heart rate. Therefore, during the process of monitoring vital signs, for any moment, the change period and data value of the physical sign parameters at the historical moment in the same anesthesia stage as this moment are combined to characterize the change and fluctuation of the physical sign parameters; and because the importance of different types of physical sign parameters is different, the weight of each physical sign parameter is combined to ensure the accuracy of the monitoring result, and thus the surgical anesthesia risk index at this moment is obtained.

[0064] Preferably, in an embodiment of the present invention, the method for obtaining the surgical anesthesia risk index includes: In an embodiment of the present invention, the physical sign parameters include the pregnant woman's blood pressure, the pregnant woman's heart rate, and the fetal heart rate.

[0065] Monitoring the blood pressure, heart rate, and fetal heart rate of the pregnant woman can timely monitor abnormal data changes during the operation. When the heart rate of the pregnant woman is abnormal or the fetus is hypoxic, the monitoring results of the pregnant woman's heart rate and the fetal heart rate will change abnormally, resulting in obvious changes in the period and data trend of the data within the current anesthesia stage. And when the period of the data becomes smaller and the data trend becomes smaller, it usually means that the vital signs are gradually deteriorating, and it is considered that the intraoperative risk increases and attention should be paid. During the operation, due to factors such as the action of anesthetic drugs, the blood pressure data of the pregnant woman will show a decaying trend. When the value is large, it indicates that the blood pressure is relatively stable and the surgical anesthesia risk is considered small. On the contrary, if the value is small, it may indicate that the physiological state of the pregnant woman is rapidly deteriorating and the surgical anesthesia risk increases, and attention also needs to be paid.

[0066] Therefore, for any moment, in the reference data segment of the pregnant woman's heart rate and the fetal heart rate at this moment (the reference data segment refers to the data segment of the same anesthesia stage, and the specific acquisition method is recorded in step S3), the AMPD algorithm is used to respectively obtain the minimum AMPD value in the reference data segment of the pregnant woman's heart rate and the fetal heart rate, and based on the time delay corresponding to the minimum AMPD value, the period value of the reference data segment of the pregnant woman's heart rate and the fetal heart rate corresponding to this moment is obtained.

[0067] Based on the foregoing analysis, a formula model for the surgical anesthesia risk index at this moment is constructed: ; Wherein, represents the surgical anesthesia risk index at this moment; represents the weight of the pregnant woman's heart rate at this moment; represents the period value of the reference data segment of the pregnant woman's heart rate at this moment; represents the value of the pregnant woman's heart rate at this moment; represents the weight of the fetal heart rate at this moment; represents the period value of the reference data segment of the fetal heart rate at this moment; represents the value of the fetal heart rate at this moment; represents the weight of the pregnant woman's blood pressure at this moment; represents the value of the pregnant woman's blood pressure at this moment; represents the natural constant; represents the normalization function.

[0068] In the formula model of surgical anesthesia risk indicators, when the cycle value and numerical value of the pregnant woman's heart rate decrease, it is considered that the vital signs are gradually deteriorating and the intraoperative risk increases. Therefore, both the cycle value and numerical value of the pregnant woman's heart rate are negatively correlated with the surgical anesthesia risk indicators. Thus, in the formula model, the product of the cycle value and numerical value of the pregnant woman's heart rate is subjected to negative correlation mapping to achieve logical relationship correction, and the first risk factor is obtained. , the larger this value is, the greater the surgical anesthesia risk at this moment; the analysis of the fetal heart rate is the same, and thus the second risk factor at the current moment is obtained. ; Similarly, since the smaller the attenuation of the pregnant woman's blood pressure data is, the more stable the blood pressure is considered, and the surgical anesthesia risk is considered to be smaller. Therefore, the numerical value of the pregnant woman's blood pressure is subjected to negative correlation mapping to achieve logical relationship correction, and thus the third risk factor at the current moment is obtained. , the larger this value is, the greater the surgical anesthesia risk at the current moment. Finally, it is necessary to integrate the surgical anesthesia risk degrees characterized by the three vital sign parameters. Based on the analysis in step S3, when the weight of a certain vital sign parameter is larger, it indicates that the fluctuation degree of this vital sign parameter during the operation is larger, and then the attention should be increased, and it should have a higher proportion in the final fusion process. Therefore, the risk factors are weighted using the weights calculated for each vital sign parameter to increase the proportion of the more important vital sign parameters, and the corresponding weighted risk factors are obtained. , and finally, the normalized value of the sum of the weighted risk factors is used as the surgical anesthesia risk indicator at this moment, and the larger the value of the surgical anesthesia risk indicator is, the higher the anesthesia risk is, and the doctor should be reminded to conduct an examination in a timely manner.

