Discrimination device and discrimination system

Abstract

[Problem] To provide an identification device and identification system for brain activity states that can identify states of brain activity of a subject undergoing the identification in a natural state and in an anesthetized state, that reduces the effects of personal differences between subjects undergoing the identification, and that can increase the accuracy of such identification. [Solution] This identification device (2) for brain activity states is provided with: a calculating unit (214) that calculates a threshold that makes the average duration of a micro-section (T) a certain value, the micro-section (T) being where the amplitude of brainwaves from the brain of a subject undergoing the identification, within a prescribed section, transition from not being in a prescribed micro-range to being in the prescribed micro-range and then further transition to not being in the prescribed micro-range, that calculates the number of said micro-sections and the average time thereof, and that calculates the potential difference in sections where the amplitude is not in the micro-range and the average time of such sections; and an identification unit (215) that identifies the brain activity state using the calculated index values of the previous period. Furthermore, this identification system (1) for brain activity states is provided with an electroencephalograph (3) that can acquire brainwaves from the brain and with the aforementioned identification device (2). The identification device (2) can acquire brainwaves using the electroencephalograph (3).

Description

[Technical Field]

[0001] In various fields including the medical field, there is a demand for an index that can distinguish the state of consciousness of a subject, such as a patient, etc. In order to meet such a demand, various indexes have been proposed, such as an index that uses the subject's response to a stimulus.

[0002] If the indicators that can distinguish the state of consciousness are subjective indicators based on the subjectivity of the discriminator who discriminates the state of consciousness, the state of consciousness discriminated by each discriminator may differ depending on the subjectivity of each discriminator.

[0003] If the index capable of discriminating the state of consciousness is an objective index, the state of consciousness of the subject can be objectively discriminated, thereby making it possible to uniquely discriminate the state of consciousness of the subject without relying on the subjectivity of the discriminator.

[0004] As an example of a device for objectively determining the state of consciousness of a subject to be determined, Patent Document 1 discloses a sleep state measuring device that performs frequency analysis on brain waves received from an electroencephalogram detector in predetermined time units, extracts at least the frequency components of alpha waves, delta waves, sigma waves, and beta waves, and determines whether the ratio or intensity of each frequency component to the brain waves is equal to or greater than a predetermined threshold value, etc. According to the invention of Patent Document 1, the sleep state of the subject to be determined can be determined based on the frequency components of alpha waves, delta waves, sigma waves, and beta waves extracted from the brain waves of the subject to be determined. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-083307 Summary of the Invention [Problem to be solved by the invention]

[0006] Among EEG waveforms, waveforms with frequencies lower than alpha waves, which are in the frequency band of 8 Hz or higher but less than 13 Hz, are also called slow waves. It is known that slow waves can appear atypically depending on the individual differences in the subject being discriminated. Therefore, methods for discriminating states of consciousness using slow waves can be affected by the individual differences in the subject being discriminated.

[0007] The slow waves include delta waves, which have a frequency band of 1 Hz or more and less than 3 Hz. Therefore, there is room for further improvement in Patent Document 1 in terms of reducing the influence of individual differences in the subject to be discriminated.

[0008] Incidentally, when administering anesthesia to a subject to be discriminated during surgery or the like, it is known that excessive anesthesia can cause circulatory depression such as severe hypotension and bradycardia and / or undesirable side effects such as postoperative delirium in the subject. It is also known that insufficient anesthesia can cause intraoperative awakening, in which the subject becomes awake during surgery. Intraoperative awakening can cause post-traumatic stress disorder (PTSD) in the subject. Therefore, there is a need for an index that can objectively determine whether the amount of anesthesia administered to a subject is appropriate.

[0009] By objectively determining the state of consciousness of a subject to be determined who has been administered anesthesia, it is possible to objectively determine whether the amount of anesthesia administered to the subject to be determined is appropriate. This can improve the safety and / or effectiveness of anesthesia. Patent Document 1 also has room for further improvement in terms of objectively determining the state of consciousness of a subject to be determined who has been administered anesthesia.

[0010] The present invention has been made in consideration of the above circumstances, and its purpose is to provide a brain activity state discrimination device and discrimination system that can distinguish the brain activity state of a subject in a natural state and a subject administered anesthesia, reduce the influence of individual differences in the subject, and improve the accuracy of discrimination. [Means for solving the problem]

[0011] In order to solve the above-mentioned problems, the inventors have conducted extensive research focusing on minute portions of electroencephalograms, which have previously been considered noise. As a result, they have discovered that the above-mentioned object can be achieved by determining a threshold value such that the average duration of minute portions, during which the amplitude of electroencephalograms acquired from the brain of a target subject changes from a state outside a predetermined minute range to a state within the predetermined minute range, and then again outside the predetermined minute range, is a certain value, and by using the time-series change in the threshold value to determine the state of brain activity, they have completed the present invention. The inventors have shown that minute portions are waveforms resulting from the instability of the electrode field, which can fluctuate even for 1 millisecond with each sampled data, and that the range of this fluctuation can reflect the degree of brain activity. They have also found that by separating the waveforms resulting from neural electrical activity from those resulting from neural electrical activity, it is possible to estimate the amount of neural electrical activity near the electrodes. Specifically, the present invention provides the following:

[0012] The invention relating to a first feature provides a brain activity state discrimination device comprising: a first micro-range determination unit that determines a predetermined micro-range so that the average time of each micro-portion in which the amplitude of an electroencephalogram within a predetermined section obtained from the brain to be discriminated changes from a state where it is not within a predetermined micro-range to a state where it is within the predetermined micro-range, and then to a state where it is not within the predetermined micro-range, is a predetermined value; a counting unit that measures changes in the time series of the micro-range; and a discrimination unit that discriminates the activity state of the brain using the measured changes.

[0013] It is known that there is a linear correlation between the number of consecutive microwaves contained in electroencephalograms within a predetermined interval acquired from the brain of a subject to be identified and the concentration of a volatile anesthetic administered to the subject. Furthermore, experiments have confirmed that the microrange, in which the number of microwaves is greatest, decreases as the anesthetic concentration increases. The higher the concentration of the volatile anesthetic, the lower the activity state of the brain of the subject to be identified. Therefore, the activity state of the brain of the subject to be identified can be determined by using the number of microranges and / or microwaves.

[0014] By determining the threshold of the minute range so that the average time of each minute portion is a predetermined value, the minute range can be determined so that the number of minute sections is large. Furthermore, it has been experimentally confirmed that there is a correlation between the average potential difference of the minute range and / or non-minute section and the state of brain activity when the threshold of the minute range is determined so that the number of minute portions is large.

[0015] According to the first aspect of the present invention, a predetermined micro range can be determined so that the average time of each micro portion is a predetermined value. Therefore, when the micro range is determined so that the number of micro portions is large, the brain activity state can be determined using the micro range.

[0016] According to the first aspect of the invention, the counting unit can measure changes in the time series of the minute range and the non-minute range. Therefore, the brain activity state can be determined by using the changes in the time series of the minute range. According to the first aspect of the invention, the discrimination device includes a discrimination unit that discriminates the brain activity state using the changes in the measured minute range, so the brain activity state can be determined. Furthermore, since brain waves are information that can be acquired without providing stimulation to the subject, non-stimulation discrimination can be performed without providing stimulation to the subject.

[0017] It is known that slow waves, which are lower in frequency than alpha waves and have a frequency band of 8 Hz or more but less than 13 Hz among electroencephalogram waveforms, can appear atypically due to individual differences in the subject to be discriminated. According to the first aspect of the invention, by discriminating the brain activity state using changes in a small range, the influence of individual differences in the subject to be discriminated can be reduced and the accuracy of discrimination can be improved.

[0018] As described above, experiments have confirmed that there is a linear correlation between the number of successive microwave segments and the concentration of a volatile anesthetic administered to a subject. Therefore, a method for determining brain activity using the number of microwave segments can determine not only the brain activity of a subject not receiving anesthesia, but also the brain activity of a subject receiving anesthesia. Therefore, according to the first aspect of the invention, it is possible to determine the brain activity of subjects receiving anesthesia in a natural state and in an anesthetized state.

[0019] Therefore, according to the invention relating to the first feature, a brain activity state discrimination device can be provided that can discriminate the brain activity state of a subject in a natural state and an anesthetized state, reduce the influence of individual differences in the subject, and improve the accuracy of discrimination.

