Anesthesia depth monitoring system and method based on skin electrical signal analysis

Abstract

The present invention discloses a system and method for monitoring the depth of anesthesia based on skin electrical signal analysis. The system includes a dynamic skin electrical analysis subsystem, a dynamic signal enhancement and adaptive noise reduction processing subsystem, a polymorphic fusion deep network subsystem, and a reinforcement learning-driven adaptive controller. The method is implemented based on the system. The present invention provides an accurate anesthesia depth monitoring solution through multimodal analysis of skin electrical signals; based on an adaptive drug adjustment algorithm and reinforcement learning, it realizes real-time adjustment of anesthesia depth to prevent anesthesia from being too deep or too shallow; combined with individual differences of patients, personalized anesthesia management is performed to improve anesthesia safety; compared with traditional electroencephalogram methods, the GSR signal acquisition method is non-invasive and more comfortable, which is convenient for long-term monitoring of patients; through innovative signal processing, deep learning and adaptive adjustment mechanisms, it can provide a more intelligent, accurate and personalized anesthesia depth monitoring solution.

Description

Technical Field

[0001] The present invention relates to the field of anesthesia depth monitoring, and in particular to an anesthesia depth monitoring system and method based on skin electrical signal analysis. Background Art

[0002] Depth of anesthesia refers to the degree to which a patient's consciousness, pain perception, and physiological functions are suppressed under the effects of anesthetic drugs. Traditional methods for monitoring depth of anesthesia rely primarily on clinical observation and physiological indicators such as heart rate and blood pressure. These methods lack direct assessment of brain function, resulting in inaccurate depth of anesthesia monitoring and potential safety risks.

[0003] Although existing technologies use technologies such as electroencephalogram (EEG) or bispectral index (BIS) to monitor the depth of anesthesia, the technology is expensive, the operation is complicated, and it causes great discomfort to the patient.

[0004] Therefore, there is an urgent need for a safer, more accurate and non-invasive anesthesia depth monitoring method and system. Summary of the Invention

[0005] In order to solve the above-described technical problems, the present invention proposes an anesthesia depth monitoring system and method based on skin electrical signal analysis.

[0006] Furthermore, an anesthesia depth monitoring system based on skin electrical signal analysis includes a neural dynamic skin electrical analysis subsystem, a dynamic signal enhancement and adaptive noise reduction processing subsystem, a polymorphic fusion deep network subsystem, and a reinforcement learning driven adaptive controller;

[0007] The neurodynamic skin electrodermal analysis subsystem uses a multi-dimensional dynamic analysis method based on skin electrodermal response, combining real-time changes and neural complexity to monitor the depth of anesthesia;

[0008] The dynamic signal enhancement and adaptive noise reduction processing subsystem removes noise and artifacts from the skin electrical signal and enhances the effective information of the signal;

[0009] The polymorphic fusion deep network subsystem combines convolutional neural networks and long short-term memory networks to extract high-precision features from multimodal signals and accurately assess the depth of anesthesia;

[0010] The reinforcement learning-driven adaptive controller ensures that the depth of anesthesia is maintained at an optimal state by dynamically adjusting the amount of anesthetic drug infusion.

[0011] Furthermore, an anesthesia depth monitoring system based on skin electrical signal analysis is provided, wherein the neurodynamic skin electrical analysis subsystem includes a skin electrical response dynamic sensitivity analysis module, a skin electrical signal complexity analysis module, and a neurodynamic index comprehensive analysis module;

[0012] The dynamic sensitivity analysis module of the skin electrical response quantifies the reaction speed of the sympathetic nervous system by calculating the instantaneous rate of change of skin conductance. The formula is:

[0013] ;

[0014] in, It represents the nerve sensitivity index and is used to capture changes in the activity of the sympathetic nervous system during anesthesia. Represents skin conductance at time intervals changes within;

[0015] The complexity analysis module of the skin electrical signal calculates the entropy value of the signal through a multi-scale method to reveal the nonlinear dynamics and complex patterns of anesthesia depth in the skin electrical signal. The formula is:

[0016] ;

[0017] in, represents the neural complexity entropy, represents the probability distribution of the skin electrical signal state, i represents the different states of the signal, and n represents the total number of states into which the signal is divided;

[0018] The neurodynamic index comprehensive analysis module combines NSI and NCE to calculate the neurodynamic index, which reflects the activity state of the patient's sympathetic nervous system and indirectly infers the depth of anesthesia. The formula is:

[0019] ;

[0020] in, represents the neural dynamic index, and Both represent weighting coefficients, which are adjusted according to different anesthesia stages to comprehensively reflect the changes in skin electrical signals.

[0021] Furthermore, in an anesthesia depth monitoring system based on skin electrical signal analysis, the dynamic signal enhancement and adaptive noise reduction processing subsystem includes an adaptive pattern enhancement module, a timing artifact removal module, and a frequency domain signal optimization module;

[0022] The adaptive mode enhancement module optimizes the signal characteristics through collaborative filtering based on the multi-dimensional signals collected in real time. The formula is:

[0023] ;

[0024] in, represents the optimized signal after the adaptive mode enhancement, represents the original signal, represents the noise component, α and β represent adjustment coefficients, which dynamically optimize and enhance the quality of the signal;

[0025] The temporal artifact removal module automatically corrects artifacts caused by patient activity or external interference by learning the temporal dependency of the signal. The formula is:

[0026] ;

[0027] in, represents the signal after removing artifacts, represents artifact components;

[0028] The frequency domain signal optimization module performs frequency domain analysis on the signal to remove invalid high-frequency noise while retaining low-frequency information to improve the overall quality of the signal. The formula is:

[0029] ;

[0030] described Represents the optimized signal at time t after frequency domain signal optimization, j represents the frequency component index in the frequency domain, and only considers the signal components within the effective frequency range. Represents the value of the original signal S(t) corresponding to the frequency component j, Represents the low-frequency range, which is the frequency domain interval of the effective signal.

