An intelligent method and system for monitoring anesthesia target control in the elderly based on EEG signals

Abstract

The present invention provides an intelligent method and system for monitoring anesthesia target control of the elderly based on EEG signals, and the method includes: obtaining the dose of anesthetics injected by the target elderly at different target time points, and collecting the EEG signals of the target elderly at different target time points in real time; determining the consciousness index and injury index of the target elderly at different doses of anesthetics based on the EEG signals, and determining the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly based on the consciousness index and the injury index; determining the physiological parameters of the target elderly at the critical point, and integrating and recording the critical point, the dose of anesthetics corresponding to the critical point and the physiological parameters. By determining the intensity of the conscious activity of the elderly under different doses of anesthetics, it is convenient to accurately find the critical point of deep and shallow sedation of the elderly, so as to facilitate the adjustment of the anesthetic dose so that the patient is in a good anesthetized state, improve the efficiency of anesthesia, enhance the level of anesthesia, and reduce the harm of anesthetics to the patient.

Description

Technical Field

[0001] The present invention relates to the field of medical research technology, and in particular to an intelligent method and system for target control of anesthesia in the elderly based on electroencephalogram signal monitoring. Background Art

[0002] At present, anesthesia has become an indispensable step in the surgical process. Anesthesia can not only prevent patients from being aware of the operation, but also improve the efficiency of the operation;

[0003] However, currently, the amount of anesthetics required by patients is mostly estimated based on the medical staff's professional experience, and the patient's current anesthesia status cannot be understood in real time during the injection process, which can easily lead to more or less anesthetics, thereby greatly reducing the anesthesia effect, and the patient's anesthesia status cannot be adjusted to the optimal anesthesia status in time according to the needs of the operation;

[0004] Therefore, the present invention provides an intelligent method and system for anesthesia target control of the elderly based on EEG signal monitoring, which is used to determine the intensity of the elderly's conscious activities under different doses of anesthetics, thereby facilitating the accurate finding of the critical point of deep and shallow anesthesia for the elderly, thereby facilitating the adjustment of the anesthetic dose according to surgical needs so that the patient is in a good anesthesia state, improving anesthesia efficiency, enhancing anesthesia level, eliminating intraoperative awareness, and reducing the harm of anesthetics to patients. Summary of the invention

[0005] The present invention provides an intelligent method and system for anesthesia target control based on EEG signal monitoring for the elderly, which is used to determine the intensity of the elderly's conscious activities under different doses of anesthetics, thereby facilitating accurate finding of the elderly's initial anesthesia loss of consciousness critical point and the critical point of deep and shallow sedation critical point, thereby facilitating adjustment of the anesthetic dose according to surgical needs so that the patient is in a good anesthetic state, improving anesthesia efficiency, enhancing anesthesia level, eliminating intraoperative awareness, and reducing the harm of anesthetics to patients.

[0006] The present invention provides an intelligent method for monitoring target control of anesthesia in the elderly based on EEG signals, comprising:

[0007] Step 1: obtaining the dose of anesthetic injected by the target elderly at different target time points, and collecting the EEG signals of the target elderly at different target time points in real time;

[0008] Step 2: determining the consciousness index and injury index of the target elderly person at different doses of anesthetics based on the EEG signal, and determining the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly person based on the consciousness index and injury index;

[0009] Step 3: Determine the brain physiological parameters of the target elderly person at the critical point of losing consciousness during initial anesthesia and the critical point of deep and shallow sedation, and integrate and record the critical point of losing consciousness during initial anesthesia and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the brain physiological parameters.

[0010] Preferably, an intelligent method for monitoring elderly anesthesia target control based on EEG signals, in step 1, obtaining the dose of anesthetic injected to the target elderly at different target time points, includes:

[0011] Obtaining the start time information of the target elderly receiving the anesthetic injection, and determining the injection speed of the preset anesthetic injection pump, wherein the injection speed of the preset anesthetic injection pump is a uniform speed;

[0012] Acquire current time information, and determine the working time of the preset anesthetic injection pump based on the current time information and the start time information of the target elderly receiving anesthetic injection;

[0013] The dosage of anesthetic injected to the target elderly person at different target time points is calculated based on the working time and the injection speed of the preset anesthetic injection pump.

[0014] Preferably, an intelligent method for target control of anesthesia for the elderly based on EEG signal monitoring is used to calculate the dose of anesthetic injected to the target elderly at different target time points based on the working time and the injection speed of the preset anesthetic injection pump, including:

[0015] Obtaining the dose of anesthetic injected to the target elderly at different target time points, and determining the remaining time of the target elderly's surgery maintenance period, wherein the dose of anesthetic injected to the target elderly at different target time points is obtained by accumulating the dose of anesthetic injected to the target elderly based on the development of time;

[0016] Obtaining the dose of anesthetic injected into the target elderly person's body at the current moment, and judging whether the dose of anesthetic currently in the target elderly person's body can complete the remaining length of the surgical maintenance period based on a preset anesthesia maintenance method;

[0017] If possible, stop injecting anesthetics into the target elderly person's body and send an early warning reminder to medical staff;

[0018] Otherwise, continue to inject anesthetics into the target elderly person's body, and monitor the EEG signals of the target elderly person in real time.

[0019] Preferably, an intelligent method for monitoring elderly anesthesia target control based on EEG signals, in step 1, real-time acquisition of EEG signals of the target elderly at different target time points, including:

[0020] Obtaining a signal collection instruction sent by the management terminal, and controlling a preset EEG signal collection device to open a signal collection channel based on the signal collection instruction;

[0021] Based on the preset EEG electrode assembly, the EEG signal of the target elderly person after receiving the injection of anesthetic is monitored in real time, and based on the signal acquisition channel, the EEG signal is transmitted to the preset EEG signal acquisition device;

[0022] The preset EEG signal acquisition device performs clutter filtering and signal enhancement processing on the EEG signal, and performs analog-to-digital conversion on the processed EEG signal based on a preset method to obtain an EEG signal to be displayed;

[0023] Based on the preset EEG signal acquisition device, the EEG signal to be displayed is displayed to complete the acquisition of EEG signals of the target elderly at different target time points.

