Anesthesia depth evaluation method and system based on physical sign data of patient

Abstract

The invention discloses an anesthesia depth evaluation method and system based on physical sign data of a patient, and relates to the technical field of medical anesthesia. An anesthesia depth evaluation system based on physical sign data of a patient comprises an anesthesia pre-operation evaluation module and an anesthesia post-operation evaluation module. By combining preoperative sign data analysis, real-time anesthesia depth evaluation, anesthesia metabolism prediction and follow-up anesthesia management, by utilizing the densely connected convolutional network, the attention mechanism and dynamic time sequence analysis, the anesthesia depth can be accurately evaluated, and the anesthesia scheme can be adjusted in real time, so that the anesthesia management of a patient is optimized, and the anesthesia efficiency of the patient is improved. The safety and individuation of the anesthesia depth in the operation process are ensured, and the anesthesia effect and the safety of a patient are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical anesthesia technology, and in particular to an anesthesia depth assessment method and system based on patient vital sign data. Background Art

[0002] Anesthesia depth assessment is a key link in anesthesia management, which aims to ensure that patients can achieve adequate sedation during anesthesia while avoiding the risks of excessive anesthesia. The main purpose of preoperative anesthesia depth assessment is to evaluate the patient's overall health status, ensure the rational selection of anesthetic drugs, and predict potential reactions during anesthesia in order to develop a personalized anesthesia plan; while postoperative anesthesia depth assessment focuses on monitoring the metabolic process of anesthetic drugs, the patient's recovery status, and whether anesthesia-related complications occur, to ensure the patient's safe recovery.

[0003] However, most existing technologies rely on monitoring methods based on a single sign, which has certain limitations. For example, EEG activity is used to assess the depth of anesthesia, but it is affected by the type of drug and individual differences among patients and may not accurately reflect the actual depth of anesthesia; and traditional monitoring methods based on blood pressure and heart rate may not fully reflect the patient's anesthetic status.

[0004] Therefore, it is particularly important to conduct a comprehensive assessment based on multidimensional physical sign data before and after surgery. By flexibly adjusting the focus of the assessment according to different needs before and after surgery, a more accurate assessment of the depth of anesthesia can be achieved, which can improve the accuracy of the assessment and thus ensure the safety and effectiveness of anesthesia management. Summary of the invention

[0005] The present invention aims to provide a method and system for evaluating the depth of anesthesia based on patient vital sign data, so as to accurately evaluate the depth of anesthesia before and after surgery.

[0006] A method for assessing anesthesia depth based on patient vital sign data comprises the following steps: Obtain the patient's preoperative vital signs data; the patient's preoperative vital signs data contains preoperative basic data, preoperative vital signs data, preoperative professional examination data and patient real-time monitoring data; among which, the patient's real-time monitoring data is based on the patient's actual state changes; based on the patient's preoperative vital signs data and the anesthesia depth preoperative assessment model, analyze and obtain the patient's real-time anesthesia depth assessment result; The preoperative assessment model of anesthesia depth combines data preprocessing, special signal processing, indicator evaluation and result output layers, and uses densely connected convolutional networks and adaptive attention mechanisms to effectively analyze patients' preoperative vital signs data and accurately assess anesthesia depth. During the patient's surgery, the patient's real-time anesthesia depth assessment results are continuously collected to obtain the patient's intraoperative anesthesia depth assessment data set; the patient's intraoperative anesthesia depth assessment data set contains N patient real-time anesthesia depth assessment results G n , n=1, 2, …, N; metabolic assessment prediction is performed based on the patient's intraoperative anesthesia depth assessment data set and the patient's anesthesia metabolic assessment model to obtain the patient's postoperative anesthesia depth assessment result; The patient anesthesia metabolism assessment model can accurately assess the patient's postoperative anesthesia depth through multi-level feature extraction and optimized metabolic prediction, combined with dynamic time series analysis and regression optimization; Subsequent anesthesia management is performed based on the patient's postoperative anesthesia depth assessment results.

[0007] As a preferred technical solution of the present invention, the anesthesia depth preoperative assessment model includes a data preprocessing layer, a special signal processing layer, an index assessment layer and a result output layer; The data preprocessing layer is used to divide the patient's preoperative vital sign data to obtain special signal vital sign data and basic vital sign data; preprocess the basic vital sign data to obtain preprocessed basic vital sign data; wherein the special signal vital sign data is image feature data; The special signal processing layer is used to perform feature analysis on special signal vital sign data to obtain the features of special signal vital sign data; The special signal processing layer is based on the improvement of traditional convolutional networks using dense connections; The indicator evaluation layer is used to evaluate the depth of anesthesia based on the special signal sign data characteristics and pre-processed basic sign data, and obtain the patient's real-time anesthesia depth evaluation results; The result output layer is used to output the patient's real-time anesthesia depth assessment results.

