Anesthesia and depth of consciousness monitoring system

Abstract

The application provides a narcosis and consciousness depth monitoring system, relates to the field of narcosis and consciousness depth monitoring, and comprises a signal acquisition module, a signal preprocessing module and a core processing module, extracts frequency domain characteristic parameters of baseline electroencephalogram signals, automatically identifies whether the subject belongs to a preset fragile brain phenotype according to the frequency domain characteristic parameters, when the identification result is that the subject belongs to the fragile brain phenotype, lowers a warning threshold of a narcosis depth index from a first preset value to a second preset value, wherein the second preset value is lower than the first preset value, continuously detects electroencephalogram burst suppression events based on real-time electroencephalogram signals during surgery, and accumulates burst suppression duration. The application effectively solves the problem that conventional narcosis depth index readings deviate from actual sedation depth due to changes in patient electroencephalogram spectrum characteristics (such as attenuation of alpha band and flattening of spectrum), and the risk of excessive narcosis caused by false high readings under unified threshold management.

Description

Technical Field

[0001] This invention relates to the field of anesthesia and depth of consciousness monitoring, and more specifically, to an anesthesia and depth of consciousness monitoring system. Background Technology

[0002] Anesthesia depth monitoring is a crucial aspect of general anesthesia management. Its purpose is to assess the patient's level of consciousness and sedation in real time, thereby guiding the precise administration of anesthetic drugs and avoiding adverse consequences caused by intraoperative awareness or excessive anesthesia. The bispectral index (BIS) is one of the most widely used indicators for anesthesia depth monitoring in clinical practice. By analyzing the frequency, power, and phase coupling characteristics of frontal lobe EEG signals, it quantifies complex EEG activity into a value of 0-100 to reflect the degree of sedation in the brain.

[0003] Current BIS monitoring systems rely primarily on algorithms based on EEG data from adult populations, failing to adequately consider age-related electrophysiological differences. Clinical electrophysiological studies have shown that with age, brain function in elderly patients undergoes degenerative changes, resulting in significant differences in their EEG spectral characteristics compared to younger individuals—manifested as a marked decrease in alpha band (8-13Hz) power and overall spectrum flattening. This age-dependent EEG alteration leads to systematic biases in BIS readings: even when signs of deep anesthesia, such as burst inhibition, are observed on EEG, BIS readings may still exceed the expected values ​​corresponding to the actual depth of sedation. If anesthesiologists use the same BIS thresholds as for younger patients to guide anesthesia management in elderly patients, it can easily lead to excessively deep anesthesia, which has been clinically proven to be closely related to an increased risk of postoperative delirium.

[0004] Therefore, we have made improvements to this and proposed an anesthesia and depth of consciousness monitoring system. Summary of the Invention

[0005] The purpose of this invention is to address the problem that existing anesthesia depth monitoring systems do not fully consider the age-related changes in the electroencephalogram (EEG) spectrum of elderly patients, leading to BIS readings deviating from the actual sedation depth, which in turn can easily cause excessive anesthesia and increase the risk of postoperative delirium.

[0006] To achieve the above-mentioned objectives, the present invention provides an anesthesia and depth of consciousness monitoring system to improve the aforementioned problems.

[0007] The application is as follows: An anesthesia and depth of consciousness monitoring system, comprising: The signal acquisition module is configured to acquire the baseline EEG signal of the subject before anesthesia induction and to continuously acquire the real-time EEG signal of the subject during the operation. The signal preprocessing module is communicatively connected to the signal acquisition module and is used to perform signal quality assessment and noise filtering on the baseline EEG signal and the real-time EEG signal. The core processing module, communicatively connected to the signal preprocessing module, is used to receive the processed EEG signals and perform the following operations: The frequency domain feature parameters of the baseline EEG signal are extracted, and the subject is automatically identified as belonging to a preset vulnerable brain phenotype based on the frequency domain feature parameters. When the identification result is that it belongs to the vulnerable brain phenotype, the warning threshold of the anesthesia depth index is lowered from the first preset value to the second preset value, wherein the second preset value is lower than the first preset value; During the operation, brainwave burst inhibition events were continuously detected based on the real-time EEG signals, and the burst inhibition duration was accumulated; The output and interaction module is communicatively connected to the core processing module. It is used to display the anesthesia depth index and the cumulative burst suppression duration, and output an anesthesia depth warning signal when the anesthesia depth index reaches the currently effective anesthesia depth index warning threshold, and output a burst suppression warning signal when the cumulative burst suppression duration reaches a preset duration threshold. Specifically, when the identification result belongs to the vulnerable brain phenotype, the currently effective anesthesia depth index warning threshold is the second preset value; when the identification result does not belong to the vulnerable brain phenotype, the currently effective anesthesia depth index warning threshold is the first preset value.

[0008] As a preferred technical solution of this application, the core processing module identifies whether the subject belongs to the vulnerable brain phenotype, specifically including: Extracting the frontal lobe of the baseline EEG signal Slow wave power parameters and slow wave power parameters; When the frontal lobe When the slow wave power parameter is lower than a preset first threshold and the slow wave power parameter is higher than a preset second threshold, the subject is determined to belong to the vulnerable brain phenotype.