[0031] Furthermore, an anesthesia depth monitoring system based on skin electrical signal analysis, the dynamic signal enhancement and adaptive noise reduction processing subsystem includes an input layer and data preprocessing module, a convolutional layer module, a long short-term memory network module, a fusion layer module, and a fully connected layer module;

[0032] The input layer and data preprocessing module receive multimodal data collected from different sensors and convert them into high-dimensional feature vectors after preprocessing. The formula is:

[0033] ;

[0034] Among them, HRV stands for heart rate variability, Resp stands for respiratory rate, Represents a high-dimensional feature vector;

[0035] The convolutional layer module learns local changes in the signal through the convolution kernel. The convolution formula is:

[0036] ;

[0037] in, Represents the output feature map of the lth layer of the convolutional neural network, represents the convolution kernel, represents the activation function, represents the output of the previous layer, represents the bias term, which is used to adjust the model;

[0038] The long short-term memory network module models the temporal changes of skin electrical signals and heart rate signals and captures the temporal characteristics of the signals. The formula is:

[0039] ;

[0040] in, Represents the cell state at the current moment, stands for the forget gate, represents the input gate, Represents the cell state at the previous moment, Represents the current candidate memory;

[0041] The fusion layer module combines the extracted local features and temporal features to obtain the final anesthesia depth assessment result;

[0042] The fully connected layer outputs the classification result of anesthesia depth;

[0043] The classification results include light anesthesia, deep anesthesia, and loss of consciousness.

[0044] Furthermore, in an anesthesia depth monitoring system based on skin electrical signal analysis, the reinforcement learning driven adaptive controller includes a reward function module, a drug regulation strategy module, and a reinforcement learning algorithm optimization module;

[0045] The reward function module provides feedback on the real-time assessment of the depth of anesthesia and the amount of drug adjustment. The formula is:

[0046] ;

[0047] in, represents the reward value at time step t, and Both represent adjustment coefficients, represents the adjusted amount of drug infusion, represents the target depth of anesthesia, Represents the current depth of anesthesia;

[0048] The drug adjustment strategy module adjusts the drug infusion volume in real time according to the difference between the current anesthesia depth and the target anesthesia depth. The formula is:

[0049] ;

[0050] in, represents the adjusted drug infusion volume, Represents the current drug infusion volume, that is, the amount of drug used by the system at the last moment or step. Represents the learning rate, which automatically adjusts the drug regulation amplitude;

[0051] The reinforcement learning algorithm optimization module selects the best drug regulation action according to the patient's anesthesia depth and physiological signals at each moment, optimizes the use of anesthetic drugs, and makes adjustments through feedback. Through reinforcement learning, it continuously interacts with the environment to learn the optimal drug regulation strategy.

[0052] Furthermore, an anesthesia depth monitoring system based on skin electrical signal analysis also includes a hardware subsystem and a software processing subsystem;

[0053] The hardware subsystem includes a skin electrode sensor, a heart rate sensor, and a respiratory rate sensor;

[0054] The galvanic skin sensor has a sampling frequency of 500Hz and is made of flexible electrodes made of conductive gel. The electrodes are placed on the patient's palms, soles, or chest and are adjusted according to the patient's body shape and anesthesia site.

[0055] The PPG sampling frequency of the heart rate sensor is 100Hz, and it uses multi-frequency infrared light source technology;

[0056] The respiratory rate sensor selects a micro airflow sensor or a pressure sensor to monitor the patient's respiratory rate in real time, with a sampling rate of 1 Hz, which is suitable for periodically changing respiratory signals;

[0057] The software processing subsystem includes a data acquisition module, a wireless transmission module, a data processing unit, a drug infusion module, and an automatic adjustment mechanism;

[0058] The data from the data acquisition module is transmitted to the data processing unit in real time via the wireless transmission module;

[0059] The wireless transmission module uses low-power Bluetooth technology to avoid signal loss or delay;

[0060] The data processing unit supports real-time multitasking and can synchronously receive data from multiple signal sources for parallel processing during anesthesia;

[0061] The drug infusion module includes an intelligent pump, a drug infusion pipeline, an infusion pressure sensor, and a flow sensor, and automatically adjusts the drug infusion volume according to the real-time assessment of the depth of anesthesia;

[0062] The automatic adjustment mechanism automatically adjusts the drug flow of the infusion module according to the deviation between the anesthesia depth and the target depth through a feedback control system, and customizes the drug infusion strategy through input parameters to ensure the anesthesia effect.

[0063] A method for monitoring anesthesia depth based on skin electrical signal analysis comprises the following steps:

[0064] S1: Signal preprocessing: Acquire physiological signals such as skin electrical signals, heart rate signals, and respiratory rate through sensors of the hardware subsystem;

[0065] S2: Signal denoising: Utilizes the multimodal signal collaborative filtering, temporal artifact removal, and frequency domain signal optimization technologies of the neural dynamic skin electrodermal analysis subsystem and the dynamic signal enhancement and adaptive noise reduction processing subsystem to eliminate noise and artifacts in the signal and retain valid information;

[0066] S3: Anesthesia Depth Assessment and Feature Extraction: Using deep learning technology and multimodal signal fusion strategies, the neural dynamic skin electrodermal analysis system is used to accurately assess the depth of anesthesia;

[0067] S4: Drug regulation and reinforcement learning: Reinforcement learning drives the adaptive controller to dynamically adjust the drug infusion volume to ensure that the depth of anesthesia is always maintained within the target range;

[0068] S41: Based on the deviation between the anesthesia depth and the target depth, the reward function module and the drug regulation strategy module adjust the effect of each drug infusion, automatically adjust the infusion volume, and minimize the anesthesia depth error in the reward function;

[0069] S5: Real-time monitoring and feedback mechanism: The patient's anesthesia depth is monitored in real time, and the drug infusion volume is adjusted promptly through the feedback mechanism. During the anesthesia process, the doctor views real-time data through a graphical interface and intervenes according to the system's suggestions. When the anesthesia depth exceeds the preset range, the doctor is reminded to intervene through sound alarms and graphical interface warnings, and the drug infusion is automatically adjusted to ensure patient safety.

[0070] S6: Clinical verification and system optimization: Continuously optimize the anesthesia depth assessment model through verification and feedback from clinical data.