[0024] Preferably, an intelligent method for monitoring elderly anesthesia target control based on EEG signals, in step 1, obtaining the dose of anesthetic injected by the target elderly at different target time points, and collecting the EEG signals of the target elderly at different target time points in real time, including:

[0025] Obtain the dose of anesthetic injected to the target elderly at different target time points and the EEG signals corresponding to the different target time points, wherein the time interval between the injected anesthetic and the collected EEG signals is consistent;

[0026] Determine the correspondence between the dose of the anesthetic injected into the target elderly person and the EEG signal based on the time interval, and mark the dose of the anesthetic and the corresponding EEG signal at the same time point based on the correspondence;

[0027] Based on the marking results, a target report sheet is generated for the dosage of the anesthetic and the corresponding electroencephalogram signal at the same time point, and the target report sheet is stored and recorded.

[0028] Preferably, an intelligent method for monitoring elderly anesthesia target control based on EEG signals, in step 2, determining the consciousness index and injury index of the target elderly at different doses of anesthetics based on EEG signals, and determining the critical point of loss of consciousness of the target elderly at the initial anesthesia and the critical point of deep and shallow sedation based on the consciousness index, including:

[0029] Obtaining the EEG signals of the target elderly at different target time points, and determining the average path length of the EEG signals at different target time points based on a preset EEG signal processing method;

[0030] Determining the time series signal characteristics of the EEG signals at the different target time points based on the average path length, and converting the EEG signals in different time domains into digital EEG signal sequences in sequence based on the time series signal characteristics;

[0031] Determine the cross information points of the digital EEG signal sequences in adjacent time domains, and determine the voltage values ​​corresponding to the cross information points based on a preset processing method;

[0032] Determine the consciousness index and injury index of the target elderly person at different time points based on the voltage value;

[0033] Obtain the total number of consciousness indexes and injury indexes of the target elderly at different time points, and determine the width of the display area in the line graph to be displayed, wherein the width of the display area includes the number of displayable data;

[0034] Based on the correspondence between the width of the display area and the number of displayable data, determining whether the width of the display area can display the consciousness index and the injury index at different time points at one time;

[0035] If not, the consciousness index and injury index at different time points are grouped to obtain N consciousness index groups to be displayed, and the plotting points of the consciousness index at different time points in the line graph to be displayed are determined based on the sequence of the time points corresponding to the consciousness index, and N consciousness index and injury index line graphs are obtained based on the plotting points;

[0036] Otherwise, the consciousness indexes at the different time points are displayed at once to obtain corresponding line graphs of the consciousness index and the injury index;

[0037] Determine the brain consciousness and brain energy activity intensity of the target elderly at different target time points based on the consciousness index and injury index line graph, and determine the turning point of the brain consciousness and brain energy activity intensity of the target elderly based on the brain consciousness and brain energy activity intensity, wherein the different target time points correspond to different doses of anesthetics;

[0038] The critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly are determined based on the turning point of the target elderly's brain consciousness and brain energy activity intensity.

[0039] Preferably, an intelligent method for monitoring elderly anesthesia target control based on EEG signals, in step 3, determining the brain physiological parameters of the target elderly at the critical point of loss of consciousness during the initial anesthesia and the critical point of deep and shallow sedation, and integrating and recording the critical point of loss of consciousness during the initial anesthesia and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters, including:

[0040] Obtaining the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly person, and obtaining brain physiological parameters of the target elderly person based on the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation, wherein the physiological parameters are at least two items;

[0041] Acquire historical brain physiological parameter values, and determine the fluctuation range values ​​of various brain physiological parameters of the patient at the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation based on the historical brain physiological parameter values;

[0042] Verifying the acquired brain physiological parameters of the target elderly based on the fluctuation range value, and determining whether the brain physiological parameters of the target elderly are within the fluctuation range value;

[0043] If not, it is determined that the acquired brain physiological parameters of the target elderly are abnormal, and the brain physiological parameters of the target elderly are input into a preset physiological parameter calibration model for calibration, wherein the preset brain physiological parameter calibration model is obtained based on training of historical brain physiological parameter values, and the preset brain physiological parameter calibration model includes multiple modules for calibrating body physiological parameters, and the modules are independent of each other;

[0044] Otherwise, it is determined that the obtained brain physiological parameters of the target elderly person are normal;

[0045] Generate a record report task based on the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters, and match a target report template from a preset report template library based on the record report task;

[0046] Visually configuring the target report template, and determining the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the target position of the brain physiological parameter in the target report template based on the visual configuration;

[0047] Based on the target position, the critical point of initial anesthesia loss of consciousness, the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters are respectively filled into the target position in the target report template to obtain an anesthesia parameter record report sheet;

[0048] The anesthesia parameter record report is sent to the management terminal and stored in a preset storage area, completing the integrated record of the critical point of initial anesthesia loss of consciousness, the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the brain physiological parameters.