[0008] As a preferred technical solution of the present invention, the specific steps of performing feature analysis in the feature signal processing layer include: In the feature signal processing layer, there are M feature recognition units D m , m=1,2,…,M; in the feature recognition unit D m In it, there are dense feature recognition blocks and adaptive attention blocks; set D m =[X 1m , X 2m , X 3m ]; where X 1m Indicated in the feature recognition unit D m The number of 1*1 convolutional layers in the dense feature recognition block, X 2m Indicated in the feature recognition unit D m The number of 3*3 convolutional layers in the dense feature recognition block, X 3m In the feature recognition unit Dm Channel attention coefficient of adaptive attention block in ; In the feature recognition unit D m In the example, the output data is the special signal sign data iterative feature T m , specific steps: Receive special signal sign data iterative features T1, T2, ..., T m-1 Perform feature recognition to obtain special signal sign data iterative feature T m ; Until all feature recognition units D are traversed m , obtain the special signal sign data characteristics; The specific steps of training the feature signal processing layer include: Collect several groups of special signal processing training samples, each of which contains target parameter matching factors and verified special signal sign data; combine several groups of special signal processing training samples to obtain a special signal processing training set; the target parameter matching factor is used to match the feature recognition unit D m Channel attention coefficient of adaptive attention block in ; The model is trained using a special signal processing training set to obtain an initial feature signal processing layer. The initial feature signal processing layer is model evaluated. If the initial feature signal processing layer passes the model evaluation, the initial feature signal processing layer is used as the feature signal processing layer in the preoperative assessment model of anesthesia depth. Otherwise, the model training is continued using the special signal processing training set.

[0009] As a preferred technical solution of the present invention, the specific steps of evaluating the depth of anesthesia at the index evaluation layer include: The depth of anesthesia is evaluated based on the improved TOPSIS method. Calculate the patient's real-time anesthesia depth assessment results; perform time slice data segmentation on the special signal sign data features and pre-processed basic sign data to obtain feature item data ; Where i is the basic time slice, i=1, 2, ..., I, I is the total time for obtaining the patient's preoperative vital sign data; J is the total number of feature items in the special signal vital sign data features and preprocessed basic vital sign data, j=1, 2, ..., J; Represents the characteristics of special signal vital sign data and the jth characteristic item data of the i-th basic time slice in the preprocessed basic vital sign data; Represents the feature item weighting factor.

[0010] As a preferred technical solution of the present invention, the patient anesthesia metabolism assessment model includes a main feature extraction layer, an optimized metabolism prediction layer and a postoperative result output layer; The main feature extraction layer is used to evaluate the real-time anesthesia depth of patients in the patient intraoperative anesthesia depth evaluation dataset G n Perform time series feature extraction to obtain the patient's real-time anesthesia depth assessment result feature G n 'and the time series characteristics of the patient's intraoperative anesthesia depth assessment data; The optimized metabolic prediction layer is used to calculate the time series characteristics of the patient's intraoperative anesthesia depth assessment data and the patient's real-time anesthesia depth assessment result characteristics G n 'And anesthesia metabolism regression function to optimize metabolic prediction analysis and obtain the patient's postoperative anesthesia depth assessment results; The postoperative result output layer is used to output the patient's postoperative anesthesia depth assessment results.

[0011] As a preferred technical solution of the present invention, the specific steps of extracting temporal features in the main feature extraction layer include: The main feature extraction layer includes a multi-point data connection layer and a dynamic time feature extraction layer; Real-time anesthesia depth assessment results of patients in multi-point data connection layer G n Perform adaptive convolution feature extraction to obtain the patient's real-time anesthesia depth assessment result feature G n '; In the dynamic time feature extraction layer, the feature G is used to evaluate the real-time anesthesia depth of all patients. n 'Perform time series feature extraction to obtain the time series features of the patient's intraoperative anesthesia depth assessment data.

[0012] As a preferred technical solution of the present invention, the specific steps of constructing the anesthesia metabolism regression function include: An initial anesthesia metabolism regression function is constructed based on linear regression analysis and historical anesthesia metabolism data; the historical anesthesia metabolism data contains a historical anesthesia depth assessment data set of several patients; Construct K prediction function adjustment factor individuals H k , k=1, 2, ..., K; wherein each prediction function adjustment factor individual contains an adjustment strategy for adjusting the initial anesthesia metabolism regression function; K prediction function adjustment factor individuals H k Combination, to obtain the iterative population of the prediction function adjustment factor; set the number of iterations p, p = 1, 2, ..., P, P is the maximum number of iterations; Prediction function adjustment factor individual H k The fitness is S k , represents the prediction function adjustment factor individual H k Improvement in the predictive ability of the initial anesthesia metabolism regression function; In the process of population iteration, each time the prediction function adjustment factor iteration population is screened, the prediction function adjustment factor individuals with poor fitness and good fitness are called the crossover mutation prediction function adjustment factor population; Using the formula B(p)=B0*e -λp (1+β*avg(p)) controls the variability of the crossover prediction function to adjust the factor population; Among them, B(p) is the variability, B0 is the initial variability, λ is the decay rate, β is the factor that controls the influence of individual distribution on the variability, and avg(p) represents the mean fitness of the prediction function adjustment factor iteration population when the number of iterations is p; According to B(p), the cross-variation prediction function adjustment factor population is updated to obtain a new cross-variation prediction function adjustment factor population; the new cross-variation prediction function adjustment factor population is combined with the prediction function adjustment factor iteration population to obtain a new prediction function adjustment factor iteration population; the new prediction function adjustment factor iteration population is used to perform the next population iteration; When the maximum number of iterations is reached, the prediction function adjustment factor individual corresponding to the current maximum fitness is output, which is the optimal prediction function adjustment factor individual. The initial anesthesia metabolism regression function is optimized based on the optimal prediction function adjustment factor individual to obtain the anesthesia metabolism regression function.

