KR102045559B1 - Method and apparatus for monitoring the depth of anaesthesia and consciousness through the brain network analysis - Google Patents

Abstract

According to the present invention, a method of monitoring anesthesia and consciousness depth through brain network analysis, performed by a computer device, includes: (a) acquiring a brain signal of a user extracted during an elapsed time of anesthesia; (b) preprocessing the measured brain signals suitable for brain network analysis; (c) calculating a functional connectivity value between two channels for each frequency based on the preprocessed brain signals and performing brain network analysis; (d) determining anesthesia and consciousness depth of the user based on the analyzed brain network characteristics; And (e) providing the determined anesthesia and consciousness depth of the user through the user interface.

Description

METHOD AND APPARATUS FOR MONITORING THE DEPTH OF ANAESTHESIA AND CONSCIOUSNESS THROUGH THE BRAIN NETWORK ANALYSIS}

The present invention relates to a method and apparatus for monitoring anesthesia and consciousness depth through brain network analysis.

In general, when performing medical activities such as surgery and treatment, it is important for patients to maintain anesthesia and depth of consciousness properly. If the anesthesia is shallow, it may wake up and suffer during surgery. On the contrary, if the anesthesia is too deep, it may lead to heart attack, complications, and death. In particular, when anesthesia is difficult, the patient may not be able to express properly even if the breathing becomes difficult. This anesthesia can happen unexpectedly to an unspecified number of people and can lead to patient death. Quantitative evaluation of anesthesia and consciousness depth is necessary to prevent such accidents, but the current anesthesia depth is determined by the experience and knowledge of anesthesiologists and physicians who understand the condition of patients and control the concentration of anesthetics. Therefore, there is a need for a device capable of objectively measuring the depth of anesthesia.

The bispectral index (BIS) or entropy index, a representative patient monitoring device that currently accounts for more than 90% of the anesthesia depth monitoring market, predicts anesthetic depth based on the degree of brain activity. In particular, BIS shows anesthesia depth from 1 to 100, the appropriate range during surgery is known to be 40 ~ 60. If it is lower than 40, anesthesia is too deep. If it is higher than 60, anesthesia is shallow. If it is above 80, the patient feels an external stimulus. It is likely to occur. Arousal during surgery means that the patient's consciousness is awake during anesthesia and the body is unable to move while feeling pain during the operation.When pain and numbness are accompanied, fear of death, anxiety, etc. -traumatic stress disorder '. In other words, current anesthesia depth monitoring technology is ideal to maintain anesthesia within the appropriate BIS range, so that after the operation is finished, you can quickly wake up from anesthesia, prevent vomiting or dizziness.

However, these techniques are only for reference in determining anesthesia and consciousness depth. Finally, discrimination of consciousness and unconsciousness requires subjective judgment of the physician, frequently differs from the sedation scale, and up to 60 seconds of quantification. Because of the time required, it is impossible to deal with a sudden medical accident. In addition, it has high reliability in general anesthesia, but has low reliability and performance in categorizing anesthesia. Therefore, there is a need for anesthesia and consciousness depth monitoring systems that can help physicians properly control anesthesia doses and minimize the occurrence of medical accidents, taking into account various environmental variables.

Meanwhile, as a conventional technique for measuring anesthesia and consciousness depth using a biosignal, Korean Patent Registration No. 10-1079785 (name of the invention: an electroencephalogram analysis device for calculating anesthesia depth index) extracts a plurality of parameters, A technique for extracting a suitable parameter according to an environment or a user based on a correlation between anesthetic depth is disclosed. However, it is not a single indicator development but a feature extraction technique, and it is difficult to identify anesthesia and consciousness depths that change according to various environmental variables. In addition, Korean Patent Registration No. 10-1111498 (name of the invention: anesthesia depth monitoring system and method) analyzes a plurality of biological signals including electrocardiogram, electrocardiogram and blood pressure. However, as the number of bio signals used increases, physical complexity and cost aspects make it difficult to use in actual clinical practice.

However, the above-mentioned techniques suggest different types of anesthesia and consciousness depth indicators, but they have limitations in not accurately distinguishing the boundary between consciousness and unconsciousness.