[0071] Furthermore, a method for monitoring the depth of anesthesia based on skin electrical signal analysis, wherein S2 comprises the following steps:

[0072] S21: Separate the noise and valid signals of each sensor signal through multimodal signal collaborative filtering to improve signal quality;

[0073] S211: Set at a certain moment , the signals collected from a sensors are ,Multimodal signal collaborative filtering uses weighted averaging to combine the characteristics of each sensor signal to calculate the final collaborative filtering signal. The formula is:

[0074] ;

[0075] in, represents the final collaborative filtering signal, Representative The weight of the sensor signal, Automatically adjusts by calculating noise level, signal strength and correlation in real time, represents the signal obtained by the b-th sensor at time t, and a represents the total number of sensors participating in collaborative filtering in the system;

[0076] S212: Automatically adjust weights based on the signal-to-noise ratio of each signal through an adaptive learning process :

[0077] When the noise is strong, increase the filtering intensity to eliminate unnecessary noise;

[0078] When the signal quality is good, reduce the filtering intensity to maintain the original characteristics of the signal;

[0079] S22: Identify and remove artifacts by modeling time series signals;

[0080] S221: Set at time , the signal collected from the sensor is , the normal model established for historical data is , calculate the artifact component , the formula is:

[0081] ;

[0082] S222: Modeling the time series signal using a deep learning algorithm. By learning the time series characteristics of the signal, the system can identify and dynamically remove artifacts caused by movement, body position changes, etc. through the time series artifact removal module in real time, correct the signal, and optimize the time series artifact removal module through a feedback mechanism.

[0083] S23: Perform frequency domain analysis on the signal through the frequency domain signal optimization module to remove high-frequency noise and retain the low-frequency effective components;

[0084] S231: Perform fast Fourier transform on the signal to convert the signal from time domain to frequency domain. The formula is:

[0085] ;

[0086] in, represents the frequency domain representation of the signal, represents the Fourier transform operation;

[0087] S232: Design a bandpass filter to remove high-frequency noise components and retain only the low-frequency signal reflecting the depth of anesthesia. Convert the optimized frequency domain signal back to the time domain through inverse Fourier transform. The formula is:

[0088] ;

[0089] in, represents the frequency domain signal after removing high-frequency noise, represents the time domain signal after denoising;

[0090] S233: Based on the spectral characteristics of each signal, the frequency band of high-frequency noise is automatically identified, and filter parameters are dynamically adjusted to remove interference signals and retain useful physiological information;

[0091] S24: performing quality assessment on the final processed signal and optimizing the signal quality by using the signal-to-noise ratio;

[0092] S241: By calculating the signal-to-noise ratio , which measures the signal quality, is expressed as:

[0093] ;

[0094] Among them, Signal Power represents the effective component power of the signal, and Noise Power represents the power of the noise component;

[0095] S242: Denoised and optimized signal and As input for subsequent anesthesia depth assessment, the deep learning model performs feature extraction and classification, and the optimized data is transmitted to the doctor in real time to provide immediate feedback and monitoring.

[0096] Furthermore, a method for monitoring the depth of anesthesia based on skin electrical signal analysis, wherein S3 comprises the following steps:

[0097] S31: The dynamic sensitivity analysis module of the skin electrical response calculates the nerve sensitivity index in real time every second, and reflects the fluctuation of the anesthesia depth according to the change trend;

[0098] S32: Quantify the complexity of the signal through the neural complexity entropy of the skin electrodermal signal complexity analysis module, infer the impact of anesthesia on the sympathetic nervous system, and provide real-time feedback;

[0099] S33: The neurodynamic index of the neurodynamic index comprehensive analysis module is used as a comprehensive evaluation indicator of anesthesia depth, which is automatically adjusted according to the patient's real-time physiological changes to help anesthesiologists accurately assess anesthesia depth;

[0100] S34: Extract local features of skin electrodermal signals and heart rate signals through convolutional neural networks, and model temporal dependencies through long short-term memory networks to capture the changes in anesthesia depth over time;

[0101] S35: The output features of the convolutional neural network and the long short-term memory network are integrated to generate an assessment result of the depth of anesthesia, which is dynamically adjusted based on the deep learning algorithm.

[0102] The beneficial effects of the present invention are: a system and method for monitoring the depth of anesthesia based on skin electrical signal analysis, which provides an accurate anesthesia depth monitoring solution through multimodal analysis of skin electrical signals; based on an adaptive drug adjustment algorithm and reinforcement learning, it realizes real-time adjustment of the anesthesia depth to prevent excessive or shallow anesthesia; combined with individual differences of patients, personalized anesthesia management is carried out to improve anesthesia safety; compared with traditional EEG methods, the GSR signal acquisition method is non-invasive and more comfortable, which is convenient for long-term monitoring of patients; through innovative signal processing, deep learning and adaptive adjustment mechanisms, it can provide a more intelligent, accurate and personalized anesthesia depth monitoring solution, providing new technical support for clinical anesthesia management. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 This is a structural diagram of an anesthesia depth monitoring system based on skin electrical signal analysis.

[0104] Figure 2 It is a flow chart of an anesthesia depth monitoring method based on skin electrodermal signal analysis. DETAILED DESCRIPTION

[0105] The present invention is further described below, but the protection scope of the present invention is not limited to the following description.

[0106] As attached Figure 1 As shown, an anesthesia depth monitoring system based on skin electrical signal analysis includes a neural dynamic skin electrical analysis subsystem, a dynamic signal enhancement and adaptive noise reduction processing subsystem, a polymorphic fusion deep network subsystem, and a reinforcement learning driven adaptive controller;

[0107] The neurodynamic skin electrodermal analysis subsystem uses a multi-dimensional dynamic analysis method based on skin electrodermal response, combining real-time changes and neural complexity to monitor the depth of anesthesia;

[0108] The neurodynamic skin electrodermal analysis subsystem is based on the neurodynamic skin electrodermal analysis method (NDSECA). By combining the analysis of the galvanic skin response (GSR) with the dynamic changes of the nervous system, it directly reflects the depth of anesthesia. The neurodynamic skin electrodermal analysis method (NDSECA) can not only capture the instantaneous changes in the skin electrodermal signal, but also deeply explore its dynamic response of the nervous system, providing a new technical path for the accurate monitoring of the depth of anesthesia.

[0109] The dynamic signal enhancement and adaptive noise reduction processing subsystem removes noise and artifacts from the skin electrical signal and enhances the effective information of the signal;

[0110] The dynamic signal enhancement and adaptive noise reduction processing subsystem is based on a dynamic signal enhancement and adaptive noise reduction processing algorithm DSE-ADA, which combines the ideas of adaptive signal enhancement, time series pattern self-learning and multi-domain optimization.

