Personalized anesthesia control system, method, equipment and medium

Abstract

The invention provides a personalized anesthesia control system, method and device and a medium, and the system comprises a collection monitoring module which is used for collecting occipital electroencephalogram signals of a user in a sober eye closing state and monitoring forehead electroencephalogram signals of the user in an anesthesia state; the control processing module is used for performing screening calculation according to the occipital electroencephalogram signals to obtain a reference Alpha peak frequency, performing calculation according to the forehead electroencephalogram signals to obtain a dynamic Alpha peak frequency, and performing analysis according to the reference Alpha peak frequency and the dynamic Alpha peak frequency to obtain the amount of anesthetic to be adjusted; the medicine infusion module is used for adjusting the anesthesia state of the user in real time according to the anesthetic dosage to be adjusted. According to the personalized anesthesia control system, the anesthesia depth can be monitored in real time by using the electroencephalogram signals of the user in the sober eye closing state, and personalized adjustment is automatically carried out.

Description

Technical Field

[0001] The present application belongs to the field of anesthesia control technology, and relates to a personalized anesthesia control system, and in particular to a personalized anesthesia control system, method, device and medium. Background Art

[0002] During clinical surgery, general anesthesia is a crucial step in ensuring the smooth implementation of the procedure. Traditionally, anesthesiologists determine the anesthetic dosage and rate based on their own experience. However, overdose or underdose can occur, potentially leading to serious complications such as intraoperative awareness, vomiting, cardiovascular disease, abnormal neurological activity, and postoperative cognitive impairment. The core contradiction in the current anesthesia control system lies in the disconnect between standardized protocols and individualized needs. Traditional anesthesia management relies heavily on the anesthesiologist's clinical experience, for example, manually adjusting the anesthetic dosage and rate by observing the patient's vital signs (such as blood pressure and heart rate) and incorporating subjective judgment. Patients vary greatly in their sensitivity to drugs due to factors such as age, metabolic capacity, and underlying medical conditions. Empirical medication administration is difficult to precisely match individual needs, and can easily result in either too-light anesthesia leading to intraoperative awareness (patient consciousness) or too-deep anesthesia causing circulatory depression and respiratory dysfunction.

[0003] In short, these pain points highlight the urgent need for clinical practice to break through the traditional experience-dependent model and develop a more objective and adaptive anesthesia control system. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a personalized anesthesia control system, method, device and medium to solve the problem of the lack of personalized real-time anesthesia control system in the prior art.

[0005] In a first aspect, the present application provides a personalized anesthesia control system. The personalized anesthesia control system includes: an acquisition and monitoring module for acquiring occipital EEG signals of a user in an awake, eyes-closed state and monitoring frontal EEG signals of a user in an anesthetized state; a control and processing module for filtering and calculating the occipital EEG signals to obtain a baseline Alpha peak frequency, calculating the frontal EEG signals to obtain a dynamic Alpha peak frequency, and analyzing the baseline Alpha peak frequency and the dynamic Alpha peak frequency to obtain an anesthetic dose to be adjusted; and a drug infusion module for adjusting the user's anesthetic state in real time using the anesthetic dose to be adjusted.

[0006] In this application, the baseline Alpha peak frequency is obtained using the occipital EEG signal obtained when the user is awake and eyes closed, and the dynamic Alpha peak frequency is obtained using the user's forehead EEG signal. The baseline Alpha peak frequency and the dynamic Alpha peak frequency are analyzed to adjust the anesthetic dose and adjust the user's anesthetic state in real time. This personalized anesthesia control system can use the EEG signal of the user in the awake and eyes closed state as a reference to monitor the dynamically changing anesthesia depth in real time, make personalized adjustments for the user, eliminate individual EEG characteristic differences, and improve the accuracy of anesthesia depth adjustment.

[0007] In an implementation of the first aspect, the control module is used to: perform time-frequency transformation processing on the occipital EEG signal using a multi-window algorithm to obtain a frequency spectrum corresponding to the occipital EEG signal; and perform screening based on the frequency spectrum corresponding to the occipital EEG signal to obtain the frequency corresponding to the Alpha energy peak as the benchmark Alpha peak frequency.

[0008] In an implementation of the first aspect, the control module is used to: segment the occipital EEG signals according to a preset window length and step size to obtain segmented occipital EEG signals; perform linear detrending processing on each of the segmented occipital EEG signals to obtain processed occipital EEG signals; perform fast Fourier transform processing on each of the processed occipital EEG signals using a discrete sequence to obtain a power spectral density corresponding to each of the processed occipital EEG signals; and arrange the power spectral densities corresponding to each of the processed occipital EEG signals in order to obtain a spectrum diagram corresponding to the occipital EEG signals.