[0013] An anesthesia depth assessment system based on patient vital sign data, comprising: The anesthesia preoperative assessment module includes a data acquisition unit and a preoperative analysis unit; the data acquisition unit is used to obtain the patient's preoperative vital signs data; the patient's preoperative vital signs data contains preoperative basic data, preoperative vital signs data, preoperative professional examination data and patient real-time monitoring data; among them, the patient's real-time monitoring data is based on the patient's actual state changes; the preoperative analysis unit is used to analyze the patient's preoperative vital signs data and the anesthesia depth preoperative assessment model to obtain the patient's real-time anesthesia depth assessment result; the anesthesia depth preoperative assessment model combines data preprocessing, special signal processing, indicator evaluation and result output layer, and uses a densely connected convolutional network and an adaptive attention mechanism to effectively analyze the patient's preoperative vital signs data and accurately assess the anesthesia depth; The postoperative anesthesia assessment module includes a data acquisition unit and a postoperative analysis unit; the data acquisition unit is used to continuously collect the patient's real-time anesthesia depth assessment results during the patient's surgery to obtain the patient's intraoperative anesthesia depth assessment data set; the patient's intraoperative anesthesia depth assessment data set contains N patient real-time anesthesia depth assessment results G n, n=1, 2, …, N; the postoperative analysis unit is used to perform metabolic assessment prediction based on the patient's intraoperative anesthesia depth assessment data set and the patient's anesthesia metabolic assessment model to obtain the patient's postoperative anesthesia depth assessment result; the patient's anesthesia metabolic assessment model can accurately assess the patient's postoperative anesthesia depth through multi-level feature extraction and optimized metabolic prediction, combined with dynamic time series analysis and regression optimization; subsequent anesthesia management is performed based on the patient's postoperative anesthesia depth assessment result.

[0014] The present invention has the following advantages: 1. The present invention combines preoperative vital sign data analysis, real-time anesthesia depth assessment, anesthesia metabolism prediction and subsequent anesthesia management, and utilizes densely connected convolutional networks, attention mechanisms and dynamic time series analysis to accurately assess anesthesia depth and adjust anesthesia plans in real time, thereby optimizing the patient's anesthesia management, ensuring the safety and personalization of anesthesia depth during surgery, and improving anesthesia effects and patient safety.