[0111] The polymorphic fusion deep network subsystem combines convolutional neural networks and long short-term memory networks to extract high-precision features from multimodal signals and accurately assess the depth of anesthesia, achieving the dual advantages of local feature extraction and temporal dependency modeling in a deep learning architecture.

[0112] The reinforcement learning-driven adaptive controller ensures that the depth of anesthesia is maintained at an optimal state by dynamically adjusting the amount of anesthetic drug infusion.

[0113] Furthermore, an anesthesia depth monitoring system based on skin electrical signal analysis is provided, wherein the neurodynamic skin electrical analysis subsystem includes a skin electrical response dynamic sensitivity analysis module, a skin electrical signal complexity analysis module, and a neurodynamic index comprehensive analysis module;

[0114] The dynamic sensitivity analysis module of the skin electrical response quantifies the reaction speed of the sympathetic nervous system by calculating the instantaneous rate of change of skin conductance. The formula is:

[0115] ;

[0116] in, It represents the nerve sensitivity index and is used to capture changes in the activity of the sympathetic nervous system during anesthesia. Represents skin conductance at time intervals changes within;

[0117] Among them, the skin electrode response (SCR) is controlled by the activity of the sympathetic nervous system and therefore reflects the changes in the depth of anesthesia;

[0118] The complexity analysis module of the skin electrical signal calculates the entropy value of the signal through a multi-scale method to reveal the nonlinear dynamics and complex patterns of anesthesia depth in the skin electrical signal. The formula is:

[0119] ;

[0120] in, represents the neural complexity entropy, represents the probability distribution of the skin electrical signal state, i represents the different states of the signal, and n represents the total number of states the signal is divided into. For example, if the signal is divided into 5 different amplitude ranges, then n=5;

[0121] Among them, neural complexity entropy, based on the Nonlinear Dynamic Mode (NDM) analysis method, is used to quantify the complexity of skin electrical signals. Skin electrical signals are highly nonlinear and self-similar, and can reflect the complex neural activities during anesthesia.

[0122] The neurodynamic index comprehensive analysis module combines NSI and NCE to calculate the neurodynamic index, which reflects the activity state of the patient's sympathetic nervous system and indirectly infers the depth of anesthesia. The formula is:

[0123] ;

[0124] in, represents the neural dynamic index, Both represent weighting coefficients, which are adjusted according to different anesthesia stages to comprehensively reflect the changes in skin electrical signals.

[0125] Furthermore, in an anesthesia depth monitoring system based on skin electrical signal analysis, the dynamic signal enhancement and adaptive noise reduction processing subsystem includes an adaptive pattern enhancement module, a timing artifact removal module, and a frequency domain signal optimization module;

[0126] The adaptive mode enhancement module optimizes the signal characteristics through collaborative filtering based on the multi-dimensional signals collected in real time. The formula is:

[0127] ;

[0128] in, represents the optimized signal after the adaptive mode enhancement, represents the original signal, represents the noise component, α and β represent adjustment coefficients, which dynamically optimize and enhance the quality of the signal;

[0129] The adaptive pattern enhancement module uses the Multi-modal Collaborative Filtering (MMCF) algorithm in the early stages of signal processing to automatically identify and enhance effective signal patterns by analyzing the timing characteristics of the signal and external environmental noise. This is based on multi-dimensional signals collected in real time, such as skin conductance, heart rate, and respiratory rate.

[0130] The temporal artifact removal module automatically corrects artifacts caused by patient activity or external interference by learning the temporal dependency of the signal. The formula is:

[0131] ;

[0132] in, represents the signal after removing artifacts, represents artifact components;

[0133] In the frequency domain, the noise components of the signal are mainly concentrated in the high-frequency area, while the useful information is usually concentrated in the low-frequency area;

[0134] The frequency domain signal optimization module performs frequency domain analysis on the signal to remove invalid high-frequency noise while retaining low-frequency information to improve the overall quality of the signal. The formula is:

[0135] ;

[0136] described Represents the optimized signal at time t after frequency domain signal optimization, j represents the frequency component index in the frequency domain, and only considers the signal components within the effective frequency range. Represents the original signal The value corresponding to the frequency component j, Represents the low-frequency range, which is the frequency domain interval of the effective signal.

[0137] Furthermore, an anesthesia depth monitoring system based on skin electrical signal analysis, the dynamic signal enhancement and adaptive noise reduction processing subsystem includes an input layer and data preprocessing module, a convolutional layer module CNN, a long short-term memory network module LSTM, a fusion layer module Fusion Layer, and a fully connected layer module;

[0138] The input layer and data preprocessing module receive multimodal data collected from different sensors and convert them into high-dimensional feature vectors after preprocessing. The formula is:

[0139] ;

[0140] Among them, HRV stands for heart rate variability, Resp stands for respiratory rate, Represents a high-dimensional feature vector;

[0141] The convolutional layer module learns local changes in the signal through the convolution kernel. The convolution formula is:

[0142] ;

[0143] in, Represents the output feature map of the lth layer of the convolutional neural network, represents the convolution kernel, represents the activation function, represents the output of the previous layer, represents the bias term, which is used to adjust the model;

[0144] Among them, the convolutional layer module CNN is used to extract the local features of each signal, especially the dynamic patterns in the skin electrode signal GSR and heart rate signal;

[0145] The long short-term memory network module models the temporal changes of skin electrical signals and heart rate signals and captures the temporal characteristics of the signals. The formula is:

[0146] ;

[0147] in, Represents the cell state at the current moment, stands for the forget gate, represents the input gate, Represents the cell state at the previous moment, Represents the current candidate memory;

[0148] The fusion layer module combines the extracted local features and temporal features to obtain the final anesthesia depth assessment result;

[0149] The fully connected layer outputs the classification result of anesthesia depth;

[0150] The classification results include light anesthesia, deep anesthesia, and loss of consciousness.