[0009] In one implementation of the first aspect, the control module is used to: perform fast Fourier transform processing on any processed occipital EEG signal using multiple discrete sequences to obtain multiple power spectral densities; average each of the power spectral densities to obtain the average value of the power spectral density as the power spectral density corresponding to the processed occipital EEG signal.

[0010] In an implementation of the first aspect, the control module is used to: calculate time-frequency diagram matrix data based on occipital EEG signals of multiple channels; screen the occipital EEG signals of each channel to obtain frequency spectra of the occipital EEG time-frequency data of multiple awake and eye-closed states; calculate the peak significance based on the frequency spectra of the occipital EEG time-frequency data to obtain the Alpha peak frequency with the largest peak significance; screen the Alpha peak frequencies corresponding to each channel to obtain the Alpha peak frequency that meets preset conditions; and average the Alpha peak frequencies that meet the preset conditions to obtain the benchmark Alpha peak frequency.

[0011] In an implementation of the first aspect, the control module is used to: obtain a corresponding baseline Alpha peak frequency threshold based on the user's age; determine whether the user's baseline Alpha peak frequency meets the corresponding baseline Alpha peak frequency threshold based on the user's age; if so, adjust the anesthetic dose based on the user's baseline Alpha peak frequency; if not, mark the user as being in an abnormal state.

[0012] In an implementation of the first aspect, the control module is used to: determine whether the difference between the dynamic Alpha peak frequency and the baseline Alpha peak frequency is greater than or equal to a preset threshold upper limit; if so, determine that the anesthesia depth is too shallow, and obtain the anesthetic dose to be adjusted; if not, determine whether the difference between the dynamic Alpha peak frequency and the baseline Alpha peak frequency is less than or equal to a preset threshold lower limit; if so, determine that the anesthesia depth is too deep; if not, determine that the anesthesia depth is moderate.

[0013] In a second aspect, the present application provides a personalized anesthesia control method. The personalized anesthesia control method includes: collecting the occipital EEG signal of the user in the awake and closed-eye state and monitoring the frontal EEG signal of the user in the anesthesia state; screening and calculating according to the occipital EEG signal to obtain a baseline Alpha peak frequency; calculating according to the frontal EEG signal to obtain a dynamic Alpha peak frequency; analyzing according to the baseline Alpha peak frequency and the dynamic Alpha peak frequency to obtain an anesthetic dose to be adjusted; and using the anesthetic dose to be adjusted to adjust the user's anesthesia state in real time.

[0014] In a third aspect, the present application provides an electronic device comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, so that the electronic device performs the personalized anesthesia control method described in the second aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the personalized anesthesia control method described in the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1A Shown is a schematic diagram of an application scenario of the personalized anesthesia control method described in this application.

[0017] Figure 1B Shown are structural diagrams of the client-cloud interaction scenarios in these implementations.

[0018] Figure 2 Shown is a structural schematic diagram of the personalized anesthesia control system described in an embodiment of the present application.

[0019] Figure 3 Shown is a structural diagram of the acquisition and monitoring module described in an embodiment of the present application.

[0020] Figure 4 Shown is a schematic diagram of the process of collection and monitoring described in an embodiment of the present application.

[0021] Figure 5 Shown is a schematic diagram of the process of obtaining the spectrum corresponding to the occipital lobe EEG signal as described in an embodiment of the present application.

[0022] Figure 6 Shown is a schematic diagram of the process of obtaining the reference Alpha peak frequency according to an embodiment of the present application.

[0023] Figure 7A Shown is a schematic diagram of the occipital EEG signal in the awake, eyes-closed state described in an embodiment of the present application.

[0024] Figure 7B Shown is a schematic diagram of the frontal EEG signal in the anesthesia state described in the embodiment of the present application.

[0025] Figure 7C Shown is a schematic diagram of the anesthesia depth range described in the embodiments of the present application.

[0026] Figure 8 Shown is a flow chart of the personalized anesthesia control method described in an embodiment of the present application.

[0027] Figure 9 Shown is a structural schematic diagram of an electronic device described in an embodiment of the present application.

[0028] Component number description

[0029] 1 Personalized anesthesia control device

[0030] 11. EEG signal acquisition equipment

[0031] 12 Local Processor

[0032] 13 Display Terminal

[0033] 2-end-cloud interactive system

[0034] 20 Terminal

[0035] 21 Cloud Servers

[0036] 100 Personalized Anesthesia Control System

[0037] 110 Data Acquisition and Monitoring Module

[0038] 120 control processing module

[0039] 130 Drug Infusion Module

[0040] 900 Electronic Equipment

[0041] 910 Memory

[0042] 920 processor

[0043] 930 Display

[0044] Steps S11 to S14

[0045] Steps S21 to S25

[0046] Steps S31 to S35 DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0048] It should be noted that in the embodiments of this application, words such as "optionally" or "for example" represent examples, illustrations, or descriptions. Any embodiment or design described in this application as "optionally" or "for example" should not be interpreted as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "optionally" or "for example" is intended to present the relevant concepts in a concrete manner.

