Anesthesia monitoring method and device

Abstract

The invention is suitable for the technical field of anesthesia monitoring, and particularly relates to an anesthesia monitoring method and equipment, and the method comprises the steps: obtaining vital sign data and anesthesia injection information of an anesthesia monitoring target, and providing a data basis for an anesthesia monitoring process. According to the anesthesia injection information and the vital sign data of the anesthesia monitoring target, the anesthesia depth index of the anesthesia monitoring target is determined, and the anesthesia depth of the anesthesia monitoring target is determined in time. Based on the anesthesia depth index and the vital sign data of the anesthesia monitoring target, the anesthesia risk dynamic value of the anesthesia monitoring target is determined, and the anesthesia risk condition of the anesthesia monitoring target is scientifically and reasonably identified. According to the anesthesia risk dynamic value of the anesthesia monitoring target, the anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output, the response efficiency and accuracy of the anesthesia monitoring process are improved, and the risk occurrence probability is reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of anesthesia monitoring, and in particular, relates to an anesthesia monitoring method and device. Background Art

[0002] Anesthesia monitoring plays a vital role in ensuring patient safety. Anesthesia monitoring requires a series of monitoring technologies and strict management processes. Anesthesia monitoring requires real-time evaluation and management of the patient's physiological state to ensure the safety and effectiveness of the anesthesia process.

[0003] Traditional methods of assessing anesthesia risk are often static, relying mainly on the anesthesiologist's experience and some basic vital signs. It is difficult to achieve precise regulation and can easily lead to intraoperative awareness or excessive anesthesia. It cannot reflect the changing situation during anesthesia in real time, resulting in slow response of the anesthesia monitoring process and low monitoring accuracy. Summary of the invention

[0004] The embodiments of the present application provide an anesthesia monitoring method and device, which can solve the problem that the anesthesia monitoring process cannot reflect the constantly changing situation during the anesthesia process in real time, resulting in slow response of the anesthesia monitoring process and low monitoring accuracy.

[0005] In a first aspect, an embodiment of the present application provides an anesthesia monitoring method, comprising: Acquiring vital sign data and anesthesia injection information of an anesthesia monitoring target; wherein the vital sign data is used to reflect the physiological state of the anesthesia monitoring target; Determining an anesthesia depth index of the anesthesia monitoring target according to the anesthesia injection information and the vital sign data of the anesthesia monitoring target; wherein the anesthesia depth index is used to reflect the anesthesia depth of the anesthesia monitoring target; Determining a dynamic value of anesthesia risk of the anesthesia monitoring target based on the anesthesia depth index and the vital sign data of the anesthesia monitoring target; wherein the dynamic value of anesthesia risk is used to reflect the anesthesia risk situation of the anesthesia monitoring target; According to the anesthesia risk dynamic value of the anesthesia monitoring target, an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The anesthesia monitoring method provided in the embodiment of the present application provides a data basis for the anesthesia monitoring process by acquiring the vital signs data and anesthesia injection information of the anesthesia monitoring target. According to the anesthesia injection information and vital signs data of the anesthesia monitoring target, the anesthesia depth index of the anesthesia monitoring target is determined, and the anesthesia depth of the anesthesia monitoring target is determined in time. Based on the anesthesia depth index and vital signs data of the anesthesia monitoring target, the anesthesia risk dynamic value of the anesthesia monitoring target is determined, and the anesthesia risk situation of the anesthesia monitoring target is scientifically and rationally identified. According to the anesthesia risk dynamic value of the anesthesia monitoring target, an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output to improve the response efficiency and accuracy of the anesthesia monitoring process and reduce the probability of risk occurrence.

[0007] In a second aspect, an embodiment of the present application provides an anesthesia monitoring system, comprising: An acquisition unit, used for acquiring vital sign data and anesthesia injection information of an anesthesia monitoring target; wherein the vital sign data is used to reflect the physiological state of the anesthesia monitoring target; A determination unit, configured to determine an anesthesia depth index of the anesthesia monitoring target according to the anesthesia injection information and the vital sign data of the anesthesia monitoring target; wherein the anesthesia depth index is used to reflect the anesthesia depth of the anesthesia monitoring target; A risk unit, configured to determine a dynamic value of anesthesia risk of the anesthesia monitoring target based on the anesthesia depth index and the vital sign data of the anesthesia monitoring target; wherein the dynamic value of anesthesia risk is used to reflect the anesthesia risk situation of the anesthesia monitoring target; The instruction unit is used to output an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value according to the anesthesia risk dynamic value of the anesthesia monitoring target.

[0008] In a third aspect, an embodiment of the present application provides an anesthesia monitoring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method as described in any one of the above aspects when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product, which, when executed on an anesthesia monitoring device, enables the anesthesia monitoring device to execute the method of any one of the above aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 It is a flowchart of an anesthesia monitoring method provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the operation of an anesthesia monitoring method provided by an embodiment of the present application; Figure 3 is a flowchart of step S300 of the anesthesia monitoring method provided in one embodiment of the present application; Figure 4 4 is a flowchart of step S400 of the anesthesia monitoring method provided in one embodiment of the present application; Figure 5 is a structural schematic diagram of an anesthesia monitoring system provided by an embodiment of the present application; Figure 6 It is a structural schematic diagram of an anesthesia monitoring device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] Traditional methods of assessing anesthesia risk are often static, relying mainly on the anesthesiologist's experience and some basic vital signs. It is difficult to achieve precise regulation and can easily lead to intraoperative awareness or excessive anesthesia. It cannot reflect the changing situation during anesthesia in real time, resulting in slow response of the anesthesia monitoring process and low monitoring accuracy.