[0020] The second feature of the invention is the first feature of the invention, and provides a discrimination device in which the electroencephalograms include electroencephalograms acquired at a predetermined sampling period, and the first infinitesimal range determination unit is capable of determining the predetermined infinitesimal range so that the ratio of the average time of each infinitesimal portion to the predetermined sampling period approximately matches a predetermined ratio.

[0021] It has been experimentally confirmed that by determining the amplitude threshold of the minute interval so that the number of minute portions is large, the correlation between changes in the minute range and the activity state of the brain can be made even stronger. When the amplitude threshold of the minute interval is determined so that the number of minute portions is large, it has been experimentally confirmed that the sampling period, which is the interval at which time-varying electroencephalograms are acquired, correlates with the average time of each minute interval. It has been experimentally confirmed that when the minute range is determined so that the number of minute portions is large, the ratio of the average time of each minute interval to the sampling period approaches a predetermined ratio.

[0022] According to the second aspect of the invention, the predetermined micro-range can be determined so that the ratio of the average time of each micro-portion to the predetermined sampling period is approximately equal to the predetermined ratio, and therefore the micro-range can be determined so that the number of micro-portions is large. This can further strengthen the correlation between changes in the micro-range and the brain activity state in an anesthetized subject. Therefore, according to the second aspect of the invention, the accuracy of the discrimination can be further improved.

[0023] Therefore, according to the second aspect of the invention, a brain activity state discrimination device can be provided that can discriminate the brain activity state of a subject to be discriminated, reduce the influence of individual differences in the subject to be discriminated, and improve the accuracy of discrimination.

[0024] A third aspect of the invention provides the discrimination device according to the second aspect of the invention, wherein the predetermined ratio is equal to or greater than 3 / 2 and equal to or less than 7 / 2.

[0025] It has been experimentally confirmed that when the amplitude threshold of the minute sections is determined to maximize the number of minute sections, the ratio of the average time of each minute section to the sampling period approaches a ratio of more than 2 / 3 and less than 2 / 7.

[0026] According to the third aspect of the invention, since the predetermined ratio is between 3 / 2 and 7 / 2, the predetermined infinitesimal range can be determined so that the number of infinitesimal parts is even greater. This can further strengthen the correlation between changes in the infinitesimal range and brain activity. Therefore, according to the third aspect of the invention, the accuracy of discrimination can be further improved.

[0027] Therefore, according to the third aspect of the invention, a brain activity state discrimination device can be provided that can discriminate the brain activity state of a subject to be discriminated, reduce the influence of individual differences in the subject to be discriminated, and improve the accuracy of discrimination.

[0028] The invention relating to the fourth feature is an invention relating to any of the first to third features, and provides a discrimination device further comprising a second infinitesimal range determination unit capable of determining the specified infinitesimal range so as to approximately maximize the number of the infinitesimal parts.

[0029] It has been experimentally confirmed that by determining the amplitude threshold of the minute section so as to increase the number of minute portions, the correlation between changes in the minute range and / or non-minute range and the brain activity state of an anesthetized subject can be further strengthened. Furthermore, when the threshold is determined so as to increase the number of minute portions, the correlation between changes in the minute range and the brain activity state can also be further strengthened. According to the fourth aspect of the invention, the second minute range determination unit is further provided, which can determine the predetermined threshold so as to substantially maximize the number of minute portions, thereby further strengthening the correlation between changes in the minute range and the brain activity state. Therefore, according to the fourth aspect of the invention, the accuracy of discrimination can be further improved.

[0030] Therefore, according to the invention relating to the fourth feature, a brain activity state discrimination device can be provided that can discriminate the brain activity state of a subject who has been administered anesthesia, reduce the influence of individual differences in the subject, and improve the accuracy of discrimination.

[0031] The invention relating to the fifth feature is an invention relating to any one of the first to fourth features, and provides a discrimination device in which the discrimination unit is capable of determining whether the activity state corresponds to one or more consciousness states selected from an awake state, a light sleep state, a REM sleep state, and a non-REM sleep state.

[0032] Experiments have confirmed that the amplitude threshold at which the number of minute parts is greatest decreases as the object of discrimination becomes awake, light sleep, REM sleep, and non-REM sleep, in that order, as the level of consciousness decreases. This trend is the same as the amplitude threshold at which the number of minute parts changes to a maximum as the anesthetic concentration increases. Therefore, there may be a correlation between the change in the minute range when the threshold is determined so that the number of minute parts increases and the state of consciousness.

[0033] According to the fifth aspect of the present invention, since the brain activity state is determined using the change in the minute range, it is possible to determine whether the active state corresponds to one or more consciousness states selected from the wakefulness state, light sleep state, REM sleep state, and non-REM sleep state. In addition, the neural electrical activity state at the electrode position where the electroencephalogram was acquired can be estimated using the potential difference between the maximum and minimum values ​​in the non-minute range.

[0034] Therefore, according to the fifth aspect of the invention, a brain activity state discrimination device can be provided that can discriminate the brain activity state of a subject to be discriminated, reduce the influence of individual differences in the subject to be discriminated, and improve the accuracy of discrimination.

[0035] The sixth feature of the invention is an invention according to any one of the first to fifth features, which provides a discrimination device, wherein the brain waves are brain waves obtained from the brain of the subject to be discriminated in a natural state or an anesthetized state.

[0036] The amplitude threshold at which the number of minute segments is maximized decreases as the level of consciousness decreases or the concentration of the volatile anesthetic administered to the subject increases. Furthermore, it has been experimentally confirmed that, at this threshold, the ratio of the average time of the minute segments to the sampling period is between 2 / 3 and 2 / 7. Therefore, it has been experimentally confirmed that there is a linear correlation between the threshold and the level of consciousness or the concentration of the volatile anesthetic administered to the subject. Therefore, a method for determining brain activity using changes in a minute range of minute segments can determine the brain activity of a subject in a natural state and / or under anesthesia. Therefore, according to the sixth aspect of the invention, brain activity can be determined using electroencephalograms obtained from the brain of a subject in a natural state or anesthetized state.

[0037] Therefore, according to the sixth aspect of the invention, a brain activity state discrimination device can be provided that can discriminate the brain activity state of a subject in a natural state or an anesthetized state, reduce the influence of individual differences in the subject, and improve the accuracy of discrimination.

[0038] The invention according to a seventh feature provides a discrimination system comprising an electroencephalograph capable of acquiring the brain waves from the brain, and a discrimination device according to any one of the first to sixth features, wherein the discrimination device is capable of acquiring the brain waves using the electroencephalograph.

[0039] According to the seventh aspect of the present invention, the electroencephalogram of the subject can be acquired in real time via an electroencephalograph. This allows the discrimination device to discriminate the brain activity state of the subject in real time. Therefore, the difference between the timing of acquiring the electroencephalogram and the timing of discrimination can be reduced, and the accuracy of discrimination can be further improved.

[0040] Therefore, according to the seventh aspect of the invention, a brain activity state discrimination system can be provided that can discriminate the brain activity state of subjects in a natural state or an anesthetized state, reduce the influence of individual differences in the subjects, and improve the accuracy of discrimination. [Effects of the Invention]

[0041] According to the present invention, it is possible to provide a brain activity state discrimination device and discrimination system that can distinguish between brain activity states in subjects in a natural state and in subjects administered anesthesia, reduce the influence of individual differences in the subjects, and improve the accuracy of discrimination. [Brief explanation of the drawings]

[0042] [Figure 1] FIG. 1 is a block diagram showing an example of the hardware configuration and software configuration of a discrimination system 1 according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram showing an example of the electroencephalogram table 221. As shown in FIG. [Figure 3] FIG. 3 is a flowchart showing an example of a preferred flow of the discrimination process executed by the discrimination device 2 of this embodiment. [Figure 4] FIG. 4 is a diagram showing an example of electroencephalograms in the awake and asleep states. [Figure 5]FIG. 5 shows the results of 20 hours of EEG analysis using an example mouse, showing the number of minute intervals from the start of the test according to the trial number when the minute range is from 0 μV to 100 μV. [Figure 6] Figure 6 shows the results of 20 hours of EEG analysis using a single mouse, showing the number of minute intervals and the average potential difference of non-minute intervals (burst intervals) for amplitude thresholds of 1 μV between 1 μV and 100 μV in deep EEG information by state of consciousness. [Figure 7] Figure 7 shows the number of minute intervals and the average potential difference of non-minute intervals (burst intervals) for amplitude thresholds of 0.2 μV between 0.2 μV and 20 μV increments in scalp EEG at different anesthetic concentrations in a dog under sevoflurane anesthesia. [Figure 8] FIG. 8 shows the results of the threshold at which the minute interval was maximized and the average potential difference of the burst interval at that threshold, divided by state of consciousness, for six mice. DETAILED DESCRIPTION OF THE INVENTION

[0043] An example of a preferred embodiment of the present invention will be described below with reference to the drawings. Note that this is merely an example, and the technical scope of the present invention is not limited to this example.