[0049] Preferably, an intelligent method for target control of anesthesia in the elderly based on EEG signal monitoring is used to obtain an anesthesia parameter record report, including:

[0050] Obtain anesthesia parameter record report sheets corresponding to different surgeries of multiple groups of elderly people, and classify the anesthesia parameter record report sheets based on the type of surgery to obtain target classification results, wherein there are at least two anesthesia parameter record report sheets corresponding to each type of surgery, and there are at least two types of surgery;

[0051] Based on the target classification results, the anesthesia parameter record report sheets corresponding to each type of surgery are packaged to obtain a target data packet;

[0052] The target data packets are stored respectively to obtain a reference library of anesthesia parameter record reports;

[0053] An index keyword is created based on the target data packet corresponding to each type of surgery at the storage address of the anesthesia parameter record report reference library, and the index keyword is sent to the medical staff terminal.

[0054] Preferably, an intelligent method for target control of anesthesia in the elderly based on EEG signal monitoring is used to send the index keywords to the medical staff terminal, including:

[0055] Acquire the current surgery data of the elderly person, and determine the target surgery type of the current surgery of the elderly person based on the surgery data;

[0056] Determine a target index keyword based on the target surgery type, and search for a target data packet corresponding to the target surgery type in the anesthesia parameter record report reference library based on the target index keyword;

[0057] Determine the target anesthesia depth required for the current surgery of the elderly based on the current surgery data of the elderly, and match the target anesthesia depth with each anesthesia parameter recorded in each anesthesia parameter record report in the target data packet to obtain a target critical point;

[0058] The target anesthetic dosage corresponding to the target critical point is determined, and the anesthetic required by the current elderly person is prepared and injected based on the target anesthetic dosage to ensure that the current elderly person is anesthetized to the target anesthetic depth required for the operation.

[0059] Preferably, an intelligent target control system for monitoring anesthesia of the elderly based on EEG signals comprises:

[0060] The EEG signal acquisition module is used to obtain the dose of anesthetic injected to the target elderly at different target time points, and to collect the EEG signals of the target elderly at different target time points in real time;

[0061] An analysis module, used to determine the consciousness index of the target elderly person under different doses of anesthetics based on the EEG signal, and determine the critical point of loss of consciousness and the critical point of deep and shallow sedation of the target elderly person based on the consciousness index;

[0062] The integrated recording module is used to determine the brain physiological parameters of the target elderly person at the critical point of losing consciousness during the initial anesthesia and the critical point of deep and shallow sedation, and to integrate and record the critical point of losing consciousness during the initial anesthesia and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the brain physiological parameters.

[0063] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0064] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0066] Figure 1 This is a flow chart of an intelligent method for monitoring target control of anesthesia in the elderly based on EEG signals in an embodiment of the present invention;

[0067] Figure 2 This is a flowchart of step 1 in an intelligent method for monitoring target control of anesthesia in the elderly based on EEG signals in an embodiment of the present invention;

[0068] Figure 3 This is a structural diagram of an intelligent target control system for monitoring anesthesia for the elderly based on EEG signals in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0070] Embodiment 1:

[0071] This embodiment provides an intelligent method for monitoring elderly anesthesia target control based on EEG signals. Figure 1 As shown, including:

[0072] Step 1: obtaining the dose of anesthetic injected by the target elderly at different target time points, and collecting the EEG signals of the target elderly at different target time points in real time;

[0073] Step 2: determining the consciousness index of the target elderly person at different doses of anesthetics based on the EEG signal, and determining the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly person based on the consciousness index;

[0074] Step 3: Determine the brain physiological parameters of the target elderly person at the critical point of losing consciousness during initial anesthesia and the critical point of deep and shallow sedation, and integrate and record the critical point of losing consciousness during initial anesthesia and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the brain physiological parameters.

[0075] In this embodiment, the target elderly refers to patients aged 61-80 years old.

[0076] In this embodiment, the target time point refers to the dose of anesthetics received by the patient at different times during the operation. For example, 10 ml of anesthetics are injected into the body one hour after the operation, and 20 ml of anesthetics are injected into the body two hours after the operation.

[0077] In this embodiment, the consciousness index and injury index are used to describe the target elderly's brain consciousness activity and brain energy intensity under different doses of anesthetics, that is, the degree of awareness of the surgery.

[0078] In this embodiment, the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation refer to the intersection of the target elderly person's consciousness index and injury index trend curve.

[0079] In this embodiment, the physiological parameters refer to the target elderly person's electrocardiogram, blood pressure, blood oxygen, etc.

[0080] The beneficial effect of the above technical solution is: by determining the brain consciousness and brain energy activity intensity of the elderly under different doses of anesthetics, it is helpful to accurately find the critical point of loss of consciousness and the critical point of deep and shallow sedation of the elderly during initial anesthesia, and record it, so as to facilitate the rapid adjustment of the anesthetic dose according to the needs of the operation so that the patient is in a good anesthesia state, improve the anesthesia efficiency, enhance the anesthesia level, eliminate intraoperative awareness, and reduce the harm of anesthetics to patients.

[0081] Embodiment 2:

[0082] Based on the above embodiment 1, this embodiment provides an intelligent method for monitoring anesthesia target control in the elderly based on EEG signals, such as Figure 2 As shown, in step 1, the dose of anesthetic injected to the target elderly at different target time points is obtained, including:

[0083] Step 101: obtaining the start time information of the target elderly receiving the anesthetic injection, and determining the injection speed of the preset anesthetic injection pump, wherein the injection speed of the preset anesthetic injection pump is a uniform speed;

[0084] Step 102: obtaining current time information, and determining the working time of the preset anesthetic injection pump based on the current time information and the start time information of the target elderly receiving anesthetic injection;

[0085] Step 103: Based on the working time and the preset injection speed of the anesthetic injection pump, the dosage of the anesthetic injected to the target elderly person at different target time points is calculated.

[0086] In this embodiment, the start time information refers to the time information when the target elderly person receives anesthesia during preoperative preparation, for example, the target elderly person receives an anesthetic injection at 9:00 in the morning.