[0009] As a preferred technical solution of this application, the core processing module lowers the warning threshold from the first preset value to the second preset value, specifically including: The core processing module determines the adjustment range of the first preset value based on the identification result of the vulnerable brain phenotype, and the adjustment range is positively correlated with the quantitative index characterizing the severity of the vulnerable brain phenotype; The core processing module adjusts the warning threshold from the first preset value to the second preset value according to the adjustment range.

[0010] As a preferred technical solution of this application, the core processing module outputs the outbreak suppression warning signal when the cumulative outbreak suppression duration reaches the preset duration threshold, specifically including: The core processing module compares the cumulative burst suppression duration with a first duration threshold and a second duration threshold, respectively, wherein the first duration threshold is less than the second duration threshold; When the cumulative burst suppression duration reaches the first duration threshold but does not reach the second duration threshold, a first-level warning signal is output; When the cumulative burst suppression duration reaches the second duration threshold, a second-level warning signal is output, and the warning intensity of the second-level warning signal is higher than that of the first-level warning signal.

[0011] As a preferred technical solution of this application, the core processing module accumulates the burst suppression time window by window, using a preset time window as the unit; When the cumulative burst suppression duration within any time window reaches the first duration threshold, a warning signal of the first level is output; when it reaches the second duration threshold, a warning signal of the second level is output. If the cumulative burst suppression duration does not reach the first duration threshold when the current time window ends, the cumulative burst suppression duration is reset to zero, and the next time window begins to accumulate again.

[0012] As a preferred technical solution of this application, the core processing module is further used for: The frontal lobe was extracted from the real-time EEG signals continuously acquired during the operation. Wave power parameters and burst suppression tendency parameters; When the real-time EEG signal is in the frontal lobe When the wave power parameter is lower than the preset third threshold and the burst inhibition tendency parameter is higher than the preset fourth threshold, it is determined that the subject conforms to the vulnerable brain phenotype during the operation, and the warning threshold of the anesthesia depth index is maintained at the second preset value. When the real-time EEG signal is in the frontal lobe If the wave power parameter is not lower than the third threshold, or the burst inhibition tendency parameter is not higher than the fourth threshold, it is determined that the subject does not conform to the vulnerable brain phenotype during the operation, and the warning threshold of the anesthesia depth index is restored from the second preset value to the first preset value.

[0013] As a preferred technical solution of this application, the signal preprocessing module performs signal quality assessment on the baseline EEG signal and the real-time EEG signal, specifically including: Calculate the artifact percentage and electrode contact impedance of each channel in the baseline EEG signal and the real-time EEG signal; Based on the artifact proportion and the electrode contact impedance index, the quality assessment results of each channel signal are generated; When the quality assessment result of any channel is lower than the preset quality threshold, the signal preprocessing module marks the signal of that channel as unavailable and outputs the corresponding channel quality prompt information in the output and interaction module.

[0014] As a preferred technical solution of this application, the signal preprocessing module performs baseline correction processing on the baseline EEG signal, specifically including: Obtain the baseline drift component from the baseline EEG signal; The baseline drift component is removed from the baseline EEG signal to obtain the corrected baseline EEG signal; The core processing module extracts the frequency domain feature parameters based on the corrected baseline EEG signal and identifies whether the subject belongs to the vulnerable brain phenotype.

[0015] As a preferred technical solution of this application, after the core processing module outputs the first-level warning signal, it is further used for: After outputting the first level of warning signal, the growth rate of the cumulative outbreak suppression duration is continuously monitored; When the growth rate exceeds the preset rate threshold, the second duration threshold is shortened to the third duration threshold, and the third duration threshold is used as the basis for determining whether to trigger the second level warning signal. The third duration threshold is greater than the first duration threshold.

[0016] As a preferred technical solution of this application, the output and interaction module presents the identification results of the vulnerable brain phenotype in a visual manner, specifically including: When the core processing module identifies that the subject belongs to the vulnerable brain phenotype, the output and interaction module displays a first visual identifier and simultaneously presents the adjustment trajectory information of the warning threshold of the anesthesia depth index from the first preset value to the second preset value. When the core processing module identifies that the subject does not belong to the vulnerable brain phenotype, the output and interaction module displays a second visual identifier, which is distinguishable from the first visual identifier in terms of color, shape, or a combination of color and shape.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: In the scheme of this application: By collecting baseline EEG signals from subjects before anesthesia induction and automatically identifying whether they belong to the vulnerable brain phenotype, the system implements dual early warning for all subjects based on the depth of anesthesia index and burst inhibition duration. For subjects with the vulnerable brain phenotype, the warning threshold for the depth of anesthesia index is adaptively lowered from a first preset value to a second preset value, thereby improving the index's warning sensitivity in the vulnerable brain population. This effectively addresses the risk of excessive anesthesia caused by deviations in the conventional depth of anesthesia index readings from the actual sedation depth due to changes in the patient's EEG spectral characteristics (such as alpha band attenuation and spectral flattening), and the potential for inflated readings under uniform threshold management. Simultaneously, the system's independently executed continuous detection of burst inhibition events and cumulative duration exceeding warnings provide an objective basis for judging deep anesthesia from the EEG time-domain characteristic dimension, independent of spectral assumptions. This complements the frequency-domain-based depth of anesthesia index warning technology, compensating for the blind spots of a single index. Furthermore, the system possesses intraoperative dynamic adaptive capabilities, restoring thresholds based on real-time EEG characteristics to ensure that the monitoring strategy accurately matches the patient's real-time brain function state throughout the entire process, achieving individualized and intelligent perioperative anesthesia depth management. Attached Figure Description