[0020] To solve the above problems, an embodiment of the present application provides an anesthesia monitoring method and device. In this method, by obtaining the vital signs data and anesthesia injection information of the anesthesia monitoring target, a data basis is provided for the anesthesia monitoring process. According to the anesthesia injection information and vital signs data of the anesthesia monitoring target, the anesthesia depth index of the anesthesia monitoring target is determined, and the anesthesia depth of the anesthesia monitoring target is determined in time. Based on the anesthesia depth index and vital signs data of the anesthesia monitoring target, the anesthesia risk dynamic value of the anesthesia monitoring target is determined, and the anesthesia risk situation of the anesthesia monitoring target is scientifically and rationally identified. According to the anesthesia risk dynamic value of the anesthesia monitoring target, an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output to improve the response efficiency and accuracy of the anesthesia monitoring process and reduce the probability of risk occurrence.

[0021] The anesthesia monitoring method provided in the embodiment of the present application can be applied to an anesthesia monitoring device. In this case, the anesthesia monitoring device is the executor of the anesthesia monitoring method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of anesthesia monitoring device.

[0022] For example, the anesthesia monitoring device may be a variety of intelligent monitoring devices, including, but not limited to, a desktop computer, a smart screen, a smart TV, a handheld device with wireless communication function, a computing device, a computer, a laptop computer, etc.

[0023] In order to better understand the anesthesia monitoring method provided in the embodiment of the present application, the specific implementation process of the anesthesia monitoring method provided in the embodiment of the present application is exemplarily introduced below.

[0024] Figure 1 A schematic flow chart of an anesthesia monitoring method provided in an embodiment of the present application is shown. Figure 2 The schematic diagram of the operation of the anesthesia monitoring method provided in the embodiment of the present application is shown, and the anesthesia monitoring method includes: S100, acquiring vital sign data and anesthesia injection information of an anesthesia monitoring target; wherein the vital sign data is used to reflect the physiological state of the anesthesia monitoring target.

[0025] It can be understood that the vital sign data of the anesthesia monitoring target is the physiological index data reflecting the current physiological state of the anesthesia monitoring target, which may include heart rate, blood pressure, blood oxygen saturation, body temperature and respiratory rate. The vital sign data can be collected in real time by medical monitoring equipment, and stored and transmitted through data storage devices. It can also be connected to the monitoring equipment through the API interface to format the discrete data into a unified data stream for transmission and acquisition. Anesthesia injection information refers to data related to anesthetics, which may include the type, dose, injection rate and time of anesthetics. Anesthesia injection information can be provided by an anesthesia infusion pump, which has an integrated precise dose calculation and recording function. Noise filtering and signal enhancement algorithms, such as Kalman Filter, can be used in the data acquisition process to reduce interference during the acquisition process. When acquiring anesthesia injection information, data can be extracted from the infusion pump through a communication protocol such as HL7 or a serial communication interface, and the data format can be standardized to adapt to subsequent analysis algorithms.

[0026] S200, determining an anesthesia depth index of the anesthesia monitoring target according to anesthesia injection information and vital sign data of the anesthesia monitoring target; wherein the anesthesia depth index is used to reflect the anesthesia degree of the anesthesia monitoring target.

[0027] It can be understood that the anesthesia depth index is a parameter used to quantify the degree of anesthesia, reflecting the degree of response of the central nervous system of the anesthesia monitoring target to anesthetic drugs. According to the anesthesia injection information and vital signs data of the anesthesia monitoring target, the parameters can be fused and analyzed through a deep learning model or a regression model to output a comprehensive anesthesia depth index value. For example, a model based on the bispectral index (BIS) can be used to combine EEG signals with cardiovascular signals to improve the accuracy of anesthesia depth monitoring.

[0028] In a possible implementation, S200, determining an anesthesia depth index of the anesthesia monitoring target according to anesthesia injection information and vital sign data of the anesthesia monitoring target, includes: S210, obtaining anesthesia depth calculation index data of the anesthesia monitoring target according to the anesthesia injection information and vital sign data of the anesthesia monitoring target; wherein the anesthesia depth calculation index data is used to reflect the anesthesia condition of the anesthesia monitoring target.

[0029] It can be understood that the anesthesia depth calculation index data is an intermediate variable describing the anesthesia state, which may include heart rate variability (HRV) index, EEG entropy value, etc. The anesthesia depth calculation index data reflects the target's physiological response degree, and can be obtained by performing frequency domain analysis on vital sign data through wavelet transform or fast Fourier transform (FFT) based on signal processing and feature engineering technology to obtain the anesthesia depth calculation index data of the anesthesia monitoring target.

[0030] Optionally, S210, obtaining anesthesia depth calculation index data of the anesthesia monitoring target according to anesthesia injection information and vital sign data of the anesthesia monitoring target, including: S211, determining an anesthesia infusion type of the anesthesia monitoring target based on the anesthesia infusion information of the anesthesia monitoring target.

[0031] It can be understood that the type of anesthesia injection is used to determine the types of key features that need to be extracted from vital signs data. Different types of anesthetic drugs have different physiological effects. For example, intravenous anesthesia tends to suppress the circulatory system, inhalation anesthesia affects the central nervous system more directly, and combined anesthesia may have a superimposed effect on multiple systems. Therefore, the identification of the type of anesthesia injection can help optimize the subsequent data processing strategy and make the subsequent calculation of the anesthesia depth index more accurate.