[0151] Furthermore, in an anesthesia depth monitoring system based on skin electrical signal analysis, the reinforcement learning driven adaptive controller includes a reward function module, a drug regulation strategy module, and a reinforcement learning algorithm optimization module;

[0152] The reward function module provides feedback on the real-time assessment of the depth of anesthesia and the amount of drug adjustment. The formula is:

[0153] ;

[0154] in, represents the reward value at time step t, and Both represent adjustment coefficients, represents the adjusted amount of drug infusion, represents the target depth of anesthesia, Represents the current depth of anesthesia;

[0155] The drug adjustment strategy module adjusts the drug infusion volume in real time according to the difference between the current anesthesia depth and the target anesthesia depth. The formula is:

[0156] ;

[0157] in, represents the adjusted drug infusion volume, Represents the current drug infusion volume, that is, the amount of drug used by the system at the last moment or step. Represents the learning rate, which automatically adjusts the drug regulation amplitude;

[0158] The reinforcement learning algorithm optimization module selects the best drug regulation action according to the patient's anesthesia depth and physiological signals at each moment, optimizes the use of anesthetic drugs, and makes adjustments through feedback. Through reinforcement learning, it continuously interacts with the environment to learn the optimal drug regulation strategy.

[0159] Furthermore, an anesthesia depth monitoring system based on skin electrical signal analysis also includes a hardware subsystem and a software processing subsystem;

[0160] The hardware subsystem includes a skin electrode sensor, a heart rate sensor, and a respiratory rate sensor;

[0161] The galvanic skin sensor, or GSR sensor, uses flexible electrode materials to improve contact stability with the skin and reduce the impact of motion artifacts. The electrodes are designed with conductive gel to reduce contact impedance with the skin and ensure stable signal acquisition. The sampling frequency is set to 500Hz to ensure that changes in the skin's electrical signals can be captured in a timely manner to reflect the dynamic response of the sympathetic nervous system. This frequency can accurately capture short-term fluctuations in the sympathetic nerves and adapt to rapid changes during anesthesia. The electrodes are placed on the patient's palms, soles of the feet, or chest, and can be adjusted according to different patient sizes and anesthesia sites.

[0162] The heart rate sensor, namely PPG technology, uses multi-frequency infrared light source LED technology to enhance its ability to sense blood flow changes and reduce the interference of ambient light on data collection. The amplitude of the PPG signal is closely related to the volume changes of blood flow and can accurately monitor heart rate. The sampling frequency of the PPG sensor is 100Hz, ensuring real-time monitoring of heart rate changes and fusion analysis with skin electrical signals.

[0163] The respiratory rate sensor selects a micro airflow sensor or a pressure sensor to monitor the patient's respiratory rate in real time with a sampling rate of 1 Hz, which is suitable for periodically changing respiratory signals and accurately assesses the effects of anesthesia based on the relationship between the patient's respiratory rate and the depth of anesthesia;

[0164] The software processing subsystem includes a data acquisition module, a wireless transmission module, a data processing unit, a drug infusion module, and an automatic adjustment mechanism;

[0165] The data from the data acquisition module is transmitted to the data processing unit in real time via the wireless transmission module;

[0166] The wireless transmission module uses low-power Bluetooth technology to avoid signal loss or delay;

[0167] The data processing unit supports real-time multitasking and can synchronously receive data from multiple signal sources for parallel processing during anesthesia;

[0168] The drug infusion module includes an intelligent pump, a drug infusion pipeline, an infusion pressure sensor, and a flow sensor, and automatically adjusts the drug infusion volume according to the real-time assessment of the depth of anesthesia;

[0169] The automatic adjustment mechanism automatically adjusts the drug flow rate of the infusion module according to the deviation between the anesthesia depth and the target depth through a feedback control system, and customizes the drug infusion strategy through input parameters to ensure the anesthesia effect;

[0170] The input parameters include weight, age, type of surgery, etc.

[0171] As attached Figure 2 As shown, a method for monitoring the depth of anesthesia based on skin electrical signal analysis includes the following steps:

[0172] S1: Signal preprocessing: Acquire physiological signals such as skin electrical signals, heart rate signals, and respiratory rate through sensors of the hardware subsystem;

[0173] Among them, the signal is usually affected by various noise sources, such as motion artifacts, electromagnetic interference, etc., so the signal must be effectively preprocessed to ensure the accuracy and stability of subsequent anesthesia depth assessment;

[0174] S2: Signal denoising: Utilizes the multimodal signal collaborative filtering, temporal artifact removal, and frequency domain signal optimization technologies of the neural dynamic skin electrodermal analysis subsystem and the dynamic signal enhancement and adaptive noise reduction processing subsystem to eliminate noise and artifacts in the signal and retain valid information;

[0175] S3: Anesthesia Depth Assessment and Feature Extraction: Using deep learning technology and multimodal signal fusion strategies, the neural dynamic skin electrodermal analysis system is used to accurately assess the depth of anesthesia;

[0176] S4: Drug regulation and reinforcement learning: Reinforcement learning drives the adaptive controller to dynamically adjust the drug infusion volume to ensure that the depth of anesthesia is always maintained within the target range;

[0177] S41: Based on the deviation between the anesthesia depth and the target depth, the reward function module and the drug regulation strategy module adjust the effect of each drug infusion, automatically adjust the infusion volume, and minimize the anesthesia depth error in the reward function;

[0178] S5: Real-time monitoring and feedback mechanism: The patient's anesthesia depth is monitored in real time, and the drug infusion volume is adjusted promptly through the feedback mechanism. During the anesthesia process, the doctor views real-time data through a graphical interface and intervenes according to the system's suggestions. When the anesthesia depth exceeds the preset range, the doctor is reminded to intervene through sound alarms and graphical interface warnings, and the drug infusion is automatically adjusted to ensure patient safety.

[0179] S6: Clinical verification and system optimization: Continuously optimize the anesthesia depth assessment model through verification and feedback from clinical data.

[0180] Furthermore, a method for monitoring the depth of anesthesia based on skin electrical signal analysis, wherein S2 comprises the following steps:

[0181] S21: Separate the noise and valid signal of each sensor signal through multimodal signal collaborative filtering to improve signal quality;

[0182] During anesthesia, physiological signals such as skin conductance signals, heart rate, and respiratory rate show a certain correlation as the depth of anesthesia changes. For example, when the depth of anesthesia is deep, the patient's sympathetic nerve activity will change significantly, and the skin conductance signals and heart rate signals will often fluctuate synchronously. Therefore, multimodal signal collaborative filtering can filter multiple signal sources through weighted averaging, thereby optimizing signal quality.

