CN117838063B

CN117838063B - Physiological information early warning processing system and electronic equipment under anesthesia scene

Info

Publication number: CN117838063B
Application number: CN202410239304.7A
Authority: CN
Inventors: 李小俚; 梁振虎; 闫佳庆; 张昊; 李继芳
Original assignee: Jiangxi Jielian Medical Equipment Co ltd
Current assignee: Jiangxi Jielian Medical Equipment Co ltd
Priority date: 2024-03-04
Filing date: 2024-03-04
Publication date: 2024-05-24
Anticipated expiration: 2044-03-04
Also published as: CN117838063A

Abstract

The application provides a physiological information early warning processing system and electronic equipment under an anesthesia scene, wherein the system comprises: the device comprises an acquisition module, a first determination module, a second determination module, a combination module, a third determination module and an alarm module, wherein the acquisition module is used for acquiring case information and brain electrical signals of a target object; the first determining module is used for determining anesthesia index data according to the brain electrical signals to obtain a plurality of groups of anesthesia index data sets; the second determining module is used for determining the anesthesia index parameters corresponding to each anesthesia index data set in the plurality of groups of anesthesia index data sets to obtain a plurality of anesthesia index parameters; the combination module is used for combining a plurality of anesthesia index parameters according to the case information to obtain target DOA parameters; the third determining module is used for determining a target DOA threshold range corresponding to the case information; and the alarm module is used for triggering an alarm event when the target DOA parameter is not in the target DOA threshold range.

Description

Physiological information early warning processing system and electronic equipment under anesthesia scene

Technical Field

The application relates to the technical field of medical monitoring, in particular to a physiological information early warning processing system and electronic equipment in an anesthesia scene.

Background

At present, patients must be subjected to enough anesthesia during surgery and intensive care therapy to relieve pain and pressure, and the patient after anesthesia can be judged and adjusted in dosage by monitoring physiological parameters of the patient, such as blood pressure, heart rate, respiratory rate, oxygen saturation and the like, so as to ensure that the patient maintains a safe anesthetic state during surgery.

However, risk assessment for patients may sometimes be less comprehensive, which may result in a lack of comprehensive knowledge of the patient's medical history or other potential risk factors prior to performing the anesthesia procedure, as well as the possible occurrence of an emergency during anesthesia, which may affect the patient's safety and health.

How to reasonably set the anesthesia depth threshold range and set an alarm event for early warning so as to improve the safety of the anesthesia process is needed to be solved.

Disclosure of Invention

The embodiment of the application provides a physiological information early warning processing system and electronic equipment in an anesthesia scene, which can timely respond and trigger an alarm event and an information prompt by determining target DOA parameters and setting corresponding threshold ranges according to the conditions of target objects, and can furthest protect the life and health of the target objects by timely responding and triggering the alarm event and the information prompt.

In a first aspect, an embodiment of the present application provides a physiological information early warning processing system in an anesthesia scenario, which is applied to an anesthesia monitoring alarm device, where the system includes an acquisition module, a first determination module, a second determination module, a combination module, a third determination module, and an alarm module, where:

The acquisition module is used for acquiring case information and brain electrical signals of the target object after the target object is inoculated with the target anesthetic;

The first determining module is used for determining anesthesia index data according to the electroencephalogram signals to obtain a plurality of groups of anesthesia index data sets, and the plurality of groups of anesthesia index data sets at least comprise: BSR data set, COH data set, PE data set;

The second determining module is used for determining the anesthesia index parameters corresponding to each anesthesia index data set in the plurality of groups of anesthesia index data sets to obtain a plurality of anesthesia index parameters, wherein the plurality of anesthesia index parameters at least comprise BSR parameters, COH parameters and PE parameters; each anesthesia index data set corresponds to an anesthesia index parameter; each anesthesia index parameter corresponds to an anesthesia index type identifier;

the combination module is used for combining the plurality of anesthesia index parameters according to the case information to obtain target DOA parameters;

The third determining module is used for determining a target DOA threshold range corresponding to the case information;

The alarm module is used for triggering an alarm event when the target DOA parameter is not in the target DOA threshold range;

wherein, in terms of the triggering alarm event, the alarm module is specifically configured to:

acquiring a target lower limit threshold and a target upper limit threshold of the target DOA threshold range;

determining a first difference between the target lower threshold and the target DOA parameter when the target DOA parameter is less than the target lower threshold;

Determining a first alarm parameter corresponding to the first difference value;

executing the alarm event according to the first alarm parameter;

Generating first prompt information according to a preset mapping relation between the first difference value and the action time of the target anesthetic, wherein the first prompt information comprises the time required by the target DOA parameter to reach the target lower limit threshold;

Determining a second difference between the target DOA parameter and the target upper threshold when the target DOA parameter is greater than the target upper threshold;

determining a second alarm parameter corresponding to the second difference value;

executing the alarm event according to the second alarm parameter;

generating second prompt information according to a preset mapping relation between the second difference value and the dosage of the target anesthetic, wherein the second prompt information comprises the supplementing quantity of the target anesthetic.

Optionally, the case information includes medical history information, and in the aspect of combining the plurality of anesthesia index parameters according to the case information to obtain a target DOA parameter, the combination module is specifically configured to:

Determining an anesthesia index type identifier corresponding to the medical history information to obtain at least one anesthesia index type identifier; acquiring anesthesia index parameters with the same anesthesia index type identifier as the at least one anesthesia index type identifier from the plurality of anesthesia index parameters to obtain at least one anesthesia index parameter; determining the target DOA parameter based on the at least one anesthesia index parameter.

Optionally, in the determining the target DOA parameter according to the at least one anesthesia index parameter, the combining module is specifically configured to:

acquiring a weight group corresponding to the medical history information, wherein the weight group comprises at least one weight, and the at least one weight corresponds to the at least one anesthesia index parameter one by one; and carrying out weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain the target DOA parameter.

Optionally, in the aspect of performing a weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain the target DOA parameter, the combination module is specifically configured to:

performing weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain a reference DOA parameter; acquiring target hardware parameters and target software parameters of the anesthesia monitoring alarm equipment; determining a first influence coefficient corresponding to the target hardware parameter; determining a second influence coefficient corresponding to the target software parameter; and dynamically adjusting the reference DOA parameter according to the first influence coefficient and the second influence coefficient to obtain the target DOA parameter.