[0018] Figure 1 A schematic diagram of a module for anesthesia and depth of consciousness monitoring system provided in this application; Figure 2 A logic flowchart of an anesthesia and depth of consciousness monitoring system provided in this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example: Refer to Figures 1 to 2 As shown, an anesthesia and depth of consciousness monitoring system includes: The signal acquisition module is configured to acquire the baseline EEG signal of the subject before anesthesia induction and to continuously acquire the real-time EEG signal of the subject during the operation. Preferably, the baseline EEG signal is acquired while the subject is in a resting state with eyes closed, for a duration of not less than 2 minutes, and at a sampling rate of not less than 256 Hz. When acquiring the baseline EEG signal before anesthesia induction, guidance prompts are presented to the operator through the output and interaction module to instruct the subject to keep their eyes closed, relax, and avoid active thinking during the acquisition. If the proportion of ocular artifacts detected during the acquisition exceeds the preset artifact tolerance threshold, the acquisition time is automatically extended or the operator is prompted to re-acquire the signal until a baseline EEG signal that meets the quality requirements is obtained.

[0022] It also includes a signal preprocessing module, which is connected in communication with the signal acquisition module, and is used to perform signal quality assessment and noise filtering on baseline EEG signals and real-time EEG signals; It also includes a core processing module, which communicates with the signal preprocessing module to receive the processed EEG signals and perform the following operations: Frequency domain feature parameters of baseline EEG signals are extracted, and the subjects are automatically identified as belonging to a pre-defined vulnerable brain phenotype based on the frequency domain feature parameters. When the identification result is that it belongs to the vulnerable brain phenotype, the warning threshold of the anesthesia depth index is lowered from the first preset value to the second preset value, wherein the second preset value is lower than the first preset value; During the operation, burst inhibition events of brain signals were continuously detected based on real-time EEG signals, and the duration of burst inhibition was accumulated; Preferably, the anesthesia depth index is a dimensionless index calculated based on the bispectral index (BIS) algorithm, with a value ranging from 0 to 100. The lower the value, the deeper the brainwave inhibition and the higher the sedation level. The first preset value is 60, and the second preset value is 50. The values ​​of the first and second preset values ​​correspond to the clinically recognized BIS index warning threshold system—a BIS value below 60 indicates that the appropriate sedation depth required for general anesthesia has been achieved, and a BIS value that is consistently below 40 indicates a risk of excessive anesthesia. When the BIS value is below 50, the probability of brainwave burst inhibition increases significantly, and the risk of postoperative delirium increases accordingly. Those skilled in the art will understand that the specific values ​​of the first and second preset values ​​mentioned above are clinically routine values ​​and can be adjusted according to actual needs in different clinical scenarios or different anesthesia depth index systems. For example, the first preset value can also be 55 or 65, and the second preset value can also be 45 or 55, all of which do not deviate from the core protection scope of this application.

[0023] A burst inhibition event is defined as follows: In a preprocessed single-channel EEG signal, the root mean square (RMS) amplitude of the EEG signal is continuously calculated within a 1-second time window. When the RMS amplitude within a 1-second window is lower than a preset burst inhibition amplitude threshold, and the continuous duration of this low-amplitude state reaches a preset minimum burst inhibition duration threshold, the time period of this low-amplitude state is marked as a burst inhibition event. If the time interval between two adjacent marked burst inhibition events is less than 0.5 seconds, the two are combined into a single continuous burst inhibition event. The burst inhibition amplitude threshold is 5 μV. This threshold is based on the burst inhibition amplitude boundary standard widely used in clinical EEG measurements, and is set to 5 μV under the conventional configuration of disc electrodes conforming to the International Federation for Clinical Neurophysiology (IFCN) standards and a medical EEG amplifier (input noise level not exceeding 2 μV peak-to-peak). Those skilled in the art can adaptively adjust it within the range of 2 μV to 10 μV according to the specific noise level of the equipment used and the clinical scenario. The minimum burst inhibition duration threshold is 0.5 seconds.

[0024] It also includes an output and interaction module, which communicates with the core processing module to display the anesthesia depth index and the cumulative burst suppression duration, and outputs an anesthesia depth warning signal when the anesthesia depth index reaches the currently effective anesthesia depth index warning threshold, and outputs a burst suppression warning signal when the cumulative burst suppression duration reaches the preset duration threshold. Specifically, when the identification result is a vulnerable brain phenotype, the currently effective anesthesia depth index warning threshold is the second preset value; when the identification result is not a vulnerable brain phenotype, the currently effective anesthesia depth index warning threshold is the first preset value.