[0015] 2. The present invention can evaluate the depth of anesthesia in real time and accurately by combining the patient's preoperative vital sign data and the preoperative assessment model of the depth of anesthesia, which helps to maintain the optimal depth of anesthesia during surgery and avoid excessive or insufficient anesthesia, thereby reducing surgical risks and improving patient safety; in the special signal processing layer, the model improves the traditional convolutional network through a densely connected convolutional network. The dense connection can effectively utilize feature information at different levels, enhance the network's expression ability, and improve the ability to extract and analyze complex signal features; the special signal sign data is combined with the basic sign data, so that complex signals such as image data can be effectively processed, thereby improving the accuracy of anesthesia depth assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a schematic diagram of the structure of an anesthesia depth assessment system based on patient vital sign data adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0018] Embodiment 1, a method for assessing anesthesia depth based on patient vital sign data, comprising the following steps: Obtaining the patient's preoperative physical sign data; the patient's preoperative physical sign data includes preoperative basic data, preoperative vital sign data, preoperative professional examination data and patient real-time monitoring data; among which, the patient's real-time monitoring data is based on the patient's actual status changes; Preoperative basic data are usually collected before surgery and do not change significantly throughout the entire operation and after the operation, including the patient's age, gender, height, weight, past medical history, etc. They usually do not change over time, but are related to the patient's basic health status; preoperative vital signs data may change over time, including blood pressure, heart rate, respiratory rate, body temperature, etc. Preoperative vital signs data are usually closely related to time, and therefore will be dynamically affected by the patient's current health status; preoperative professional examination data are usually obtained by professional examinations, such as blood tests, imaging examinations, electrocardiograms, etc.; patient real-time monitoring data is based on changes in the patient's actual status and is collected in real time, usually with various factors during the operation, including electrocardiograms, blood oxygen saturation, ventilator parameters, etc. These data are collected in real time and continuously updated over time for real-time evaluation of patient status; Based on the patient's preoperative physical sign data and the preoperative anesthesia depth assessment model, the patient's real-time anesthesia depth assessment result is obtained; The preoperative assessment model of anesthesia depth combines data preprocessing, special signal processing, indicator evaluation and result output layers, and uses densely connected convolutional networks and adaptive attention mechanisms to effectively analyze patients' preoperative vital signs data and accurately assess anesthesia depth. The preoperative evaluation model of anesthesia depth includes a data preprocessing layer, a special signal processing layer, an index evaluation layer and a result output layer; The data preprocessing layer is used to divide the patient's preoperative vital sign data to obtain special signal vital sign data and basic vital sign data; preprocess the basic vital sign data to obtain preprocessed basic vital sign data; wherein the special signal vital sign data is image feature data; The special signal processing layer is used to perform feature analysis on special signal vital sign data to obtain the features of special signal vital sign data; The special signal processing layer is based on the improvement of traditional convolutional networks using dense connections; The indicator evaluation layer is used to evaluate the depth of anesthesia based on the special signal sign data characteristics and pre-processed basic sign data, and obtain the patient's real-time anesthesia depth evaluation results; The result output layer is used to output the patient's real-time anesthesia depth assessment results; By combining the patient's preoperative vital signs data and the preoperative assessment model of the depth of anesthesia, the depth of anesthesia can be assessed in real time and accurately, which helps to maintain the optimal depth of anesthesia during surgery and avoid over-anesthesia or under-anesthesia, thereby reducing surgical risks and improving patient safety. In the special signal processing layer, the model improves the traditional convolutional network through a densely connected convolutional network. The dense connection can effectively utilize feature information at different levels, enhance the network's expression ability, and improve the ability to extract and analyze complex signal features. The special signal vital signs data is combined with the basic vital signs data, so that complex signals such as image data can be effectively processed, thereby improving the accuracy of anesthesia depth assessment. For example, image data from signals such as electroencephalogram (EEG) and electrocardiogram (ECG) can be deeply analyzed to better predict the patient's anesthesia status. The specific steps of feature analysis in the feature signal processing layer include: In the feature signal processing layer, there are M feature recognition units D m , m=1,2,…,M; in the feature recognition unit D m In it, there are dense feature recognition blocks and adaptive attention blocks; set D m =[X 1m , X 2m , X 3m ]; where X 1m Indicated in the feature recognition unit D m The number of 1*1 convolutional layers in the dense feature recognition block, X 2m Indicated in the feature recognition unit D m The number of 3*3 convolutional layers in the dense feature recognition block, X 3m In the feature recognition unit D m Channel attention coefficient of adaptive attention block in ; In the feature recognition unit D m In the example, the output data is the special signal sign data iterative feature T m , specific steps: Receive special signal sign data iterative features T1, T2, ..., T m-1 Perform feature recognition to obtain special signal sign data iterative feature T m ; Until all feature recognition units D are traversed m , obtain the special signal sign data characteristics; The specific steps of training the feature signal processing layer include: Collect several groups of special signal processing training samples, each of which contains target parameter matching factors and verified special signal sign data; combine several groups of special signal processing training samples to obtain a special signal processing training set; the target parameter matching factor is used to match the feature recognition unit D m Channel attention coefficient of adaptive attention block in ; The special signal processing training set is used to train the model to obtain an initial characteristic signal processing layer; the initial characteristic signal processing layer is evaluated by the model, and if the initial characteristic signal processing layer passes the model evaluation, the initial characteristic signal processing layer is used as the characteristic signal processing layer in the preoperative evaluation model of the depth of anesthesia; otherwise, the special signal processing training set is used to continue the model training; The dense feature recognition block in the feature recognition unit is composed of 1×1 convolutional layers and 3×3 convolutional layers, which can effectively capture multi-level local and global features; the 1×1 convolutional layer can be used to reduce the number of channels and improve computational efficiency, while the 3×3 convolutional layer helps capture richer contextual information. This combination improves the model's expressiveness when processing complex signals and enhances its sensitivity to signals related to anesthesia depth assessment; by introducing the channel attention coefficient through the adaptive attention block, the model can dynamically adjust the attention of different channels according to the characteristics of the input data. The channel attention mechanism can effectively distinguish the importance of different features when processing anesthesia depth-related features, thereby optimizing feature representation and improving the model's adaptability and accuracy to different patient vital signs. Through layer-by-layer iterative processing, the model can gradually dig deeper and refine more abstract and efficient feature representations. The output features of each feature recognition unit will gradually form more refined anesthesia depth-related signal features, thereby improving the accuracy of anesthesia depth assessment. The specific steps for evaluating the depth of anesthesia at the indicator evaluation level include: The depth of anesthesia is evaluated based on the improved TOPSIS method. Calculate the patient's real-time anesthesia depth assessment results; perform time slice data segmentation on the special signal sign data features and pre-processed basic sign data to obtain feature item data ; Where i is the basic time slice, i=1, 2, ..., I, I is the total time for obtaining the patient's preoperative vital sign data; J is the total number of feature items in the special signal vital sign data features and preprocessed basic vital sign data, j=1, 2, ..., J; Represents the characteristics of special signal vital sign data and the jth characteristic item data of the i-th basic time slice in the preprocessed basic vital sign data; represents the weighting factor of feature item; The TOPSIS method is used to identify the anesthesia depth assessment matrix. By calculating the relative proximity of each feature index to the ideal solution, the anesthesia depth can be quantified and evaluated more scientifically, and the objectivity and accuracy of the assessment can be improved. By introducing feature weights, the indicator weights can be dynamically adjusted according to the importance of specific features. In this way, when evaluating the anesthesia depth, key features can be highlighted, the interference of secondary features on the results can be reduced, and the sensitivity and accuracy of the model can be ensured. Special signal features and pre-processed basic sign data are segmented and synthesized to form a feature item data matrix. This multi-feature fusion method can fully reflect the patient's anesthesia-related sign information and avoid misjudgment that may be caused by a single feature. The matrix-based anesthesia depth assessment result is data-driven, which reduces the impact of subjective factors on anesthesia depth assessment and provides clinicians with more reliable auxiliary decision support. During the patient's surgery, the patient's real-time anesthesia depth assessment results are continuously collected to obtain the patient's intraoperative anesthesia depth assessment data set; the patient's intraoperative anesthesia depth assessment data set contains N patient real-time anesthesia depth assessment results G n , n=1, 2, …, N; metabolic assessment prediction is performed based on the patient's intraoperative anesthesia depth assessment data set and the patient's anesthesia metabolic assessment model to obtain the patient's postoperative anesthesia depth assessment result; The patient anesthesia metabolism assessment model can accurately assess the patient's postoperative anesthesia depth through multi-level feature extraction and optimized metabolic prediction, combined with dynamic time series analysis and regression optimization; The patient anesthesia metabolism assessment model includes a main feature extraction layer, an optimized metabolism prediction layer, and a postoperative result output layer; The main feature extraction layer is used to evaluate the real-time anesthesia depth of patients in the patient intraoperative anesthesia depth evaluation dataset G n Perform time series feature extraction to obtain the patient's real-time anesthesia depth assessment result feature G n 'and the time series characteristics of the patient's intraoperative anesthesia depth assessment data; The optimized metabolic prediction layer is used to calculate the time series characteristics of the patient's intraoperative anesthesia depth assessment data and the patient's real-time anesthesia depth assessment result characteristics G n 'And anesthesia metabolism regression function to optimize metabolic prediction analysis and obtain the patient's postoperative anesthesia depth assessment results; The postoperative result output layer is used to output the patient's postoperative anesthesia depth assessment results; The specific steps of extracting temporal features in the main feature extraction layer include: The main feature extraction layer includes a multi-point data connection layer and a dynamic time feature extraction layer; Real-time anesthesia depth assessment results of patients in multi-point data connection layer G n Perform adaptive convolution feature extraction to obtain the patient's real-time anesthesia depth assessment result feature G n'; In the dynamic time feature extraction layer, the feature G is used to evaluate the real-time anesthesia depth of all patients. n 'Extract time series features to obtain the time series features of the patient's intraoperative anesthesia depth assessment data; Through dynamic time series analysis and regression optimization, the patient anesthesia metabolic assessment model can efficiently analyze the intraoperative anesthesia depth assessment data, and combine the results of multi-level feature extraction to accurately assess the patient's postoperative anesthesia depth, which helps to reduce postoperative anesthesia-related complications; in the main feature extraction layer, multi-point connection and dynamic time feature extraction are performed on the patient's real-time anesthesia depth assessment results, which can not only capture single-point features, but also mine the global pattern of time series data, providing more comprehensive information for metabolic assessment prediction; the optimized metabolic prediction layer combines the time series characteristics of the patient's intraoperative anesthesia depth assessment data, the real-time assessment result characteristics and the anesthesia metabolism regression function to optimize the metabolic process and analyze it, which can effectively reflect the individual differences of patients and improve the accuracy and reliability of postoperative anesthesia depth assessment; in the dynamic time feature extraction layer, using the time series model, the model can capture the dynamic change trend of intraoperative anesthesia depth over time, and provide more timely input information for postoperative anesthesia depth prediction. This method can better adapt to the changes in anesthesia depth during long-term surgery; The specific steps to construct the anesthesia metabolism regression function include: An initial anesthesia metabolism regression function is constructed based on linear regression analysis and historical anesthesia metabolism data; the historical anesthesia metabolism data contains a historical anesthesia depth assessment data set of several patients; Construct K prediction function adjustment factor individuals H k , k=1, 2, ..., K; wherein each prediction function adjustment factor individual contains an adjustment strategy for adjusting the initial anesthesia metabolism regression function; K prediction function adjustment factor individuals H k Combination, to obtain the iterative population of the prediction function adjustment factor; set the number of iterations p, p=1, 2, ..., P, P is the maximum number of iterations; the maximum number of iterations is set by professional technicians according to actual conditions; Prediction function adjustment factor individual H k The fitness is S k , represents the prediction function adjustment factor individual H k Improvement in the predictive ability of the initial anesthesia metabolism regression function; In the process of population iteration, each time the prediction function adjustment factor iteration population is screened, the prediction function adjustment factor individuals with poor fitness and good fitness are called the crossover mutation prediction function adjustment factor population; Using the formula B(p)=B0*e -λp (1+β*avg(p)) controls the variability of the crossover prediction function to adjust the factor population; Among them, B(p) is the variability, B0 is the initial variability, λ is the decay rate, β is the factor that controls the influence of individual distribution on the variability, and avg(p) represents the fitness mean of the prediction function adjustment factor iteration population when the number of iterations is p; the initial variability is set by professional technicians according to actual conditions; According to B(p), the cross-variation prediction function adjustment factor population is updated to obtain a new cross-variation prediction function adjustment factor population; the new cross-variation prediction function adjustment factor population is combined with the prediction function adjustment factor iteration population to obtain a new prediction function adjustment factor iteration population; the new prediction function adjustment factor iteration population is used to perform the next population iteration; When the maximum number of iterations is reached, the prediction function adjustment factor individual corresponding to the current maximum fitness is output, which is the optimal prediction function adjustment factor individual. The initial anesthesia metabolism regression function is optimized based on the optimal prediction function adjustment factor individual to obtain the anesthesia metabolism regression function; By introducing the prediction function adjustment factor and iterative optimization mechanism, the prediction ability of the initial anesthesia metabolism regression function has been significantly improved. The model can fit the historical anesthesia metabolism data more accurately, thereby improving the accuracy of the patient's postoperative anesthesia depth assessment. By using the cross-mutation and fitness screening strategies, the adjustment factor population can dynamically evolve and gradually optimize the initial anesthesia metabolism regression function. This adaptive optimization method enables the model to continuously approach the optimal prediction result. By dynamically controlling the variability through the formula, the exploration ability can be increased in the early stage of optimization, while the randomness can be reduced in the later stage of iteration to concentrate on development. This balance mechanism improves the algorithm convergence speed and the final optimization effect. By introducing control factors and using the influence of the population fitness mean on the variability, the diversity of the prediction function adjustment factor population is effectively maintained, the population is prevented from falling into a single mode, and the global optimization capability is improved; in each iteration, the prediction function adjustment factor individuals with better and worse fitness are cross-mutated and updated to ensure the dynamic adjustment of the model optimization direction. This mechanism can accelerate the screening process of the optimal adjustment factor and improve the optimization efficiency of the model; the optimized anesthesia metabolism regression function can quickly provide high-precision prediction results in clinical applications, provide doctors with reliable postoperative anesthesia depth management decision support, and improve the safety and efficiency of postoperative patient recovery; Subsequent anesthesia management is carried out based on the results of the patient's postoperative anesthesia depth assessment. For example, after the patient's surgery is completed, the postoperative anesthesia depth assessment results are monitored in real time to determine whether the patient has reached the awakening standard, prevent delayed awakening due to residual anesthetics, and improve the safety of the patient's postoperative awakening. When the assessment results show that the patient's anesthesia depth is gradually reduced to a safe level, the doctor can remove the endotracheal tube in a timely manner and arrange for the patient to enter the postoperative intensive care unit; based on the anesthesia depth assessment results, assess whether the patient has fully recovered consciousness, and formulate a postoperative analgesia strategy based on the patient's pain feedback. If the assessment results show that the anesthetic is metabolized too quickly, analgesics may need to be supplemented in time to relieve the patient's pain. By assessing the patient's metabolism and recovery, the dosage of analgesics can be adjusted to avoid side effects caused by excessive medication; the postoperative anesthesia depth assessment results can significantly improve the safety, personalization and efficiency of postoperative management, and provide patients with better recovery guarantees.