[0044] <Discrimination System 1> 1 is a block diagram showing an example of the hardware and software configurations of a discrimination system 1 according to an embodiment of the present invention. Hereinafter, an example of a preferable configuration of the discrimination system 1 according to an embodiment of the present invention will be described with reference to FIG.

[0045] The discrimination system 1 includes a discrimination device 2 and an electroencephalograph 3. The discrimination device 2 and the electroencephalograph 3 are configured to be connectable to each other via a network N.

[0046] [Discrimination device 2] The discrimination device 2 includes a control unit 21, a storage unit 22, and a communication unit 23. Although not an essential aspect, the discrimination device 2 preferably includes a display unit 24 configured to be able to display the brain activity state discriminated by the discrimination device 2.

[0047] [Control unit 21] The control unit 21 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and the like.

[0048] The control unit 21 loads a predetermined program and, as necessary, cooperates with the memory unit 22, the communication unit 23, and / or the display unit 24 to realize the elements of the software configuration in the discrimination device 2, such as the acquisition unit 211, the first minute range determination unit 212, the second minute range determination unit 213, the counting unit 214, and the discrimination unit 215.

[0049] [Storage section 22] Data and files are stored in the memory unit 22. The memory unit 22 has a data storage unit including components capable of storing data and files, such as semiconductor memory, recording media, and memory cards. The memory unit 22 may have a mechanism that enables connection to a storage device or storage system, such as a network attached storage (NAS), a storage area network (SAN), cloud storage, a file server, and / or a distributed file system, via the network N.

[0050] The memory unit 22 stores a control program executed by the microcomputer, an electroencephalogram table 221, a history of predetermined thresholds for a predetermined micro-range described below, threshold change information regarding changes in the predetermined thresholds, acquired electroencephalograms, etc.

[0051] (EEG Table 221) 2 is a diagram showing an example of the electroencephalogram table 221. The electroencephalogram table 221 is a table for storing electroencephalograms acquired from a subject to be discriminated. The electroencephalogram table 221 stores one or more electroencephalograms. This allows the control unit 21 to perform a discrimination process, which will be described later, and discriminate the brain activity state in the discrimination process.

[0052] The electroencephalogram table 221 is preferably capable of storing an ID that is associated with an electroencephalogram and that can identify the electroencephalogram, so that the control unit 21 can store and / or acquire the electroencephalogram using the ID.

[0053] It is preferable that the electroencephalogram table 221 can store information about the sampling period associated with the electroencephalogram. This allows the control unit 21 to use the information about the sampling period. The information about the sampling period is not particularly limited, and may be, for example, information indicating a sampling interval related to the sampling period and / or information indicating a sampling frequency related to the sampling period.

[0054] It is preferable that minute ranges related to the amplitude of brain waves can be stored in association with the brain waves in the brain wave table 221. By being able to store minute ranges in the brain wave table 221, the control unit 21 can identify minute brain waves whose amplitudes are in the minute range.

[0055] It is preferable that the electroencephalogram table 221 be capable of storing the number of minute intervals (also referred to as minute portions) counted in step S4, which will be described later, in association with the electroencephalogram. This allows the control unit 21 to store the counted number of minute intervals in the electroencephalogram table 221. The control unit 21 can also acquire and use the stored minute intervals.

[0056] The EEG table 221 is preferably capable of storing, in association with the EEG, the average time of minute intervals during which the EEG amplitude changes from a state outside a predetermined minute range determined in step S3 described below to a state within the predetermined minute range and then again to a state outside the predetermined minute range. This allows the control unit 21 to store the average time of the minute intervals in the EEG table 221. The control unit 21 can also acquire and use the stored average time of the minute intervals. The EEG table 221 is preferably capable of storing, in association with the EEG, the average potential difference of non-minute intervals during which the EEG amplitude changes from a state within a predetermined minute range described below to a state outside the predetermined minute range and then again to a state within the predetermined minute range. This allows the control unit 21 to store the average potential difference of the non-minute intervals in association with the EEG. This allows the control unit 21 to store the average potential difference of the non-minute intervals in the EEG table 221. The control unit 21 can also acquire and use the stored average potential difference of the non-minute intervals.

[0057] ID "B1" in Figure 2 stores the EEG in an awake state, the sampling period "4 ms" when this EEG was acquired, the minute range "0-0.067 mV" used when counting the number of minute intervals in this EEG, the number of minute intervals counted "1500", the average time of the minute interval "10 ms", and the average potential difference of the non-minute interval "0.2 mV".

[0058] ID "B2" in Figure 2 stores the EEG during non-REM sleep, the sampling period "4 ms" when this EEG was acquired, the minute range "0-0.025 mV" used when counting the number of minute intervals in this EEG, the number of minute intervals counted "1900", the average time of the minute interval "10 ms", and the average potential difference of the non-minute interval "0.1 mV".

[0059] The number of minute sections stored in ID "B1" in FIG. 2 is different from the number of minute sections stored in ID "B2". Therefore, the number of stored minute sections can be used to determine the brain activity state of the discrimination target. Also, the minute range stored in ID "B1" in FIG. 2 is different from the minute range stored in ID "B2". Therefore, the stored minute range can be used to determine the brain activity range of the discrimination target.

[0060] [Communications Department 23] Returning to Fig. 1, the communication unit 23 is not particularly limited as long as it connects the discrimination device 2 to the network N and enables communication with the electroencephalograph 3. Examples of the communication unit 23 include a connector capable of transmitting an electrical signal related to an electroencephalogram, a bus such as a general-purpose bus conforming to the USB standard, a serial port such as an RS-232C standard port, a parallel port such as an interface conforming to the IEEE 1284 standard, a connector conforming to the Serial ATA standard, a communication device conforming to the Ethernet standard, a wireless device conforming to a mobile phone network, a Wi-Fi (Wireless Fidelity) compatible device conforming to IEEE802.11, an optical wireless device conforming to optical wireless communication, The communication unit may include one or more of the above.

[0061] [Display section 24] The display unit 24 is not particularly limited as long as it can output the determined brain activity state, etc. Examples of the display unit 24 include a display unit having a touch panel, an organic EL display, a liquid crystal display, a monitor, a projector, etc. By providing the display unit 24 in the discrimination device 2, the brain activity state determined by the discrimination device 2 can be displayed on the display unit 24. In this way, the discrimination device 2 can notify the user using the discrimination device 2 of the determined brain activity state.

[0062] The display unit 24 may include an audio output device such as a speaker. When the display unit 24 includes an audio output device, the determined brain activity state, etc. can be output via audio. This allows the discrimination device 2 to notify the user of the determined brain activity state, etc., even if the user has difficulty seeing the display unit 24.

[0063] [Input section] Although not an essential aspect, it is preferable that the discrimination device 2 includes an input unit (not shown) that can receive commands from a user. By including the input unit, it is possible to perform discrimination processing based on commands from the user. The type of input unit is not particularly limited. Examples of input units include an input device including multiple switches, a keyboard, a mouse, a touch panel, a software keyboard, a microphone that recognizes voice, and a communication device that receives input from an external device.

[0064] There is no particular limitation on the commands from the user that the input unit can receive, including, for example, a command to make the discriminator 2 start the discrimination process, a command to make the discriminator 2 end the discrimination process, a command to change the infinitesimal range, a command to change the method for determining the infinitesimal range, a command to change a predetermined ratio (described later), and / or a command to change the sampling period.

[0065] [Electroencephalogram 3] The electroencephalograph 3 is not particularly limited as long as it is an electroencephalograph that can acquire the electroencephalogram of the discrimination target. The electroencephalograph 3 is configured to be able to provide the acquired electroencephalogram to the discrimination device 2 via the network N.