[0025] By collecting baseline EEG signals from subjects before anesthesia induction and automatically identifying whether they belong to the vulnerable brain phenotype, the system implements dual early warning for all subjects based on the depth of anesthesia index and burst inhibition duration. For subjects with the vulnerable brain phenotype, the warning threshold for the depth of anesthesia index is adaptively lowered from a first preset value to a second preset value, significantly improving the index's warning sensitivity in the vulnerable brain population. This effectively addresses the risk of excessive anesthesia caused by deviations in the conventional depth of anesthesia index readings from the actual sedation depth due to changes in the patient's EEG spectral characteristics (such as alpha band attenuation and spectral flattening), and the potential for inflated readings under uniform threshold management. Simultaneously, the system's independently executed continuous detection of burst inhibition events and cumulative duration exceeding warning limits provide an objective basis for judging deep anesthesia from the EEG time-domain characteristic dimension, independent of spectral assumptions. This complements the frequency-domain-based depth of anesthesia index warning technology, compensating for the monitoring blind spots of a single index. Furthermore, the system possesses intraoperative dynamic adaptive capabilities, restoring thresholds based on real-time EEG characteristics to ensure that the monitoring strategy accurately matches the patient's real-time brain function state throughout the entire process, achieving individualized and intelligent perioperative anesthesia depth management.

[0026] As a preferred technical solution of this application, the core processing module identifies whether the subject belongs to the vulnerable brain phenotype, specifically including: Frontal lobe for extracting baseline EEG signals Slow wave power parameters and slow wave power parameters; The frontal lobe alpha wave power parameters are as follows: Using EEG signals from at least one channel in leads Fp1, Fp2, F3, and F4 as the analysis object, a Fast Fourier Transform is performed on the preprocessed baseline EEG signal to obtain the power spectral density in the 8-13 Hz frequency band. The average power spectral density of the analyzed channels is then calculated to obtain the frontal lobe alpha wave power parameters. Slow-wave power parameters. Slow-wave power parameters are obtained by acquiring the power spectral density within the 0.5-4Hz frequency band using EEG signals from the same analysis channel, and averaging the values ​​across the analysis channels. Power spectral density is expressed in μV. 2 The unit is / Hz.

[0027] When frontal lobe When the slow wave power parameter is lower than the preset first recognition threshold and the slow wave power parameter is higher than the preset second recognition threshold, the subject is determined to have a vulnerable brain phenotype.

[0028] The first identification threshold ranges from 2.0 μV. 2 / Hz to 8.0μV 2 / Hz, preferably 4.0μV. 2 / Hz; the second identification threshold ranges from 15.0μV. 2 / Hz to 40.0μV 2 / Hz, preferably 25.0μV. 2 / Hz. The above thresholds were obtained through preoperative resting-state EEG data collection and statistical analysis of a healthy adult population stratified by age (each 10 years as a stratum): [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Using the mean and standard deviation of the wave power and slow wave power as references, Subjects whose wave power is lower than the mean of the same age group minus 1.5 standard deviations and whose slow wave power is higher than the mean of the same age group plus 1.5 standard deviations are defined as meeting the vulnerable brain phenotype. The above preferred values ​​correspond to the statistical results of the 55-75 age group. In specific implementation, the corresponding age stratification threshold can be selected according to the actual age of the subject, or multiple sets of age stratification parameters can be preset in the system for the operator to choose from.

[0029] It should be noted that the above-mentioned "slow wave power parameter exceeding the preset second recognition threshold" as one of the criteria for determining the vulnerable brain phenotype is based on the following physiological basis: a certain subgroup of the elderly population (especially those with early neurodegenerative changes or a tendency for cortical deinhibition) exhibits a relative increase in slow wave activity in resting-state EEG. This phenomenon is referred to as "age-related slow wave deinhibition" or "cortical dedifferentiation" in the EEG literature, and its essence is the electrophysiological manifestation of the functional degeneration of cortical inhibitory neural circuits. Therefore, the relative increase in slow wave power is consistent with the brain functional degenerative changes pointed to by the "vulnerable brain phenotype" defined in this application at the pathophysiological level. Those skilled in the art can understand the setting logic of this criterion based on the above physiological principles.

[0030] Preferably, the core processing module lowers the warning threshold from a first preset value to a second preset value, specifically including: The core processing module determines the adjustment range of the first preset value based on the identification results of the vulnerable brain phenotype. The adjustment range is positively correlated with the quantitative indicators that characterize the severity of the vulnerable brain phenotype. The core processing module adjusts the warning threshold from the first preset value to the second preset value according to the adjustment range.