Optionally, the case information further includes personal information; the personal information includes: age information, height information, and weight information; in the aspect of determining the target DOA threshold range corresponding to the case information, the third determining module is specifically configured to:

Acquiring a reference DOA threshold range corresponding to the personal information; determining a target body metabolic rate of the target subject from the personal information; determining a target metabolic rate of the target anesthetic drug based on the target body metabolic rate; determining the anesthesia depth influence degree parameters corresponding to the medical history information and the target anesthetic; and adjusting the reference DOA threshold range according to the target metabolic rate and the anesthesia depth influence degree parameter to obtain the target DOA threshold range.

Optionally, in the aspect that the reference DOA threshold range is adjusted according to the target metabolic rate and the anesthesia depth influence degree parameter to obtain the target DOA threshold range, the third determining module is specifically configured to:

Acquiring a first lower threshold and a first upper threshold of the reference DOA threshold range; determining a target regulation parameter corresponding to the target metabolic rate; adjusting the first lower limit threshold and the first upper limit threshold according to the target adjustment parameter to obtain a reference first lower limit threshold and a reference first upper limit threshold; determining a target fine tuning parameter corresponding to the anesthesia depth influence degree parameter; adjusting the reference first lower limit threshold according to the target fine tuning parameter to obtain a target first lower limit threshold; and determining the target DOA threshold range according to the target first lower limit threshold and the reference first upper limit threshold.

In a second aspect, an embodiment of the present application provides an electronic device, including a physiological information early warning processing system in an anesthesia scenario according to the first aspect of the present application.

By implementing the embodiment of the application, the deviation condition of the target DOA parameter and the target DOA threshold range is monitored in real time, the alarm event and the information prompt can be responded and triggered in time, the efficiency and the safety of the anesthesia process are improved, and the life and the health of a target object are protected to the greatest extent.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a system architecture diagram of an anesthesia monitoring alarm device provided by an embodiment of the present application;

fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

FIG. 3 is a schematic workflow diagram of a physiological information early warning processing system under an anesthesia scenario provided by an embodiment of the present application;

fig. 4 is a functional block diagram of a physiological information early warning processing system in an anesthesia scenario according to an embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, the character "/" indicates that the front and rear associated objects are an "or" relationship. The term "plurality" as used in the embodiments of the present application means two or more.

"At least one" or the like in the embodiments of the present application means any combination of these items, including any combination of single item(s) or plural items(s), meaning one or more, and plural means two or more. For example, at least one (one) of a, b or c may represent the following seven cases: a, b, c, a and b, a and c, b and c, a, b and c. Wherein each of a, b, c may be an element or a set comprising one or more elements.

The "connection" in the embodiment of the present application refers to various connection manners such as direct connection or indirect connection, so as to implement communication between devices, which is not limited in the embodiment of the present application.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

The following explanation is given to the related nouns related to the present application as follows:

Burst suppression ratio (burst suppression ratio, BSR): BSR is an indicator used to measure the degree of balance between burst and inhibition in an electroencephalogram signal. Burst is a high frequency oscillation in brain activity, while inhibition is a low frequency oscillation. BSR is commonly used to assess the characteristics of brain activity in conditions of anesthesia, coma, or epilepsy. After the electroencephalogram signal is divided into two phases of an outbreak period and a suppression period, calculating the ratio of the duration of the suppression period to the total duration, and thus obtaining the BSR. A higher BSR indicates more inhibitory activity, possibly associated with loss of consciousness or impaired brain function.

Spectral Coherence (COH): COH is an indicator for measuring the degree of signal synchrony between two brain regions. It can be evaluated by calculating the relationship between the phase and amplitude of the two signals in different frequency ranges. COH is often used to study functional connections and information transfer of neural networks, which can help us to understand the coordination mechanisms of the brain in different cognitive tasks.

Permutation entropy (Permutation Entropy, PE): PE is an index that describes signal complexity and irregularities. It measures the complexity of the signal by calculating the probability distribution of different permutation modes based on the permutation order of the signal values. In brain electrical signal studies, higher permutation entropy may represent more complex and flexible brain dynamics, associated with information processing and cognitive abilities.

Depth of anesthesia (Depth Of Anaesthesia, DOA): DOA refers to the extent to which the patient's consciousness, sensation and physiological response is inhibited during general anesthesia. General anesthesia is the loss of consciousness and sensation in a patient through the use of anesthetic drugs to perform surgery or other medical procedures. Assessment of the depth of anesthesia is important to ensure patient safety and comfort.

The system architecture of the anesthesia monitoring and alarming device in the embodiment of the present application will be described with reference to fig. 1, and fig. 1 is a system architecture diagram of the anesthesia monitoring and alarming device provided in the embodiment of the present application, where the anesthesia monitoring and alarming device 110 includes four modules, that is, a display module 111, an information review module 112, an information management module 113, and an abnormality detection module 114.

The display module 111 is configured to display waveforms and anesthesia index parameters corresponding to the anesthesia index data.

In one possible embodiment, the display module 111 obtains the BSR data set, the COH data set, and the PE data set, processes the multiple sets of anesthesia index data sets through the display module 111, and displays the corresponding waveforms in the display area, where the display area is refreshed in real time, and different waveform display modes may be set, where the waveform display modes include but are not limited to scrolling, stepping, and sweeping, the number of seconds on screen defaults to 2s, and the amplitude defaults to 200uV.

In one possible embodiment, the display module 111 obtains the BSR data set, the COH data set, and the anesthesia index parameters corresponding to the PE data set, that is, the BSR parameter, the COH parameter, and the PE parameter, where the BSR parameter, the COH parameter, and the PE parameter are all the latest data in the corresponding data sets, performs a weighted operation on the BSR parameter, the COH parameter, and the PE parameter to obtain the DOA parameter, and finally displays the BSR parameter, the COH parameter, the PE parameter, and the DOA parameter in a parameter display area, that is, visually presents the status information of the target object during anesthesia by a numerical display manner.

The information review module 112 is configured to retrospectively display the history information.