[0183] S211: Set at a certain moment , the signals collected from a sensors are ,Multimodal signal collaborative filtering uses weighted averaging to combine the characteristics of each sensor signal to calculate the final collaborative filtering signal. The formula is:

[0184] ;

[0185] in, represents the final collaborative filtering signal, Representative The weight of the sensor signal, Automatically adjusts by calculating noise level, signal strength and correlation in real time, represents the signal obtained by the b-th sensor at time t, and a represents the total number of sensors participating in collaborative filtering in the system;

[0186] S212: Automatically adjust weights based on the signal-to-noise ratio of each signal through an adaptive learning process :

[0187] When the noise is strong, increase the filtering intensity to eliminate unnecessary noise;

[0188] When the signal quality is good, reduce the filtering intensity to maintain the original characteristics of the signal;

[0189] It can also be understood as dynamically optimizing the enhanced signal by adjusting the coefficients of the original signal S(t) and the noise component N(t) quality;

[0190] S22: Identify and remove artifacts by modeling time series signals;

[0191] Artifacts are usually caused by changes in patient position, movement, or poor sensor contact. Traditional artifact removal methods are usually based on threshold correction, which sometimes mistakenly removes valid signals.

[0192] S221: Set at time , the signal collected from the sensor is , the normal model established for historical data is , calculate the artifact component , the formula is:

[0193] ;

[0194] S222: Modeling the time series signal using a deep learning algorithm. By learning the time series characteristics of the signal, the system can identify and dynamically remove artifacts caused by movement, body position changes, etc. through the time series artifact removal module in real time, correct the signal, and optimize the time series artifact removal module through a feedback mechanism.

[0195] S23: Perform frequency domain analysis on the signal through the frequency domain signal optimization module to remove high-frequency noise and retain the low-frequency effective components;

[0196] S231: Perform fast Fourier transform on the signal to convert the signal from time domain to frequency domain. The formula is:

[0197] ;

[0198] in, represents the frequency domain representation of the signal, represents the Fourier transform operation;

[0199] S232: Design a bandpass filter to remove high-frequency noise components and retain only the low-frequency signal reflecting the depth of anesthesia. Convert the optimized frequency domain signal back to the time domain through inverse Fourier transform. The formula is:

[0200] ;

[0201] in, represents the frequency domain signal after removing high-frequency noise, represents the time domain signal after denoising;

[0202] S233: Based on the spectral characteristics of each signal, the frequency band of high-frequency noise is automatically identified, and filter parameters are dynamically adjusted to remove interference signals and retain useful physiological information;

[0203] S24: performing quality assessment on the final processed signal and optimizing the signal quality by using the signal-to-noise ratio;

[0204] S241: By calculating the signal-to-noise ratio , which measures the signal quality, is expressed as:

[0205] ;

[0206] Among them, Signal Power represents the effective component power of the signal, and Noise Power represents the power of the noise component;

[0207] S242: Denoised and optimized signal and As input for subsequent anesthesia depth assessment, the deep learning model performs feature extraction and classification, and the optimized data is transmitted to the doctor in real time to provide immediate feedback and monitoring.

[0208] Furthermore, a method for monitoring the depth of anesthesia based on skin electrical signal analysis, wherein S3 comprises the following steps:

[0209] S31: The dynamic sensitivity analysis module of the skin electrical response calculates the nerve sensitivity index in real time every second, and reflects the fluctuation of the anesthesia depth according to the change trend;

[0210] S32: Quantify the complexity of the signal through the neural complexity entropy of the skin electrodermal signal complexity analysis module, infer the impact of anesthesia on the sympathetic nervous system, and provide real-time feedback;

[0211] S33: The neurodynamic index of the neurodynamic index comprehensive analysis module is used as a comprehensive evaluation indicator of anesthesia depth, which is automatically adjusted according to the patient's real-time physiological changes to help anesthesiologists accurately assess anesthesia depth;

[0212] S34: Extract local features of skin electrodermal signals and heart rate signals through convolutional neural networks, and model temporal dependencies through long short-term memory networks to capture the changes in anesthesia depth over time;

[0213] S35: The output features of the convolutional neural network and the long short-term memory network are integrated to generate an assessment result of the depth of anesthesia, which is dynamically adjusted based on the deep learning algorithm.

[0214] The benefits of the present invention are:

[0215] 1. Improving the accuracy and real-time performance of anesthesia depth monitoring: Traditional anesthesia depth monitoring methods rely on physiological indicators such as heart rate, blood pressure, or electroencephalogram (EEG), which have certain limitations. By introducing GSR as an objective physiological signal, combined with advanced signal processing and feature extraction algorithms, more accurate anesthesia depth assessment results can be obtained in real-time monitoring, thereby reducing uncertainty during clinical anesthesia.

[0216] 2. Personalized anesthesia management: Through comprehensive analysis of multimodal physiological signals, such as galvanic skin signals, heart rate variability, and respiratory rate, the anesthetic drug dosage can be dynamically adjusted based on individual patient differences and real-time changes in anesthesia depth, ensuring that anesthesia depth is maintained within the safest and most effective range, reducing the risk of drug overdose or underdose.

[0217] 3. Improving the non-invasiveness and comfort of anesthesia depth monitoring: Compared to traditional EEG monitoring methods, the use of GSR monitoring is non-invasive, convenient, and does not require complex equipment, avoiding the discomfort caused by EEG monitoring for patients. It also offers greater portability and ease of use, making it suitable for various clinical scenarios.

[0218] 4. Reduce anesthesia risks and ensure surgical safety: By establishing a precise anesthesia depth assessment model and combining it with adaptive adjustment technology, it can promptly detect changes in anesthesia depth, avoid excessive or shallow anesthesia, ensure patient safety during surgery, and effectively reduce the occurrence of anesthesia-related complications;

[0219] 5. Optimize the use of anesthetic drugs and save medical resources: The real-time feedback mechanism helps anesthesiologists adjust drug use in a timely manner to ensure that the depth of anesthesia remains within the appropriate range, avoid excessive use of anesthetic drugs, reduce drug waste and side effects, and improve the efficiency of anesthetic drug use.