[0019] Example 2, an anesthesia depth assessment system based on patient vital signs data, see Figure 1 As shown, including: The anesthesia preoperative assessment module includes a data acquisition unit and a preoperative analysis unit; the data acquisition unit is used to obtain the patient's preoperative vital signs data; the patient's preoperative vital signs data contains preoperative basic data, preoperative vital signs data, preoperative professional examination data and patient real-time monitoring data; among them, the patient's real-time monitoring data is based on the patient's actual state changes; the preoperative analysis unit is used to analyze the patient's preoperative vital signs data and the anesthesia depth preoperative assessment model to obtain the patient's real-time anesthesia depth assessment result; the anesthesia depth preoperative assessment model combines data preprocessing, special signal processing, indicator evaluation and result output layer, and uses a densely connected convolutional network and an adaptive attention mechanism to effectively analyze the patient's preoperative vital signs data and accurately assess the anesthesia depth; The postoperative anesthesia assessment module includes a data acquisition unit and a postoperative analysis unit; the data acquisition unit is used to continuously collect the patient's real-time anesthesia depth assessment results during the patient's surgery to obtain the patient's intraoperative anesthesia depth assessment data set; the patient's intraoperative anesthesia depth assessment data set contains N patient real-time anesthesia depth assessment results G n , n=1, 2, …, N; the postoperative analysis unit is used to perform metabolic assessment prediction based on the patient's intraoperative anesthesia depth assessment data set and the patient's anesthesia metabolic assessment model to obtain the patient's postoperative anesthesia depth assessment result; the patient's anesthesia metabolic assessment model can accurately assess the patient's postoperative anesthesia depth through multi-level feature extraction and optimized metabolic prediction, combined with dynamic time series analysis and regression optimization; subsequent anesthesia management is performed based on the patient's postoperative anesthesia depth assessment result.