In one possible embodiment, the information review module 112 reads in the physiological signal data of the stored target object, reproduces the monitoring environment in operation, realizes the function of postoperative review, displays the physiological signal data of the target object in a curve form in a display area, and intuitively reflects the dynamic change process of the physiological signal data of the target object so as to assist the medical staff in postoperative analysis.

The information management module 113 is configured to search, modify, and delete case information of the target object, and store the case information and the electroencephalogram signal.

In one possible embodiment, the information management module 113 records physiological signal data of the target object, i.e. brain electrical signals, stores case information of the target object, files the case information, facilitates later work, and can also provide data support for the information review module 112, and meanwhile, the information management module 113 can search, modify and delete the case information of the target object, wherein the case information includes, but is not limited to, medical history information, personal information, contact information and hospitalization information.

The anomaly detection module 114 is configured to detect an anomaly event.

In one possible embodiment, the abnormal event detection includes, but is not limited to, electrode falling detection, impedance detection, anesthesia index parameter detection, when electrode falling is detected, an alarm indicator is used for flashing or an interface popup window to prompt a guardian of the abnormal event, when the anesthesia index parameter exceeds a set normal range, an alarm event is triggered, and the anesthesia index parameter display is changed from green to red and is accompanied by a beep alarm.

Therefore, by the system architecture, the anesthesia state of the target object can be known more accurately and intuitively, and when an abnormal event occurs, an alarm event can be responded and triggered in time, so that the efficiency and safety of the anesthesia process are improved.

Referring to fig. 2, fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 2, the electronic device 20 includes one or more processors 220, a memory 230, a communication interface 240, and one or more programs 231, where the processor 220 is communicatively connected to the memory 230 and the communication interface 240 through an internal communication bus.

The processor 220 is mainly configured to:

After inoculating a target anesthetic to a target object, acquiring case information and an electroencephalogram signal of the target object;

determining anesthesia index data according to the brain electrical signals to obtain a plurality of groups of anesthesia index data sets, wherein the plurality of groups of anesthesia index data sets at least comprise: BSR data set, COH data set, PE data set;

Determining anesthesia index parameters corresponding to each anesthesia index data set in the plurality of groups of anesthesia index data sets to obtain a plurality of anesthesia index parameters, wherein the plurality of anesthesia index parameters at least comprise BSR parameters, COH parameters and PE parameters; each anesthesia index data set corresponds to an anesthesia index parameter; each anesthesia index parameter corresponds to an anesthesia index type identifier;

Combining a plurality of anesthesia index parameters according to the case information to obtain target DOA parameters;

determining a target DOA threshold range corresponding to the case information;

If the target DOA parameter is not in the target DOA threshold range, triggering an alarm event;

wherein triggering an alarm event comprises:

Acquiring a target lower limit threshold and a target upper limit threshold of a target DOA threshold range;

Executing an alarm event according to the first alarm parameter;

generating first prompt information according to a preset mapping relation between a first difference value and the acting time of the target anesthetic, wherein the first prompt information is the time required by the target DOA parameter to reach a target lower limit threshold;

Executing an alarm event according to the second alarm parameter;

Generating second prompt information according to a preset mapping relation between a second difference value and the dosage of the target anesthetic, wherein the second prompt information comprises the supplementing quantity of the target anesthetic.

Wherein the one or more programs 231 are stored in the memory 230 and configured to be executed by the processor 220, the one or more programs 231 comprising instructions for performing any of the steps of the embodiments described above.

The Processor 220 may be, for example, a central processing unit (Central Processing Unit, CPU), a general purpose Processor, a digital signal Processor (DIGITAL SIGNAL Processor, DSP), an Application-specific integrated Circuit (ASIC), a field programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, units and circuits described in connection with this disclosure. Processor 220 may also be a combination that performs computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, and the like. The communication unit may be a communication interface 240, a transceiver, a transceiving circuit, etc., and the storage unit may be a memory 230.

Memory 230 may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an erasable programmable ROM (erasable PROM), an electrically erasable programmable EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (random access memory, RAM) which acts as external cache memory. By way of example, and not limitation, many forms of random access memory (random access memory, RAM) are available, such as static random access memory (STATIC RAM, SRAM), dynamic Random Access Memory (DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (double DATA RATE SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCHLINK DRAM, SLDRAM), and direct memory bus random access memory (direct rambus RAM, DR RAM).

It will be appreciated that the electronic device 20 may include more or fewer structural elements than those described in the above-described block diagrams, including, for example, a power module, physical key, wi-Fi module, speaker, bluetooth module, sensor, display module, etc., without limitation. It is understood that the electronic device 20 may be configured with a system architecture as described in fig. 1.

After understanding the software and hardware architecture of the present application, the following describes a physiological information early warning processing system under an anesthesia scenario in the embodiment of the present application with reference to fig. 3, and fig. 3 is a schematic workflow diagram of the physiological information early warning processing system under an anesthesia scenario provided by the embodiment of the present application, applied to an anesthesia monitoring alarm device, specifically including the following steps:

step S301, after the target object is inoculated with the target anesthetic, obtaining case information and an electroencephalogram signal of the target object.

Specifically, prior to receiving an anesthetic procedure, the target subject should be comprehensively evaluated and patient case information recorded, wherein the case information includes medical history, medication, surgical history, allergic reaction history, and the like. Meanwhile, in the anesthesia monitoring process, an electroencephalogram signal is a very important monitoring index. During the monitoring process, electrodes need to be attached to the scalp of the patient in order to collect the brain electrical signals.

Wherein the target object may comprise a human or other animal, which may comprise at least one of: cats, dogs, pigs, cows, monkeys, etc., are not limited herein. When the target object is a person, the target object may include at least one of: the elderly, children, young people, etc., are not limited herein.

Step S302, determining anesthesia index data according to the brain electrical signals to obtain a plurality of groups of anesthesia index data sets.

Wherein the plurality of sets of anesthesia index data sets comprises at least: BSR data set, COH data set, PE data set.

Specifically, the electroencephalogram signal is analyzed by using a nonlinear energy operator (Nonlinear Energy Operator, NLEO), so that the explosion suppression calculation is performed, and finally, a BSR data set is obtained. It is clear that NLEO is an indicator for reflecting non-linear activity in brain electrical signals. Is provided with a single-channel electroencephalogram signalThe sampling rate of the signal is/>Then the signal time is/>Second.