[0032] For example, the classification rules or models established in advance according to the anesthetic drug database can be used to analyze the anesthetic injection information. By matching the drug name, it is possible to identify whether it is an intravenous drug (such as propofol), an inhaled anesthetic drug (such as isoflurane), or an auxiliary drug in a combined anesthesia scheme. The injection type can be further refined in combination with the administration method. If the injection rate of a certain anesthetic drug presents a constant characteristic, it may be a continuous intravenous infusion; if the drug is injected in intermittent large doses, it may be an induced intravenous anesthesia. For a combined anesthesia scheme, by analyzing the combination pattern of multiple drugs and time series data, it can be determined whether it is mainly based on inhalation anesthesia or intravenous injection, and the determined anesthetic injection type can be output. The anesthetic injection type can not only be a single result (such as "intravenous injection" or "inhalation anesthesia"), but also include specific parameters, such as "propofol intravenous injection, rate 3mg / kg / h". The information contained in the anesthetic injection type can directly guide the subsequent vital sign data extraction process. For example, intravenous anesthesia may focus on heart rate variability (HRV), while inhalation anesthesia focuses on the frequency band characteristics of brain waves.

[0033] S212, identifying the vital sign data according to the anesthesia injection type of the anesthesia monitoring target, and determining an anesthesia depth calculation index of the anesthesia monitoring target.

[0034] It can be understood that the most relevant calculation indicators for the type of anesthesia injection, i.e., the anesthesia depth calculation indicators, can be extracted from the vital signs data using feature selection algorithms, rule-based mapping methods, data-driven machine learning prediction frameworks, etc. The goal of feature selection is to reduce the data dimension while retaining the most important features for anesthesia depth prediction.

[0035] Exemplarily, based on the goal that the anesthetic injection type is intravenous anesthesia, the standard deviation of HRV (SDNN) or the low frequency / high frequency ratio (LF / HF Ratio) can be selected as an indicator for calculating the depth of anesthesia; based on the goal that the anesthetic injection type is inhalation anesthesia, features such as the slow wave power (SWP) in the B-wave power spectral density are selected as indicators for calculating the depth of anesthesia.

[0036] S213, extracting the vital sign data according to the anesthesia depth calculation index of the anesthesia monitoring target to obtain the anesthesia depth calculation index data of the anesthesia monitoring target.

[0037] It can be understood that for each anesthesia depth calculation indicator, the corresponding extraction rules or algorithms can be pre-set to extract the corresponding specific data from the vital signs data to construct an anesthesia depth calculation indicator data set. The extraction rules can be based on statistical methods, such as the mean calculation of heart rate, the extraction of the rate of change of respiratory rate, etc.; or they can be based on signal analysis methods, such as the spectrum analysis of brain waves or the peak detection of blood oxygen saturation signals. The extraction process can be combined with a time window so that the extracted data can accurately reflect the real-time changes of the target state.

[0038] For example, for EEG data, a bandpass filter can be used to select signals in specific frequency bands (such as delta waves, theta waves, etc.) and calculate the relative energy of the signals in each frequency band; for respiratory data, parameters such as the waveform change rate and frequency distribution are analyzed. These filtered data are marked as core analysis areas; for heart rate data, discrete Fourier transform (DFT) can be used to calculate its spectral characteristics; for EEG signals, instantaneous frequency and amplitude information can be extracted through wavelet decomposition.

[0039] S220, analyzing the anesthesia depth calculation index data of the anesthesia monitoring target to determine the anesthesia depth index of the anesthesia monitoring target.

[0040] It can be understood that the anesthesia depth index is used to measure the depth of anesthesia. A comprehensive anesthesia depth index can be obtained by analyzing the anesthesia depth calculation index data. A comprehensive anesthesia depth index can be calculated based on multiple calculation indicators (such as BIS index, ECG, heart rate, etc.) through weighted average or machine learning model.

[0041] Exemplarily, the electroencephalogram (EEG) signal, heart rate, blood oxygen saturation (SpO2), blood pressure and other data can be annotated to obtain the machine learning model input sample, and the corresponding anesthesia depth index can be annotated as the target output value and the annotated data can be divided into a training set and a validation set. The parameters of the model are adjusted through the training set so that the model can minimize the prediction error (for example, using the mean square error loss function). After the training is completed, the model can output the predicted anesthesia depth index based on the new physiological data (EEG signal, heart rate, blood oxygen saturation, blood pressure, etc.). For example, the new input data (EEG signal, heart rate, SpO2, BP) is input into the trained model, and the model outputs an anesthesia depth index (such as a BIS value).

[0042] S300, determining a dynamic value of anesthesia risk of the anesthesia monitoring target based on anesthesia depth index and vital sign data of the anesthesia monitoring target; wherein the dynamic value of anesthesia risk is used to reflect the anesthesia risk situation of the anesthesia monitoring target.

[0043] It can be understood that the dynamic value of anesthesia risk is a comprehensive indicator used to quantitatively evaluate the anesthesia risk faced by patients during anesthesia. It can be calculated by combining the anesthesia depth index and vital signs data of the anesthesia monitoring target, please refer to Figure 3 , determine the relative appropriateness of anesthesia depth and vital signs in time, provide a comprehensive risk value, achieve a comprehensive evaluation of the anesthesia risk situation of anesthesia monitoring targets, and improve the response efficiency and accuracy of the anesthesia monitoring process.