[0220] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An anesthesia depth monitoring system based on skin electrical signal analysis, characterized in that: Including neural dynamic skin electricity analysis subsystem, dynamic signal enhancement and adaptive noise reduction processing subsystem, polymorphic fusion deep network subsystem, reinforcement learning driven adaptive controller; The neurodynamic skin electrodermal analysis subsystem uses a multi-dimensional dynamic analysis method based on skin electrodermal response, combining real-time changes and neural complexity to monitor the depth of anesthesia; The dynamic signal enhancement and adaptive noise reduction processing subsystem removes noise and artifacts from the skin electrical signal and enhances the effective information of the signal; The polymorphic fusion deep network subsystem combines convolutional neural networks and long short-term memory networks to extract high-precision features from multimodal signals and accurately assess the depth of anesthesia; The reinforcement learning-driven adaptive controller dynamically adjusts the anesthetic drug infusion volume to ensure that the anesthesia depth is maintained at an optimal state; The dynamic signal enhancement and adaptive noise reduction processing subsystem includes an input layer and data preprocessing module, a convolutional layer module, a long short-term memory network module, a fusion layer module, and a fully connected layer module; The input layer and data preprocessing module receive multimodal data collected from different sensors and convert them into high-dimensional feature vectors after preprocessing. The formula is: ； in, It represents the nerve sensitivity index and is used to capture changes in the activity of the sympathetic nervous system during anesthesia. represents neural complexity entropy, HRV represents heart rate variability, and Resp represents respiratory rate. Represents a high-dimensional feature vector; The convolutional layer module learns local changes in the signal through the convolution kernel. The convolution formula is: ； in, Represents the output feature map of the lth layer of the convolutional neural network, represents the convolution kernel, represents the activation function, Represents the output of the previous layer, represents the bias term, which is used to adjust the model; The long short-term memory network module models the temporal changes of skin electrical signals and heart rate signals and captures the temporal characteristics of the signals. The formula is: ； in, Represents the cell state at the current moment, stands for the forget gate, represents the input gate, Represents the cell state at the previous moment, Represents the current candidate memory; The fusion layer module combines the extracted local features and temporal features to obtain the final anesthesia depth assessment result; The fully connected layer outputs the classification result of anesthesia depth; The classification results include light anesthesia, deep anesthesia, and loss of consciousness.

2. The anesthesia depth monitoring system based on skin electrical signal analysis according to claim 1, characterized in that: The neural dynamic skin electrode analysis subsystem includes a skin electrode response dynamic sensitivity analysis module, a skin electrode signal complexity analysis module, and a neural dynamic index comprehensive analysis module; The dynamic sensitivity analysis module of the skin electrical response quantifies the reaction speed of the sympathetic nervous system by calculating the instantaneous rate of change of skin conductance. The formula is: ； in, Represents skin conductance at time intervals changes within; The complexity analysis module of the skin electrical signal calculates the entropy value of the signal through a multi-scale method to reveal the nonlinear dynamics and complex patterns of anesthesia depth in the skin electrical signal. The formula is: ； in, represents the probability distribution of the skin electrical signal state, i represents the different states of the signal, and n represents the total number of states into which the signal is divided; The neurodynamic index comprehensive analysis module combines NSI and NCE to calculate the neurodynamic index, which reflects the activity state of the patient's sympathetic nervous system and indirectly infers the depth of anesthesia. The formula is: ； in, represents the neural dynamic index, and Both represent weighting coefficients, which are adjusted according to different anesthesia stages to comprehensively reflect the changes in skin electrical signals.

3. The anesthesia depth monitoring system based on skin electrical signal analysis according to claim 1, characterized in that: The dynamic signal enhancement and adaptive noise reduction processing subsystem includes an adaptive pattern enhancement module, a timing artifact removal module, and a frequency domain signal optimization module; The adaptive mode enhancement module optimizes the signal characteristics through collaborative filtering based on the multi-dimensional signals collected in real time. The formula is: ； in, represents the optimized signal after the adaptive mode enhancement, represents the original signal, represents the noise component, α and β represent adjustment coefficients, which dynamically optimize and enhance the quality of the signal; The temporal artifact removal module automatically corrects artifacts caused by patient activity or external interference by learning the temporal dependency of the signal. The formula is: ； in, represents the signal after removing artifacts, represents artifact components; The frequency domain signal optimization module performs frequency domain analysis on the signal to remove invalid high-frequency noise while retaining low-frequency information to improve the overall quality of the signal. The formula is: ； described Represents the optimized signal at time t after frequency domain signal optimization, j represents the frequency component index in the frequency domain, and only considers the signal components within the effective frequency range. Represents the value of the original signal S(t) corresponding to the frequency component j, Represents the low-frequency range, which is the frequency domain interval of the effective signal.

4. The anesthesia depth monitoring system based on skin electrical signal analysis according to claim 1, characterized in that: The reinforcement learning driven adaptive controller includes a reward function module, a drug regulation strategy module, and a reinforcement learning algorithm optimization module; The reward function module provides feedback on the real-time assessment of the depth of anesthesia and the amount of drug adjustment. The formula is: ； in, represents the reward value at time step t, and Both represent adjustment coefficients, represents the adjusted amount of drug infusion, represents the target depth of anesthesia, Represents the current depth of anesthesia; The drug adjustment strategy module adjusts the drug infusion volume in real time according to the difference between the current anesthesia depth and the target anesthesia depth. The formula is: ； in, represents the adjusted drug infusion volume, Represents the current drug infusion volume, that is, the amount of drug used by the system at the last moment or step. Represents the learning rate, which automatically adjusts the drug regulation amplitude; The reinforcement learning algorithm optimization module selects the best drug regulation action according to the patient's anesthesia depth and physiological signals at each moment, optimizes the use of anesthetic drugs, and makes adjustments through feedback. Through reinforcement learning, it continuously interacts with the environment to learn the optimal drug regulation strategy.

5. The anesthesia depth monitoring system based on skin electrical signal analysis according to claim 1, characterized in that: It also includes hardware subsystems and software processing subsystems; The hardware subsystem includes a skin electrode sensor, a heart rate sensor, and a respiratory rate sensor; The galvanic skin sensor has a sampling frequency of 500Hz and is made of flexible electrodes made of conductive gel. The electrodes are placed on the patient's palms, soles, or chest and are adjusted according to the patient's body shape and anesthesia site. The PPG sampling frequency of the heart rate sensor is 100Hz, and it uses multi-frequency infrared light source technology; The respiratory rate sensor selects a micro airflow sensor or a pressure sensor to monitor the patient's respiratory rate in real time, with a sampling rate of 1 Hz, which is suitable for periodically changing respiratory signals; The software processing subsystem includes a data acquisition module, a wireless transmission module, a data processing unit, a drug infusion module, and an automatic adjustment mechanism; The data from the data acquisition module is transmitted to the data processing unit in real time via the wireless transmission module; The wireless transmission module uses low-power Bluetooth technology to avoid signal loss or delay; The data processing unit supports real-time multitasking and can synchronously receive data from multiple signal sources for parallel processing during anesthesia; The drug infusion module includes an intelligent pump, a drug infusion pipeline, an infusion pressure sensor, and a flow sensor, and automatically adjusts the drug infusion volume according to the real-time assessment of the depth of anesthesia; The automatic adjustment mechanism automatically adjusts the drug flow of the infusion module according to the deviation between the anesthesia depth and the target depth through a feedback control system, and customizes the drug infusion strategy through input parameters to ensure the anesthesia effect.