First, a single-segment NLEO is estimated. Let the data period time for calculating NLEO be(Seconds), then the number of data segment points used to estimate NLEO is/>Therefore, according to the NLEO calculation formula, the signal/>NLEO signal/>The calculation formula is as follows:

wherein, Is the original electroencephalogram signal,/>Each point/>Are all original signals/>At time/>Internal NLEO values.

Then, there may be interference of the electrocardiographic signal in the electroencephalogram signal, and NLEO needs to be removed. Set the data period time for calculating electrocardiosignal as(Seconds) then used to estimate the NLEO signal/>The number of data segments of the central electric signal is。

For the followingIs/>Is a continuous data point of (1):

Will be Scaling to a standard normal distribution/>. If/>Middle is greater than/>If the number of data points is greater than 4, then these points are found at the original/>The value of (a) is replaced with/>The processed NLEO signal is denoted as/>。

Finally, the NLEO value within the window is calculated. Let the window time for calculating NLEO be(Seconds) then contain the NLEO signal/>, within the windowData points of (2) are/>。

The resulting intra-window NLEO values are:

further, the window length for calculating the burst suppression ratio is designed to be (Seconds), the window length in NLEO is。

Now for each window NLEO valueClassifying outbreaks and suppression, and setting the result after classification as/>And let the judgment threshold be 4.865182.

For window NLEO。

Set the startThe sum of data points in is/>Sum of squares of，

The following is obtained:

for window NLEO (For each/>)Time period).

Each is provided withThe sum of data points in is/>Sum of squares ofThen the following steps are obtained:

let the number of consecutive seconds of burst be The number of consecutive bursts is/>。

If it isIf there is/>And/>Order in principle。

Finally, the classification of outbreaks and suppression needs to be translated into an outbreak suppression ratio.

Setting the obtained burst suppression ratioThe definition is as follows:

Wherein the method comprises the steps of Meaning in the range i=a, …, b,/>(I) Number equal to 0.

The correlation degree of the two channel brain electrical signals on a specific frequency band is measured by calculating the frequency spectrum coherence of the two channel brain electrical signals, so as to obtain a COH data set.

First, the power spectral density of the forehead two-channel brain electrical signal is calculated. Let forehead two-channel EEG signal be x and y, their power spectrum densities are respectivelyAnd/>. Next, the cross spectral density/>, between them needs to be calculatedThis step is used to measure the degree of correlation of two channel electroencephalogram signals on a specific frequency band. Finally, calculate the frequency band/>, at a specific frequency bandIn the two channels, the spectral coherence of the brain electrical signals.

The general calculation is as follows:

Wherein i represents the number of the frequency bin, Number indicating upper limit of frequency range,/>A sequence number indicating the lower end of the frequency range.

The method comprises the following specific steps of:

let the time sequence of the brain electrical signal be { X (i), i=1, 2, …, n }, and perform phase space reconstruction to obtain a matrix Y:

Wherein m refers to the embedding dimension, Refers to delay time and has the equation/>. Each row in the matrix is a reconstruction component and a total of K reconstruction components are represented.

Suppose that the jth reconstruction component in the time series, i.eArranging according to the size, setting the index value of each element column in the reconstruction component before the component is arranged as1, 2, …, m, and settingFor the index value after permutation, namely:

Thus, the reconstructed components of each row obtained for any time series X reconstruction can be arranged to obtain a set of position index sequences as follows:

the sequences are possible in R permutations and there are Record each arrangement/>The probability of occurrence is/>R=1, 2, …, R, the permutation entropy of the time series X thus obtained is:

When (when) Time,/>Taking the maximum value, and normalizing the permutation entropy of the time sequence X to obtain:

Step S303, determining the anesthesia index parameters corresponding to each anesthesia index data set in the plurality of anesthesia index data sets to obtain a plurality of anesthesia index parameters.

Wherein the plurality of anesthesia index parameters at least comprise a BSR parameter, a COH parameter and a PE parameter; each anesthesia index data set corresponds to an anesthesia index parameter; each anesthesia index parameter corresponds to an anesthesia index type identifier.

Specifically, anesthesia index data at the latest moment in a plurality of groups of anesthesia index data sets are used as corresponding anesthesia index parameters, namely BSR data at the latest moment in a BSR data set are used as BSR parameters, wherein the numerical range of the BSR parameters is 0-1, and anesthesia index type marks corresponding to the BSR parameters are BSR; taking COH data at the latest moment in the COH data set as COH parameters, wherein the numerical range of the COH parameters is 0-1, and the anesthesia index type corresponding to the COH parameters is identified as COH; and taking PE data at the latest moment in the PE data set as PE parameters, wherein the numerical range of the PE parameters is 0-1, and the anesthesia index type corresponding to the PE parameters is marked as PE.

And step S304, combining the plurality of anesthesia index parameters according to the case information to obtain target DOA parameters.

The case information comprises medical history information, and firstly, anesthesia index type identifiers corresponding to the medical history information are determined to obtain at least one anesthesia index type identifier; then, acquiring anesthesia index parameters with the same anesthesia index type identifier as the at least one anesthesia index type identifier from the plurality of anesthesia index parameters to obtain at least one anesthesia index parameter; finally, determining the target DOA parameter according to the at least one anesthesia index parameter.

Wherein determining the target DOA parameter according to the at least one anesthesia index parameter specifically comprises: acquiring a weight group corresponding to the medical history information, wherein the weight group comprises at least one weight, and the at least one weight corresponds to the at least one anesthesia index parameter one by one; and carrying out weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain the target DOA parameter.

The weighting operation is performed according to the at least one weight and the at least one anesthesia index parameter to obtain the target DOA parameter, which specifically comprises the following steps: performing weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain a reference DOA parameter; acquiring target hardware parameters and target software parameters of the anesthesia monitoring alarm equipment; determining a first influence coefficient corresponding to the target hardware parameter; determining a second influence coefficient corresponding to the target software parameter; and dynamically adjusting the reference DOA parameter according to the first influence coefficient and the second influence coefficient to obtain the target DOA parameter.