[0020] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A method for assessing anesthesia depth based on patient vital sign data, characterized in that: The following steps are involved: Obtain the patient's preoperative vital signs data; the patient's preoperative vital signs data contains preoperative basic data, preoperative vital signs data, preoperative professional examination data and patient real-time monitoring data; among which, the patient's real-time monitoring data is based on the patient's actual state changes; based on the patient's preoperative vital signs data and the anesthesia depth preoperative assessment model, analyze and obtain the patient's real-time anesthesia depth assessment result; The preoperative assessment model of anesthesia depth combines data preprocessing, special signal processing, indicator evaluation and result output layers, and uses densely connected convolutional networks and adaptive attention mechanisms to effectively analyze patients' preoperative vital signs data and accurately assess anesthesia depth. During the patient's surgery, the patient's real-time anesthesia depth assessment results are continuously collected to obtain the patient's intraoperative anesthesia depth assessment data set; the patient's intraoperative anesthesia depth assessment data set contains N patient real-time anesthesia depth assessment results G n , n=1, 2, …, N; metabolic assessment prediction is performed based on the patient's intraoperative anesthesia depth assessment data set and the patient's anesthesia metabolic assessment model to obtain the patient's postoperative anesthesia depth assessment result; The patient anesthesia metabolism assessment model can accurately assess the patient's postoperative anesthesia depth through multi-level feature extraction and optimized metabolic prediction, combined with dynamic time series analysis and regression optimization; Subsequent anesthesia management is performed based on the patient's postoperative anesthesia depth assessment results.