[0031] Quantitative indicators characterizing the severity of the vulnerable brain phenotype are calculated as follows: Frontal lobe of baseline EEG signals... The deviation of the slow wave power parameter from the first recognition threshold and the excess of the slow wave power parameter from the second recognition threshold are weighted and summed to obtain a comprehensive deviation index, which serves as a quantitative indicator characterizing the severity of the vulnerable brain phenotype. Specifically: make For actual measurement of the frontal lobe Wave power, The first identification threshold; To measure the slow wave power, This is the second identification threshold. Normalization deviation of wave power The normalization of slow wave power exceeds the degree They are defined as follows: in < and > The above definition is valid at that time.

[0032] Adjustment range Determine according to the following formula: in, The adjustment range is expressed in BIS index points. The maximum allowable adjustment range ( =15), and These are the preset weighting coefficients.

[0033] Second preset value Calculate using the following formula: in The first preset value is 60.

[0034] Weighting coefficient and The values ​​are respectively =5, =4; The above weighting coefficients were determined based on: multiple linear regression analysis of the deviation between preoperative EEG data and intraoperative BIS readings of no less than 200 subjects of different age groups, to... Wave power parameters and slow wave power parameters were used as independent variables, and the deviation of BIS readings from the actual sedation depth was used as the dependent variable. After fitting a regression equation, the standardized regression coefficients of each variable were obtained. The standardized regression coefficient for the slow wave power parameter is approximately 5, and the standardized regression coefficient for the slow wave power parameter is approximately 4. Based on this, the following settings are made: =5、 =4; The above weighted summation normalization formula ensures that the adjustment range is proportional to the severity of the vulnerable brain phenotype. Those skilled in the art can recalibrate the weight coefficients using the same regression analysis method based on actual clinical data, or perform stratification according to different age groups or different anesthesia protocols, all without departing from the scope of protection of this application.

[0035] Preferably, the core processing module outputs an outbreak suppression warning signal when the cumulative outbreak suppression duration reaches a preset duration threshold, specifically including: The core processing module compares the cumulative burst suppression time with a first duration threshold and a second duration threshold, respectively, and the first duration threshold is less than the second duration threshold; When the cumulative burst suppression time reaches the first duration threshold but does not reach the second duration threshold, a first-level warning signal is output. When the cumulative outbreak suppression time reaches the second duration threshold, a second-level warning signal is output. The warning intensity of the second-level warning signal is higher than that of the first-level warning signal.

[0036] The first duration threshold ranges from 5 to 30 seconds, with a preferred value of 10 seconds; the second duration threshold ranges from 30 to 120 seconds, with a preferred value of 60 seconds. These values ​​are based on the following clinical considerations: within a 60-second time window, a cumulative burst inhibition duration of 10 seconds indicates a significant level of EEG inhibition, which should attract the attention of the anesthesiologist; a cumulative duration of 60 seconds indicates severe deep EEG inhibition, requiring immediate intervention. Those skilled in the art can adaptively adjust these thresholds during clinical practice based on the type of surgery, the patient's underlying condition, and the pharmacokinetic characteristics of the anesthetic drugs used.

[0037] Preferably, the core processing module accumulates the burst suppression time window by window, using a preset time window as the unit; When the cumulative burst suppression duration within any time window reaches the first duration threshold, a first-level warning signal is output; when it reaches the second duration threshold, a second-level warning signal is output. If the cumulative burst suppression duration does not reach the first duration threshold when the current time window ends, the cumulative burst suppression duration is reset to zero, and the next time window begins to accumulate again.

[0038] The preset time window length is 60 seconds. The time window is implemented using a sliding window method or an adjacent non-overlapping window method, with the adjacent non-overlapping window method being preferred. That is, starting from the start time of the operation, a time window is divided every 60 seconds, and the burst suppression duration is independently accumulated within each window. When the accumulated burst suppression duration reaches the first duration threshold within any time window, the first-level warning is triggered; when it reaches the second duration threshold, the second-level warning is triggered.

[0039] The core processing module also records the total cumulative burst suppression time throughout the entire surgery and displays it synchronously with the cumulative burst suppression time of the current time window in the output and interaction module. When the total cumulative burst suppression time throughout the entire surgery reaches the preset global warning threshold (the global warning threshold is 180 seconds), the core processing module outputs a global burst suppression warning signal. The recording of the global cumulative time is not affected by the time window clearing mechanism to prevent the omission of brief but cumulatively significant burst suppression events occurring in multiple windows. The global burst suppression warning signal is a supplementary safety warning mechanism independent of the first-level and second-level warning signals, and its output does not affect the normal triggering and clearing logic of the graded warnings within the time window.

[0040] Preferably, the core processing module is also used for: Extracting frontal lobe from real-time EEG signals continuously acquired during surgery Wave power parameters and burst suppression tendency parameters; When real-time EEG signals in the frontal lobe When the wave power parameter is lower than the preset third threshold and the burst inhibition tendency parameter is higher than the preset fourth threshold, the subject is determined to have a vulnerable brain phenotype during the operation, and the warning threshold of the anesthesia depth index is maintained at the second preset value. When real-time EEG signals in the frontal lobe If the wave power parameter is not lower than the third threshold, or the burst inhibition tendency parameter is not higher than the fourth threshold, the subject is determined to not conform to the vulnerable brain phenotype during the operation, and the warning threshold of the anesthesia depth index is restored from the second preset value to the first preset value.