[0044] In a possible implementation, S300, based on the anesthesia depth index and vital sign data of the anesthesia monitoring target, determining the anesthesia risk dynamic value of the anesthesia monitoring target includes: S310, performing a first gradient analysis on the anesthesia depth index of the anesthesia monitoring target to obtain anesthesia depth index gradient data corresponding to the anesthesia depth index; wherein the anesthesia depth index gradient data is used to reflect a change trend of the anesthesia depth of the anesthesia monitoring target.

[0045] It can be understood that the purpose of the first gradient analysis of the anesthesia depth index is to quantify the rate of change of the anesthesia depth over time by performing temporal and numerical gradient analysis on the anesthesia depth index, and then obtain the anesthesia depth index gradient data reflecting the trend of anesthesia depth change. The gradient analysis not only reveals the direction of change of the anesthesia depth (increase or decrease), but also reveals the rate or severity of change, which helps to determine whether the anesthesia depth is appropriate at the current moment.

[0046] Optionally, S310, performing a first gradient analysis on the anesthesia depth index of the anesthesia monitoring target to obtain anesthesia depth index gradient data corresponding to the anesthesia depth index includes: S311, obtaining a preset anesthesia depth index gradient segment and anesthesia depth time gradient segment.

[0047] It can be understood that the anesthesia depth index gradient segment and the anesthesia depth time gradient segment are preset based on empirical data and historical data. Each anesthesia depth index gradient segment represents a range of variation of an anesthesia depth index. The anesthesia depth index gradient segment can be divided according to different stages of anesthesia depth. Each anesthesia depth time gradient segment represents a range of variation of anesthesia depth time. The anesthesia depth time gradient segment can be pre-divided and selected based on the characteristics of anesthetic drugs. Specifically, each preset anesthesia depth index gradient segment will correspond to a specific anesthesia depth index value, which is used to evaluate whether the depth of anesthesia is appropriate. Each preset anesthesia depth time gradient segment will correspond to a specific moment of the anesthesia depth index, which is used to evaluate whether the anesthesia depth is appropriate at the corresponding moment.

[0048] Exemplarily, assuming that the value range of the anesthesia depth index is 0 to 100, the following gradient segments can be set: 0 to 10: Gradient value is 1 10 to 20: Gradient value is 2 20 to 30: Gradient value 3 30 to 40: Gradient value 4 40 to 50: Gradient value 5 Rules for dividing anesthesia depth time gradient segments: 0 to 20 minutes: gradient value 1 20 to 40 minutes: Gradient value 2 40 to 60 minutes: gradient value 3 60 to 80 minutes: gradient value 4 80 to 100 minutes: gradient value 5 100 to 120 minutes: gradient value 6 S312, performing numerical gradient classification on the anesthesia depth index of the anesthesia monitoring target according to the preset anesthesia depth index gradient section, and obtaining anesthesia depth index numerical gradient data corresponding to the anesthesia depth index.

[0049] It can be understood that the anesthesia depth index is a numerical indicator of anesthesia intensity or depth, indicating the strength of the current anesthesia state. According to the pre-set anesthesia depth index gradient segment, the actually measured anesthesia depth index value can be classified into the corresponding gradient segment. For example, if the current anesthesia depth index is 18, then according to the preset gradient segment (10-20 is gradient 2), the anesthesia depth index numerical gradient data corresponding to the anesthesia depth index is classified as gradient 2. Through the classification of numerical gradients, the changes in anesthesia depth can be quantified, which helps to analyze whether the anesthesia is within the expected safety range.

[0050] S313, performing time gradient classification on the anesthesia depth index of the anesthesia monitoring target according to the preset anesthesia depth time gradient segment, and obtaining anesthesia depth index time gradient data corresponding to the anesthesia depth index.

[0051] It can be understood that the time segment corresponding to the anesthesia depth index at a certain moment can be classified by the preset anesthesia depth time gradient segment. For example, if the anesthesia process has lasted for 30 minutes, then according to the preset time gradient segment (20 to 40 minutes is gradient 2), the anesthesia depth index time gradient data corresponding to the anesthesia depth index will be classified as gradient 2. Gradient analysis can be combined with the changing trend of time and anesthesia depth to assist in determining whether anesthesia is at an appropriate stage.

[0052] Exemplarily, the method further comprises: S340, determining first moment information corresponding to an anesthesia depth index of an anesthesia monitoring target.

[0053] It is understandable that for each data record of the anesthesia depth index (such as anesthesia depth index, heart rate, blood oxygen, etc.), a timestamp can be added to each data in advance. The timestamp records the specific time when the data is collected as the first time information corresponding to the anesthesia depth index.

[0054] S350: Determine second time information corresponding to the initial anesthesia injection time of the anesthesia monitoring target based on the anesthesia injection information of the anesthesia monitoring target.

[0055] It can be understood that the second moment information refers to the moment when the anesthetic drug injection starts. The start time of the injection can be obtained by querying the anesthetic injection information in the injection pump or database. The injection system will provide an API interface, through which various injection data including the start time of the drug injection can be directly extracted. The second moment information provides a benchmark for the subsequent evaluation and adjustment of the anesthesia process.

[0056] S360: Obtain an absolute time difference between the first time information and the second time information according to the first time information and the second time information.

[0057] It can be understood that the absolute time difference refers to the absolute value of the time difference between the current moment (first moment information) and the initial anesthesia injection moment (second moment information). By calculating the absolute time difference between these two moments, the time elapsed from the start of anesthetic injection to the current moment can be measured, which can help evaluate the change of anesthetic effect over time and then determine the risk situation during anesthesia. It can be calculated using the following formula: Absolute time difference = |current moment-time of anesthesia injection start|.