[0049] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.

[0050] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0051] For patients undergoing general anesthesia, especially those who are elderly or at high risk, the current clinical practice is to use anesthesia depth monitors—devices that collect and analyze brain waves—to precisely control the intraoperative anesthetic dosage and maintain an appropriate depth of anesthesia. Anesthetic sedatives primarily act on the brain by altering the firing activity of neurons, thereby changing brain activity. These altered firing activity generates electrical signals, known as EEG (electroencephalogram) signals, which can be detected non-invasively by electrodes placed on the scalp. Anesthetic sedatives produce specific EEG patterns in the forehead. Anesthesia depth monitors use an algorithm to convert these EEG patterns into a numerical value on a scale of 0 to 100, known as the "anesthesia depth index." Lower numbers indicate deeper anesthesia. Anesthesiologists adjust anesthetic dosage based on changes in the anesthesia depth index. Existing anesthesia depth algorithms lack accuracy, primarily because they rely on empirical statistical formulas based on a small sample of adult patients. The development process is as follows: 1. EEG data is collected from hundreds to thousands of anesthetized patients. 2. Several anesthesiologists and EEG experts analyze EEG waveforms, anesthetic dosage, and vital signs to manually determine the patient's depth of anesthesia index at each moment. 3. A statistical method is used to fit EEG characteristics and anesthesia depth index formulas. 4. This formula is integrated into the anesthesia depth monitor. Consequently, existing anesthesia depth index algorithms fail to reflect individual patient differences.

[0052] To address at least the above-mentioned issues, an embodiment of the present application provides a personalized anesthesia control system. The personalized anesthesia control system includes: an acquisition and monitoring module for acquiring occipital EEG signals of a user in an awake, eyes-closed state and monitoring frontal EEG signals of a user in an anesthetized state; a control and processing module for filtering and calculating the occipital EEG signals to obtain a baseline Alpha peak frequency, calculating the frontal EEG signals to obtain a dynamic Alpha peak frequency, and analyzing the baseline Alpha peak frequency and the dynamic Alpha peak frequency to obtain an anesthetic dose to be adjusted; and a drug infusion module for adjusting the user's anesthetic state in real time using the anesthetic dose to be adjusted.

[0053] In the embodiment of the present application, the baseline Alpha peak frequency is obtained using the occipital EEG signal obtained while the user is awake and eyes closed, and the dynamic Alpha peak frequency is obtained using the user's forehead EEG signal. The baseline Alpha peak frequency and the dynamic Alpha peak frequency are analyzed to adjust the anesthetic dose, thereby adjusting the user's anesthetic state in real time. This personalized anesthesia control system can use the user's EEG signal while awake and eyes closed as a reference to monitor the dynamically changing anesthetic depth in real time, perform personalized adjustments for the user, eliminate individual EEG characteristic differences, and improve the accuracy of anesthetic depth adjustment.

[0054] Figure 1A The figure shows an application scenario of the personalized anesthesia control method described in this application. The personalized anesthesia control device 1 can be used to implement the personalized anesthesia control method provided in the embodiment of this application, but the application scenario of the personalized anesthesia control method provided in the embodiment of this application is not limited to Figure 1A The personalized anesthesia control device 1 shown. Figure 1A As shown, the personalized anesthesia control device 1 includes an EEG signal acquisition device 11, a local processor 12, and a display terminal 13. The personalized anesthesia control method provided in the embodiment of the present application can be applied to the local processor 12.

[0055] in, Figure 1A The local processor 12 in the embodiment can be a local processor or a local processor cluster composed of multiple local processors or a cloud computing center, etc., which is not limited here. Figure 1A Only one EEG signal acquisition device 11, one local processor 12 and one display terminal 13 are shown, but it should be understood that Figure 1A The examples are only used to understand this solution, and the specific numbers of local processors 12 and display terminals 13 should be flexibly determined based on actual conditions.

[0056] In other implementations, the personalized anesthesia control device 1 may not include the display terminal 13, but may only include a local processor 12 with a display function and an EEG signal acquisition device 11. The personalized anesthesia control method provided in the embodiment of the present application can be applied to the local processor 12. The local processor 12 with a display function may include a tablet computer, a laptop computer, a PDA, a mobile phone, a personal computer, and a voice interaction device, or may be a medical monitoring device, a medical detection device, etc., which is not limited here.