[0041] Furthermore, as a more preferred implementation, to avoid frequent switching of the warning threshold due to fluctuations in EEG characteristics near the threshold boundary during surgery, the core processing module performs the following hysteresis judgment before restoring the warning threshold from the second preset value to the first preset value: within M consecutive time windows (M is ≥3, preferably M=5), the frontal lobe of the real-time EEG signal... When the wave power parameters are all not lower than the third threshold and the burst suppression tendency parameters are all not higher than the fourth threshold, the warning threshold will be restored from the second preset value to the first preset value. Before the above conditions of M consecutive time windows are met, even if the real-time EEG characteristic parameters have crossed the recovery threshold boundary, the warning threshold will still be maintained at the second preset value to prevent unnecessary threshold recovery caused by instantaneous fluctuations within a single time window.

[0042] Third threshold The determination method is as follows: in The first identification threshold (preoperative vulnerable phenotype identification threshold, typical value 4.0 μV) is used. 2 / Hz). This represents the intraoperative inhibition coefficient. The value is dynamically adjusted based on the current value of the intraoperative anesthesia depth index: The first recognition threshold is 4.0 μV 2 Taking / Hz as an example, when the BIS is in the 40-60 range, the third threshold is 2.8μV. 2 / Hz, the third threshold is 2.0μV when BIS is below 40. 2 / Hz. The above. The values ​​were obtained from frontal lobe EEG data of subjects collected in clinical trials at different target concentrations of propofol (1.0-4.0 μg / mL). The wave power attenuation ratio relative to the preoperative baseline was obtained by curve fitting; in clinical settings without target-controlled infusion (TCI) systems, or in anesthesia regimens primarily using inhaled anesthetics, The value can also be manually set by the operator based on clinical experience and the anesthesia depth index, or it can be adaptively estimated by the system based on historical data, neither of which deviates from the scope of protection of this application.

[0043] Burst suppression tendency parameter Defined as the starting frequency of burst suppression events detected within the current time window, expressed in times per minute, calculated using the following formula: in This represents the initial number of burst suppression events within the current time window. The time window length (minutes) is typically 1 minute (60 seconds); the fourth threshold ranges from 1 to 4 times per minute, with 2 times per minute being the preferred value.

[0044] Preferably, the signal preprocessing module performs signal quality assessment on the baseline EEG signal and the real-time EEG signal, specifically including: Calculate the artifact percentage and electrode contact impedance of each channel in the baseline and real-time EEG signals. Based on the artifact percentage and electrode contact impedance, quality assessment results for each channel signal are generated. When the quality assessment result of any channel is lower than the preset quality threshold, the signal preprocessing module marks the signal of that channel as unavailable and outputs the corresponding channel quality prompt information in the output and interaction module.

[0045] The artifact percentage is calculated as follows: Using 1 second as the basic analysis unit, each unit is checked for artifacts such as electrooculography (EOG) artifacts (characterized by transient waveforms with a frequency below 4Hz and an amplitude exceeding 50μV), electromyography (EMG) artifacts (characterized by high-frequency interference with a frequency above 30Hz and an amplitude exceeding 20μV), or motion artifacts (characterized by drastic jumps in signal amplitude, i.e., an amplitude change rate exceeding 100μV / ms between adjacent sampling points). The proportion of analysis units marked as containing any of these artifact types out of the total number of analysis units is taken as the artifact percentage. The calculation formula is: in The number of 1-second analysis units marked as containing artifacts. This represents the total number of analysis units. Electrode contact impedance is obtained by applying a test current of 10Hz with an amplitude not exceeding 1μA to each electrode and measuring the impedance between the electrode and the skin. If the artifact rate of a channel exceeds 20% or the electrode contact impedance exceeds 5kΩ, the quality assessment result is unqualified, and the channel is marked as unusable. Channels marked as unusable do not participate in the frequency domain feature parameter extraction and burst suppression event detection of the core processing module; the anesthesia depth index is calculated based on the signals from the remaining available channels.

[0046] Preferably, the signal preprocessing module performs baseline correction processing on the baseline EEG signal, specifically including: Obtain the baseline drift component from the baseline EEG signal; Baseline drift components are removed from the baseline EEG signal to obtain the corrected baseline EEG signal; The core processing module extracts frequency domain feature parameters based on the corrected baseline EEG signal and identifies whether the subject belongs to the vulnerable brain phenotype.

[0047] The baseline drift component is obtained by applying a high-pass filter with a cutoff frequency of 0.5 Hz to the original EEG signal, or by using a polynomial fitting method of order no less than 3 to estimate the low-frequency trend term in the signal; after subtracting the baseline drift component from the original signal, the corrected baseline EEG signal is obtained.