The present invention provides an anesthesia and consciousness depth monitoring method and apparatus that accurately informs the timing of switching between consciousness and unconsciousness, and further reduces the physical and mental damage caused by anesthesia by further improving the accuracy of unconsciousness and consciousness discrimination. For the purpose of

According to a first embodiment of the present invention, a method for monitoring anesthesia and consciousness depth through brain network analysis, performed by a computer device, includes: (a) acquiring a brain signal of a user extracted during an elapsed time of anesthesia; (b) preprocessing the measured brain signals suitable for brain network analysis; (c) calculating a functional connectivity value between two channels for each frequency based on the preprocessed brain signals and performing brain network analysis; (d) determining anesthesia and consciousness depth of the user based on the analyzed brain network characteristics; And (e) providing the determined anesthesia and consciousness depth of the user through the user interface.

In accordance with a second embodiment of the present invention, an anesthesia and consciousness depth monitoring apparatus through brain network analysis may include: a memory in which a program for performing anesthesia and consciousness depth monitoring method through brain network analysis is stored; And a processor that executes a program, wherein the processor, according to the execution of the program, obtains the brain signal of the user extracted during the elapsed time of anesthesia, and preprocesses the measured brain signals to be suitable for brain network analysis. Calculate functional connectivity between two channels for each frequency based on brain signals, perform brain network analysis, determine anesthesia and consciousness depth of user based on analyzed brain network characteristics, determine anesthesia and consciousness of user Depth is provided through the user interface.

The present invention determines anesthesia and consciousness depth based on the amount of change in how the brain network state changes as an anesthetic is administered based on the brain network state before the anesthesia of the patient. Accordingly, since the criteria for anesthesia and consciousness are brain signals when the patient is awake, solving the problem that the criteria of the indicator may differ for each individual may improve the reliability of the system.

In particular, since it is possible to accurately determine the boundary point between the conscious and the unconscious, accurate conscious depth can be measured, and the conscious depth can be measured only by the signal of the occipital lobe itself.

1 is a structural diagram of an anesthetic and consciousness depth monitoring system through brain network analysis according to an embodiment of the present invention.
2 is a flowchart illustrating a method of performing brain network analysis by an anesthesia and consciousness depth monitoring apparatus according to an embodiment of the present invention.
Figure 3 (a) shows the value of the overall efficiency indicator (GE) divided by each anesthesia and consciousness depth in the delta wave frequency band of the brain signals measured according to an embodiment of the present invention, Figure 3 (b) ) Represents the point efficiency (LE) value divided into each anesthesia and consciousness depth in the delta wave frequency band, and (c) of FIG. 3 is a total efficiency indicator divided into each anesthetic and consciousness depth in the beta wave frequency band. A value of GE is shown, and FIG. 3D shows a point efficiency LE which is divided into anesthesia and consciousness depth in the beta wave frequency band.
4 is an exemplary view of a user interface displaying anesthesia and consciousness depth of a user determined according to an anesthesia and consciousness depth monitoring apparatus through brain network analysis according to an embodiment of the present invention.
5 is a flowchart illustrating a method for monitoring anesthesia and consciousness depth through brain network analysis according to an embodiment of the present invention.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and like reference numerals designate like parts throughout the specification.

Throughout the specification, when a part is "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element in between. . In addition, when a part is said to "include" a certain component, which means that it may further include other components, except to exclude other components unless otherwise stated.

In the present specification, the term 'unit' includes a unit realized by hardware, a unit realized by software, and a unit realized by both. In addition, one unit may be realized using two or more pieces of hardware, and two or more units may be realized by one piece of hardware. Meanwhile, '~' is not limited to software or hardware, and '~' may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors. Thus, as an example, '~' means components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, procedures, and the like. Subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and the 'parts' may be combined into a smaller number of components and the 'parts' or further separated into additional components and the 'parts'. In addition, the components and '~' may be implemented to play one or more CPUs in the device or secure multimedia card.

The anesthesia and consciousness depth monitoring device referred to below may be implemented as a computer or a portable terminal that can be connected to a server or another terminal through a network. Here, the computer includes, for example, a laptop, desktop, laptop, etc., which is equipped with a web browser, and the portable terminal is, for example, a wireless communication device that ensures portability and mobility. International Mobile Telecommunication (IMT) -2000, Code Division Multiple Access (CDMA) -2000, W-Code Division Multiple Access (W-CDMA), Wireless Broadband Internet (Wibro), Long Term Evolution (LTE) communication-based terminal, smart It can include all kinds of handheld based wireless communication devices such as phones, tablet PCs, and the like. In addition, the “network” may be a wired or mobile radio communication network or satellite, such as a local area network (LAN), wide area network (WAN), or value added network (VAN). It can be implemented in all kinds of wireless networks such as communication networks.