2. The method for evaluating anesthesia depth based on patient vital sign data according to claim 1, characterized in that: The preoperative evaluation model of anesthesia depth includes a data preprocessing layer, a special signal processing layer, an index evaluation layer and a result output layer; The data preprocessing layer is used to divide the patient's preoperative vital sign data into special signal vital sign data and basic vital sign data; Preprocessing the basic vital sign data to obtain preprocessed basic vital sign data; wherein the special signal vital sign data is image feature data; The special signal processing layer is used to perform feature analysis on special signal vital sign data to obtain the features of special signal vital sign data; The special signal processing layer is based on the improvement of traditional convolutional networks using dense connections; The indicator evaluation layer is used to evaluate the depth of anesthesia based on the special signal sign data characteristics and pre-processed basic sign data, and obtain the patient's real-time anesthesia depth evaluation results; The result output layer is used to output the patient's real-time anesthesia depth assessment results.

3. The method for evaluating anesthesia depth based on patient vital sign data according to claim 2, characterized in that: The specific steps of feature analysis in the feature signal processing layer include: In the feature signal processing layer, there are M feature recognition units D m , m=1,2,…,M; in the feature recognition unit D m In it, there are dense feature recognition blocks and adaptive attention blocks; set D m =[X 1m , X 2m , X 3m ]; where X 1m Indicated in the feature recognition unit D m The number of 1*1 convolutional layers in the dense feature recognition block, X 2m Indicated in the feature recognition unit D m The number of 3*3 convolutional layers in the dense feature recognition block, X 3m In the feature recognition unit D m Channel attention coefficient of adaptive attention block in ; In the feature recognition unit D m In the example, the output data is the special signal sign data iterative feature T m , specific steps: Receive special signal sign data iterative features T1, T2, ..., T m-1 Perform feature recognition to obtain special signal sign data iterative feature T m ; Until all feature recognition units D are traversed m , obtain the special signal sign data characteristics; The specific steps of training the feature signal processing layer include: Collect several groups of special signal processing training samples, each of which contains target parameter matching factors and verified special signal sign data; combine several groups of special signal processing training samples to obtain a special signal processing training set; the target parameter matching factor is used to match the feature recognition unit D m Channel attention coefficient of adaptive attention block in ; The model is trained using a special signal processing training set to obtain an initial feature signal processing layer. The initial feature signal processing layer is model evaluated. If the initial feature signal processing layer passes the model evaluation, the initial feature signal processing layer is used as the feature signal processing layer in the preoperative assessment model of anesthesia depth. Otherwise, the model training is continued using the special signal processing training set.

4. The method for evaluating anesthesia depth based on patient vital sign data according to claim 3, characterized in that: The specific steps for evaluating the depth of anesthesia at the indicator evaluation level include: The depth of anesthesia is evaluated based on the improved TOPSIS method. Calculate the patient's real-time anesthesia depth assessment results; perform time slice data segmentation on the special signal sign data features and pre-processed basic sign data to obtain feature item data ; Where i is the basic time slice, i=1, 2, ..., I, I is the total time for obtaining the patient's preoperative vital sign data; J is the total number of feature items in the special signal vital sign data features and preprocessed basic vital sign data, j=1, 2, ..., J; Represents the characteristics of special signal vital sign data and the jth characteristic item data of the i-th basic time slice in the preprocessed basic vital sign data; Represents the feature item weighting factor.