[0087] In this embodiment, the preset anesthetic injection pump is pre-set, including multiple injection pumps, such as a controlled sedation injection pump, a sedation injection pump, and the like.

[0088] In this embodiment, the current time information refers to the time information currently used to determine the dosage of the anesthetic, and the target elderly person is still receiving an anesthetic injection at the current moment.

[0089] The beneficial effects of the above technical solution are: by determining the injection time and injection speed of the preset anesthetic injection pump, the dosage of anesthetics at different time points is ensured to be accurately calculated, which facilitates the determination of the EEG signals of the target elderly after different doses of anesthetics are injected into the body of the target elderly, thereby improving the accuracy of the analysis of the anesthetic state of the target elderly, and also providing convenience for determining the dosage of anesthetics at the critical point of deep and shallow anesthesia.

[0090] Embodiment 3:

[0091] On the basis of the above-mentioned embodiment 2, this embodiment provides an intelligent method for target control of anesthesia for the elderly based on EEG signal monitoring, which calculates the dose of anesthetic injected to the target elderly at different target time points based on the working time and the injection speed of the preset anesthetic injection pump, including:

[0092] Obtaining the dose of anesthetic injected to the target elderly at different target time points, and determining the remaining time of the target elderly's surgery maintenance period, wherein the dose of anesthetic injected to the target elderly at different target time points is obtained by accumulating the dose of anesthetic injected to the target elderly based on the development of time;

[0093] Obtaining the dose of anesthetic injected into the target elderly person's body at the current moment, and judging whether the dose of anesthetic currently in the target elderly person's body can complete the remaining length of the surgical maintenance period based on a preset anesthesia maintenance method;

[0094] If possible, stop injecting anesthetics into the target elderly person's body and send an early warning reminder to medical staff;

[0095] Otherwise, continue to inject anesthetics into the target elderly person's body, and monitor the EEG signals of the target elderly person in real time.

[0096] In this embodiment, the remaining time of the operation maintenance period refers to the remaining time of the current operation of the target elderly person. For example, the entire operation takes 3 hours, and the operation has been going on for two hours, then the remaining time of the operation maintenance period is 1 hour.

[0097] In this embodiment, the dose of anesthetic injected into the body of the target elderly person at the current moment refers to the total amount of anesthetic injected into the body of the target elderly person from the start of the surgery to the current moment.

[0098] In this embodiment, the preset anesthesia maintenance method is set in advance, for example, it can be predicted based on historical surgical anesthesia data.

[0099] The beneficial effect of the above technical solution is: by determining whether the current dose of anesthetics received by the target elderly person is sufficient to complete the remaining surgical tasks of the target elderly person, it is convenient to stop the injection of anesthetics in time and reduce the harm of anesthetics to the body. At the same time, it is convenient to adjust the anesthetic dose according to the needs of the operation so that the patient is in a good anesthesia state, thereby improving the anesthesia effect.

[0100] Embodiment 4:

[0101] On the basis of the above-mentioned embodiment 1, this embodiment provides an intelligent method for monitoring elderly anesthesia target control based on EEG signals. In step 1, the EEG signals of the target elderly at different target time points are collected in real time, including:

[0102] Obtaining a signal collection instruction sent by the management terminal, and controlling a preset EEG signal collection device to open a signal collection channel based on the signal collection instruction;

[0103] Based on the preset EEG electrode assembly, the EEG signal of the target elderly person after receiving the injection of anesthetic is monitored in real time, and based on the signal acquisition channel, the EEG signal is transmitted to the preset EEG signal acquisition device;

[0104] The preset EEG signal acquisition device performs clutter filtering and signal enhancement processing on the EEG signal, and performs analog-to-digital conversion on the processed EEG signal based on a preset method to obtain an EEG signal to be displayed;

[0105] Based on the preset EEG signal acquisition device, the EEG signal to be displayed is displayed to complete the acquisition of EEG signals of the target elderly at different target time points.

[0106] In this embodiment, the preset EEG signal acquisition device is set in advance, for example, it can be an EEG collector in the existing medical field.

[0107] In this embodiment, the preset EEG electrode assembly refers to a component in a preset EEG signal acquisition device that contacts the scalp of the target elderly person, and can be a tool for accurately acquiring EEG signals of the target elderly person through the cerebral cortex of the target elderly person.

[0108] In this embodiment, the clutter filtering process and the signal enhancement process refer to filtering out the noise waves in the EEG signal and amplifying the filtered EEG signal, so that the medical staff can clearly check the specific situation of the EEG signal of the target elderly.

[0109] In this embodiment, the preset method is set in advance. For example, the collected EEG signal can be converted into a corresponding digital signal through an analog-to-digital converter, thereby realizing the display of the EEG signal.

[0110] The beneficial effect of the above technical solution is: by controlling the preset EEG signal acquisition device to collect the EEG signals of the target elderly in real time, it is ensured that the EEG signals of the target elderly under different anesthetic doses can be accurately obtained, thereby facilitating the accurate determination of the critical point of loss of consciousness of the target elderly due to initial anesthesia and the critical point of deep and shallow sedation, thereby improving the anesthetic effect.

[0111] Embodiment 5:

[0112] On the basis of the above-mentioned embodiment 1, this embodiment provides an intelligent method for monitoring elderly anesthesia target control based on EEG signals. In step 1, the dose of anesthetic injected by the target elderly at different target time points is obtained, and the EEG signals of the target elderly at different target time points are collected in real time, including:

[0113] Obtain the dose of anesthetic injected to the target elderly at different target time points and the EEG signals corresponding to the different target time points, wherein the time interval between the injected anesthetic and the collected EEG signals is consistent;

[0114] Determine the correspondence between the dose of the anesthetic injected into the target elderly person and the EEG signal based on the time interval, and mark the dose of the anesthetic and the corresponding EEG signal at the same time point based on the correspondence;

[0115] Based on the marking results, a target report sheet is generated for the dosage of the anesthetic and the corresponding electroencephalogram signal at the same time point, and the target report sheet is stored and recorded.