[0066] The discrimination system 1 includes the electroencephalograph 3 capable of acquiring the electroencephalogram of the subject to be discriminated, and thus the discrimination device 2 can acquire the electroencephalogram of the subject to be discriminated in real time via the electroencephalograph 3. This allows the discrimination device 2 to discriminate the brain activity state of the subject to be discriminated in real time. This reduces the difference between the timing at which the electroencephalogram is acquired and the timing at which discrimination is performed, and can further improve the accuracy of discrimination.

[0067] Examples of the electroencephalograph 3 include a scalp electroencephalograph that acquires scalp electroencephalograms (also called Electro Encephalo Gram, EEG) via electrodes placed on the scalp, a cortico-electroencephalograph that acquires cortical electroencephalograms (also called Electro Cotico Gram, ECoG) via electrodes placed on the cortex of the brain, and / or a depth electroencephalograph that acquires deep electroencephalograms (also called Local Field Potential, LFP) via electrodes inserted into the cortex of the brain.

[0068] By including a scalp electroencephalograph in the electroencephalograph 3, it is possible to acquire electroencephalograms by a relatively simple method of placing electrodes on the scalp.

[0069] The electroencephalograph 3 includes a cortico-electroencephalograph, which can acquire cortical electroencephalograms. Cortical electroencephalograms are acquired via electrodes placed on the cortex of the brain, which is closer to the neurons, and therefore can acquire electroencephalograms with higher accuracy than scalp electroencephalographs.

[0070] The deep electroencephalogram (EEG) monitor 3 can acquire deep electroencephalograms. Deep electroencephalograms are acquired through electrodes inserted into the cortex of the brain, which is closer to the neurons, and therefore can acquire electroencephalograms with higher accuracy than scalp electroencephalograms and cortical electroencephalograms.

[0071] [Network N] The network N is not particularly limited as long as it can connect the discrimination device 2 and the electroencephalograph 3. The network N may be, for example, a personal area network, a local area network, an intranet, an extranet, the Internet, a Wi-Fi network, a bus network conforming to the Universal Serial Bus (USB), a bus network conforming to the Serial ATA standard, a signal line capable of transmitting an electrical signal, or a network that combines a plurality of networks including these.

[0072] 〔flowchart〕 3 is a flowchart showing an example of a preferable flow of the discrimination process executed by the discrimination device 2 of this embodiment. Hereinafter, an example of a preferable procedure of the discrimination process executed by the discrimination device 2 will be described with reference to FIG.

[0073] [Step S1: Acquire EEG] The control unit 21 cooperates with the storage unit 22 and the communication unit 23 to execute the acquisition unit 211 to acquire brain waves from the subject to be discriminated (step S1). The control unit 21 moves the process to step S2. The process of acquiring brain waves includes a process of storing the acquired brain waves in the brain wave table 221. When the control unit 21 acquires the brain waves, the discrimination device 2 can discriminate the brain activity state of the subject to be discriminated using the acquired brain waves and their amplitude. By storing the brain waves acquired by the control unit 21 in the brain wave table 221, discrimination can be performed using the stored brain waves.

[0074] The method for acquiring the brain waves is not particularly limited. The method for acquiring the brain waves preferably includes a method for acquiring the brain waves from the electroencephalograph 3 via the communication unit 23. This allows the brain waves of the subject to be distinguished to be acquired in real time via the electroencephalograph 3. This allows the discrimination device 2 to distinguish the brain activity state of the subject to be distinguished in real time. This reduces the difference between the timing at which the brain waves are acquired and the timing at which the discrimination is performed, thereby further improving the accuracy of the discrimination.

[0075] When the method of acquiring electroencephalograms includes acquiring electroencephalograms from the electroencephalograph 3, the process of acquiring electroencephalograms preferably includes a process of acquiring electroencephalograms at a predetermined sampling period, thereby making it possible to determine the activity state of the brain by a process using the sampling period.

[0076] The process of acquiring an electroencephalogram preferably includes a process of storing information about the sampling period in the electroencephalogram table 221. This makes it possible to determine the active state of the brain through a process using the sampling period.

[0077] Although not an essential aspect, it is preferable that the electroencephalogram includes an electroencephalogram acquired from the brain of the subject of discrimination that is not contaminated by myoelectric potentials.

[0078] [Step S2: Measure the amplitude] The control unit 21, in cooperation with the storage unit 22 and the communication unit 23, executes the acquisition unit 211 to measure the amplitude of the electroencephalogram acquired in step S1 (step S2). The control unit 21 moves the process to step S3.

[0079] (About EEG amplitude) In this embodiment, the amplitude of the electroencephalogram refers to the absolute value of the difference between the maximum value of the electroencephalogram and the minimum value of the electroencephalogram, which does not include any other minimum values ​​between the maximum value and the minimum value. This makes it possible to identify a small section in which the electroencephalogram amplitude remains within a predetermined small range, which will be described later.

[0080] (Processing for measuring brain wave amplitude) The process of measuring the amplitude of the electroencephalogram is not particularly limited, and may include, for example, calculating the value of the first derivative of the electroencephalogram, determining that a portion where the value of the first derivative changes from a value greater than zero to a value less than zero is a portion where the electroencephalogram takes a maximum value, determining that a portion where the value of the first derivative changes from a value less than zero to a value greater than zero is a portion where the electroencephalogram takes a minimum value, and measuring the absolute value of the difference in the electroencephalogram between the portion where the electroencephalogram takes a maximum value and the portion where the electroencephalogram takes a minimum value.

[0081] The process of measuring the absolute value of the difference between electroencephalograms by, for example, calculating the value of the first derivative of the electroencephalogram is simpler than the process of acquiring the frequency components of the electroencephalogram using Fourier transform, etc. Therefore, since the process of measuring the amplitude of the electroencephalograms includes the process of measuring the absolute value of the difference between the electroencephalograms by, for example, calculating the value of the first derivative of the electroencephalograms, the discrimination device 2 can be configured more easily than a brain activity state discrimination device that acquires the frequency components of the electroencephalograms.

[0082] Although not an essential aspect, it is preferable that the control unit 21 executes a process of determining a predetermined minute range in step S3.

[0083] [Step S3: Determine the threshold (micro range)] The control unit 21 determines predetermined thresholds and the like for the predetermined minute range according to the electroencephalograms acquired from the subject to be discriminated, and updates the history of the predetermined thresholds for the predetermined minute range (step S3). The control unit 21 then proceeds to step S4. By determining predetermined thresholds and the like for the predetermined minute range according to the electroencephalograms acquired from the subject to be discriminated, the number of minute sections can be counted using the predetermined thresholds and the like for the predetermined minute range determined according to the electroencephalograms acquired from the subject to be discriminated. This can further improve the accuracy of discrimination.

[0084] (For a given small range) The predetermined minute range is not particularly limited, and may be, for example, a minute range exemplified by a range in which the amplitude of the electroencephalogram is equal to or greater than 0 mV and equal to or less than a predetermined threshold, or a range in which the amplitude of the electroencephalogram is equal to or greater than 0 mV and less than a predetermined threshold.

[0085] The lower limit of the predetermined threshold is not particularly limited. The upper limit of the predetermined threshold is preferably 0.6 mV or less, more preferably 0.5 mV or less, and even more preferably 0.4 mV or less. This allows the minute brain waves contained in the minute section to become even weaker. Therefore, the accuracy of discrimination based on minute waves can be further improved.

[0086] (Regarding the process of determining a predetermined micro range so that the ratio of the average time of each micro interval to a predetermined sampling period approximately matches the predetermined ratio) Although not a required aspect, it is preferable that the process of determining the specified micro-range includes a process of executing the first micro-range determination unit 212 and determining the specified micro-range so that the ratio of the average time of each micro-interval to the sampling period for the brain waves acquired in step S1 approximately matches the specified ratio.

[0087] It has been experimentally confirmed that determining a micro-range so as to increase the number of micro-intervals can further strengthen the correlation between the number of micro-intervals and the brain activity state of a discrimination subject who has been anesthetized. When a micro-range is determined so as to increase the number of micro-intervals, it has been experimentally confirmed that the sampling period, which is the interval at which time-varying electroencephalograms are acquired, correlates with the average time of each micro-interval. It has been experimentally confirmed that when a micro-range is determined so as to increase the number of micro-intervals, the ratio of the average time of each micro-interval to the sampling period approaches a predetermined ratio.