Specifically, firstly, according to the mapping relation between medical history information and anesthesia index type preset, the anesthesia index type identification needing to be concerned is determined, for example, if the target object suffers from a nervous system disease such as epilepsy, the COH parameter is needed to be used as an important index for evaluating brain function, and consciousness and brain function of the target object are closely monitored, so that any abnormal situation can be found and treated in time. Then, the anesthesia index parameters corresponding to the anesthesia index type identifier to be focused are obtained from the anesthesia index parameters, then the weight group corresponding to the medical history information is obtained, for example, the target brain injury, stroke or other nervous system diseases are required to be focused on the BSR parameters and the PE parameters, so that the safety of the patient in the operation process is ensured, the weights corresponding to the BSR parameters and the PE parameters are larger, namely, the weight corresponding to the BSR parameters is 0.35, the weight corresponding to the COH parameters is 0.30, and the weight corresponding to the PE parameters is 0.35. And carrying out weighted operation on at least one anesthesia index parameter and a corresponding weight to obtain a reference DOA parameter, obtaining a target hardware parameter and a target software parameter of the anesthesia monitoring alarm device, wherein the target hardware parameter comprises, but is not limited to, temperature, humidity and sensor sensitivity, the target software parameter comprises, but is not limited to, software version and algorithm selection, the first influence coefficient is determined according to a preset mapping relation between the target hardware parameter and the first influence coefficient, the second influence coefficient is determined according to a preset mapping relation between the target software parameter and the second influence coefficient, the reference DOA parameter is dynamically adjusted according to the first influence coefficient and the second influence coefficient, and the reference DOA parameter is multiplied by a coefficient with the value of 100 to obtain the target DOA parameter, wherein the numerical range of the target DOA parameter is 0-100.

Step S305, determining a target DOA threshold range corresponding to the case information.

Wherein the case information further includes personal information; the personal information includes: age information, height information, and weight information; firstly, acquiring a reference DOA threshold range corresponding to the personal information; determining a target body metabolic rate of the target object according to the personal information; then, determining a target metabolic rate of the target anesthetic drug based on the target body metabolic rate; determining the anesthesia depth influence degree parameters corresponding to the medical history information and the target anesthetic; and finally, adjusting the reference DOA threshold range according to the target metabolic rate and the anesthesia depth influence degree parameter to obtain the target DOA threshold range.

In particular, different target subjects have different needs and responses to the depth of anesthesia, wherein the differences between individuals may be related to age, height, weight, etc., and older target subjects may require shallower depth of anesthesia, while older target subjects may require deeper anesthesia, and height and weight may also have an impact on depth of anesthesia, with a greater range of reference DOA thresholds for higher or heavier target subjects.

Determining different target body metabolic rates according to the difference of the personal information, wherein:

male: 10 Xweight (KG) +6.25 Xheight (CM) -5 Xage +5

Female: 10 Xweight (KG) +6.25 Xheight (CM) -5 Xage-161

Thus, the corresponding reference DOA threshold range and target body metabolic rate are obtained according to personal information, meanwhile, different narcotics have different metabolic pathways and metabolic speeds, some narcotics are metabolized by the liver, and other narcotics may be metabolized mainly by the kidney or other pathways, so that the metabolic speed of each drug is different. Finally, comprehensively evaluating the characteristics of the target anesthetic and the target body metabolism rate to obtain the target metabolism rate. Further, determining the parameter of the influence degree of the anesthesia depth corresponding to the medical history information and the target anesthetic, wherein the medical history information influences the selection and the dosage adjustment of the anesthetic, thereby influencing the anesthesia depth, and different anesthetics have different pharmacological characteristics and metabolic pathways, and some drugs may have a longer half-life or obvious accumulation effect, which also causes the anesthesia depth to be influenced. Therefore, corresponding anesthesia depth influence degree parameters can be obtained, and the reference DOA threshold range is adjusted according to the target metabolic rate and the anesthesia depth influence degree parameters to obtain a target DOA threshold range.

Therefore, the target DOA threshold range is determined according to the personal characteristics and the target drug metabolism condition, so that anesthesia management is more personalized and accurate, the special condition of a target object can be better adapted, and the anesthesia effect and the safety of the anesthesia process are improved.

The reference DOA threshold range is adjusted according to the target metabolic rate and the anesthesia depth influence degree parameter to obtain a target DOA threshold range, and the method specifically comprises the following steps: firstly, a first lower threshold value and a first upper threshold value of the reference DOA threshold value range are obtained; determining a target regulation parameter corresponding to the target metabolic rate; then, the first lower limit threshold value and the first upper limit threshold value are adjusted according to the target adjustment parameter, so that a reference first lower limit threshold value and a reference first upper limit threshold value are obtained; determining a target fine tuning parameter corresponding to the anesthesia depth influence degree parameter; adjusting the reference first lower limit threshold according to the target fine tuning parameter to obtain a target first lower limit threshold; finally, the target DOA threshold range is determined according to the target first lower threshold and the reference first upper threshold.

Specifically, a first lower threshold and a first upper threshold of the reference DOA threshold range are obtained, and then a target regulation parameter corresponding to the target metabolism speed is determined, wherein the faster the target metabolism speed is, the faster the target anesthetic is metabolized and cleared, so that the reference DOA threshold range needs to be increased to ensure that the target object maintains a sufficient anesthetic effect in the anesthetic process. And then, adjusting the first lower limit threshold and the first upper limit threshold according to the target adjustment parameter to obtain a reference first lower limit threshold and a reference first upper limit threshold. And determining a corresponding target fine tuning parameter according to the anesthesia depth influence degree parameter, and adjusting the reference first lower limit threshold according to the target fine tuning parameter to obtain a target first lower limit threshold. The smaller the target DOA parameter is, the higher the corresponding anesthetic depth degree is, the further adjustment is needed to be performed on the reference first lower limit threshold value to avoid the situation that the target object is over anesthetized and endangers the safety of the target object, so as to improve the safety of the anesthetic process, and the larger the target DOA parameter is, the lower the corresponding anesthetic depth degree is, and the situation has no safety risk for the target object, so that the reference first upper limit value does not need to be further adjusted. Finally, determining a target DOA threshold range according to the target first lower threshold and the reference first upper threshold, wherein the target DOA threshold range is a DOA threshold range corresponding to the optimal anesthetic effect, and the target DOA threshold range is generally 40-60.

Step S306, if the target DOA parameter is not within the target DOA threshold range, triggering an alarm event.