[0058] S313, classifying the anesthesia depth index of the anesthesia monitoring target by time gradient according to the preset anesthesia depth time gradient segment, and obtaining anesthesia depth index time gradient data corresponding to the anesthesia depth index, including: S3131, performing time gradient classification on the absolute time difference between the first moment information and the second moment information according to a preset anesthesia depth time gradient segment, and obtaining anesthesia depth index time gradient data corresponding to the anesthesia depth index.

[0059] For example, if the absolute time difference is 15 minutes, and the preset time gradient segment is 0 to 20 minutes corresponding to a gradient value of 1, then the anesthesia depth index time gradient data corresponding to the anesthesia depth index is 1.

[0060] With this setting, the progress of the anesthesia process can be better tracked through the classified time gradient data, and data support can be provided for the subsequent determination of anesthesia risks.

[0061] S314, determining the anesthesia depth index numerical gradient data and the anesthesia depth index time gradient data as anesthesia depth index gradient data corresponding to the anesthesia depth index.

[0062] With this setting, a comprehensive anesthesia depth index gradient data can be obtained, which can be further used to analyze and adjust the anesthesia depth to improve the accuracy of the risk analysis of the anesthesia process.

[0063] S320, performing a second gradient analysis on the vital sign data of the anesthesia monitoring target to obtain vital sign gradient data corresponding to the vital sign data; wherein the vital sign gradient data is used to reflect the change trend of the vital signs of the anesthesia monitoring target.

[0064] It can be understood that by presetting the gradient segments of vital signs data, different vital signs data can be classified according to the changes in vital signs through gradient segments, thereby capturing the details of the changes in vital signs during anesthesia, especially the abnormal fluctuations that may occur during anesthesia, and providing a basis for risk analysis of the anesthesia process.

[0065] Optionally, S320, performing a second gradient analysis on the vital sign data of the anesthesia monitoring target to obtain vital sign gradient data corresponding to the vital sign data, including: S321, obtaining a preset vital sign data gradient segment.

[0066] It is understandable that the gradient segments of the vital sign data can be preset, and the gradient segments of the vital sign data are used to classify the changes in the vital signs. The gradient segments of the vital sign data can be set based on historical data and medical experience. For example, there may be different gradient segments corresponding to different vital sign fluctuation ranges, and each gradient segment reflects the changes in vital signs within a specific range. For example, the range of changes in heart rate may be divided into different gradient intervals.

[0067] Exemplarily, the heart rate gradient segments are preset: 0 to 40 beats / minute: Gradient value 1 (indicates a very low heart rate, requiring special attention and intervention) 40 to 60 beats / minute: Gradient value 2 (indicates a low heart rate and a possible risk of over-deep anesthesia) 60 to 80 times / minute: Gradient value 3 (normal range) 80 to 100 beats / minute: Gradient value is 4 (high, may be a stress response or other physiological abnormality) 100 to 120 beats / minute: Gradient value is 5 (higher values ​​may indicate too light anesthesia or patient anxiety, etc.) 120 beats / minute or more: Gradient value 6 (indicates that the heart rate is too high and there may be potential risks) S322, gradient classification is performed on the vital sign data of the anesthesia monitoring target according to preset vital sign data gradient segments to obtain vital sign gradient data corresponding to the vital sign data.

[0068] It is understandable that the vital sign data of the anesthesia monitoring target can be divided into certain vital sign data gradient segments. For example, if the heart rate change at a certain moment is within the set segment range, it will be classified as the gradient value of the segment, thereby providing a basis for the risk analysis of the anesthesia process.

[0069] S330, determining a dynamic value of anesthesia risk of an anesthesia monitoring target based on the anesthesia depth index gradient data corresponding to the anesthesia depth index and the vital sign gradient data corresponding to the vital sign data.

[0070] It can be understood that based on the anesthesia depth index gradient data and the vital sign gradient data, the changing trends of the two can be combined through certain weighting or calculation methods to obtain the dynamic value of the anesthesia risk of the anesthesia monitoring target, which dynamically reflects the risk level of the current anesthesia process.

[0071] Optionally, S330, determining a dynamic value of anesthesia risk of an anesthesia monitoring target based on anesthesia depth index gradient data corresponding to anesthesia depth index and vital sign gradient data corresponding to vital sign data, includes: S331, performing a first calculation based on the anesthesia depth index gradient data corresponding to the anesthesia depth index to obtain a first anesthesia risk dynamic value of the anesthesia monitoring target; wherein the first anesthesia risk dynamic value is used to reflect the anesthesia depth risk situation of the anesthesia monitoring target.

[0072] It can be understood that the first anesthesia risk dynamic value is an evaluation value of the anesthesia risk based on the comprehensive calculation of the anesthesia depth index numerical gradient and the anesthesia depth index time gradient data, reflecting the relative suitability of the anesthesia degree at the current moment. The anesthesia depth index numerical gradient and the anesthesia depth index time gradient data can be directly weighted and summed to obtain a comprehensive anesthesia risk assessment value. The anesthesia depth index numerical gradient and the anesthesia depth index time gradient data can also be weighted and standardized and mapped into a score value as the first anesthesia risk dynamic value.

[0073] S332, performing a second calculation based on the vital sign gradient data corresponding to the vital sign data to obtain a second anesthesia risk dynamic value of the anesthesia monitoring target; wherein the second anesthesia risk dynamic value is used to reflect the vital sign risk situation of the anesthesia monitoring target.