Hereinafter, an embodiment of the present invention will be described in detail.

1 is a structural diagram of an anesthetic and consciousness depth monitoring system through brain network analysis according to an embodiment of the present invention.

Referring to FIG. 1, a system according to an embodiment of the present invention includes a brain signal measuring unit 100, anesthesia and consciousness depth monitoring device 200, and a feedback presenter 300.

The brain signal measuring unit 100 measures an EEG (electroencephalography) signal through a plurality of electrodes in contact with the scalp or adjacent to the scalp in order to measure an electrical signal that changes according to anesthesia and consciousness depth of the user. For example, the brain signal measuring unit 100 may measure only the brain signal of the occipital lobe for practical purposes. Here, the user may be a patient under anesthesia. In particular, the brain signal measuring unit 100 may measure the brain signal in the waking state before anesthetic administration. At this time, the brain network value calculated by performing brain network analysis in the anesthesia and consciousness depth monitoring apparatus 200 based on the measured brain signal may be used as reference data for determining anesthesia and consciousness depth.

Anesthesia and consciousness depth monitoring device 200 is connected to the brain signal measuring unit 100 by wireless or wired to obtain the brain signal of the user extracted during the anesthesia elapsed time, and to pre-process the measured brain signals suitable for brain network analysis Based on the preprocessed brain signals, functional connectivity values between two channels are calculated for each frequency, and brain network analysis is performed. The anesthesia and consciousness depth of the user may be determined based on the analyzed brain network characteristics, and the determined anesthesia and consciousness depth of the user may be provided to the feedback presenter 300. Here, the anesthesia and consciousness of the user may be determined by comparing the reference data analyzed from the brain signal of the user measured in awake state before administration of the anesthetic and the value analyzed from the brain signal measured in the anesthetic state after administration of the anesthetic. Detailed methods for determining anesthesia and consciousness depth will be described later with reference to FIG. 2.

The feedback presenter 300 visually presents the determined anesthesia and consciousness depth of the user through the user interface. For example, a quantitative indicator of anesthesia and consciousness depth may be provided, and a notification may be provided to indicate the time of conscious / unconscious transition. Detailed description of the user interface will be described later with reference to FIG. 4.

On the other hand, according to the integrated information theory (integrated information theory), the cerebral information integration of the cerebral information when the conscious, but the unconscious, there is an electrical signal, but the information integration of the brain is broken. Recently, the occipital lobe is called the Posterior Hot Zone, which has the highest neural correlates of consciousness, and has been identified as the brain region that is most clearly characterized by the level of consciousness. In other words, when the body is anesthetized, the information integration ability of the nervous system is remarkably decreased as the body transitions from consciousness to the unconscious state, and loss of consciousness occurs when the temporal and spatial self-organization of brain waves is broken. In particular, when the consciousness disappears, the information flowing from the frontal lobe dealing with cognition to the occipital lobe dealing with the senses decreases drastically. This change in brain in consciousness and unconsciousness cannot be explained simply by simple indicators, such as the magnitude and frequency power of EEG.

Therefore, the present invention proposes a method that can overcome the limitations of the aforementioned techniques by quantifying the interaction of brain nerves important in consciousness and unconsciousness, rather than simply analyzing the electrical signal itself of the brain. In other words, based on the cognitive integrated paradigm that information integration of the brain's nervous system is changed by anesthesia, brain networks can be analyzed to determine precise anesthesia and consciousness depth. In addition, the depth of consciousness can be measured only by interactions in the occipital lobe. To this end, the brain signal of the patient can be interpreted in real time to measure the functional connectivity values that change as the anesthetic is administered based on the user's waking functional connectivity before anesthesia.

In particular, the functional connectivity index of the brain signal during anesthesia can be extracted to provide anesthesia and consciousness depth of the user as compared to the brain network state when awake before anesthesia. This provides not only the depth of anesthesia and consciousness of the user, but also the point of transition from consciousness to unconsciousness or from unconsciousness to consciousness, which can be used as a biomarker to accurately distinguish between consciousness and unconsciousness. have.

Hereinafter, the configuration of the anesthesia and consciousness depth monitoring apparatus 200 through the analysis of the brain network according to an embodiment of the present invention will be described in detail.