Firstly, acquiring a target lower limit threshold and a target upper limit threshold of the target DOA threshold range; determining a first difference between the target lower threshold and the target DOA parameter when the target DOA parameter is less than the target lower threshold; determining a first alarm parameter corresponding to the first difference value; executing the alarm event according to the first alarm parameter; generating first prompt information according to a preset mapping relation between the first difference value and the action time of the target anesthetic, wherein the first prompt information comprises the time required by the target DOA parameter to reach the target lower limit threshold; determining a second difference between the target DOA parameter and the target upper threshold when the target DOA parameter is greater than the target upper threshold; determining a second alarm parameter corresponding to the second difference value; executing the alarm event according to the second alarm parameter; generating second prompt information according to a preset mapping relation between the second difference value and the dosage of the target anesthetic, wherein the second prompt information comprises the supplementing quantity of the target anesthetic.

Specifically, a target lower limit threshold and a target upper limit threshold of a target DOA threshold range are obtained; when the target DOA parameter is smaller than the target lower limit threshold, a first difference value between the target lower limit threshold and the target DOA parameter is firstly determined, then a first alarm parameter corresponding to the first difference value is determined according to a preset mapping relation between the difference value and the alarm parameter, wherein the alarm parameter comprises but is not limited to an alarm grade, an alarm frequency and an alarm mode, and finally an alarm event is executed according to the first alarm parameter, wherein the alarm event comprises but is not limited to a flashing alarm, a popup alarm and a sound alarm. Generating first prompt information according to a preset mapping relation between the first difference value and the acting time of the target anesthetic, wherein the first prompt information comprises the time required by the target DOA parameter to reach the target lower limit threshold. When the target DOA parameter is larger than the target upper limit threshold, determining a second difference value between the target DOA parameter and the target upper limit threshold, determining a second alarm parameter corresponding to the second difference value according to a preset mapping relation between the difference value and the alarm parameter, executing an alarm event according to the second alarm parameter, and generating second prompt information according to the preset mapping relation between the second difference value and the dosage of the target anesthetic, wherein the second prompt information comprises the supplementing quantity of the target anesthetic.

In one possible embodiment, the different differences correspond to different alarm levels in the alarm parameters, wherein the alarm levels include a low level, a medium level and a high level, the differences are 1-10 corresponding to a low level, the differences are 11-30 corresponding to a medium level, the differences are 31-40 corresponding to a high level, when the target DOA parameter is smaller than the target lower limit threshold or larger than the target upper limit threshold, and the alarm level corresponding to the first difference or the second difference is low, at this time, the anesthesia monitoring alarm device executes a low-level alarm event, changes the display color of the target DOA parameter from green to yellow, and the alarm mode is a flashing alarm, namely, an indicator lamp or other light source on the anesthesia monitoring alarm device is indicated to flash every 10 seconds so as to increase the alarm effect.

In one possible embodiment, the alarm level corresponding to the first difference value or the second difference value is medium, at this time, the anesthesia monitoring alarm device executes a medium alarm event, the display color of the target DOA parameter is changed from green to orange, the alarm mode is a flashing alarm and a popup alarm, that is, an indicator lamp or other light source on the anesthesia monitoring alarm device is indicated to flash once every 5 seconds, and alarm information is popped up in a visual window of the anesthesia monitoring alarm device to prompt that the target DOA parameter is abnormal.

In one possible embodiment, the alarm level corresponding to the first difference value or the second difference value is high, at this time, the anesthesia monitoring alarm device executes a high-level alarm event, the display color of the target DOA parameter is changed from green to red, the alarm mode is a flashing alarm, a popup alarm and a sound alarm, that is, an indicator lamp or other light source on the anesthesia monitoring alarm device is instructed to flash once every 2 seconds, alarm information is popped up in a visual window of the anesthesia monitoring alarm device to prompt that the target DOA parameter is abnormal, and at the same time, a buzzing alarm is sent out for 1 beep every second, each buzzing alarm lasts for 2 minutes, and a middle interval is one minute, so that a guardian can be timely and effectively prompted, the safety of the anesthesia process is further improved, and the risk in the anesthesia process is reduced.

In one possible embodiment, when the target DOA parameter is smaller than the lower threshold of the target DOA threshold range, determining a first difference between the lower threshold and the target DOA parameter, determining and triggering a corresponding alarm event according to the first difference, generating a first prompt message according to a preset mapping relation between the first difference and the acting time of the target anesthetic, displaying the first prompt message through a visual window, for example, the target DOA parameter is 35, the lower threshold of the target DOA threshold range is 40, the first difference is 5, triggering a low-level alarm event, and the generated first prompt message is that "the anesthetic depth of the target object exceeds the expected level, please stop the drug injection, the target DOA parameter reaches the lower threshold of the target DOA threshold range after 5 minutes, so as to prompt a guardian not to inject or stop injecting the anesthetic into the target object, and reach the proper anesthetic depth after the expected time.

In one possible embodiment, when the target DOA parameter is greater than the upper threshold of the target DOA threshold range, determining a second difference between the target DOA parameter and the upper threshold, determining and triggering a corresponding alarm event according to the second difference, determining an anesthetic drug supplementing amount corresponding to the second difference according to a preset mapping relation between the second difference and the dosage of the target anesthetic drug, generating a second prompting message according to the anesthetic drug supplementing amount, and finally displaying the second prompting message through a visual window, for example, the target DOA parameter is 71, the upper threshold of the target DOA threshold range is 60, the second difference is 11, and triggering a middle-level alarm event. According to a preset mapping relation, assuming that the anesthetic supplement amount corresponding to the second difference value of 5 is 3mg, the generated second prompting information is 'the target object still is 3mg of anesthetic worse', and the second prompting information is used for prompting a guardian to inject 3mg of anesthetic to the target object so as to enable the guardian to reach a proper anesthetic depth.

In summary, by implementing the embodiment of the application, the anesthesia state of the target object can be known more accurately, the target DOA parameter can be obtained by determining and combining a plurality of anesthesia index parameters, such as BSR parameters, COH parameters, PE parameters and the like, the anesthesia depth can be monitored more accurately, the personalized threshold range of the target DOA can be determined according to the specific condition of the target object, the deviation condition of the target DOA parameter and the target DOA threshold range can be monitored in real time, the alarm event and the information prompt can be responded and triggered in time, the efficiency and the safety of the anesthesia process are improved, and the life and the health of the target object are protected to the greatest extent.