[0074] It can be understood that the second anesthesia risk dynamic value can be obtained by weighted calculation of all vital sign data gradients of the anesthesia monitoring target, and the second anesthesia risk dynamic value is used to reflect the changing trend and risk situation of the vital signs of the anesthesia monitoring target during anesthesia. Assuming that the vital sign gradient data includes several dimensions, such as heart rate (HR), blood pressure (BP), respiratory rate (RR), etc., the second anesthesia risk dynamic value (S) can be calculated by the following formula: S=w1*HR+w2*BP+w3*RR, where: w1, w2, w3 are the weights of each vital sign data gradient, which can be set in advance based on their relative impact on anesthesia risk.

[0075] S333: Perform weighted calculation according to the first anesthesia risk dynamic value and the second anesthesia risk dynamic value of the anesthesia monitoring target to determine the anesthesia risk dynamic value of the anesthesia monitoring target.

[0076] It can be understood that the first anesthesia risk dynamic value (related to anesthesia depth) and the second anesthesia risk dynamic value (related to changes in vital signs) reflect the risk of anesthesia monitoring targets in terms of anesthesia depth and vital signs, respectively. In order to obtain a comprehensive risk assessment, these two dynamic values ​​can be weighted and synthesized to obtain an anesthesia risk dynamic value. The anesthesia risk dynamic value is a key indicator that dynamically reflects the risk of the anesthesia process by real-time monitoring of the patient's vital signs and anesthesia status. It reflects the relative suitability and risk level of the vital signs and anesthesia level of the anesthesia monitoring target at the corresponding moment.

[0077] Exemplarily, assuming that the first anesthesia risk dynamic value is S1 and the second anesthesia risk dynamic value is S2, the anesthesia risk dynamic value S0 can be calculated by the following formula: S0=S1*α1+S2*α2.

[0078] S400: Outputting an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value according to the anesthesia risk dynamic value of the anesthesia monitoring target.

[0079] It can be understood that the anesthesia risk dynamic value dynamically reflects the relative suitability and risk level of the vital signs and anesthesia level at the corresponding moment of the anesthesia process. According to the change of the anesthesia risk dynamic value, the corresponding anesthesia risk signal instruction can be output. Please refer to Figure 4. The anesthesia risk signal instruction output by the anesthesia risk dynamic value can be managed by setting different thresholds to define different risk intervals. For example, normal risk interval, warning interval, emergency intervention interval, etc. The setting of the risk interval is not only to ensure that the patient's anesthesia process is within a safe range, but also to take appropriate intervention measures for anesthesia risks at different stages. If the risk dynamic value exceeds the preset safety threshold, the corresponding risk signal can be automatically issued to achieve a comprehensive assessment and early warning of anesthesia risks, improve the response efficiency and accuracy of the anesthesia monitoring process, and reduce the probability of risk occurrence.

[0080] In a possible implementation, S400, according to the anesthesia risk dynamic value of the anesthesia monitoring target, outputting an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value includes: S410, obtaining preset anesthesia risk dynamic value boundaries; wherein the anesthesia risk dynamic value boundaries include an anesthesia risk dynamic value upper boundary and anesthesia risk dynamic value lower boundary.

[0081] It can be understood that the dynamic value boundary of anesthesia risk is used to define the upper and lower limit thresholds of the anesthesia risk level, including the upper boundary of the dynamic value of anesthesia risk and the lower boundary of the dynamic value of anesthesia risk. The upper boundary and lower boundary of the dynamic value of anesthesia risk define different risk levels respectively. When the dynamic value of anesthesia risk exceeds these boundaries, a response can be made according to the set rules to output the corresponding risk signal. The upper boundary of the dynamic value of anesthesia risk is the upper limit of the dynamic value of anesthesia risk. When the dynamic value of anesthesia risk exceeds the upper boundary, it means that the patient's anesthesia depth may be too deep, and there is a risk of excessive anesthesia. The patient may have respiratory depression, cardiovascular instability, delayed anesthesia awakening and other problems. An emergency signal should be issued to prompt the anesthesiologist to adjust the anesthesia depth or take other intervention measures. The lower boundary of the dynamic value of anesthesia risk is the lower limit of the anesthesia risk. The lower boundary is usually used to indicate the situation where the depth of anesthesia is insufficient. For example, too low anesthesia dose may cause the patient to recover consciousness, which in turn triggers a violent physiological reaction, or fail to achieve the expected analgesic effect.

[0082] S420, comparing the anesthesia risk dynamic value of the anesthesia monitoring target with a preset anesthesia risk dynamic value boundary, and outputting an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value.

[0083] It can be understood that after obtaining the anesthesia risk dynamic value boundary, the anesthesia risk dynamic value of the anesthesia monitoring target can be directly compared with the preset anesthesia risk dynamic value boundary, and the corresponding anesthesia risk signal instruction can be output according to the comparison result. The comparison of the anesthesia risk dynamic value with the preset anesthesia risk dynamic value boundary can be achieved through a conditional judgment method or directly through a comparison algorithm, and the anesthesia risk signal instruction can be output by setting an automatic feedback mechanism.

[0084] S430: When the anesthesia risk dynamic value of the anesthesia monitoring target exceeds the anesthesia risk dynamic value upper boundary, a first anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output.