2 is a flowchart illustrating a method of performing brain network analysis by an anesthesia and consciousness depth monitoring apparatus according to an embodiment of the present invention.

Anesthesia and consciousness depth monitoring apparatus 200 may include a processor for performing a memory and a program (or application) for measuring the consciousness-based anesthesia and consciousness depth using the brain connectivity. Here, the processor may perform various functions according to the execution of the program stored in the memory, and according to each function, the detailed modules included in the processor are preprocessor 210, brain network analyzer 220, anesthesia and consciousness depth determiner. 230.

The preprocessor 210 receives the brain signal of the user extracted during the elapsed time of anesthesia from the brain signal measuring unit 100. Brain signals are EEG signals measured at electrodes in contact with or near the scalp. In addition, the preprocessor 210 may remove noise caused by head and eye movements or eye blinks from the measured brain signals, and filter the brain signals from which the noise is removed to a specific frequency band related to sleep or consciousness. .

Delta (δ) waves, on the other hand, have a frequency of 0.1 to 4 Hz and an amplitude of 20 to 200 μV, and are often found in deep sleep or normal newborns. Theta (θ) waves have a frequency of 4 to 8 Hz and an amplitude of 20 to 100 μV and appear in an emotionally stable or sleeping state. The alpha (α) wave has a frequency of 8 to 12 Hz and an amplitude of 20 to 60 μV. The alpha (α) wave appears in a relaxed state in which tension is relaxed. The beta (β) wave has a frequency of 12-30 Hz and an amplitude of 2-20 μV and appears when you are awake or conscious. Gamma (γ) waves have a frequency of 30-50 Hz and an amplitude of 2-20 μV, and appear in a strong excited state.

Therefore, the brain network analyzer 220 of the present invention may use the frequency bands of the delta wave and the beta wave that best reflects the state of consciousness and unconsciousness.

Referring to FIG. 2, the brain network analyzer 220 may extract frequency bands of delta waves and beta waves of brain signals through frequency analysis. Subsequently, a functional connectivity value between two channels may be calculated based on the amplitude and phase values of the extracted frequency band (S131). Subsequently, functional connectivity values between all channels for each frequency may be calculated as a matrix (S133). Next, by applying a threshold value for determining whether the calculated functional connectivity value has a significant connectivity (S133), it is possible to calculate the brain network value based on the graph theory (S134).

Here, in the functional connectivity calculation step (S131), the functional connectivity value is a measure for determining the degree of synchronization of phases between brain signals for two channels, and includes a phase locking value, a phase lag index, One or more of a weighted phase lag index, an imaginary coherence, and a synchronization likelihood.

Subsequently, in a functional connectivity matrix generation step S132 and a threshold application step S133, connectivity below a specific threshold value is converted to 0 so that only meaningful functional connectivity is identified. In this case, the threshold value may be set to a value having a largest difference between a global efficiency and a local efficiency calculated through a plurality of random matrices. For example, the threshold value may be set to a value having a largest difference between a global efficiency and a local efficiency calculated through a plurality of (preferably 1000 or more) random matrices.

Subsequently, in the calculation of the brain network value (S134), the brain network value based on graph theory may be calculated using a functional connectivity matrix representing only meaningful functional connectivity and compared with the waking state before anesthesia. Brain network values refer to values of brain connectivity.

Specifically, brain network values are graph theory figures known in the field of graph theory. For example, global efficiency is an integrated measure of brain connectivity and indicates the efficiency of information integration flow throughout the brain. Local efficiency may be a measure of separation of brain connectivity and indicates a network efficiency of information integration flow in a specific cerebral region. In addition, the present invention is not limited thereto, and includes clustering coefficient, characteristic path length, modularity, closeness centrality, betweenness centrality, and eigenvector centrality. and at least one graph theory such as values representing various characteristics of brain connectivity, such as centrality.

Preferably, in the calculation of the brain network value (S134), one or more brain network values of global efficiency and local efficiency may be calculated.

Equation 1 below represents a global efficiency index (Global efficiency).

<Equation 1>

Where N denotes the number of columns of the matrix representing functional connectivity, and d _ij denotes the length of the shortest path between nodes i and j.

Equation 2 below represents Local efficiency, and includes Equation 1.

<Equation 2>

Where N represents the number of columns of the matrix representing functional connectivity and A _i represents a subgraph of the neighbor of node i.