In the case of dividing each functional module by adopting a corresponding function, fig. 4 is a block diagram of functional modules of a physiological information early warning processing system under an anesthesia scene provided by an embodiment of the present application, where the physiological information early warning processing system 400 under an anesthesia scene is applied to an anesthesia monitoring alarm device, and includes an acquisition module 410, a first determination module 420, a second determination module 430, a combination module 440, a third determination module 450, and an alarm module 460, where:

An acquisition module 410, configured to acquire case information and an electroencephalogram signal of a target object after the target object is inoculated with a target anesthetic;

the first determining module 420 is configured to determine anesthesia index data according to the electroencephalogram signal, and obtain a plurality of groups of anesthesia index data sets, where the plurality of groups of anesthesia index data sets at least include: BSR data set, COH data set, PE data set;

A second determining module 430, configured to determine anesthesia index parameters corresponding to each of the plurality of anesthesia index data sets, to obtain a plurality of anesthesia index parameters, where the plurality of anesthesia index parameters at least includes a BSR parameter, a COH parameter, and a PE parameter; each anesthesia index data set corresponds to an anesthesia index parameter; each anesthesia index parameter corresponds to an anesthesia index type identifier;

a combination module 440, configured to combine the plurality of anesthesia index parameters according to the case information to obtain a target DOA parameter;

a third determining module 450, configured to determine a target DOA threshold range corresponding to the case information;

An alarm module 460, configured to trigger an alarm event when the target DOA parameter is not within the target DOA threshold range;

specifically, in terms of the triggering alarm event, the alarm module 460 is specifically configured to:

Acquiring a target lower limit threshold and a target upper limit threshold of the target DOA threshold range; determining a first difference between the target lower threshold and the target DOA parameter when the target DOA parameter is less than the target lower threshold; determining a first alarm parameter corresponding to the first difference value; executing the alarm event according to the first alarm parameter; generating first prompt information according to a preset mapping relation between the first difference value and the action time of the target anesthetic, wherein the first prompt information comprises the time required by the target DOA parameter to reach the target lower limit threshold; determining a second difference between the target DOA parameter and the target upper threshold when the target DOA parameter is greater than the target upper threshold; determining a second alarm parameter corresponding to the second difference value; executing the alarm event according to the second alarm parameter; generating second prompt information according to a preset mapping relation between the second difference value and the dosage of the target anesthetic, wherein the second prompt information comprises the supplementing quantity of the target anesthetic.

Specifically, the case information includes medical history information, and in the aspect of combining the plurality of anesthesia index parameters according to the case information to obtain a target DOA parameter, the combining module 440 is specifically configured to:

Wherein, the determining the target DOA parameter according to the at least one anesthesia index parameter specifically includes: acquiring a weight group corresponding to the medical history information, wherein the weight group comprises at least one weight, and the at least one weight corresponds to the at least one anesthesia index parameter one by one; and carrying out weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain the target DOA parameter.

The step of performing a weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain the target DOA parameter specifically includes: performing weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain a reference DOA parameter; acquiring target hardware parameters and target software parameters of the anesthesia monitoring alarm equipment; determining a first influence coefficient corresponding to the target hardware parameter; determining a second influence coefficient corresponding to the target software parameter; and dynamically adjusting the reference DOA parameter according to the first influence coefficient and the second influence coefficient to obtain the target DOA parameter.

Specifically, the case information further includes personal information; the personal information includes: age information, height information, and weight information; in the aspect of determining the target DOA threshold range corresponding to the case information, the third determining module 450 is specifically configured to:

The adjusting the reference DOA threshold range according to the target metabolic rate and the anesthesia depth influence degree parameter to obtain the target DOA threshold range specifically includes:

Therefore, the target DOA parameters are obtained by determining and combining the plurality of anesthesia index parameters, the anesthesia depth can be monitored more accurately, the personalized threshold range of the target DOA is determined according to the case information of the target object, the deviation condition of the target DOA parameters and the target DOA threshold range is monitored in real time, an alarm event and an information prompt can be responded and triggered in time, and the efficiency and the safety of the anesthesia process are improved.

It should be noted that, the specific implementation of each operation may be described in the above embodiments, and the physiological information early warning processing system 400 in the anesthesia scenario may be used to execute the above embodiments of the present application, which is not described herein.

For the above embodiments, for simplicity of description, the same is denoted as a series of combinations of actions. It will be appreciated by persons skilled in the art that the application is not limited by the order of acts described, as some steps in embodiments of the application may be performed in other orders or concurrently. In addition, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts, steps, modules, or units, etc. that are described are not necessarily required by the embodiments of the application.

In the foregoing embodiments, the descriptions of the embodiments of the present application are emphasized, and in part, not described in detail in one embodiment, reference may be made to related descriptions of other embodiments.

Those skilled in the art will appreciate that implementing all or part of the above described embodiments may be accomplished by computer programs to instruct related hardware, the programs may be stored in computer readable storage media, and the programs may include the processes of the above described method embodiments when executed. And the aforementioned storage medium includes: ROM or random access memory RAM, magnetic or optical disk, etc.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, or may be embodied in software instructions executed by a processor. The software instructions may be comprised of corresponding software modules that may be stored in RAM, flash memory, ROM, EPROM, electrically Erasable EPROM (EEPROM), registers, hard disk, a removable disk, a compact disk read-only (CD-ROM), or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. In addition, the ASIC may be located in a terminal device or a management device. The processor and the storage medium may reside as discrete components in a terminal device or management device.

Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the embodiments of the present application may be implemented, in whole or in part, in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Drive (SSD)), or the like.