[0057] In still other implementations, the personalized anesthesia control method described in this application can be applied to end-cloud interaction scenarios. Figure 1B Shown is a schematic diagram of the structure of the end-cloud interaction scenario in these implementation methods. Figure 1BAs shown, the terminal-cloud interaction system 2 includes a terminal 20 and a cloud server 21. The terminal 20 and the cloud server 21 can communicate with each other, and the communication method is not limited to wired or wireless.

[0058] Among them, the terminal 20 can be mobile or fixed. For example, the terminal 20 can be a wireless terminal or a wired terminal. The wireless terminal can refer to a device with wireless transceiver functions, which can be deployed indoors, outdoors, and in industrial workshops. The terminal 20 can be a medical monitor, a mobile phone, a tablet computer, a laptop computer, etc., which is not limited here. The cloud server 21 can include one or more servers, or one or more processing nodes, or one or more virtual machines running on the server. The cloud server 21 can also be called a server cluster, a management platform, a data processing center, etc., which is not limited in the embodiments of the present application.

[0059] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings in the embodiments of the present application.

[0060] The following embodiments of the present application provide a personalized anesthesia control system. Figure 2 The diagram shows the structure of the personalized anesthesia control system according to the embodiment of the present application. Figure 2 As shown, the personalized anesthesia control system 100 includes an acquisition and monitoring module 110 , a control and processing module 120 and a drug infusion module 130 .

[0061] The acquisition and monitoring module 110 is used to acquire the occipital EEG signals of the user in the awake and closed-eye state and to monitor the forehead EEG signals of the user in the anesthetized state.

[0062] The control processing module 120 is used to perform screening calculations based on the occipital EEG signal to obtain a baseline Alpha peak frequency, perform calculations based on the forehead EEG signal to obtain a dynamic Alpha peak frequency, and perform analysis based on the baseline Alpha peak frequency and the dynamic Alpha peak frequency to obtain the anesthetic dose to be adjusted. Among them, the Alpha wave is a rhythm of brain electrical activity within a specific frequency range, which is used to characterize the activity state of the brain. The Alpha Peak Frequency (APF) is the frequency corresponding to the maximum energy value of the Alpha frequency band (7-13Hz) in the EEG spectrum. As the anesthetic dose increases, the forehead Alpha peak frequency gradually decreases.

[0063] The drug infusion module 130 is used to adjust the user's anesthetic state in real time using the anesthetic dose to be adjusted.

[0064] In some possible implementations, Figure 3Shown is a schematic diagram of the structure of the acquisition and monitoring module described in the embodiment of this application. Figure 3 As shown, a head-mounted wireless wearable EEG acquisition device is used as the acquisition and monitoring module 110. When the user is awake and eyes closed, the EEG electrodes are placed in the occipital visual cortex. When the user is anesthetized, the EEG electrodes are placed in the forehead. The EEG electrodes use a total of 10 motors, of which 8 motors are measurement electrodes, 1 is a reference electrode REF, and 1 is a ground electrode GND. The EEG signals of the brain regions where the 8 electrodes 1 to 8 are located are collected, and the sampling frequency is greater than or equal to 256Hz.

[0065] Specifically, Figure 4 Shown is a flow chart of the acquisition and monitoring process described in the embodiment of this application. Figure 4 As shown, the acquisition monitoring module 110 uses TTS (Text-to-Speech) to synthesize voice instructions, and the user performs the eye closing and eye opening actions for 20 seconds according to the voice instructions, and repeats it three times.

[0066] The acquisition monitoring module 110 is configured at the occipital lobe position when the user is awake with eyes closed, and is configured at the forehead position when the user is anesthetized. The EEG features of the occipital lobe position in the awake with eyes closed state are used as a reference for the EEG features in the anesthetized state. The control processing module 120 is used to filter and calculate based on the occipital EEG signals of the user in the awake with eyes closed state to obtain the baseline Alpha peak frequency. The dynamic Alpha peak frequency is obtained by calculation based on the forehead EEG signals of the user in the anesthetized state. The control processing module 120 is used to analyze based on the difference between the obtained baseline Alpha peak frequency and the dynamic Alpha peak frequency to obtain the corresponding anesthetic dose to be adjusted. The drug infusion module 130 is used to use the anesthetic dose to be adjusted to adjust the user's anesthetic state in real time.

[0067] In the embodiment of the present application, the baseline Alpha peak frequency is obtained using the occipital EEG signal obtained while the user is awake and eyes closed, and the dynamic Alpha peak frequency is obtained using the user's forehead EEG signal. The baseline Alpha peak frequency and the dynamic Alpha peak frequency are analyzed to adjust the anesthetic dose, thereby adjusting the user's anesthetic state in real time. This personalized anesthesia control system 100 can use the user's EEG signal while awake and eyes closed as a reference to monitor the dynamically changing depth of anesthesia in real time, perform personalized adjustments for the user, eliminate individual EEG characteristic differences, and improve the accuracy of anesthetic depth adjustment.