[0088] By executing the first minute range determination unit 212, it is possible to determine a predetermined minute range so that the ratio of the average time of each minute interval to the sampling period substantially matches a predetermined ratio, and therefore it is possible to determine minute ranges so that the number of minute intervals is large. This can further strengthen the correlation between the number of minute intervals and the brain activity state, thereby further improving the accuracy of the discrimination.

[0089] When the first infinitesimal range determination unit 212 is executed, the lower limit of the predetermined ratio is preferably 3 / 2 or more, more preferably 7 / 4 or more, and even more preferably 2 or more.

[0090] It has been experimentally confirmed that when the infinitesimal range is determined so as to maximize the number of infinitesimal intervals, the ratio of the average time of each infinitesimal interval to the sampling period approaches the vicinity of 2 / 5. By setting the lower limit of the predetermined ratio as described above, the predetermined ratio approaches the vicinity of 2 / 5 even more.

[0091] Therefore, the predetermined minute range can be determined so that the number of minute sections is even greater. Furthermore, the fact that the threshold is specified to be around 2 / 5 of the sampling interval time regardless of the number of sampling periods means that the threshold in this case is proportional to the amplitude of minute waves. In other words, it is possible to determine a minute range that reflects the fluidity around the electrode position where the EEG was acquired. Therefore, the accuracy of the discrimination can be further improved.

[0092] Figure 4 shows examples of electroencephalograms in the wakefulness state and non-REM sleep state. The electroencephalograms shown in Figure 4 are deep electroencephalograms in layer 5 of the secondary motor cortex of a mouse. Using Figure 4, we will explain the process of determining a predetermined micro-range so that the ratio of the average time of each micro-interval to a predetermined sampling period approximately matches a predetermined ratio.

[0093] The electroencephalogram Wa in the awake state and the electroencephalogram Wn in the sleep state shown in Fig. 4(A) are sampled. The EEG data were obtained at a sampling frequency of 1000 Hz. Therefore, the sampling period of these EEGs was 1 ms, and the measured potential differences are indicated by circles. Furthermore, using the threshold at which the number of minute intervals was greatest in the 60-second EEG, the regions where the adjacent peak potential differences are within this threshold are color-coded as minute intervals (black lines), and regions where the potential differences are greater than this threshold are color-coded as burst intervals (gray lines).

[0094] The awake state EEG Wa is in the minute range T (first minute range) from 0 mV to 0.044 mV. The minute intervals T include a first minute interval T1, a second minute interval T2, a third minute interval T3, and a fourth minute interval T4, etc. Each minute interval T has a minute interval time D (a first minute interval time D1, a second minute interval time D2, a third minute interval time D3, and a fourth minute interval time D4, etc.).

[0095] In the process of determining a predetermined infinitesimal range so that the ratio of the average time of each infinitesimal interval to a predetermined sampling period approximately matches a predetermined ratio, the predetermined infinitesimal range is determined so that the average time of infinitesimal interval T, which is the average of infinitesimal interval time D, approximately matches the time obtained by multiplying the predetermined sampling period by a predetermined ratio. For example, if the predetermined ratio is 5 / 2, the predetermined infinitesimal range is determined so that the average time of infinitesimal interval T is ms. As a result, the ratio of the average time of each infinitesimal interval, 2.5 ms, to the predetermined sampling period of 1 ms approximately matches 5 / 2.

[0096] In the example of the awake state shown in Figure 4(A), we counted the number of minute intervals T where the amplitude changed from not less than 0.044 mV to not less than 0.044 mV, and then again to not less than 0.044 mV. Similarly, in the example of the sleep state, we counted the number of minute intervals T where the amplitude changed from not less than 0.025 mV to not less than 0.025 mV, and then again to not less than 0.025 mV. This maximizes the number of minute intervals T, and at the same time, aligns the average time of each minute interval around 5 / 2 the sampling period. Aligning the average time of each minute portion can further strengthen the correlation between the minute range and the brain activity state. This can further improve the accuracy of the discrimination.

[0097] (Regarding the process of determining a predetermined micro range so as to substantially maximize the number of micro sections) Although not an essential aspect, it is preferable that the process of determining the predetermined infinitesimal range includes a process of executing the second infinitesimal range determination unit 213 to determine the predetermined infinitesimal range so as to substantially maximize the number of infinitesimal sections.

[0098] It has been experimentally confirmed that by determining a predetermined micro-range so as to increase the number of micro-intervals, the correlation between the number of micro-intervals and the brain activity state of a discrimination subject administered anesthesia can be further strengthened. By executing the second micro-range determination unit 213, the correlation between the number of micro-intervals and the brain activity state can be further strengthened. Therefore, the accuracy of discrimination can be further improved. Furthermore, by meeting the conditions, it is possible to easily compare the brain activity state of the discrimination area and the discrimination subject.

[0099] [Step S4: Measure changes in thresholds, etc.] The control unit 21 executes the counting unit 214 to measure the change in the threshold value, etc. (step S4) The control unit 21 moves the process to step S5.

[0100] The process of measuring the change in the threshold value preferably includes a process of determining whether the threshold value has increased and / or decreased using the history of the threshold value for a predetermined small range and storing information on the determination result as threshold change information, thereby enabling a determination process based on the threshold change information to be performed in step S5 described later.

[0101] The process of measuring threshold changes preferably includes a process of counting the number of minute intervals. The process of counting the number of minute intervals includes a process of determining whether the EEG amplitude is within a predetermined minute range. This makes it possible to count the number of minute intervals in which the EEG amplitude changes from not being within the predetermined minute range to being within the predetermined minute range and then back to not being within the predetermined minute range.

[0102] The process of counting the number of minute sections may be a process of counting the number of burst sections in which the amplitude of the electroencephalogram changes from a state in a predetermined minute range to a state outside the predetermined minute range and then back to a state in the predetermined minute range. Typically, the state of the electroencephalogram alternates between a state in a minute section and a state in a burst section. Therefore, the process of counting the number of burst sections can count the number of minute sections.

[0103] It is known that there is a linear correlation between the number of consecutive sections of minute waves contained in electroencephalograms within a predetermined interval acquired from the brain of a subject to be identified and the concentration of a volatile anesthetic administered to the subject. The higher the concentration of the volatile anesthetic, the lower the activity state of the brain of the subject to be identified. Therefore, the activity state of the brain of the subject to be identified can be determined by using the number of consecutive sections of minute waves.

[0104] According to the process executed in step S4, the counting unit 214 can count the number of consecutive micro-intervals in which the amplitude of the electroencephalogram (EEG) is within a predetermined micro-range. In the micro-intervals, the electroencephalogram (EEG) is a microwave whose amplitude is within a predetermined micro-range. Therefore, the brain activity state can be determined by using the number of micro-intervals.

[0105] The process of counting the number of minute intervals will be explained using Figure 4. The awake state electroencephalogram Wa shown in Figure 4(A) has maximum values ​​where the potential difference of the electroencephalogram changes from an increase to a decrease, and minimum values ​​where the potential difference changes from a decrease to an increase. The amplitude of the awake state electroencephalogram Wa can be measured using the difference (symbol Pt, symbol Pb, etc.) between the maximum and minimum values ​​of adjacent electroencephalograms.

[0106] When the amplitude of the awake state electroencephalogram Wa is within a predetermined minute range (e.g., symbol Pt), the awake state electroencephalogram Wa is in a minute interval T (e.g., the first minute interval T1, second minute interval T2, third minute interval T3, and fourth minute interval T4 in FIG. 4). More specifically, when the amplitude of the awake state electroencephalogram Wa changes from a state where it is not within the predetermined minute range to a state where it is within the predetermined minute range, and then again to a state where it is not within the predetermined minute range, the awake state electroencephalogram Wa is in a minute interval T.

[0107] On the other hand, when the amplitude of the awake state electroencephalogram Wa is not within a predetermined minute range (e.g., symbol Pb), the awake state electroencephalogram Wa is in burst interval B (e.g., first burst interval B1, second burst interval B2, third burst interval B3, and fourth burst interval B4 in Figure 4(A)).

[0108] The discrimination device 2 counts the number of minute sections T discriminated in this way. As shown in Fig. 4, minute sections T and burst sections B appear alternately. Therefore, the number of minute sections T can also be counted by the process of counting the number of burst sections B.