The anesthesia and consciousness depth determination unit 230 may detect an increase in the brain network value as compared with reference data in the delta wave and beta wave frequency bands according to the elapsed time of anesthesia of the user. Here, the reference data means a value of the brain network analyzed from the brain signal of the user measured in the waking state before administration of the anesthetic.

Referring back to FIG. 2, for example, in the case of the delta wave frequency band, a change (increase amount) of the brain network value after anesthetic administration may be detected in comparison with reference data (S141). That is, the amount of increase in the brain network value based on the reference data in the delta wave frequency band means a quantitative index indicating anesthesia and consciousness depth. A detailed description of the change (increase amount) of the brain network value in the delta wave frequency band will be described later with reference to FIGS. 3A and 3B.

In addition, in the case of the beta wave frequency band, it is possible to detect the point of time when the brain network value after anesthesia administration increases above a predetermined value in comparison with the reference data (S142). That is, the point of time when the brain network value is increased more than a predetermined value compared to the reference data in the beta wave frequency band means a point of time when the state of transition from the conscious state to the unconscious state or the state of unconscious state to the conscious state. A detailed description of the increase (sudden increase time) of the brain network value in the beta-wave frequency band will be described later with reference to FIGS. 3C and 3D.

Figure 3 (a) shows the value of the overall efficiency indicator (GE) divided by each anesthesia and consciousness depth in the delta wave frequency band of the brain signals measured according to an embodiment of the present invention, Figure 3 (b) ) Represents the point efficiency (LE) value divided into each anesthesia and consciousness depth in the delta wave frequency band, and (c) of FIG. 3 is a total efficiency indicator divided into each anesthetic and consciousness depth in the beta wave frequency band. A value of GE is shown, and FIG. 3D shows a point efficiency LE which is divided into anesthesia and consciousness depth in the beta wave frequency band.

Referring to Figures 3 (a) to 3 (d), I indicated on the bar graph means standard deviation. In particular, anesthesia and conscious depth conditions (Baseline), anesthetics administered after the switch to skip to the unconscious in the conscious point (Trans _UN), the unconscious, unresponsive to stimuli due to anesthetic administration (UCS), in the unconscious awake before anesthesia administration Trans _CON can be divided into five stages of recovery, which can respond to the stimulus as a recovery period. At this time, the network value of the awake state (Baseline) before administration of the anesthetic agent is a reference data, and serves as a reference for comparing the degree of increase of the network value during the elapsed anesthesia time.

In other words, as shown in (a) and (b) of FIG. 3, in the case of the delta wave, the brain network values GE and LE in the remaining steps except for recovery compared to the baseline. Indicates that this is increased. In other words, in the case of delta waves, the depth of anesthesia and consciousness increases as the brain network values GE and LE increase.

As shown in (c) and (d) of FIG. 3, in case of the beta wave, a transition point (Trans _UN ) that passes from consciousness to the unconsciousness or transition from unconsciousness to consciousness compared to the baseline Only the time point Trans _{CON indicates} that the brain network values GE and LE are increased. That is, in the case of beta wave, the point of time (the moment) when the brain network values (GE, LE) increase more than a predetermined value more rapidly than the awake state (Baseline) is the point of transition from consciousness to unconsciousness, or from unconsciousness to consciousness. It means the point of time.

4 is an exemplary view of a user interface displaying anesthesia and consciousness depth of a user determined according to an anesthesia and consciousness depth monitoring apparatus through brain network analysis according to an embodiment of the present invention.

As shown in FIG. 4, the feedback presenter 300 visually presents the brain network value of the delta wave indicating anesthesia and consciousness depth as a quantitative indicator, and the brain network value of the beta wave is notified because it is a transition time between the conscious and unconscious. May occur.

Specifically, referring back to FIG. 2, the network value that increases and changes with respect to the reference data in the delta wave frequency band may be provided as a quantitative indicator of anesthesia and consciousness through the feedback presenter 300 (S151). In addition, in the beta-wave frequency band, by detecting the time when the brain network value is increased above the predetermined value, it can provide a notification to the conscious / unconscious switching point through the feedback presentation unit 300 (S152).

As shown in FIG. 4, the notification may be provided as an audible sound that can be recognized audibly or in a form that can be visually recognized by displaying a warning light. For example, the feedback presenter 300 may display a brain network value (delta index, beta index) in the delta wave or beta wave frequency band as a quantitative indicator of anesthesia and consciousness depth. As another example, a functional connectivity pattern of an initial state (awake state) and a current state (anesthetic state under anesthesia) in a delta or beta wave frequency band may be displayed.