[0068] It should be noted that the depth of anesthesia is positively correlated with the anesthetic dose relative to the awake state. Different users have different sensitivities to anesthetics, and the same user of different ages also has different sensitivities to anesthetics.

[0069] In one embodiment of the present application, the control module 120 is used to: perform time-frequency transformation processing on the occipital EEG signal using a multi-window algorithm to obtain a frequency spectrum corresponding to the occipital EEG signal; and perform screening based on the frequency spectrum corresponding to the occipital EEG signal to obtain the frequency corresponding to the Alpha energy peak as the benchmark Alpha peak frequency.

[0070] In some possible implementations, a multi-taper algorithm is a mathematical processing method. It uses the multi-taper algorithm to perform time-frequency transformation on the occipital EEG signal to obtain a corresponding spectrogram of the occipital EEG signal. The spectrogram of the occipital EEG signal is then filtered to obtain the frequency corresponding to the alpha energy peak as the benchmark alpha peak frequency.

[0071] In other possible implementations, a multi-window algorithm is also used to perform time-frequency processing on the frontal EEG signal to obtain a spectrum corresponding to the frontal EEG signal. The spectrum corresponding to the frontal EEG signal is then filtered to obtain the frequency corresponding to the alpha energy peak as the dynamic alpha peak frequency.

[0072] Figure 5 The figure shows the process flow of obtaining the spectrum corresponding to the occipital lobe EEG signal according to the embodiment of the present application. Figure 5 As shown, performing time-frequency transformation processing on the occipital EEG signal using a multi-window algorithm to obtain a frequency spectrum corresponding to the occipital EEG signal includes the following steps S11 to S14.

[0073] Step S11 : segmenting the occipital lobe EEG signal according to a preset window length and step size to obtain a segmented occipital lobe EEG signal.

[0074] Step S12: performing linear detrending processing on each segmented occipital lobe EEG signal to obtain a processed occipital lobe EEG signal.

[0075] Step S13: performing fast Fourier transform processing on each of the processed occipital lobe EEG signals using a discrete sequence to obtain a power spectrum density corresponding to each of the processed occipital lobe EEG signals.

[0076] Step S14, arranging the power spectrum densities corresponding to the processed occipital lobe EEG signals in order to obtain a spectrum diagram corresponding to the occipital lobe EEG signals.

[0077] In some possible implementations, the preset window length is t1 seconds and the step length is t2 seconds. The continuous occipital EEG signal is segmented and processed according to the preset window length and step length, and multiple time windows after segmentation are obtained as the segmented occipital EEG signal. Specifically, for an occipital EEG signal with a total duration of 10 seconds, the segmented occipital EEG signal is: 0-4s, 1-5s, 2-6s, ... 6-10s, a total of 7 windows. The segmented occipital EEG signal is linearly detrended to remove low-frequency drift and obtain the processed occipital EEG signal. Multiple orthogonal Slepian function window discrete prolate spherical sequences are generated as discrete sequences, and multiple discrete sequence windows are applied to any processed occipital EEG signal to obtain multiple windowed signal segments. Each windowed signal segment is fast Fourier transformed to obtain the power spectral density corresponding to any processed occipital EEG signal. Repeat the above steps for each processed occipital EEG signal to obtain the power spectral density of each processed occipital EEG signal within the 0-40 Hz range. Arrange the power spectral densities corresponding to each processed occipital EEG signal in chronological order to obtain a spectrogram corresponding to the occipital EEG signal. The horizontal axis of the spectrogram represents time, the vertical axis represents frequency, and the color represents power intensity. The Slepian function is a function commonly used in signal processing and spectral analysis.

[0078] In one embodiment of the present application, the control module 120 is used to: perform fast Fourier transform processing on any processed occipital EEG signal using multiple discrete sequences to obtain multiple power spectral densities; average each of the power spectral densities to obtain the average value of the power spectral density as the power spectral density corresponding to the processed occipital EEG signal.

[0079] In some possible implementations, five orthogonal Slepian function window discrete prolate spherical sequences are generated and applied to any processed occipital EEG signal to obtain five windowed signal segments. Each windowed signal segment is then subjected to a fast Fourier transform to obtain the power spectral density of each window. The power spectral density results of the five windows are averaged, and the average power spectral density is calculated as the power spectral density corresponding to the processed occipital EEG signal.

[0080] Figure 6 The flowchart of obtaining the reference Alpha peak frequency according to the embodiment of the present application is shown. Figure 6 As shown, obtaining the reference Alpha peak frequency includes the following steps S21 to S25.

[0081] Step S21 , calculating time-frequency map matrix data based on multiple channel occipital lobe EEG signals.