The respective apparatuses and the respective modules/units included in the products described in the above embodiments may be software modules/units, may be hardware modules/units, or may be partly software modules/units, and partly hardware modules/units. For example, for each device or product applied to or integrated on a chip, each module/unit included in the device or product may be implemented in hardware such as a circuit, or at least some modules/units may be implemented in software program, where the software program runs on a processor integrated inside the chip, and the remaining (if any) part of modules/units may be implemented in hardware such as a circuit; for each device and product applied to or integrated in the chip module, each module/unit contained in the device and product can be realized in a hardware manner such as a circuit, different modules/units can be located in the same component (such as a chip, a circuit module and the like) or different components of the chip module, or at least part of the modules/units can be realized in a software program, the software program runs on a processor integrated in the chip module, and the rest (if any) of the modules/units can be realized in a hardware manner such as a circuit; for each device, product, or application to or integrated with the terminal device, each module/unit included in the device may be implemented in hardware such as a circuit, and different modules/units may be located in the same component (e.g., a chip, a circuit module, etc.) or different components in the terminal device, or at least some modules/units may be implemented in a software program, where the software program runs on a processor integrated within the terminal device, and the remaining (if any) some modules/units may be implemented in hardware such as a circuit.

The foregoing detailed description of the embodiments of the present application further illustrates the purposes, technical solutions and advantageous effects of the embodiments of the present application, and it should be understood that the foregoing description is only a specific implementation of the embodiments of the present application, and is not intended to limit the scope of the embodiments of the present application, and any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of the embodiments of the present application.

Claims

1. The utility model provides a physiological information early warning processing system under anesthesia scene, its characterized in that is applied to anesthesia monitoring alarm equipment, including acquisition module, first determination module, second determination module, combination module, third determination module and alarm module, wherein:

the first determining module is configured to determine anesthesia index data according to the electroencephalogram signal, and obtain a plurality of groups of anesthesia index data sets, where the plurality of groups of anesthesia index data sets at least include: a burst suppression ratio dataset, a spectral coherence dataset, an permutation entropy dataset;

the second determining module is configured to determine anesthesia index parameters corresponding to each of the plurality of sets of anesthesia index data sets, to obtain a plurality of anesthesia index parameters, where the plurality of anesthesia index parameters at least includes an explosion suppression ratio parameter, a spectral coherence parameter, and an permutation entropy parameter; each anesthesia index data set corresponds to an anesthesia index parameter; each anesthesia index parameter corresponds to an anesthesia index type identifier;

the combination module is used for combining the plurality of anesthesia index parameters according to the case information to obtain target anesthesia depth parameters;

The third determining module is used for determining a target anesthesia depth threshold range corresponding to the case information;

the alarm module is used for triggering an alarm event when the target anesthesia depth parameter is not in the target anesthesia depth threshold range;

Acquiring a target lower limit threshold and a target upper limit threshold of the target anesthesia depth threshold range;

Determining a first difference between the target lower threshold and the target anesthesia depth parameter when the target anesthesia depth parameter is less than the target lower threshold;

executing the alarm event according to the first alarm parameter;

generating first prompt information according to a preset mapping relation between the first difference value and the action time of the target anesthetic, wherein the first prompt information comprises the time required by the target anesthetic depth parameter to reach the target lower limit threshold;

Determining a second difference between the target anesthesia depth parameter and the target upper threshold when the target anesthesia depth parameter is greater than the target upper threshold;

executing the alarm event according to the second alarm parameter;

generating second prompt information according to a preset mapping relation between the second difference value and the dosage of the target anesthetic, wherein the second prompt information comprises the supplementing quantity of the target anesthetic;

The case information comprises medical history information, and the combination module is specifically configured to:

Determining an anesthesia index type identifier corresponding to the medical history information to obtain at least one anesthesia index type identifier;

Acquiring anesthesia index parameters with the same anesthesia index type identifier as the at least one anesthesia index type identifier from the plurality of anesthesia index parameters to obtain at least one anesthesia index parameter;

determining the target anesthesia depth parameter according to the at least one anesthesia index parameter;

wherein, in the aspect of determining the target anesthesia depth parameter according to the at least one anesthesia index parameter, the combination module is specifically configured to:

Acquiring a weight group corresponding to the medical history information, wherein the weight group comprises at least one weight, and the at least one weight corresponds to the at least one anesthesia index parameter one by one;

performing weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain the target anesthesia depth parameter;

Wherein, in the aspect of obtaining the target anesthesia depth parameter by performing a weighted operation according to the at least one weight and the at least one anesthesia index parameter, the combination module is specifically configured to:

performing weighted operation according to the at least one weight and the at least one anesthesia index parameter to obtain a reference anesthesia depth parameter;

acquiring target hardware parameters and target software parameters of the anesthesia monitoring alarm equipment;

determining a first influence coefficient corresponding to the target hardware parameter;

determining a second influence coefficient corresponding to the target software parameter;

dynamically adjusting the reference anesthesia depth parameter according to the first influence coefficient and the second influence coefficient to obtain the target anesthesia depth parameter;

Wherein the case information further includes personal information; the personal information includes: age information, height information, and weight information; in the aspect of determining the target anesthesia depth threshold range corresponding to the case information, the third determining module is specifically configured to:

Acquiring a reference anesthesia depth threshold range corresponding to the personal information;

determining a target body metabolic rate of the target subject from the personal information;

Determining a target metabolic rate of the target anesthetic drug based on the target body metabolic rate;

determining the anesthesia depth influence degree parameters corresponding to the medical history information and the target anesthetic;

And adjusting the reference anesthesia depth threshold range according to the target metabolism speed and the anesthesia depth influence degree parameter to obtain the target anesthesia depth threshold range.

2. The system of claim 1, wherein the third determination module is configured to, in terms of said adjusting the reference depth of anesthesia threshold range based on the target metabolic rate and the depth of anesthesia influence parameter, obtain the target depth of anesthesia threshold range:

Acquiring a first lower threshold and a first upper threshold of the reference anesthesia depth threshold range;

Determining a target regulation parameter corresponding to the target metabolic rate;

Adjusting the first lower limit threshold and the first upper limit threshold according to the target adjustment parameter to obtain a reference first lower limit threshold and a reference first upper limit threshold;

determining a target fine tuning parameter corresponding to the anesthesia depth influence degree parameter;

Adjusting the reference first lower limit threshold according to the target fine tuning parameter to obtain a target first lower limit threshold;

the target anesthesia depth threshold range is determined from the target first lower threshold and the reference first upper threshold.

3. An electronic device, characterized in that the electronic device comprises the physiological information pre-warning processing system in an anesthesia scenario according to claim 1 or 2.