[0069] It should be noted that the AMPD algorithm is a well-known technology, and the specific process will not be elaborated here.

[0070] Thus, the surgical anesthesia risk indicator at each moment can be obtained.

[0071] Step S5: Perform vital sign monitoring according to the surgical anesthesia risk indicator at each moment.

[0072] When performing real-time monitoring of the vital signs during the general anesthesia operation of a pregnant woman, the vital signs can be monitored according to the surgical anesthesia risk indicator at a certain moment.

[0073] Preferably, in an embodiment of the present invention, performing vital sign monitoring according to the surgical anesthesia risk indicator at each moment includes: Based on the foregoing analysis, it can be seen that the greater the surgical anesthesia risk index, the higher the surgical anesthesia risk, and the doctor should be reminded to perform an examination in a timely manner. Therefore, when the surgical anesthesia risk index at a certain moment is greater than or equal to the preset risk threshold, it is prompted that there is a surgical anesthesia risk at this moment for early warning; on the contrary, when the surgical anesthesia risk index at a certain moment is less than the preset risk threshold, it is prompted that there is no anesthesia risk at this moment and no early warning is required.

[0074] It should be noted that the preset risk threshold is 0.65, and the specific value can be adjusted according to the implementation scenario and will not be limited here.

[0075] In summary, in the embodiment of the present invention, first, by acquiring the time series signal of the electroencephalogram of the pregnant woman and the time series data of various physical sign parameters, the physiological state changes of the pregnant woman during the anesthesia process can be comprehensively and carefully reflected. In different anesthesia stages, the fluctuation states of the physical sign parameters are different, so the weights will vary. Therefore, it is necessary to judge the anesthesia stage corresponding to each moment. Since when in different anesthesia stages, the brain electrical activities of the pregnant woman usually show different band changes, so at each moment, the frequency domain analysis is performed on the local data segment in the electroencephalogram time series signal, and based on the data change situation in the frequency domain data, the anesthesia stage corresponding to each moment is determined. Then for each moment, in the time series data of each physical sign parameter, the change fluctuation between its data value and the data value of the historical moment in the same anesthesia stage is analyzed to determine the fluctuation index. This process can quantify the fluctuation situation of each physical sign parameter, which helps to comprehensively compare the fluctuation indexes of all types of physical sign parameters in the subsequent process and dynamically calculate the weight of each physical sign parameter at each moment; this way of dynamically allocating weights more conforms to the physiological change characteristics in the actual anesthesia process. Further, combining the change period, data value of the physical sign parameter at the historical moment in the same anesthesia stage as the current moment and the weight of each physical sign parameter, the surgical anesthesia risk index is calculated. Finally, the vital sign monitoring is performed based on the surgical anesthesia risk index at each moment. The embodiment of the present invention can adaptively set the fusion weight of each physical sign parameter at each moment according to the progress stage of the anesthesia process and in combination with the physiological condition of the pregnant woman herself, so as to obtain a more accurate surgical anesthesia risk index for vital sign monitoring, ensuring the accuracy of the monitoring result.

[0076] The embodiment of the present invention also provides a vital sign monitoring system during anesthesia. Please refer to Figure 7, which shows a system block diagram, including a data acquisition module 701 for implementing step S1 in the above method embodiment; an anesthesia stage judgment module 702 for implementing step S2 in the above method embodiment; a weight analysis module 703 for implementing step S3 in the above method embodiment; a surgical anesthesia risk index calculation module 704 for implementing step S4 in the above method embodiment; and a vital sign monitoring module 705 for implementing step S5 in the above method embodiment.