[0085] It can be understood that when the dynamic value of anesthesia risk exceeds the preset upper limit, it means that the risk in the anesthesia process has exceeded the predetermined safety range, and a "first anesthesia risk signal instruction" can be generated, indicating that the anesthesia monitoring target is in a risky state. The first anesthesia risk signal instruction is an alarm or instruction that is automatically triggered when the monitored dynamic value of anesthesia risk exceeds the preset safety upper limit. The first anesthesia risk signal refers to the ability to convey risk information by setting a specific alarm sound or voice prompt.

[0086] In a possible implementation, S400, according to the anesthesia risk dynamic value of the anesthesia monitoring target, outputting an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value includes: S440: When the anesthesia risk dynamic value of the anesthesia monitoring target exceeds the lower boundary of the anesthesia risk dynamic value, a second anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output.

[0087] It can be understood that when the dynamic value of anesthesia risk exceeds the preset lower boundary, it means that the risk in the anesthesia process has exceeded the predetermined safety range, and a "second anesthesia risk signal instruction" can be generated, indicating that the anesthesia monitoring target is in a risky state. The second anesthesia risk signal instruction is an alarm or instruction that is automatically triggered when the monitored dynamic value of anesthesia risk exceeds the preset safety lower boundary. The first anesthesia risk signal refers to the ability to convey risk information by setting a specific alarm sound or voice prompt.

[0088] Corresponding to the anesthesia monitoring method of the above embodiment, the embodiment of the present application also provides an anesthesia monitoring system, and each unit of the system can implement each step of the anesthesia monitoring method. Figure 5 A structural block diagram of an anesthesia monitoring system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0089] Reference Figure 5 , the anesthesia monitoring system includes: An acquisition unit, used for acquiring vital sign data and anesthesia injection information of an anesthesia monitoring target; wherein the vital sign data is used to reflect the physiological state of the anesthesia monitoring target; A determination unit, configured to determine an anesthesia depth index of the anesthesia monitoring target according to the anesthesia injection information and the vital sign data of the anesthesia monitoring target; wherein the anesthesia depth index is used to reflect the anesthesia depth of the anesthesia monitoring target; A risk unit, configured to determine a dynamic value of anesthesia risk of the anesthesia monitoring target based on the anesthesia depth index and the vital sign data of the anesthesia monitoring target; wherein the dynamic value of anesthesia risk is used to reflect the anesthesia risk situation of the anesthesia monitoring target; The instruction unit is used to output an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value according to the anesthesia risk dynamic value of the anesthesia monitoring target.

[0090] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0091] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0092] The present application also provides an anesthesia monitoring device, Figure 6 This is a schematic diagram of the structure of an anesthesia monitoring device provided in one embodiment of the present application. Figure 6 As shown, the anesthesia monitoring device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown), at least one memory 61 ( Figure 6 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the anesthesia monitoring device 6 implements the steps in any of the above-mentioned anesthesia monitoring method embodiments, or the anesthesia monitoring device 6 implements the functions of the units in the above-mentioned system embodiments.

[0093] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the anesthesia monitoring device 6.

[0094] The anesthesia monitoring device may be a variety of intelligent monitoring devices. The anesthesia monitoring device may include but is not limited to a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 6 It is only an example of the anesthesia monitoring device 6 and does not constitute a limitation on the anesthesia monitoring device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0095] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0096] In some embodiments, the memory 61 may be an internal storage unit of the anesthesia monitoring device 6, such as a hard disk or memory of the anesthesia monitoring device 6. In other embodiments, the memory 61 may also be an external storage device of the anesthesia monitoring device 6, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the anesthesia monitoring device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the anesthesia monitoring device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0097] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0098] An embodiment of the present application provides a computer program product. When the computer program product is executed on an anesthesia monitoring device, the anesthesia monitoring device implements the steps in any of the above method embodiments.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the anesthesia monitoring device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0100] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0102] In the embodiments provided in the present application, it should be understood that the disclosed anesthesia monitoring system / anesthesia monitoring equipment and method can be implemented in other ways. For example, the anesthesia monitoring system / anesthesia monitoring equipment embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An anesthesia monitoring method, characterized in that: include: Acquiring vital sign data and anesthesia injection information of an anesthesia monitoring target; wherein the vital sign data is used to reflect the physiological state of the anesthesia monitoring target; Determining an anesthesia depth index of the anesthesia monitoring target according to the anesthesia injection information and the vital sign data of the anesthesia monitoring target; wherein the anesthesia depth index is used to reflect the anesthesia degree of the anesthesia monitoring target; Determining a dynamic value of anesthesia risk of the anesthesia monitoring target based on the anesthesia depth index and the vital sign data of the anesthesia monitoring target; wherein the dynamic value of anesthesia risk is used to reflect the anesthesia risk situation of the anesthesia monitoring target; According to the anesthesia risk dynamic value of the anesthesia monitoring target, an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output.

2. The anesthesia monitoring method according to claim 1, characterized in that: Determining the anesthesia depth index of the anesthesia monitoring target according to the anesthesia injection information and the vital sign data of the anesthesia monitoring target includes: According to the anesthesia injection information and the vital sign data of the anesthesia monitoring target, anesthesia depth calculation index data of the anesthesia monitoring target is obtained; wherein the anesthesia depth calculation index data is used to reflect the anesthesia condition of the anesthesia monitoring target; The anesthesia depth calculation index data of the anesthesia monitoring target is analyzed to determine the anesthesia depth index of the anesthesia monitoring target.