[0109] The process of counting the average potential difference in a burst section will now be described. The difference between the minimum and maximum values ​​in one burst section is taken as the potential difference in the burst section. The average of the potential differences in the burst section is counted as the average potential difference in the burst section.

[0110] [Step S5: Determine brain activity] Returning to Fig. 3, the control unit 21 executes the determination unit 215 to determine the brain activity state based on the information about the change in the threshold stored in the threshold change information in step S4 (step S5). The control unit 21 shifts the process to step S1 and repeats the processes from step S1 to step S5.

[0111] When determining a predetermined micro-range so that the ratio of the average time of each micro-interval to a predetermined sampling period approximately matches a predetermined ratio, and / or when determining a predetermined micro-range so as to maximize the number of micro-intervals, it is preferable that the process of determining the brain activity state includes a process of determining that the brain activity state has become a more active brain activity state when the threshold change information is information indicating that the predetermined threshold has increased, and determining that the brain activity state has become a less active brain activity state when the threshold change information is information indicating that the predetermined threshold has decreased (hereinafter also simply referred to as ``determination process based on threshold change information'').

[0112] When the predetermined microrange is determined so that the ratio of the average time of each microinterval to the sampling period is approximately equal to a predetermined ratio, and / or when the predetermined microrange is determined so as to maximize the number of microintervals, it has been experimentally confirmed that there is a linear correlation between the predetermined threshold for the predetermined microrange and the concentration of the volatile anesthetic administered to the discrimination target (Figure 7). In this correlation, the predetermined threshold tends to be smaller when the concentration of the volatile anesthetic is higher.

[0113] When the predetermined micro-range is determined so that the ratio of the average time of each micro-interval to the sampling period is approximately equal to a predetermined ratio, and / or when the predetermined micro-range is determined so that the number of micro-intervals is maximized, it is known that the predetermined threshold fluctuates at a relatively high value in the awake state. On the other hand, it is known that the predetermined threshold fluctuates at a relatively low value in the non-REM sleep state. It has been experimentally confirmed that the predetermined threshold increases slightly in the dream state (e.g., REM sleep state), and this is more noticeable in the electroencephalograms in the primary somatosensory cortex than in the secondary motor cortex (Figure 8). Note that REM sleep is characterized by relaxation of the skeletal muscles throughout the body. can be done.

[0114] Therefore, when the predetermined threshold value increases to a larger value, it can be determined that the brain activity state is an active brain activity state such as an awake state. On the other hand, when the predetermined threshold value decreases to a smaller value, it can be determined that the brain activity state is a relatively inactive brain activity state such as a non-REM sleep state. Therefore, the process of determining the brain activity state includes a determination process based on threshold change information, so that the brain activity state of the determination target can be determined.

[0115] It is known that there is a linear correlation between the number of consecutive sections of minute waves contained in electroencephalograms within a predetermined interval acquired from the brain of a subject to be identified and the concentration of a volatile anesthetic administered to the subject. The higher the concentration of the volatile anesthetic, the lower the activity state of the brain of the subject to be identified. Therefore, the activity state of the brain of the subject to be identified can be determined by using the number of consecutive sections of minute waves.

[0116] It is preferable that the discrimination unit 215 executes a process of discriminating the brain activity state using the counted number of minute intervals. A minute interval is an interval in which minute waves continue. Therefore, the process executed in step S5 can discriminate the brain activity state.

[0117] It is known that slow waves, which are waveforms of electroencephalograms with a frequency band of 8 Hz or more but less than 13 Hz and have a lower frequency than alpha waves, can appear atypically due to individual differences in the subject to be discriminated. The discriminator 215 discriminates the brain activity state using the number of minute sections, thereby reducing the influence of individual differences in the subject to be discriminated and improving the accuracy of discrimination.

[0118] As described above, it is known that there is a linear correlation between the number of successive sections of minute waves and the concentration of a volatile anesthetic administered to a subject. Therefore, a method for determining the brain activity state using the number of minute sections can determine the brain activity state of a subject to which anesthesia has been administered. Therefore, the determination unit 215 can determine the brain activity state of a subject to which anesthesia has been administered.

[0119] Although not a required feature, the process of determining the brain activity state using the counted number of micro-intervals preferably includes a process of determining whether the number of micro-intervals is within a predetermined range associated with the brain activity state. This allows the number of micro-intervals to be associated with the brain activity state via the predetermined range. Therefore, the brain activity state to be determined can be more easily determined.

[0120] When the specified micro-range, exemplified by when the first micro-range determination unit 212 and / or the second micro-range determination unit 213 are executed in step S3, is a micro-range determined so that the number of micro-intervals is even larger, it is preferable that the discrimination unit 215 discriminates the brain activity state based on the specified micro-range and the number of micro-intervals.

[0121] When the predetermined microrange is determined so as to have a larger number of microintervals, it has been experimentally confirmed that there is a correlation between the determined predetermined microrange and the brain activity state. When the brain activity state is a relatively inactive activity state such as a non-REM sleep state, the determined predetermined microrange can be narrower than when the brain activity state is an active activity state such as an awake state.

[0122] Therefore, by the discrimination unit 215 discriminating the brain activity state based on the number of predetermined minute ranges and minute sections, the accuracy of discrimination can be further improved.

[0123] It has been experimentally confirmed that when the micro-range is narrow, the greater the number of micro-intervals, the more active the brain is, and when the micro-range is wide, the fewer the number of micro-intervals, the more active the brain is.

[0124] Although not an essential aspect, the process of determining the brain activity state using the counted number of micro-intervals preferably includes a process of determining, when the micro-range is narrower than a predetermined range, that the greater the number of micro-intervals, the more active the brain activity state, and, when the micro-range is wider than the predetermined range, that the fewer the number of micro-intervals, the more active the brain activity state. This can further improve the accuracy of the determination based on the experimentally confirmed relationship between the micro-range and the number of micro-intervals.

[0125] The predetermined range is not particularly limited, and may be, for example, a range in which the amplitude of the electroencephalogram is equal to or less than a predetermined threshold. For example, for 125 Hz deep electroencephalogram data, the lower limit of the predetermined threshold is preferably 0.001 mV or more, more preferably 0.005 mV or more, and even more preferably 0.010 mV or more. This can further improve the accuracy of the discrimination by using a range determined by a threshold where the experimentally confirmed relationship between the number of minute sections and the brain activity state changes.

[0126] The upper limit of the predetermined threshold is preferably 0.100 mV or less, more preferably 0.075 mV or less, and even more preferably 0.060 mV or less, which can further improve the accuracy of discrimination by using the range determined by the threshold where the experimentally confirmed relationship between the number of minute sections and the brain activity state changes.

[0127] (Regarding determining whether or not a person is in a conscious state) Although not an essential aspect, it is preferable that the discrimination unit 215 executes a process of determining whether or not the active state corresponds to one or more consciousness states selected from the wakefulness state, the light sleep state, the REM sleep state, and the non-REM sleep state.

[0128] It is known that there is a linear correlation between the number of consecutive periods of microwaves and the concentration of a volatile anesthetic administered to the subject. As the concentration of the volatile anesthetic increases, the state of consciousness may change sequentially from an awake state to a light sleep state, a REM sleep state, and a non-REM sleep state. Therefore, there may be a correlation between the number of consecutive periods of microwaves and one or more states of consciousness selected from an awake state, a light sleep state, a REM sleep state, and a non-REM sleep state.

[0129] The discrimination unit 215 discriminates the brain activity state using the number of micro-intervals, and can therefore determine whether the activity state corresponds to one or more consciousness states selected from the wakefulness state, light sleep state, REM sleep state, and non-REM sleep state.

[0130] When the discrimination unit 215 executes a process of discriminating whether or not an active state corresponds to one or more consciousness states selected from an awake state, a light sleep state, a REM sleep state, and a non-REM sleep state, the electroencephalogram is preferably a deep electroencephalogram (Local Field Potential; LFP). When the electroencephalogram is a deep electroencephalogram, the active state can be discriminated in more detail. Therefore, when the electroencephalogram is a deep electroencephalogram, the discrimination unit 215 can discriminate whether or not an active state corresponds to one or more consciousness states selected from an awake state, a light sleep state, a REM sleep state, and a non-REM sleep state.

[0131] 4(B) is a diagram showing an example of an electroencephalogram in a non-REM sleep state. Hereinafter, with reference to FIGS. 4(A) and 4(B), a description will be given of how to determine the brain activity state of a subject based on the number of minute intervals T.