[0082] Step S22: Screen the occipital EEG signals of each channel to obtain spectrograms of the occipital EEG time-frequency data of multiple awake and eye-closed states.

[0083] Step S23 , performing peak significance calculation based on the spectrum of the occipital EEG time-frequency data to obtain the Alpha peak frequency with the maximum peak significance.

[0084] Step S24 , screening is performed according to the Alpha peak frequencies corresponding to the respective channels to obtain Alpha peak frequencies that meet preset conditions.

[0085] Step S25 , averaging the Alpha peak frequencies that meet the preset conditions to obtain the reference Alpha peak frequency.

[0086] In some possible implementations, in the process of collecting the occipital EEG signal of the user in the awake and closed-eye state, occipital EEG signal data of 8 channels is obtained. Calculations are performed based on the occipital EEG signal data of the 8 channels to obtain the time-frequency matrix data of the occipital EEG signal of the 8 channels. For any channel, 3 occipital EEG time-frequency measurements in the awake and closed-eye state are selected in sequence to generate a spectrogram. The peak frequency of the Alpha frequency band (7-13Hz) is calculated based on the spectrogram to obtain the Alpha peak frequency with the largest peak significance. The Alpha peak frequencies of the 8 channels are calculated in sequence, and channels with peak significance greater than 10 are screened out. The Alpha peak frequencies corresponding to the screened channels are averaged to obtain the baseline Alpha peak frequency. Among them, the peak significance is also known as the peak prominence, which is characterized by the vertical distance from the peak to the nearest valley.

[0087] In one embodiment of the present application, the control module 120 is configured to obtain a corresponding baseline Alpha peak frequency threshold based on the user's age. Based on the user's age, the control module 120 determines whether the user's baseline Alpha peak frequency meets the corresponding baseline Alpha peak frequency threshold. If so, the control module 120 adjusts the anesthetic dose based on the user's baseline Alpha peak frequency. If not, the control module 120 marks the user as being in an abnormal state.

[0088] In some possible implementations, the baseline Alpha peak frequency varies with the user's age. A threshold corresponding to the baseline Alpha peak frequency is obtained based on the user's age. A determination is made as to whether the obtained baseline Alpha peak frequency meets the threshold. If so, the baseline Alpha peak frequency is deemed valid, and the anesthetic dose is adjusted based on the baseline Alpha peak frequency. If not, the user is determined to have a fragile brain and is flagged as abnormal. For example, the user's age may be determined to be greater than 75 years old. If so, the baseline Alpha peak frequency is determined to meet a first threshold. If so, the anesthetic dose is adjusted based on the user's baseline Alpha peak frequency. If not, the user is flagged as abnormal. The user's age may be determined to be greater than 60 years old. If so, the baseline Alpha peak frequency is determined to meet a second threshold. If so, the anesthetic dose is adjusted based on the user's baseline Alpha peak frequency. If not, the user is flagged as abnormal. The user's age may be determined to be greater than 18 years old. If so, the baseline Alpha peak frequency is determined to meet a third threshold. If so, the anesthetic dose is adjusted based on the user's baseline Alpha peak frequency. If not, the user is flagged as abnormal. Determine whether the user is older than 6 years old. If so, determine whether the baseline Alpha peak frequency meets a fourth threshold. If so, adjust the anesthetic dose based on the user's baseline Alpha peak frequency. If not, mark the user as being in an abnormal state. The first, second, third, and fourth thresholds may be specific Alpha peak frequency values ​​or ranges of Alpha peak frequency values, and this application is not limited thereto.

[0089] In one embodiment of the present application, the control module 120 is used to: determine whether the difference between the dynamic Alpha peak frequency and the baseline Alpha peak frequency is greater than or equal to the preset threshold upper limit; if so, determine that the anesthesia depth is too shallow and obtain the anesthetic dose to be adjusted; if not, determine whether the difference between the dynamic Alpha peak frequency and the baseline Alpha peak frequency is less than or equal to the preset threshold lower limit; if so, determine that the anesthesia depth is too deep; if not, determine that the anesthesia depth is moderate.

[0090] In some possible implementations, during the process of monitoring the user's anesthesia state, it is determined whether the difference between the obtained dynamic Alpha peak frequency and the baseline Alpha peak frequency is within the range from the lower threshold to the upper threshold. If so, the anesthesia depth is determined to be moderate. If not, the anesthesia depth is determined to be too deep or too shallow based on the value of the dynamic Alpha peak frequency. Specifically, the Alpha peak frequencies of the forehead EEG signals of the 8 channels in the anesthesia state are dynamically calculated, and the channels with peak significance greater than 10 are screened out. The average value of the Alpha peak frequencies corresponding to these channels is calculated to obtain the dynamic Alpha peak frequency. It is determined whether the difference between the dynamic Alpha peak frequency and the Alpha peak frequency is greater than or equal to the upper threshold. If so, the anesthesia depth is determined to be too shallow, and the anesthetic dose to be adjusted is obtained. If not, it is determined whether the difference between the dynamic Alpha peak frequency and the baseline Alpha peak frequency is less than or equal to the preset lower threshold. If so, the anesthesia depth is determined to be too deep. If not, the anesthesia depth is determined to be moderate.