[0048] Preferably, after the core processing module outputs the first-level warning signal, it is also used for: After issuing the first-level warning signal, continuously monitor the growth rate of the cumulative outbreak suppression duration; When the growth rate exceeds the preset rate threshold, the second duration threshold is shortened to the third duration threshold, and the third duration threshold is used as the basis for determining whether to trigger the second-level warning signal. The third duration threshold is greater than the first duration threshold.

[0049] growth rate The calculation is based on the cumulative increase in burst suppression duration over the last 10 seconds, divided by 10 seconds, using the burst suppression duration increment per second (seconds / second) as the unit. in This represents the cumulative increment (in seconds) of the burst suppression duration within the last 10 seconds. The rate threshold ranges from 0.3 seconds / second to 0.7 seconds / second, with a preferred value of 0.5 seconds / second. That is, when the cumulative burst suppression duration increases by more than 5 seconds within the last 10 seconds, it is determined that the growth rate exceeds the threshold. When the growth rate exceeds the threshold, the core processing module shortens the second duration threshold from 60 seconds to the third duration threshold (the third duration threshold ranges from 20 seconds to 40 seconds, with a preferred value of 30 seconds).

[0050] After triggering the second-level warning, the core processing module automatically resets the monitoring count of the growth rate and continues to monitor the growth of the outbreak suppression duration in the subsequent process.

[0051] As a further preferred implementation, the recovery condition for the second duration threshold is: the growth rate falls below the rate threshold and remains below it for at least 30 seconds, while the cumulative burst suppression duration within the current time window does not exceed 80% of the first duration threshold. Here, "cumulative burst suppression duration within the current time window" refers to the cumulative value from the start of the current time window to the current time. If both conditions are met simultaneously, the second duration threshold is restored from the third duration threshold to the second duration threshold (i.e., 60 seconds). If the cumulative burst suppression duration has reached 80% or more of the first duration threshold, the third duration threshold is maintained until the current time window ends or a second-level warning is triggered, to prevent premature relaxation of the warning standard due to a temporary drop in the growth rate when the cumulative suppression duration is already high. Those skilled in the art can adaptively adjust the above thresholds according to the specific anesthesia plan and the patient's condition.

[0052] Preferably, the output and interaction module presents the identification results of vulnerable brain phenotypes in a visual manner, specifically including: When the core processing module identifies that the subject has a vulnerable brain phenotype, the output and interaction module displays the first visual identifier and simultaneously presents the adjustment trajectory information of the warning threshold of the anesthesia depth index from the first preset value to the second preset value. When the core processing module identifies that the subject does not belong to the vulnerable brain phenotype, the output and interaction module displays a second visual identifier, which can be distinguished from the first visual identifier in terms of color, shape, or a combination of color and shape.

[0053] The primary visual identifier is a yellow triangular warning icon, and the secondary visual identifier is a green circular normal icon. Adjustment trajectory information is presented through numerical change animations or trend arrows: when the warning threshold is lowered, the output and interaction module uses a dynamic arrow to indicate the process of the threshold moving from the first preset value to the second preset value on the side of the anesthesia depth index display area, and simultaneously displays the values ​​before and after the adjustment in the threshold value display area for at least 5 seconds; when the warning threshold is restored, a dynamic arrow indicates the reverse movement process; the presentation of adjustment trajectory information is triggered by the threshold change event and continues to be displayed for at least 30 seconds after the event occurs, so that the anesthesia operator is aware of the currently effective warning threshold and its change history, avoiding misinterpretation of warning signals due to threshold changes.

[0054] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. An anesthesia and depth of consciousness monitoring system, characterized in that, include: The signal acquisition module is configured to acquire the baseline EEG signal of the subject before anesthesia induction and to continuously acquire the real-time EEG signal of the subject during the operation. The signal preprocessing module is communicatively connected to the signal acquisition module and is used to perform signal quality assessment and noise filtering on the baseline EEG signal and the real-time EEG signal. The core processing module, communicatively connected to the signal preprocessing module, is used to receive the processed EEG signals and perform the following operations: The frequency domain feature parameters of the baseline EEG signal are extracted, and the subject is automatically identified as belonging to a preset vulnerable brain phenotype based on the frequency domain feature parameters. When the identification result is that it belongs to the vulnerable brain phenotype, the warning threshold of the anesthesia depth index is lowered from the first preset value to the second preset value, wherein the second preset value is lower than the first preset value; During the operation, brainwave burst inhibition events were continuously detected based on the real-time EEG signals, and the burst inhibition duration was accumulated; The output and interaction module is communicatively connected to the core processing module. It is used to display the anesthesia depth index and the cumulative burst suppression duration, and output an anesthesia depth warning signal when the anesthesia depth index reaches the currently effective anesthesia depth index warning threshold, and output a burst suppression warning signal when the cumulative burst suppression duration reaches a preset duration threshold. Specifically, when the identification result belongs to the vulnerable brain phenotype, the currently effective anesthesia depth index warning threshold is the second preset value; when the identification result does not belong to the vulnerable brain phenotype, the currently effective anesthesia depth index warning threshold is the first preset value.