[0132] The awake state electroencephalogram Wa shown in FIG. 4(A) includes four minute intervals T (first minute interval T1, second minute interval T2, third minute interval T3, fourth minute interval T4, etc.) between 0 and 0.2 seconds.

[0133] On the other hand, the non-REM sleep state EEG Wn in the non-REM sleep state shown in Figure 4(B) includes five micro-intervals (fifth micro-interval T5, sixth micro-interval T6, seventh micro-interval T7, eighth micro-interval T8, and ninth micro-interval T9) between 0 seconds and 0.2 seconds in which the amplitude changes from a state not below 0.025 mV to a state below 0.025 mV, and then again to a state not below 0.025 mV.

[0134] 4(A) and 4(B), the awake state electroencephalogram Wa and the non-REM sleep state electroencephalogram Wn have different numbers of minute sections T in a predetermined section. Therefore, the number of minute sections T can be used to determine whether the brain's activity state corresponds to the awake state or the non-REM sleep state.

[0135] (Discriminating brain activity in anesthetized subjects) Although not an essential aspect, it is preferable that the discrimination unit 215 executes a process of discriminating the brain activity state of a discrimination subject that is not affected by myoelectric potential, such as a subject under anesthesia management.

[0136] It is known that the brain becomes almost inactive under conditions such as general anesthesia, coma, and hypothermia, etc. In such brain activity, the brain shows a pattern called burst suppression, which is distinguished by the alternating appearance of a short period in which the average amplitude of the brain waves is large and a long period in which the average amplitude of the brain waves is small.

[0137] It has been experimentally confirmed that in the absence of burst suppression, the mean potential difference of EEGs in non-microinterval states increases as the anesthetic concentration increases. Therefore, the mean potential difference of EEGs in non-microinterval states can be used to determine the brain activity state of a subject who has been anesthetized (Figure 7(B)).

[0138] When the specified micro-range, such as when the first micro-range determination unit 212 and / or the second micro-range determination unit 213 are executed in step S3, is a micro-range determined so that the number of micro-intervals becomes even larger, it has been experimentally confirmed that the number of micro-intervals when burst suppression is not occurring decreases as the anesthetic concentration increases (Figure 7(A)).

[0139] Therefore, if the specified micro-range is a micro-range determined so that the number of micro-intervals is even larger, the number of micro-intervals can be used to determine the brain activity state of a subject who has been administered an appropriate amount of anesthesia.

[0140] When the specified micro-range is determined so that the number of micro-intervals becomes larger, experiments have confirmed that when the anesthetic concentration is higher than the appropriate level (also called an excessively deep anesthetic level), the EEG shows burst suppression, and the average time of each micro-interval increases at any amplitude threshold, resulting in a significant decrease in the amplitude threshold that defines the micro-range (Figure 7(A)).

[0141] Therefore, the average time of each micro-interval and / or the number of micro-intervals and the amplitude threshold can be used to determine the brain activity state of a subject who has been anesthetized, and can provide an indication of whether the anesthesia concentration is higher than appropriate.

[0142] (About outputting the determined brain activity state) Although not an essential aspect, it is preferable that the discrimination unit 215 outputs the determined brain activity state via the display unit 24. This makes it possible to output the determined brain activity state to the user who uses the discrimination device 2.

[0143] (Effect of discrimination processing) By having the discrimination device 2 execute the processing from step S1 to step S5, it is possible to provide a brain activity state discrimination device 2 that can discriminate the brain activity state of a subject who has been administered anesthesia, reduce the influence of individual differences in the subject, and improve the accuracy of discrimination. [Example]

[0144] The present invention will be specifically described below with reference to examples of the present embodiment, but the present invention is not limited to these examples.

[0145] <Common conditions for Test 1 and Test 2> [Experimental animals] Mice were used in Study 1, and beagle dogs in Study 2, as described below. In the mouse study, mice were housed in individually ventilated cages. The lighting in the cages was controlled to alternate between 12 hours of light and 12 hours of darkness, with lights on at 8:00 AM and off at 8:00 PM. In the dog study, the mice were kept warm to maintain body temperature at 37.5 to 38.0°C and ventilated to maintain end-tidal carbon dioxide levels at 33 to 40 mmHg. Sevoflurane inhalation anesthetic and rocuronium muscle relaxant were administered.

[0146] <Test 1: Discrimination experiment using deep EEG> [Example 1-1] Discrimination by the discrimination device 2 of this embodiment, Part 1 [Electroencephalogram acquisition] Electroencephalograph electrodes (75 μm platinum electrodes) were placed in the secondary motor cortex (M2) and primary somatosensory cortex (S1) of the mice, and deep electroencephalograms (LOEs) were recorded at a sampling frequency of 10 kHz. Deep EEG (LFP) was acquired. Deep EEG is a waveform obtained by inserting electrodes into the cortex of the subject animal and measuring the sum of the nearby electrical potentials. To acquire deep EEG from cerebral cortical layer 5, electrodes were inserted to a depth of 670 μm. Deep EEG acquisition was carried out for 1200 minutes starting at 2:00 AM, creating 1200 EEG data sets of 60 seconds each. The 60-second data were subjected to state discrimination every 4 seconds using an existing method. EEG data that were in the same state for all 15 judged 60-second periods were downsampled to 1000 Hz, 500 Hz, 250 Hz, and 125 Hz for analysis.

[0147] [Distinguishing brain activity] The threshold was set from 1 μV to 100 μV in 1 μV increments. The acquired deep EEG was recorded every minute. The brain waves were divided into minute intervals where the potential difference between adjacent peaks was below the threshold and burst intervals where it was above the threshold, and the number of minute intervals and the average time of the burst intervals, as well as the average time and average potential difference, were counted. The potential difference of the burst interval was taken as the difference between the maximum and minimum values ​​of the brain waves measured in the burst interval. These new indices can quantitatively describe the visual brain wave morphology.

[0148] The number of minute intervals calculated using a threshold of 100 μV for the 125 Hz deep EEG of M2 was Figure 5 shows an example of a plot of the EEG data plotted against the elapsed time from the start of measurement. For all mice, regardless of their state, when the average duration of the burst interval was 5 to 8 times the sampling period, a scatter plot similar to that shown in Figure 5 was obtained. The horizontal axis of Figure 5 represents the trial number (unit: -; dimensionless), and the vertical axis represents the number of microintervals corresponding to the elapsed time (unit: -; dimensionless). The length of one trial is 60 seconds. The trial number increases with the elapsed time from the start of acquisition. State discrimination was confirmed by visual inspection of the EEG waveform. In Figure 5, a one-minute period with 425 or more micro-intervals was determined to be an awake state, a period between 320 and 425 micro-intervals was determined to be a light sleep state, a period between 110 and 320 micro-intervals was determined to be a non-REM sleep state (sleep without rapid eye movement, so-called deep sleep state), and a period less than 110 micro-intervals was determined to be a REM sleep state (sleep with rapid eye movement, often accompanied by dreams, in which the body is asleep but the brain is active). This method made it possible to distinguish approximately 10% of REM sleep-like waveforms during sleep.

[0149] Micro-thresholds for 1000Hz deep EEG from 1μV to 100μV in 1μV increments Figure 6 shows examples of the number of microintervals or the potential difference during burst intervals. Regardless of the state of consciousness, the variation in the number of microintervals relative to the threshold was consistent, with the number of microintervals increasing and decreasing as the threshold increased. Furthermore, the potential difference during burst intervals decreased and increased as the threshold increased. These patterns were consistent regardless of the electrode location or individual, and were also consistent across mice and dogs. The threshold with the maximum number of microintervals is proportional to the amplitude of microwaves and is thought to reflect fluctuations in the electrode field. In other words, this threshold can increase with increased brain activity. The decrease in the mean potential difference during burst intervals at the threshold where the mean potential difference is minimum is due to the disruption of the large waves that connect the microwaves. Therefore, the potential difference during burst intervals at this threshold in EEGs with a high sampling frequency, such as 1000 Hz, is thought to reflect electrical activity between fluctuations.