[0077] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above function modules. In practical applications, the above functions can be allocated to different function modules according to needs, that is, the internal structure of the computer device is divided into different function modules to complete all or part of the functions described above. In addition, a vital sign monitoring system during anesthesia and a method embodiment of a vital sign monitoring method during anesthesia provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0078] Please refer to Figure 8 , which shows a schematic diagram of the system structure of a vital sign monitoring system during anesthesia provided by an embodiment of the present invention, including a processor 800, a memory 801, a bus 802, and a communication interface 803. The processor 800, the communication interface 803, and the memory 801 are connected through the bus 802. Among them, the memory 801 may include a high-speed random access memory. The bus 802 may be an ISA bus, a PCI bus, an EISA bus, etc. The processor 800 may be an integrated circuit chip with signal processing capabilities. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory 801. When at least one instruction, at least one program, a code set, or an instruction set is loaded and executed by the processor, the steps in a vital sign monitoring method during anesthesia are implemented.

[0079] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the 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.

[0080] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized.

Claims

1. A method for monitoring vital signs during anesthesia, characterized in that: The method comprises: Obtain the brain wave timing signals and timing data of various vital sign parameters of pregnant women; At each moment, frequency domain analysis is performed on the local data corresponding to each moment in the brain wave time series signal, and based on the data changes in the frequency domain data, the anesthesia stage corresponding to each moment is determined; In the time series data of each vital sign parameter, the fluctuation of the data value at each moment and the historical moment in the same anesthesia stage is analyzed to determine the fluctuation index of each vital sign parameter at each moment; at each moment, the fluctuation indexes of all types of vital sign parameters are comprehensively compared to calculate the weight of each vital sign parameter; In the time series data of multiple vital sign parameters, for any moment, the change cycle, data value, and weight of each vital sign parameter at the historical moments in the same anesthesia stage as that moment are combined to obtain the surgical anesthesia risk index at that moment; Vital signs are monitored based on surgical anesthesia risk indicators at each moment.

2. The method for monitoring vital signs during anesthesia according to claim 1, characterized in that: The method for determining the anesthesia stage comprises: In terms of time series, for any moment, starting from this moment, a preset number of consecutive moments are selected in the historical moments to obtain the time series data segment corresponding to this moment in the brain wave time series signal; Perform fast Fourier transform on the time series data segment corresponding to the moment to obtain frequency domain data; The frequency domain data corresponding to the moment is divided based on a preset frequency to obtain different frequency bands, wherein the frequency bands include an Alpha frequency band, a Beta frequency band, a Theta frequency band, and a Delta frequency band; In each frequency band, the fluctuation of frequency amplitude is analyzed to obtain the intensity characteristic value of each frequency band; In the frequency domain data corresponding to the moment, when the frequency band corresponding to the maximum intensity eigenvalue is the Alpha band or the Beta band, it is judged that the moment is in the anesthesia awakening stage; when the frequency band corresponding to the maximum intensity eigenvalue is the Theta band, it is judged that the moment is in the anesthesia induction stage; when the frequency band corresponding to the maximum intensity eigenvalue is the Delta band, it is judged that the moment is in the anesthesia maintenance stage.

3. The method for monitoring vital signs during anesthesia according to claim 2, characterized in that: The method for obtaining the intensity characteristic value comprises: In the frequency domain data at this moment, in each frequency band, the accumulated value of the amplitude at all frequencies is used as the first intensity factor of each frequency band; In each frequency band, the mean of the amplitudes at all frequencies is taken as the intensity mean, and the proportion of the intensity mean of each frequency band in the intensity mean of all frequency bands is taken as the second intensity factor of each frequency band; The value obtained by normalizing the product of the first intensity factor and the second intensity factor of each frequency band is used as the intensity characteristic value of each frequency band.