5. The method for evaluating anesthesia depth based on patient vital sign data according to claim 4, characterized in that: The patient anesthesia metabolism assessment model includes a main feature extraction layer, an optimized metabolism prediction layer, and a postoperative result output layer; The main feature extraction layer is used to evaluate the real-time anesthesia depth of patients in the patient intraoperative anesthesia depth evaluation dataset G n Perform time series feature extraction to obtain the patient's real-time anesthesia depth assessment result feature G n 'and the time series characteristics of the patient's intraoperative anesthesia depth assessment data; The optimized metabolic prediction layer is used to calculate the time series characteristics of the patient's intraoperative anesthesia depth assessment data and the patient's real-time anesthesia depth assessment result characteristics G n 'And anesthesia metabolism regression function to optimize metabolic prediction analysis and obtain the patient's postoperative anesthesia depth assessment results; The postoperative result output layer is used to output the patient's postoperative anesthesia depth assessment results.

6. The method for evaluating anesthesia depth based on patient vital sign data according to claim 5, characterized in that: The specific steps of extracting temporal features in the main feature extraction layer include: The main feature extraction layer includes a multi-point data connection layer and a dynamic time feature extraction layer; Real-time anesthesia depth assessment results of patients in multi-point data connection layer G n Perform adaptive convolution feature extraction to obtain the patient's real-time anesthesia depth assessment result feature G n '; In the dynamic time feature extraction layer, the feature G is used to evaluate the real-time anesthesia depth of all patients. n 'Perform time series feature extraction to obtain the time series features of the patient's intraoperative anesthesia depth assessment data.

7. The method for evaluating anesthesia depth based on patient vital sign data according to claim 6, characterized in that: The specific steps to construct the anesthesia metabolism regression function include: An initial anesthesia metabolism regression function is constructed based on linear regression analysis and historical anesthesia metabolism data; the historical anesthesia metabolism data contains a historical anesthesia depth assessment data set of several patients; Construct K prediction function adjustment factor individuals H k , k=1, 2, ..., K; wherein each prediction function adjustment factor individual contains an adjustment strategy for adjusting the initial anesthesia metabolism regression function; K prediction function adjustment factor individuals H k Combination, to obtain the iterative population of the prediction function adjustment factor; set the number of iterations p, p = 1, 2, ..., P, P is the maximum number of iterations; Prediction function adjustment factor individual H k The fitness is S k , represents the prediction function adjustment factor individual H k Improvement in the predictive ability of the initial anesthesia metabolism regression function; In the process of population iteration, each time the prediction function adjustment factor iteration population is screened, the prediction function adjustment factor individuals with poor fitness and good fitness are called the crossover mutation prediction function adjustment factor population; Using the formula B(p)=B0*e -λp (1+β*avg(p)) controls the variability of the crossover prediction function to adjust the factor population; Among them, B(p) is the variability, B0 is the initial variability, λ is the decay rate, β is the factor that controls the influence of individual distribution on the variability, and avg(p) represents the mean fitness of the prediction function adjustment factor iteration population when the number of iterations is p; According to B(p), the cross-variation prediction function adjustment factor population is updated to obtain a new cross-variation prediction function adjustment factor population; the new cross-variation prediction function adjustment factor population is combined with the prediction function adjustment factor iteration population to obtain a new prediction function adjustment factor iteration population; the new prediction function adjustment factor iteration population is used to perform the next population iteration; When the maximum number of iterations is reached, the prediction function adjustment factor individual corresponding to the current maximum fitness is output, which is the optimal prediction function adjustment factor individual. The initial anesthesia metabolism regression function is optimized based on the optimal prediction function adjustment factor individual to obtain the anesthesia metabolism regression function.

8. An anesthesia depth assessment system based on patient vital sign data, characterized in that: The system applies a method for assessing anesthesia depth based on patient vital sign data as described in any one of claims 1 to 7, comprising: The anesthesia preoperative assessment module includes a data acquisition unit and a preoperative analysis unit; the data acquisition unit is used to obtain the patient's preoperative vital signs data; the patient's preoperative vital signs data contains preoperative basic data, preoperative vital signs data, preoperative professional examination data and patient real-time monitoring data; among them, the patient's real-time monitoring data is based on the patient's actual state changes; the preoperative analysis unit is used to analyze the patient's preoperative vital signs data and the anesthesia depth preoperative assessment model to obtain the patient's real-time anesthesia depth assessment result; the anesthesia depth preoperative assessment model combines data preprocessing, special signal processing, indicator evaluation and result output layer, and uses a densely connected convolutional network and an adaptive attention mechanism to effectively analyze the patient's preoperative vital signs data and accurately assess the anesthesia depth; The postoperative anesthesia assessment module includes a data acquisition unit and a postoperative analysis unit; the data acquisition unit is used to continuously collect the patient's real-time anesthesia depth assessment results during the patient's surgery to obtain the patient's intraoperative anesthesia depth assessment data set; the patient's intraoperative anesthesia depth assessment data set contains N patient real-time anesthesia depth assessment results G n , n=1, 2, …, N; the postoperative analysis unit is used to perform metabolic assessment prediction based on the patient's intraoperative anesthesia depth assessment data set and the patient's anesthesia metabolic assessment model to obtain the patient's postoperative anesthesia depth assessment result; the patient's anesthesia metabolic assessment model can accurately assess the patient's postoperative anesthesia depth through multi-level feature extraction and optimized metabolic prediction, combined with dynamic time series analysis and regression optimization; subsequent anesthesia management is performed based on the patient's postoperative anesthesia depth assessment result.

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