[0150] In the conventional method, electroencephalograms (EEGs) obtained from the secondary motor cortex (also called M2) of mice and myoelectric potentials (MEPs) obtained from the mice were analyzed using sleep analysis software, SleepSign (registered trademark), manufactured by Kissei Comtec Co., Ltd. REM sleep was difficult to detect using conventional methods. In the 20-hour EEG analysis experiment conducted in this study, which consisted of 1,200 trials, no trials were identified as REM sleep in three of six mice. In two of the remaining three mice, the number of trials diagnosed as REM sleep was eight and four, respectively, indicating few trials identified as REM sleep. In this discrimination method, electroencephalograms (EEGs) and myoelectric potentials (MEPs) were used.

[0151] In the plots shown in Figure 5, as a result of the discrimination in Reference Example 1, plots corresponding to non-REM sleep states are indicated by "*" (a shape combining "+" and "x"), plots corresponding to REM sleep states are indicated by "+", plots corresponding to light sleep are indicated by "△", and plots corresponding to wakefulness states are indicated by "x".

[0152] [Results and Discussion] [Discrimination accuracy] In Example 1, where slow waves tended to appear during wakefulness, state discrimination was difficult using frequency analysis. However, with this method, by adjusting the average duration of the burst interval to 5-8 times the sampling frequency, scatter plots showing similar patterns were obtained without being affected by the likelihood of slow waves appearing, without individual differences. In two individuals in which slow waves did not tend to appear during wakefulness, the results of sleep / wake discrimination using the existing method and this method were identical. Trials diagnosed as REM sleep were observed in approximately 10% of trials diagnosed as sleep, which is in line with conventional wisdom. Furthermore, the states discriminated by the number of minute intervals were consistent with state discrimination confirmed visually.

[0153] According to Test 1, the discrimination device 2 according to this embodiment can discriminate the brain activity state of a test subject mouse without using myoelectric potential and without adjusting the discrimination criteria according to the test subject mouse. Therefore, deep EEG can discriminate the brain activity state of a discrimination subject, reducing the influence of individual differences in the discrimination subject and improving the discrimination accuracy.

[0154] Figure 6 shows the thresholds for 1000 Hz deep EEG from 1 μV to 100 μV in 1 μV increments. The figures show the number of microintervals and the average potential difference of the burst intervals in each individual. Regardless of the electrode location, at the threshold where the number of microintervals was at its maximum, the average value of each microinterval was around 5 / 2 of the sampling period. The threshold decreased in the order of wakefulness, light sleep, REM sleep, and non-REM sleep. Additionally, the average potential difference of the burst intervals around the threshold decreased in the order of wakefulness, light sleep, and sleep (REM and non-REM). Therefore, it was confirmed that the accuracy of discrimination can be further improved by determining the threshold where the number of microintervals is at its maximum. Regardless of the sampling frequency (125 Hz-1000 Hz) or electrode position (S1, M2), the threshold decreased in all six animals in the following order: wakefulness, light sleep, REM sleep, and non-REM sleep (Figure 8). This strongly supports the idea that the microwaves previously considered noise are fluctuations in the electrode field that reflect brain activity. The mean potential difference during the burst period around the threshold differed between REM and non-REM sleep at 125 Hz and 250 Hz. This indicates that EEG information above 500 Hz is desirable for the mean potential difference during the burst period to reflect neural electrical activity at the electrode site, and that low-sampling information is affected by slow waves.

[0155] <Test 2: Discrimination experiment using scalp EEG according to anesthetic concentration> [Example 2] Discrimination by the discrimination device 2 of this embodiment [Electroencephalogram acquisition] Anesthetized beagle dogs were fitted with electroencephalograph needle electrodes on the forehead, and scalp electroencephalograms (EEG) were obtained at a sampling frequency of 250 Hz. Under continuous administration of the muscle relaxant rocuronium, sevoflurane anesthesia was maintained at 2.0%, 2.5%, 3.0%, 3.5%, 4.0%, and 5.0% for 20 minutes each, followed by 5-minute EEG measurements. Three 64-second data segments were extracted from the 5-minute 250 Hz EEG information and used as analysis data. The same analysis method as in Example 1-1 was used.

[0156] [Distinguishing brain activity] Using a vital sign monitor, the exhaled sevoflurane concentration was measured in intubated dogs. After 20 minutes of stable equilibrium at the same concentration, it was estimated that the sevoflurane concentration in the cerebrospinal fluid was maintained at the same concentration. It was determined that the suppression of brain activity increased with increasing anesthetic concentration. In dogs, 1.3% ± 0.3% sevoflurane anesthesia is known to be the maximum concentration at which sound stimulation can awaken the dog. Therefore, 2.0% sevoflurane anesthesia was considered to be the maximum concentration at which tracheal intubation was possible. It is judged to be a shallow level of anesthesia. Sevoflurane anesthesia at 5.0% shows burst suppression in the EEG. This was the lowest concentration at which a burst appeared, and was determined to be a deep anesthesia level. Three 64-second EEG records measured from needle electrodes placed on the scalp for each concentration were used as the analysis data. Figure 7 shows the number of minute intervals and the average voltage of the burst interval at thresholds in 0.2 μV increments from 0.2 μV to 20 μV. The phase difference is shown. In all individuals, regardless of the anesthetic concentration, at the threshold where the number of minute intervals was at its maximum, the average value of each minute interval was around 2 / 5 times the sampling period. In addition, the threshold where the number of minute intervals was at its maximum decreased with increasing anesthetic concentration. At a sevoflurane concentration of 2.0, excluding under excessively deep anesthesia where burst suppression appeared on the EEG, The number of micro-segments decreased with increasing anesthetic concentration from 0.5% to 4.0%. The mean potential difference between the anesthetic and the anesthetic was 0.2 μV to 20 μV for all thresholds. increased with

[0157] [Reference Example 2] Discrimination using conventional methods Currently available perioperative anesthesia depth monitors do not have an index that is linearly proportional to the anesthetic concentration, and do not significantly suppress intraoperative awakening.

[0158] According to Test 1, the discrimination device 2 of this embodiment can determine whether the active state corresponds to one or more consciousness states selected from the wakefulness state, light sleep state, REM sleep state, and non-REM sleep state. According to Test 2, the depth of hypnosis, which increases with increasing anesthetic concentration, can be quantified.

[0159] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments. Furthermore, the effects described in the above-described embodiments are merely a list of the most preferable effects resulting from the present invention, and the effects of the present invention are not limited to those described in the above-described embodiments. Furthermore, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all of the configurations described. [Explanation of symbols]

[0160] 1. Discrimination System 2 Discrimination device 21 Control section 211 Acquisition Department 212 First minute range determination unit 213 Second minute range determination unit 214 Counting Department 215 Discrimination part 22 Memory section 221 EEG Table 23 Communications Department 24 Display section 3. Electroencephalography B Burst section D. Minute interval time N Network P potential difference T Minute Section Wa awake state EEG Wn Non-REM sleep state EEG

Claims

1. a first minute range determination unit that determines the predetermined minute range so that the average time of each minute portion during which the amplitude of the electroencephalogram within a predetermined section acquired from the brain of the subject to discrimination changes from a state where it is not within the predetermined minute range to a state where it is within the predetermined minute range and then again to a state where it is not within the predetermined minute range, is a predetermined value; a counting unit that measures changes in the time series of the minute range; A brain activity state discrimination device comprising: a discrimination unit that discriminates the brain activity state using the measured change.

2. the electroencephalogram includes an electroencephalogram acquired at a predetermined sampling period; The discrimination device according to claim 1 , wherein the first infinitesimal range determination unit is capable of determining the infinitesimal range so that a ratio of an average time of each infinitesimal portion to the predetermined sampling period substantially coincides with a predetermined ratio.

3. 3. The discrimination device according to claim 2, wherein the predetermined ratio is between 3 / 2 and 7 / 2.

4. The discrimination device according to claim 1 , further comprising a second infinitesimal range determination unit capable of determining the predetermined infinitesimal range so as to substantially maximize the number of the infinitesimal portions.

5. 5. The discrimination device according to claim 1, wherein the discrimination unit is capable of determining whether the activity state corresponds to one or more consciousness states selected from an awake state, a light sleep state, a REM sleep state, and a non-REM sleep state.

6. The discrimination device according to claim 1 , wherein the electroencephalogram is obtained from the brain of the subject in a natural state or in a state where anesthesia is administered.

7. an electroencephalograph capable of acquiring the electroencephalogram from the brain; The discrimination device according to any one of claims 1 to 6, Equipped with The discrimination device is capable of acquiring the electroencephalogram using the electroencephalograph.

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