[0116] In this embodiment, the consistency between the time interval of receiving the injected anesthetic and the collected EEG signal means that after the target elderly person receives the anesthetic injection, the collected EEG signal is collected in real time according to the amount of anesthetic in the target elderly person's body. For example, when the operation has been going on for ten minutes, the amount of anesthetic in the target elderly person's body is 5 ml. At this time, the EEG signal of the target elderly person is collected from the beginning of the anesthesia injection to ten minutes.

[0117] In this embodiment, the corresponding relationship means that each time point corresponds to an EEG signal, and the time point may be one second, one minute, etc.

[0118] In this embodiment, marking refers to marking the dose of anesthetic and the EEG signal with a unified marking symbol at the same time point, so as to facilitate the determination of the EEG signal of the target elderly person under different doses of anesthetic.

[0119] In this embodiment, the target report refers to recording the dosage of anesthetic and the corresponding EEG signal at the same time point, so as to facilitate the later analysis of the critical point of loss of consciousness due to initial anesthesia and the critical point of deep and shallow sedation.

[0120] The beneficial effect of the above technical solution is: by determining the EEG signals of the target elderly under different doses of anesthetics at different time points, determining the correspondence between the two, and recording the correspondence between the two, it is convenient to analyze the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly, thereby improving the accuracy of the critical point of deep and shallow anesthesia, enhancing the anesthetic effect, and reducing the harm of anesthetics to patients.

[0121] Embodiment 6:

[0122] On the basis of the above-mentioned embodiment 1, this embodiment provides an intelligent method for monitoring anesthesia target control of the elderly based on EEG signals. In step 2, the consciousness index of the target elderly at different doses of anesthetics is determined based on the EEG signals, and the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly are determined based on the consciousness index, including:

[0123] Obtaining the EEG signals of the target elderly at different target time points, and determining the average path length of the EEG signals at different target time points based on a preset EEG signal processing method;

[0124] Determining the time series signal characteristics of the EEG signals at the different target time points based on the average path length, and converting the EEG signals in different time domains into digital EEG signal sequences in sequence based on the time series signal characteristics;

[0125] Determine the cross information points of the digital EEG signal sequences in adjacent time domains, and determine the voltage values ​​corresponding to the cross information points based on a preset processing method;

[0126] Determine the consciousness index and injury index of the target elderly person at different time points based on the voltage value;

[0127] Obtain the total number of consciousness indexes and injury indexes of the target elderly at different time points, and determine the width of the display area in the line graph to be displayed, wherein the width of the display area includes the number of displayable data;

[0128] Based on the correspondence between the width of the display area and the number of displayable data, determining whether the width of the display area can display the consciousness index and the injury index at different time points at one time;

[0129] If not, the consciousness index and injury index at different time points are grouped to obtain N consciousness index and injury index groups to be displayed, and the plotting points of the consciousness index and injury index at different time points in the line graph to be displayed are determined based on the sequence of the time points corresponding to the consciousness index and injury index, and N consciousness index and injury index line graphs are obtained based on the plotting points;

[0130] Otherwise, the consciousness indexes at the different time points are displayed at once to obtain corresponding line graphs of the consciousness index and the injury index;

[0131] Determine the brain consciousness and brain energy activity intensity of the target elderly at different target time points based on the consciousness index line graph, and determine the turning point of the brain consciousness and brain energy activity intensity of the target elderly based on the brain consciousness and brain energy activity intensity, wherein the different target time points correspond to different doses of anesthetics;

[0132] The critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly are determined based on the turning point of the target elderly's brain consciousness and brain energy activity intensity.

[0133] In this embodiment, the preset EEG signal processing method is set in advance, for example, the average path length of the EEG signal may be determined by wavelet reconstruction, windowed horizontal visibility graph complex network conversion and complex network analysis.

[0134] In this embodiment, the average path length of the EEG signal refers to the distance from the EEG signal collected from the head of the target elderly person to the EEG signal display terminal, which is used to determine the order in which the EEG signal is transmitted to the display terminal.

[0135] In this embodiment, the time series signal feature refers to the time sequence in which the EEG signal is transmitted from the head of the target elderly person to the EEG display.

[0136] In this embodiment, the digital EEG signal sequence refers to converting the analog EEG signals collected from the target elderly person's head into digital signals that can be read and processed by a machine.

[0137] In this embodiment, the cross information point refers to the part of the EEG signal of the target elderly person that has not changed at adjacent time points.

[0138] In this embodiment, the preset processing method is set in advance, for example, it can be converted through a professional machine, for example, it can be determined by an EEG machine the voltage value corresponding to the EEG signal of the target elderly person under different circumstances.

[0139] In this embodiment, the consciousness index is used to measure the current level of brain alertness of the target elderly, and the injury index is used to measure the current sensitivity of the elderly's brain to pain in the early stage of surgery. After the analgesia is achieved, the index can measure the current level of brain alertness of the target elderly.

[0140] In this embodiment, one-time display refers to displaying the consciousness index and injury index of the current target elderly at different time points in a unified line graph through a line graph.

[0141] In this embodiment, the drawing points refer to the positions of different consciousness indexes and injury indexes in the line graph, which is convenient for determining the position information of each consciousness index and injury index according to the values ​​of each consciousness index and injury index, thereby realizing a detailed display of the consciousness index and injury index through a line graph.

[0142] In this embodiment, the intensity of brain consciousness activity refers to the degree of brain wakefulness and level of wakefulness of the target elderly person under different doses of anesthetics.