3. The anesthesia monitoring method according to claim 2, characterized in that: The step of obtaining the anesthesia depth calculation index data of the anesthesia monitoring target according to the anesthesia injection information and the vital sign data of the anesthesia monitoring target includes: Determining an anesthesia infusion type of the anesthesia monitoring target based on the anesthesia infusion information of the anesthesia monitoring target; Identifying the vital sign data according to the anesthesia injection type of the anesthesia monitoring target, and determining an anesthesia depth calculation index of the anesthesia monitoring target; The vital sign data is extracted according to the anesthesia depth calculation index of the anesthesia monitoring target to obtain the anesthesia depth calculation index data of the anesthesia monitoring target.

4. The anesthesia monitoring method according to claim 1, characterized in that: The step of determining the anesthesia risk dynamic value of the anesthesia monitoring target based on the anesthesia depth index and the vital sign data of the anesthesia monitoring target includes: Performing a first gradient analysis on the anesthesia depth index of the anesthesia monitoring target to obtain anesthesia depth index gradient data corresponding to the anesthesia depth index; wherein the anesthesia depth index gradient data is used to reflect a change trend of the anesthesia depth of the anesthesia monitoring target; Performing a second gradient analysis on the vital sign data of the anesthesia monitoring target to obtain vital sign gradient data corresponding to the vital sign data; wherein the vital sign gradient data is used to reflect the change trend of the vital sign of the anesthesia monitoring target; Based on the anesthesia depth index gradient data corresponding to the anesthesia depth index and the vital sign gradient data corresponding to the vital sign data, a dynamic value of anesthesia risk of the anesthesia monitoring target is determined.

5. The anesthesia monitoring method according to claim 4, characterized in that: The performing a first gradient analysis on the anesthesia depth index of the anesthesia monitoring target to obtain anesthesia depth index gradient data corresponding to the anesthesia depth index includes: Obtaining preset anesthesia depth index gradient segments and anesthesia depth time gradient segments; According to the preset anesthesia depth index gradient section, the anesthesia depth index of the anesthesia monitoring target is numerically graded to obtain anesthesia depth index numerical gradient data corresponding to the anesthesia depth index; Performing time gradient classification on the anesthesia depth index of the anesthesia monitoring target according to the preset anesthesia depth time gradient section to obtain anesthesia depth index time gradient data corresponding to the anesthesia depth index; The anesthesia depth index numerical gradient data and the anesthesia depth index time gradient data are determined as the anesthesia depth index gradient data corresponding to the anesthesia depth index.

6. The anesthesia monitoring method according to claim 5, characterized in that: The method further comprises: Determining first moment information corresponding to the anesthesia depth index of the anesthesia monitoring target; Based on the anesthesia injection information of the anesthesia monitoring target, determining second time information corresponding to the initial anesthesia injection time of the anesthesia monitoring target; Obtaining an absolute time difference between the first time information and the second time information according to the first time information and the second time information; The step of performing time gradient classification on the anesthesia depth index of the anesthesia monitoring target according to the preset anesthesia depth time gradient section to obtain anesthesia depth index time gradient data corresponding to the anesthesia depth index includes: The absolute time difference between the first time information and the second time information is classified into time gradients according to the preset anesthesia depth time gradient section to obtain anesthesia depth index time gradient data corresponding to the anesthesia depth index.

7. The anesthesia monitoring method according to claim 4, characterized in that: The performing a second gradient analysis on the vital sign data of the anesthesia monitoring target to obtain vital sign gradient data corresponding to the vital sign data includes: Obtaining preset vital sign data gradient segments; The vital sign data of the anesthesia monitoring target is gradient classified according to the preset vital sign data gradient section to obtain vital sign gradient data corresponding to the vital sign data.

8. The anesthesia monitoring method according to claim 4, characterized in that: The determining the dynamic value of the anesthesia risk of the anesthesia monitoring target based on the anesthesia depth index gradient data corresponding to the anesthesia depth index and the vital sign gradient data corresponding to the vital sign data includes: Performing a first calculation based on the anesthesia depth index gradient data corresponding to the anesthesia depth index to obtain a first anesthesia risk dynamic value of the anesthesia monitoring target; wherein the first anesthesia risk dynamic value is used to reflect the anesthesia depth risk situation of the anesthesia monitoring target; Performing a second calculation based on the vital sign gradient data corresponding to the vital sign data to obtain a second anesthesia risk dynamic value of the anesthesia monitoring target; wherein the second anesthesia risk dynamic value is used to reflect the vital sign risk situation of the anesthesia monitoring target; A weighted calculation is performed according to the first anesthesia risk dynamic value and the second anesthesia risk dynamic value of the anesthesia monitoring target to determine the anesthesia risk dynamic value of the anesthesia monitoring target.

9. The anesthesia monitoring method according to claim 1, characterized in that: The step of outputting an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value according to the anesthesia risk dynamic value of the anesthesia monitoring target includes: Acquire a preset anesthesia risk dynamic value boundary; wherein the anesthesia risk dynamic value boundary includes an anesthesia risk dynamic value upper boundary and an anesthesia risk dynamic value lower boundary; Comparing the anesthesia risk dynamic value of the anesthesia monitoring target with a preset anesthesia risk dynamic value boundary, and outputting an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value; When the anesthesia risk dynamic value of the anesthesia monitoring target exceeds the anesthesia risk dynamic value upper boundary, outputting a first anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value; And / or, outputting an anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value according to the anesthesia risk dynamic value of the anesthesia monitoring target includes: When the anesthesia risk dynamic value of the anesthesia monitoring target exceeds the anesthesia risk dynamic value lower boundary, a second anesthesia risk signal instruction corresponding to the anesthesia risk dynamic value is output.

10. An anesthesia monitoring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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