4. The method for monitoring vital signs during anesthesia according to claim 1, characterized in that: The method for obtaining the volatility index includes: For any moment, among the historical moments before this moment in the time series, the historical moment in the same anesthesia stage as this moment is taken as the reference moment, and the corresponding values ​​of this moment and all the reference moments in the time series data of each vital sign parameter constitute the reference data segment of this moment; At this moment, in the reference data segment of each vital sign parameter, adaptive segmentation is performed based on the distribution of data to obtain multiple segments, and the segmentation of the reference data segment is analyzed to determine the first fluctuation factor of each vital sign parameter at this moment; In the reference data segment of each vital sign parameter at this moment, the fluctuation of the data values ​​in all segments is comprehensively analyzed to determine the second fluctuation factor of each vital sign parameter at this moment; The normalized value of the product of the first fluctuation factor and the second fluctuation factor for each vital sign parameter at that moment is used as the fluctuation index of each vital sign parameter at that moment.

5. The method for monitoring vital signs during anesthesia according to claim 4, characterized in that: The method for obtaining the first fluctuation factor includes: At any moment, in the reference data segment of each vital sign parameter at the moment, the reference data segment is segmented using an adaptive piecewise constant approximation method to obtain a plurality of segments and an APCA approximation value of each segment; The absolute value of the difference between the APCA approximation of each segment and the mean APCA approximation of all segments is used as a deviation factor, and the ratio of the deviation factor to the length of each segment is used as a first fluctuation coefficient of each segment; The cumulative sum of the fluctuation coefficients of all segments is taken as the first fluctuation factor of each vital sign parameter at that moment.

6. The method for monitoring vital signs during anesthesia according to claim 5, characterized in that: The method for obtaining the second fluctuation factor includes: In each segment, the absolute value of the difference between each data value and the mean of all data values ​​is calculated as a difference factor, and the mean of the difference factors of all data values ​​is taken as the second fluctuation coefficient of each segment; The mean of the fluctuation coefficients of all segments is taken as the second fluctuation factor of each vital sign parameter at that moment.

7. The method for monitoring vital signs during anesthesia according to claim 1, characterized in that: The method for obtaining the weight includes: At each moment, the proportion of the fluctuation index of each vital sign parameter in the fluctuation index of all types of vital sign parameters is used as the weight of each vital sign parameter at each moment.

8. The method for monitoring vital signs during anesthesia according to claim 4, characterized in that: The method for obtaining the surgical anesthesia risk index includes: The physical sign parameters include maternal blood pressure, maternal heart rate and fetal heart rate; For any moment, in the reference data segment of the pregnant woman's heart rate and the fetal heart rate at that moment, the minimum AMPD value in the reference data segment is obtained by using the AMPD algorithm, and the period value of the reference data segment of the pregnant woman's heart rate and the fetal heart rate corresponding to that moment is obtained based on the time delay corresponding to the minimum AMPD value; The formula model of surgical anesthesia risk index at this moment includes: ； in, It represents the surgical anesthesia risk index at that moment; Indicates the weight of the pregnant woman's heart rate at that moment; Indicates the period value of the reference data segment of the pregnant woman's heart rate at that moment; Indicates the value of the pregnant woman’s heart rate at that moment; Indicates the weight of the fetal heart rate at that moment; Indicates the period value of the reference data segment of the fetal heart rate at that moment; Indicates the value of the fetal heart rate at that moment; Indicates the weight of the pregnant woman's blood pressure at that moment; Indicates the blood pressure value of the pregnant woman at that moment; represents a natural constant; Represents the normalization function.

9. The method for monitoring vital signs during anesthesia according to claim 1, characterized in that: The vital signs monitoring according to the surgical anesthesia risk indicators at each moment includes: When the surgical anesthesia risk index at a certain moment is greater than or equal to the preset risk threshold, it indicates that there is a surgical anesthesia risk at that moment and issues an early warning; When the surgical anesthesia risk index at a certain moment is less than the preset risk threshold, it indicates that there is no surgical anesthesia risk at that moment and no early warning is needed.

10. A vital sign monitoring system during anesthesia, characterized in that: The invention comprises a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps of a method for monitoring vital signs during anesthesia as described in any one of claims 1 to 9 are implemented.

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