[0143] In this embodiment, the turning point of the activity intensity of brain consciousness and brain energy refers to the point where brain consciousness and brain energy change from active to inactive.

[0144] In this embodiment, the critical point of the target elderly person's initial anesthesia loss of consciousness and the critical point of the depth and shallowness of sedation are determined based on the turning point of the target elderly person's brain consciousness and brain energy activity intensity, including:

[0145] The anesthetic dose injected by the target elderly at the target time point is obtained, the comprehensive evaluation value of the brain consciousness index and the injury index of the target elderly at the target time point is calculated based on the anesthetic dose, and the anesthetic efficiency of the anesthetic is calculated based on the comprehensive evaluation of the brain consciousness index and the injury index. The specific steps include:

[0146] The comprehensive evaluation value of the brain consciousness index and injury index of the target elderly is calculated according to the following formula:

[0147]

[0148] Among them, α represents the comprehensive evaluation value of the brain consciousness index and injury index of the target elderly; T represents the average change period of the EEG signal of the target elderly; β represents the dose of anesthetic currently injected by the target elderly; θ represents the anesthesia coefficient, and the value range is (0.8, 0.95); F represents the frequency value of the EEG signal when the target elderly is not injected with anesthetics; f represents the frequency value of the EEG signal after the target elderly receives the target dose of anesthetics; V represents the speed value of the target elderly absorbing anesthetics; t represents the total length of time for the target elderly to absorb anesthetics;

[0149] The anesthetic efficiency of the anesthetic at the target time point was calculated according to the following formula:

[0150]

[0151] Among them, η represents the anesthetic efficiency of the anesthetic at the target time point, and its value range is (0, 1); μ represents the error factor, and its value range is (0.1, 0.15); k1 represents the time length used by the target elderly from receiving the anesthetic injection to the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation; k2 represents the average time length used by the elderly from receiving the anesthetic injection to the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation; α1 represents the comprehensive evaluation value of the brain consciousness index and injury index of the target elderly when the anesthetic is not injected, and its value is greater than α; h2 represents the maintenance time length of the anesthetic after the anesthetic injection is stopped; h1 represents the time length required for the remaining surgery of the target elderly, and its value is less than h2;

[0152] The calculated anesthetic efficiency is compared with the preset anesthetic efficiency;

[0153] If the anesthetic efficiency is greater than or equal to the preset anesthetic efficiency, it is determined that the anesthetic is used properly;

[0154] Otherwise, it is determined that the anesthetic is unqualified, and the injection speed of the anesthetic is adjusted until the anesthetic efficiency is greater than or equal to the preset anesthetic efficiency.

[0155] The above-mentioned preset anesthesia efficiency is set in advance.

[0156] The above-mentioned brain consciousness index and injury index are calculated based on the fluctuation frequency and energy change of the target elderly's brain electrical signal after the target elderly receives an anesthetic injection.

[0157] The above-mentioned anesthesia coefficient refers to the degree of paralysis of the target elderly's cranial nerves by anesthetics after the target elderly person receives an anesthetic injection. The larger the value, the faster the cranial nerves of the elderly person are paralyzed.

[0158] The above average time length value refers to the average value of the time taken by multiple groups of elderly people to reach the critical point of anesthesia, and the operations performed on the multiple groups of elderly people are consistent with the operations performed on the target elderly person.

[0159] The beneficial effects of the above technical scheme are: by converting the collected EEG signals into data, accurate analysis of the values ​​of various data of the EEG signals can be achieved; by judging the consciousness index of the target elderly person in the form of voltage, accurate confirmation of the brain activity intensity of the target elderly person can be achieved; finally, the consciousness index and injury index of the target elderly person at different time points are displayed in a line graph, which is convenient for intuitively determining the target elderly person's initial anesthesia loss of consciousness critical point and the critical point of deep and shallow sedation. This scheme ensures accurate analysis of the target elderly person's initial anesthesia loss of consciousness critical point and the critical point of deep and shallow sedation, so that it is convenient to adjust the anesthetic dose according to the needs of the operation so that the patient is in a good anesthetic state, thereby improving the anesthetic effect.

[0160] Embodiment 7:

[0161] On the basis of the above-mentioned embodiment 1, this embodiment provides an intelligent method for monitoring elderly anesthesia target control based on EEG signals. In step 3, the physiological parameters of the target elderly at the critical point of losing consciousness during the initial anesthesia and the critical point of deep and shallow sedation are determined, and the critical point of losing consciousness during the initial anesthesia and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters are integrated and recorded, including:

[0162] Obtaining the critical point of loss of consciousness during initial anesthesia and the critical point of deep and shallow sedation of the target elderly person, and obtaining brain physiological parameters of the target elderly person based on the critical point of loss of consciousness during initial anesthesia and the critical point of deep and shallow sedation, wherein the brain physiological parameters are at least two items;

[0163] Acquire historical brain physiological parameter values, and determine the fluctuation range values ​​of various brain physiological parameters of the patient at the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation based on the historical brain physiological parameter values;

[0164] Verifying the acquired brain physiological parameters of the target elderly based on the fluctuation range value, and determining whether the brain physiological parameters of the target elderly are within the fluctuation range value;

[0165] If not, it is determined that the acquired brain physiological parameters of the target elderly are abnormal, and the brain physiological parameters of the target elderly are input into a preset brain physiological parameter calibration model for calibration, wherein the preset brain physiological parameter calibration model is obtained based on training of historical brain physiological parameter values, and the preset brain physiological parameter calibration model includes multiple modules for verifying body physiological parameters, and the modules are independent of each other;

[0166] Otherwise, it is determined that the acquired physiological parameters of the target elderly person are normal;

[0167] Generate a record report task based on the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters, and match a target report template from a preset report template library based on the record report task;

[0168] Visually configuring the target report template, and determining the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the target position of the brain physiological parameter in the target report template based on the visual configuration;

[0169] Based on the target position, the critical point of initial anesthesia loss of consciousness, the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters are respectively filled into the target position in the target report template to obtain an anesthesia parameter record report sheet;

[0170] The anesthesia parameter record report is sent to the management terminal and stored in a preset storage area, completing the integrated record of the critical point of initial anesthesia loss of consciousness, the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the brain physiological parameters.