2. The anesthesia and depth of consciousness monitoring system according to claim 1, characterized in that, The core processing module identifies whether the subject belongs to the vulnerable brain phenotype, specifically including: Extracting the frontal lobe of the baseline EEG signal Slow wave power parameters and slow wave power parameters; When the frontal lobe When the slow wave power parameter is lower than a preset first threshold and the slow wave power parameter is higher than a preset second threshold, the subject is determined to belong to the vulnerable brain phenotype.

3. The anesthesia and depth of consciousness monitoring system according to claim 2, characterized in that, The core processing module lowers the warning threshold from the first preset value to the second preset value, specifically including: The core processing module determines the adjustment range of the first preset value based on the identification result of the vulnerable brain phenotype, and the adjustment range is positively correlated with the quantitative index characterizing the severity of the vulnerable brain phenotype; The core processing module adjusts the warning threshold from the first preset value to the second preset value according to the adjustment range.

4. The anesthesia and depth of consciousness monitoring system according to claim 3, characterized in that, The core processing module outputs the outbreak suppression warning signal when the cumulative outbreak suppression duration reaches the preset duration threshold, specifically including: The core processing module compares the cumulative burst suppression duration with a first duration threshold and a second duration threshold, respectively, wherein the first duration threshold is less than the second duration threshold; When the cumulative burst suppression duration reaches the first duration threshold but does not reach the second duration threshold, a first-level warning signal is output; When the cumulative burst suppression duration reaches the second duration threshold, a second-level warning signal is output, and the warning intensity of the second-level warning signal is higher than that of the first-level warning signal.

5. The anesthesia and depth of consciousness monitoring system according to claim 4, characterized in that, The core processing module accumulates the burst suppression time window by window, using a preset time window as the unit. When the cumulative burst suppression duration within any time window reaches the first duration threshold, a warning signal of the first level is output; when it reaches the second duration threshold, a warning signal of the second level is output. If the cumulative burst suppression duration does not reach the first duration threshold when the current time window ends, the cumulative burst suppression duration is reset to zero, and the next time window begins to accumulate again.

6. The anesthesia and depth of consciousness monitoring system according to claim 5, characterized in that, The core processing module is also used for: The frontal lobe was extracted from the real-time EEG signals continuously acquired during the operation. Wave power parameters and burst suppression tendency parameters; When the real-time EEG signal is in the frontal lobe When the wave power parameter is lower than the preset third threshold and the burst inhibition tendency parameter is higher than the preset fourth threshold, it is determined that the subject conforms to the vulnerable brain phenotype during the operation, and the warning threshold of the anesthesia depth index is maintained at the second preset value. When the real-time EEG signal is in the frontal lobe If the wave power parameter is not lower than the third threshold, or the burst inhibition tendency parameter is not higher than the fourth threshold, it is determined that the subject does not conform to the vulnerable brain phenotype during the operation, and the warning threshold of the anesthesia depth index is restored from the second preset value to the first preset value.

7. The anesthesia and depth of consciousness monitoring system according to claim 6, characterized in that, The signal preprocessing module performs signal quality assessment on the baseline EEG signal and the real-time EEG signal, specifically including: Calculate the artifact percentage and electrode contact impedance of each channel in the baseline EEG signal and the real-time EEG signal; Based on the artifact proportion and the electrode contact impedance index, the quality assessment results of each channel signal are generated; When the quality assessment result of any channel is lower than the preset quality threshold, the signal preprocessing module marks the signal of that channel as unavailable and outputs the corresponding channel quality prompt information in the output and interaction module.

8. The anesthesia and depth of consciousness monitoring system according to claim 7, characterized in that, The signal preprocessing module performs baseline correction processing on the baseline EEG signal, specifically including: Obtain the baseline drift component from the baseline EEG signal; The baseline drift component is removed from the baseline EEG signal to obtain the corrected baseline EEG signal; The core processing module extracts the frequency domain feature parameters based on the corrected baseline EEG signal and identifies whether the subject belongs to the vulnerable brain phenotype.

9. The anesthesia and depth of consciousness monitoring system according to claim 4, characterized in that, After the core processing module outputs the first-level warning signal, it is also used for: After outputting the first level of warning signal, the growth rate of the cumulative outbreak suppression duration is continuously monitored; When the growth rate exceeds the preset rate threshold, the second duration threshold is shortened to the third duration threshold, and the third duration threshold is used as the basis for determining whether to trigger the second level warning signal. The third duration threshold is greater than the first duration threshold.

10. The anesthesia and depth of consciousness monitoring system according to claim 9, characterized in that, The output and interaction module presents the identification results of the vulnerable brain phenotype in a visual manner, specifically including: When the core processing module identifies that the subject belongs to the vulnerable brain phenotype, the output and interaction module displays a first visual identifier and simultaneously presents the adjustment trajectory information of the warning threshold of the anesthesia depth index from the first preset value to the second preset value. When the core processing module identifies that the subject does not belong to the vulnerable brain phenotype, the output and interaction module displays a second visual identifier, which is distinguishable from the first visual identifier in terms of color, shape, or a combination of color and shape.

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