[0091] Figure 7A The diagram shows the occipital EEG signal in the awake eyes-closed state described in the embodiment of the present application. Figure 7A As shown, the user's baseline Alpha peak frequency is 9.5215Hz. Figure 7B The diagram shows the frontal EEG signal of the anesthesia state described in the embodiment of the present application. Figure 7B As shown, the user's dynamic alpha peak frequency is 11.5Hz. Figure 7C Shown is a schematic diagram of the depth of anesthesia range described in the embodiment of this application. Figure 7C As shown, the difference between the user's dynamic Alpha peak frequency and the baseline Alpha peak frequency is between the lower threshold and the upper threshold, and it is considered that the user is in an appropriate anesthesia depth.

[0092] The present application embodiment provides a personalized anesthesia control method, which can be used, for example, Figure 1A The local processor 12 shown or Figure 1B It is implemented by the cloud server 21 shown. Figure 8 The flow chart of the personalized anesthesia control method described in the embodiment of the present application is shown. Figure 8 As shown, the personalized anesthesia control method includes steps S31 to S35.

[0093] Step S31 , collecting the occipital EEG signal of the user in the awake eyes-closed state and monitoring the forehead EEG signal of the user in the anesthesia state.

[0094] Step S32: performing screening calculation based on the occipital lobe EEG signal to obtain a reference Alpha peak frequency.

[0095] Step S33: Calculate based on the frontal EEG signal to obtain the dynamic Alpha peak frequency.

[0096] Step S34 , analyzing the reference Alpha peak frequency and the dynamic Alpha peak frequency to obtain the anesthetic dose to be adjusted.

[0097] Step S35: Using the anesthetic dose to be adjusted, the user's anesthetic state is adjusted in real time.

[0098] In some possible implementations, a baseline Alpha peak frequency is obtained by screening and calculating the user's occipital EEG signal while awake and eyes closed. A dynamic Alpha peak frequency is obtained by calculating the user's forehead EEG signal while anesthetized. The difference between the obtained baseline Alpha peak frequency and the dynamic Alpha peak frequency is analyzed to obtain a corresponding anesthetic dose to be adjusted. The anesthetic dose to be adjusted is used to adjust the user's anesthesia state in real time.

[0099] In the embodiment of the present application, the baseline Alpha peak frequency is obtained using the occipital EEG signal obtained when the user is awake and eyes closed, and the dynamic Alpha peak frequency is obtained using the user's forehead EEG signal. The baseline Alpha peak frequency and the dynamic Alpha peak frequency are analyzed to adjust the anesthetic dose, thereby adjusting the user's anesthetic state in real time. This personalized anesthesia control method can use the user's EEG signal in the awake and eyes closed state as a reference to monitor the dynamically changing anesthesia depth in real time, perform personalized adjustments for the user, eliminate individual EEG characteristic differences, and improve the accuracy of anesthesia depth adjustment.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices or methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0101] The modules / units described as separate components may or may not be physically separate, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into a processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit.

[0102] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0103] An embodiment of the present application also provides an electronic device. Figure 9 The structure diagram of the electronic device 900 according to the embodiment of the present application is shown. Figure 9 As shown, in this embodiment, the electronic device 900 includes a memory 910 and a processor 920 .

[0104] The memory 910 is used to store computer programs; preferably, the memory 910 includes: ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk, etc., various media that can store program codes.

[0105] Specifically, the memory 910 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The electronic device 900 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 910 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application. It will be understood that the memory 910 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM) or a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). Memory described in the embodiments of the present invention is intended to include, but not be limited to, these and any other suitable types of memory.

[0106] The processor 920 is connected to the memory 910 and is used to execute the computer program stored in the memory 910 so that the electronic device 900 executes the personalized anesthesia control method described in any embodiment of the present application.

[0107] Optionally, the processor 920 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0108] Optionally, the electronic device 900 in this embodiment may further include a display 930. The display 930 is communicatively connected to the memory 910 and the processor 920, and is used to display a graphical user interface (GUI) interactive interface related to the personalized anesthesia control method described in the embodiment of the present application.

[0109] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the personalized anesthesia control method described in any embodiment of the present application is implemented.