Therefore, the anesthesia and consciousness depth monitoring system through the analysis of the brain network of the present invention can predict the objective anesthesia and consciousness depth, and can be practically used in the clinical practice such as adding anesthetics by accurately identifying the transition point of consciousness and unconsciousness.

In addition, by measuring the anesthesia and consciousness depth through the above-described brain network value, while providing the depth of the user's consciousness, at the same time to determine the point of transition from consciousness to unconsciousness, or from unconsciousness to consciousness, a notification is generated and generated during surgery. It can prevent accidents such as arousal during surgery. In addition, since the user's waking state before anesthesia is a reference, it is possible to correct anesthesia and consciousness depth by identifying variables such as the state of the user's surgery day.

5 is a flowchart illustrating a method for monitoring anesthesia and consciousness depth through brain network analysis according to an embodiment of the present invention.

Hereinafter, an anesthetic and consciousness depth monitoring method through brain network analysis according to an embodiment of the present invention will be described in detail with reference to FIG. 5.

Since the following method is performed by the apparatus 200 described above, the description thereof will be replaced with the above description even if the contents are omitted below.

Anesthesia and consciousness depth monitoring method through brain network analysis, performed by the computer device is to obtain a brain signal of the user extracted during the elapsed time of anesthesia (S110), pre-processing the measured brain signals suitable for brain network analysis Step S120, calculating functional connectivity values between two channels for each frequency based on preprocessed brain signals, performing brain network analysis (S130), anesthesia and consciousness of the user based on the analyzed brain network characteristics Determining the depth (S140) and providing the determined depth of anesthesia and consciousness of the user through the user interface (S150).

Pre-processing (S120) includes removing noise from the measured brain signals and filtering the noise-removed brain signals to a specific frequency band associated with sleep or consciousness.

Performing the brain network analysis (S130) is a step of extracting the frequency band of the delta wave and beta wave of the brain signals through the frequency analysis, based on the amplitude and phase value of the extracted frequency band Calculating brain connectivity values based on a graph theory by calculating a functional connectivity value between channels and applying a threshold value for determining whether the calculated functional connectivity value has a significant connectivity.

Here, the functional connectivity value is a measure for determining the degree of phase synchronization between brain signals for two channels, and includes a phase locking value, a phase lag index, and a weighted phase lag index. ), Virtual coherence, and synchronization likelihood.

In addition, the threshold value is set to the largest difference between the global efficiency and the local efficiency calculated through a plurality of random matrices, and the brain network value is a quantified value of brain connectivity.

 Determining the depth of anesthesia and consciousness of the user (S140) includes the step of detecting the increase in the brain network value compared to the reference data in the delta wave and beta wave frequency band as the elapsed time of the anesthesia of the user . Here, the increase amount of the brain network value based on the reference data in the delta wave frequency band is a quantitative index indicating anesthesia and consciousness depth, and the brain network value increases more than a predetermined value compared to the reference data in the beta wave frequency band. The point of time is a point of time from the state of consciousness to an unconscious state or a point of time from the state of unconsciousness to a state of consciousness. At this time, the reference data is the value of the brain network analyzed from the brain signal of the user measured in the waking state before administration of the anesthetic.

One embodiment of the present invention can also be implemented in the form of a recording medium containing instructions executable by a computer, such as a program module executed by the computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer readable medium may include a computer storage medium. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Although the methods and systems of the present invention have been described in connection with specific embodiments, some or all of their components or operations may be implemented using a computer system having a general purpose hardware architecture.

The foregoing description of the present invention is intended for illustration, and it will be understood by those skilled in the art that the present invention may be easily modified in other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is shown by the following claims rather than the detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present invention. do.