[0171] In this embodiment, the brain physiological parameters may be the values ​​corresponding to various physical signs of the target elderly person when they reach the critical point of losing consciousness during initial anesthesia and the critical point of deep and shallow sedation, such as blood pressure, blood oxygen, etc.

[0172] In this embodiment, the historical brain physiological parameter values ​​refer to various physical sign parameters corresponding to the critical point of loss of consciousness during initial anesthesia and the critical point of deep and shallow sedation of the elderly who had undergone surgery before.

[0173] In this embodiment, the fluctuation range of each brain physiological parameter refers to the value range of each brain physiological parameter under normal conditions when the elderly person loses consciousness at the critical point of initial anesthesia and the critical point of deep and shallow sedation.

[0174] In this embodiment, the preset brain physiological parameter calibration model is set in advance and is used to calibrate the values ​​of abnormal brain physiological parameters.

[0175] In this embodiment, the recording and reporting task refers to determining the number of items to be recorded, and the amount of recorded data corresponding to each recorded item, etc.

[0176] In this embodiment, the preset report template library is pre-set and stores a variety of report templates for recording surgical data that need to be recorded during the surgical process.

[0177] In this embodiment, the target report template refers to a report template suitable for recording the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters, and is one of the preset report template libraries.

[0178] In this embodiment, the visual configuration refers to setting the length, width, height, etc. of each unit in the target report template and the position of each record item in the target report template.

[0179] In this embodiment, the target position refers to the position to be filled in for each anesthesia parameter in the target report template.

[0180] In this embodiment, the preset storage area is set in advance and is used to store the final generated report, and may be a hard disk or the like.

[0181] The beneficial effect of the above technical solution is: by determining the various physiological parameters of the target elderly person at the critical point, and generating a corresponding anesthesia parameter record report form based on the brain physiological parameters, critical point and the anesthetic dose corresponding to the critical point for storage, the various parameters of the anesthesia critical point can be integrated and recorded, which is convenient for medical staff to determine the corresponding anesthetic dose in time according to the different anesthesia depths required for the elderly surgery during the later surgery, thereby improving the anesthesia effect and also improving the anesthesia efficiency.

[0182] Embodiment 8:

[0183] On the basis of the above-mentioned embodiment 7, this embodiment provides an intelligent method for target control of anesthesia for the elderly based on EEG signal monitoring, and obtains an anesthesia parameter record report, including:

[0184] Obtain anesthesia parameter record report sheets corresponding to different surgeries of multiple groups of elderly people, and classify the anesthesia parameter record report sheets based on the type of surgery to obtain target classification results, wherein there are at least two anesthesia parameter record report sheets corresponding to each type of surgery, and there are at least two types of surgery;

[0185] Based on the target classification results, the anesthesia parameter record report sheets corresponding to each type of surgery are packaged to obtain a target data packet;

[0186] The target data packets are stored respectively to obtain a reference library of anesthesia parameter record reports;

[0187] An index keyword is created based on the target data packet corresponding to each type of surgery at the storage address of the anesthesia parameter record report reference library, and the index keyword is sent to the medical staff terminal.

[0188] In this embodiment, the target classification result refers to the result of finally classifying the anesthesia parameter record report sheet according to the surgery type, for example, it may be a cardiac surgery type, a wound suture type, and the like.

[0189] In this embodiment, the target data packet refers to a data packet obtained by packaging anesthesia parameter record reports corresponding to various types of surgeries.

[0190] In this embodiment, the anesthesia parameter record report sheet reference library refers to anesthesia parameter record report sheets corresponding to various types of surgeries, with the purpose of providing a convenient reference for later surgical anesthesia.

[0191] In this embodiment, the index keyword means that medical staff can quickly find the anesthesia parameter record report sheet corresponding to the corresponding operation type according to the index keyword, thereby improving work efficiency.

[0192] The beneficial effect of the above technical solution is: by storing and recording the anesthesia parameter record report corresponding to each type of surgery, it is convenient for medical staff in the later stage to anesthetize the elderly in time according to the reference data to anesthetize the elderly to the anesthesia depth required for the operation, thereby improving the anesthesia level, enhancing the anesthesia efficiency, and reducing the harm of anesthetics to patients.

[0193] Embodiment 9:

[0194] On the basis of the above-mentioned embodiment 8, this embodiment provides an intelligent method for target control of anesthesia in the elderly based on EEG signal monitoring, and sends the index keywords to the medical staff terminal, including:

[0195] Acquire the current surgery data of the elderly person, and determine the target surgery type of the current surgery of the elderly person based on the surgery data;

[0196] Determine a target index keyword based on the target surgery type, and search for a target data packet corresponding to the target surgery type in the anesthesia parameter record report reference library based on the target index keyword;

[0197] Determine the target anesthesia depth required for the current surgery of the elderly based on the current surgery data of the elderly, and match the target anesthesia depth with each anesthesia parameter recorded in each anesthesia parameter record report in the target data packet to obtain a target critical point;

[0198] The target anesthetic dosage corresponding to the target critical point is determined, and the anesthetic required by the current elderly person is prepared and injected based on the target anesthetic dosage to ensure that the current elderly person is anesthetized to the target anesthetic depth required for the operation.