[0110] As used in this specification, the terms "component," "module," "system," and the like are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. By way of illustration, both an application running on a computing device and a computing device can be a component. One or more components can reside in a process and / or an execution thread, and a component can be located on a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures stored thereon. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component on a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0111] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0112] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A personalized anesthesia control system, characterized in that: include: The acquisition and monitoring module is used to collect the occipital EEG signals of the user when the user is awake and eyes closed, and to monitor the forehead EEG signals of the user when the user is anesthetized; a control processing module, configured to perform screening calculations based on the occipital EEG signal to obtain a baseline Alpha peak frequency, perform calculations based on the frontal EEG signal to obtain a dynamic Alpha peak frequency, and perform analysis based on the baseline Alpha peak frequency and the dynamic Alpha peak frequency to obtain an anesthetic dose to be adjusted; The drug infusion module is used to adjust the user's anesthetic state in real time using the anesthetic dose to be adjusted.

2. The personalized anesthesia control system according to claim 1, characterized in that: The control module is used for: Performing time-frequency transformation on the occipital EEG signal using a multi-window algorithm to obtain a frequency spectrum corresponding to the occipital EEG signal; Screening is performed according to the spectrum diagram corresponding to the occipital lobe EEG signal to obtain the frequency corresponding to the Alpha energy peak as the reference Alpha peak frequency.

3. The personalized anesthesia control system according to claim 2, characterized in that: The control module is used for: Segmenting the occipital lobe EEG signal according to a preset window length and step size to obtain a segmented occipital lobe EEG signal; performing linear detrending processing on each segmented occipital lobe EEG signal to obtain a processed occipital lobe EEG signal; Performing fast Fourier transform processing on each of the processed occipital lobe EEG signals using a discrete sequence to obtain a power spectral density corresponding to each of the processed occipital lobe EEG signals; The power spectrum densities corresponding to the processed occipital lobe EEG signals are arranged in order to obtain a spectrum diagram corresponding to the occipital lobe EEG signals.

4. The personalized anesthesia control system according to claim 3, characterized in that: The control module is used for: Performing fast Fourier transform on any processed occipital EEG signal using multiple discrete sequences to obtain multiple power spectral densities; The power spectrum densities are averaged and the average value of the power spectrum densities is obtained as the power spectrum density corresponding to the processed occipital lobe EEG signal.

5. The personalized anesthesia control system according to claim 1, characterized in that: The control module is used for: Calculate time-frequency map matrix data based on multiple channel occipital EEG signals; Screening the occipital EEG signals of each channel to obtain spectrograms of the occipital EEG time-frequency data in multiple awake and eye-closed states; Calculating peak significance based on the spectrum of the occipital EEG time-frequency data to obtain the Alpha peak frequency with the greatest peak significance; Filter according to the Alpha peak frequency corresponding to each channel to obtain the Alpha peak frequency that meets the preset conditions; The Alpha peak frequencies that meet the preset conditions are averaged to obtain the reference Alpha peak frequency.

6. The personalized anesthesia control system according to claim 1, characterized in that: The control module is used for: Obtain the corresponding baseline Alpha peak frequency threshold based on the user's age; Determine whether the user's baseline Alpha peak frequency meets the corresponding baseline Alpha peak frequency threshold based on the user's age. If so, adjust the anesthetic dose based on the user's baseline Alpha peak frequency. If not, mark the user as abnormal.

7. The personalized anesthesia control system according to claim 1, characterized in that: The control module is used for: determining whether the difference between the dynamic Alpha peak frequency and the reference Alpha peak frequency is greater than or equal to a preset upper threshold value; if so, determining that the anesthesia depth is too shallow, and obtaining the anesthetic dose to be adjusted; If not, it is determined whether the difference between the dynamic Alpha peak frequency and the reference Alpha peak frequency is less than or equal to the preset threshold lower limit. If so, it is determined that the anesthesia depth is too deep; if not, it is determined that the anesthesia depth is moderate.

8. A personalized anesthesia control method, characterized in that: include: Collect the user's occipital EEG signals when the user is awake with eyes closed and monitor the user's forehead EEG signals when the user is anesthetized; Performing screening calculations based on the occipital EEG signal to obtain a baseline Alpha peak frequency; Calculating based on the frontal EEG signal to obtain a dynamic Alpha peak frequency; Analyze the reference Alpha peak frequency and the dynamic Alpha peak frequency to obtain an anesthetic dose to be adjusted; The anesthetic dose to be adjusted is used to adjust the user's anesthetic state in real time.

9. An electronic device, characterized in that: The electronic device comprises: memory for storing computer programs; A processor, wherein the processor is configured to execute the computer program stored in the memory so as to enable the electronic device to execute the personalized anesthesia control method according to claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the personalized anesthesia control method according to claim 8 is implemented.

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