6. A method for monitoring depth of anesthesia based on skin electrical signal analysis, implemented by the system for monitoring depth of anesthesia based on skin electrical signal analysis according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1: Signal preprocessing: Acquire physiological signals such as skin electrical signals, heart rate signals, and respiratory rate through sensors of the hardware subsystem; S2: Signal denoising: Utilizes the multimodal signal collaborative filtering, temporal artifact removal, and frequency domain signal optimization technologies of the neural dynamic skin electrodermal analysis subsystem and the dynamic signal enhancement and adaptive noise reduction processing subsystem to eliminate noise and artifacts in the signal and retain valid information; S3: Anesthesia Depth Assessment and Feature Extraction: Using deep learning technology and multimodal signal fusion strategies, the neural dynamic skin electrodermal analysis system is used to accurately assess the depth of anesthesia; S4: Drug regulation and reinforcement learning: Reinforcement learning drives the adaptive controller to dynamically adjust the drug infusion volume to ensure that the depth of anesthesia is always maintained within the target range; S41: Based on the deviation between the anesthesia depth and the target depth, the reward function module and the drug regulation strategy module adjust the effect of each drug infusion, automatically adjust the infusion volume, and minimize the anesthesia depth error in the reward function; S5: Real-time monitoring and feedback mechanism: The patient's anesthesia depth is monitored in real time, and the drug infusion volume is adjusted promptly through the feedback mechanism. During the anesthesia process, the doctor views real-time data through a graphical interface and intervenes according to the system's suggestions. When the anesthesia depth exceeds the preset range, the doctor is reminded to intervene through sound alarms and graphical interface warnings, and the drug infusion is automatically adjusted to ensure patient safety. S6: Clinical verification and system optimization: Continuously optimize the anesthesia depth assessment model through verification and feedback from clinical data.

7. The method for monitoring the depth of anesthesia based on skin electrical signal analysis according to claim 6, characterized in that: The S2 comprises the following steps: S21: Separate the noise and valid signals of each sensor signal through multimodal signal collaborative filtering to improve signal quality; S211: Set at a certain moment , the signals collected from a sensors are ,Multimodal signal collaborative filtering uses weighted averaging to combine the characteristics of each sensor signal to calculate the final collaborative filtering signal. The formula is: ； in, represents the final collaborative filtering signal, Representative The weight of the sensor signal, Automatically adjusts by calculating noise level, signal strength and correlation in real time, represents the signal obtained by the b-th sensor at time t, and a represents the total number of sensors participating in collaborative filtering in the system; S212: Automatically adjust weights based on the signal-to-noise ratio of each signal through an adaptive learning process : When the noise is strong, increase the filtering intensity to eliminate unnecessary noise; When the signal quality is good, reduce the filtering intensity to maintain the original characteristics of the signal; S22: Identify and remove artifacts by modeling time series signals; S221: Set at time , the signal collected from the sensor is , the normal model established for historical data is , calculate the artifact component , the formula is: ； S222: Modeling the time series signal using a deep learning algorithm. By learning the time series characteristics of the signal, the artifacts caused by the artifacts are identified in real time and dynamically removed through the time series artifact removal module. The signal is corrected and the time series artifact removal module is optimized through a feedback mechanism. S23: Perform frequency domain analysis on the signal through the frequency domain signal optimization module to remove high-frequency noise and retain the low-frequency effective components; S231: Perform fast Fourier transform on the signal to convert the signal from time domain to frequency domain. The formula is: ； in, represents the frequency domain representation of the signal, represents the Fourier transform operation; S232: Design a bandpass filter to remove high-frequency noise components and retain only the low-frequency signal reflecting the depth of anesthesia. Convert the optimized frequency domain signal back to the time domain through inverse Fourier transform. The formula is: ； in, represents the frequency domain signal after removing high-frequency noise, represents the time domain signal after denoising; S233: Based on the spectral characteristics of each signal, the frequency band of high-frequency noise is automatically identified, and filter parameters are dynamically adjusted to remove interference signals and retain useful physiological information; S24: performing quality assessment on the final processed signal and optimizing the signal quality by signal-to-noise ratio; S241: By calculating the signal-to-noise ratio , which measures the signal quality, is expressed as: ； Among them, Signal Power represents the power of the effective component of the signal, and Noise Power represents the power of the noise component; S242: Denoised and optimized signal and As input for subsequent anesthesia depth assessment, the deep learning model performs feature extraction and classification, and the optimized data is transmitted to the doctor in real time to provide immediate feedback and monitoring.

8. The method for monitoring the depth of anesthesia based on skin electrical signal analysis according to claim 6, characterized in that: The S3 includes the following steps: S31: The dynamic sensitivity analysis module of the skin electrical response calculates the nerve sensitivity index in real time every second, and reflects the fluctuation of the anesthesia depth according to the change trend; S32: Quantify the complexity of the signal through the neural complexity entropy of the skin electrodermal signal complexity analysis module, infer the impact of anesthesia on the sympathetic nervous system, and provide real-time feedback; S33: The neurodynamic index of the neurodynamic index comprehensive analysis module is used as a comprehensive evaluation indicator of anesthesia depth, which is automatically adjusted according to the patient's real-time physiological changes to help anesthesiologists accurately assess anesthesia depth; S34: Extract local features of skin electrodermal signals and heart rate signals through convolutional neural networks, and model temporal dependencies through long short-term memory networks to capture the changes in anesthesia depth over time; S35: The output features of the convolutional neural network and the long short-term memory network are integrated to generate an assessment result of the depth of anesthesia, which is dynamically adjusted based on the deep learning algorithm.

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