[0199] In this embodiment, the surgical data of the current elderly person refers to the surgical data of the elderly person who is undergoing surgery at the current moment, which is different from the target elderly person. The target elderly person is used to determine the critical point of loss of consciousness of the initial anesthetized person and the critical point of deep and shallow sedation, and the critical point is obtained by analyzing multiple elderly people through different surgeries. The target elderly person is a general term, and the current elderly person is a specific term.

[0200] In this embodiment, the target surgery type refers to the surgery type corresponding to the current surgery pair of the elderly person.

[0201] In this embodiment, the target keyword refers to the index keyword corresponding to the current surgery type of the elderly person, which is one of many index keywords.

[0202] In this embodiment, the target data packet refers to the data packet corresponding to the anesthesia parameter record report sheet corresponding to the current surgery type of the elderly person, and the anesthesia parameter record report sheet contained in the data packet is consistent with the current surgery type of the elderly person.

[0203] In this embodiment, the target anesthesia depth refers to the degree of anesthesia required by the elderly person determined according to the type of surgery performed on the elderly person.

[0204] The beneficial effect of the above technical solution is: by determining the type of surgery for the elderly and the required anesthesia, it is convenient to quickly and accurately determine the dosage of anesthetics required by the elderly through the anesthesia parameter record report, thereby improving the anesthesia efficiency and anesthesia level, and also reducing the harm caused by anesthesia.

[0205] Embodiment 10:

[0206] This embodiment provides an intelligent target control system for monitoring elderly anesthesia based on EEG signals. Figure 3 As shown, including:

[0207] The EEG signal acquisition module is used to obtain the dose of anesthetic injected to the target elderly at different target time points, and to collect the EEG signals of the target elderly at different target time points in real time;

[0208] An analysis module, used to determine the consciousness index of the target elderly person under different doses of anesthetics based on the EEG signal, and determine the critical point of loss of consciousness and the critical point of deep and shallow sedation of the target elderly person based on the consciousness index;

[0209] The integrated recording module is used to determine the physiological parameters of the target elderly person at the critical point of deep and shallow anesthesia, and integrate and record the critical point of initial anesthesia loss of consciousness, the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point and the brain physiological parameters.

[0210] The beneficial effect of the above technical solution is: by determining the intensity of the elderly's conscious activities under different doses of anesthetics, it is helpful to accurately find the critical point of the elderly's initial anesthesia loss of consciousness and the critical point of deep and shallow sedation, so as to adjust the anesthetic dose according to the needs of the operation so that the patient is in a good anesthetic state, improve the anesthetic effect, enhance the anesthesia level, eliminate intraoperative awareness, and reduce the harm of anesthetics to patients.

[0211] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent target control system for monitoring elderly anesthesia based on EEG signals, characterized in that: include: The EEG signal acquisition module is used to obtain the dose of anesthetic injected to the target elderly at different target time points, and to collect the EEG signals of the target elderly at different target time points in real time; An analysis module, for determining the consciousness index and injury index of the target elderly person at different doses of anesthetics based on the EEG signal, and determining the critical point of loss of consciousness during initial anesthesia and the critical point of deep and shallow sedation of the target elderly person based on the consciousness index and injury index; An integrated recording module is used to determine the brain physiological parameters of the target elderly person at the critical point of losing consciousness during the initial anesthesia and the critical point of deep and shallow sedation, and to integrate and record the critical point of losing consciousness during the initial anesthesia and the critical point of deep and shallow sedation, the dose of anesthetic corresponding to the critical point, and the brain physiological parameters; Wherein, determining the consciousness index and injury index of the target elderly person at different doses of anesthetics based on the EEG signal, and determining the critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly person based on the consciousness index and injury index, including: Obtaining the EEG signals of the target elderly at different target time points, and determining the average path length of the EEG signals at different target time points based on a preset EEG signal processing method; the average path length of the EEG signal refers to the distance from the EEG signal collected from the head of the target elderly to the EEG signal display terminal, which is used to determine the order in which the EEG signals are transmitted to the display terminal; Determine the time series signal characteristics of the EEG signals at the different target time points based on the average path length, and convert the EEG signals in different time domains into digital EEG signal sequences in sequence based on the time series signal characteristics; the time series signal characteristics refer to the time sequence of the EEG signals being transmitted from the head of the target elderly person to the EEG display; Determine the cross information points of the digital EEG signal sequences in adjacent time domains, and determine the voltage values ​​corresponding to the cross information points based on a preset processing method; Determine the consciousness index and injury index of the target elderly person at different time points based on the voltage value; Obtain the total number of consciousness indexes and injury indexes of the target elderly at different time points, and determine the width of the display area in the line graph to be displayed, wherein the width of the display area includes the number of displayable data; Based on the correspondence between the width of the display area and the number of displayable data, determining whether the width of the display area can display the consciousness index and the injury index at different time points at one time; If not, the consciousness index and injury index at different time points are grouped to obtain N consciousness index groups and injury index groups to be displayed, and the plotting points of the consciousness index and injury index at different time points in the line graph to be displayed are determined based on the sequence of the time points corresponding to the consciousness index and the injury index, and N consciousness index and injury index line graphs are obtained based on the plotting points; Otherwise, the consciousness index and injury index at different time points are displayed at once to obtain corresponding line graphs of the consciousness index and injury index; Determine the brain consciousness and brain energy activity intensity of the target elderly at different target time points based on the consciousness index and injury index line graph, and determine the turning point of the brain consciousness and brain energy activity intensity of the target elderly based on the brain consciousness and brain energy activity intensity, wherein the different target time points correspond to different doses of anesthetics; The critical point of initial anesthesia loss of consciousness and the critical point of deep and shallow sedation of the target elderly are determined based on the turning point of the target elderly's brain consciousness and brain energy activity intensity.

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