100: brain signal measuring unit
200: anesthesia and consciousness depth monitoring device
210: preprocessing unit
220: brain network analysis unit
230: Determination of anesthesia and consciousness depth
300: feedback presentation

Claims (10)

In the method of monitoring anesthesia and consciousness depth through brain network analysis, performed by a computer device,
(a) obtaining a brain signal of a user extracted during an elapsed time of anesthesia;
(b) preprocessing the measured brain signals suitable for brain network analysis;
(c) calculating a functional connectivity value between two channels for each frequency based on the preprocessed brain signals and performing brain network analysis;
(d) determining anesthesia and consciousness depth of the user based on the analyzed brain network characteristics; And
(e) providing the determined anesthesia and consciousness depth of the user through a user interface,
Step (c) is
(c-1) extracting frequency bands of the delta wave and the beta wave of the brain signals through frequency analysis;
(c-2) calculating a functional connectivity value between the two channels based on the amplitude and phase values of the extracted frequency band; And
(c-3) calculating a brain network value based on a graph theory by applying a threshold value for determining whether the calculated functional connectivity value has a significant connectivity;
The functional connectivity value is
As a measure for determining the degree of phase synchronization between the brain signals for the two channels, Phase locking value, Phase lag index, Weighted phase lag index, Virtual coherence (Imaginary coherence) and Synchronization likelihood,
The threshold value is set to a value at which a difference between a global efficiency and a local efficiency calculated through a plurality of random matrices is the largest.
The brain network value is to quantify the connectivity of the brain,
Anesthesia and consciousness depth monitoring method through brain network analysis. The method of claim 1,
Step (b) is
(b-1) removing noise from the measured brain signals; And
(b-2) filtering the noise canceled brain signals to a specific frequency band associated with sleep or consciousness;
Anesthesia and consciousness depth monitoring method through brain network analysis. delete delete The method of claim 1,
Step (d)
Detecting an increase in the value of the brain network as compared with reference data in the delta wave and beta wave frequency bands according to the elapsed time of anesthesia of the user;
The increase amount of the brain network value based on the reference data in the delta wave frequency band is a quantitative index indicating the anesthesia and consciousness depth,
The time point at which the brain network value increases above a predetermined value in comparison with the reference data in the beta-wave frequency band may be a time point at which the state of consciousness is switched from the unconscious state to the state of unconsciousness or the state of unconsciousness is changed to the conscious state,
The reference data is the brain network value analyzed from the brain signal of the user measured in awake state before administration of the anesthetic,
Anesthesia and consciousness depth monitoring method through brain network analysis. In the anesthesia and consciousness depth monitoring device through brain network analysis,
A memory for storing a program for performing anesthesia and consciousness depth monitoring method through brain network analysis; And
A processor that executes the program;
The processor, according to the execution of the program,
Acquire brain signals of the extracted user during the elapsed time of anesthesia,
Pre-processing the measured brain signals suitable for brain network analysis,
Calculate functional connectivity values between two channels for each frequency based on the preprocessed brain signals, perform brain network analysis,
Determine anesthesia and consciousness depth of the user based on the analyzed brain network characteristics,
Provide the determined anesthesia and consciousness of the user through the user interface,
The processor is
Frequency analysis extracts the delta and beta wave frequency bands of the brain signals,
Calculating a functional connectivity value between the two channels based on the amplitude and phase values of the extracted frequency band,
Calculating a brain network value based on a graph theory by applying a threshold value for determining whether the calculated functional connectivity value has a significant connectivity,
The functional connectivity value is
As a measure for determining the degree of phase synchronization between the brain signals for the two channels, Phase locking value, Phase lag index, Weighted phase lag index, Virtual coherence (Imaginary coherence) and Synchronization likelihood,
The threshold value is set to a value at which a difference between a global efficiency and a local efficiency calculated through a plurality of random matrices is the largest.
The brain network value is to quantify the connectivity of the brain,
Anesthesia and consciousness depth monitoring device through brain network analysis. The method of claim 6,
The processor is
Removing noise from the measured brain signals and filtering the noise-removed brain signals to a specific frequency band associated with sleep or consciousness,
Anesthesia and consciousness depth monitoring device through brain network analysis. delete delete The method of claim 6,
The processor is
As the elapsed time of anesthesia of the user flows, the increase of the brain network value is detected by comparing with reference data in the delta wave and beta wave frequency bands.
The increase amount of the brain network value based on the reference data in the delta wave frequency band is a quantitative index indicating the anesthesia and consciousness depth,
The time point at which the brain network value increases above a predetermined value in comparison with the reference data in the beta-wave frequency band may be a time point at which the state of consciousness is switched from the unconscious state to the state of unconsciousness or the state of unconsciousness is changed to the conscious state,
The reference data is the brain network value analyzed from the brain signal of the user measured in awake state before administration of the anesthetic,
Anesthesia and consciousness depth monitoring device through brain network analysis.

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