Secretion amount estimation system and secretion amount estimation program

Abstract

To make it possible to grasp the amount of secretion of a hormone by an easier method without collecting and examining a sample individually.SOLUTION: A secretion amount estimation system S includes: a frequency component detection unit 212 and an estimation unit 215. The frequency component detection unit 212 detects a frequency component of a brain wave in a specific region of a brain. An estimation unit 215 estimates the amount of secretion of a hormone as an estimation target of an estimation subject on the basis of the result of frequency between a frequency component detected by the frequency component detection unit 212 from the estimation subject and a frequency component as a reference for estimating the amount of secretion of the estimation target hormone.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a secretion amount estimation system and a secretion amount estimation program. [Background technology]

[0002] Conventionally, the amount of hormone secreted in the body has been measured for various purposes. Measuring hormone secretion allows medical professionals to select appropriate medical treatment and allows subjects to accurately understand their own physical condition.

[0003] An example of such technology for measuring hormone secretion levels is disclosed in Patent Document 1. In the technology disclosed in Patent Document 1, hormone secretion levels are measured at medical institutions or testing institutions. Medical advice corresponding to the measured hormone secretion levels is then provided to the subject, allowing the measurement results to be used more effectively. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-108177 Summary of the Invention [Problem to be solved by the invention]

[0005] In the general technology disclosed in the above-mentioned Patent Document 1, it is necessary to carry out a specimen test. That is, in order to measure the amount of hormone secretion, it is necessary to collect a specimen such as blood or saliva from the subject and carry out a specimen test on this specimen. However, in order to conduct a specimen test, it is necessary to have the specimen collected at a medical institution or to collect the specimen using a predetermined test kit, which is very troublesome for the subject. Therefore, for example, it is difficult and impractical for a woman to measure the amount of hormone secretion on a daily basis in order to understand her menstrual status.

[0006] The present invention has been made in view of the above circumstances, and an object of the present invention is to make it possible to grasp the amount of hormone secretion by a simpler method without the need to collect and test a sample each time. [Means for solving the problem]

[0007] In order to solve the above problem, a secretion amount estimation system according to one embodiment of the present invention comprises: a detection means for detecting frequency components of electroencephalograms in a specific brain region; an estimation means for estimating the secretion amount of the hormone to be estimated in the subject to be estimated based on a comparison result between the frequency component detected from the subject to be estimated by the detection means and a frequency component that serves as a reference for estimating the secretion amount of the hormone to be estimated; The present invention is characterized by comprising: [Effects of the Invention]

[0008] According to the present invention, it is not necessary to collect a sample each time and perform a sample test, and it is possible to grasp the amount of hormone secretion in a simpler manner. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an example of the overall configuration of a secretion amount estimation system according to one embodiment of the present invention. [Figure 2] 1 is a block diagram showing an example of the configuration of an electroencephalogram measuring device according to one embodiment of the present invention. [Figure 3] 1 is a block diagram showing an example of the configuration of a secretion amount estimation device according to an embodiment of the present invention. [Figure 4] 1 is a table showing an example of subject information stored in a secretion amount estimation device according to an embodiment of the present invention. [Figure 5] 1 is a flowchart showing the flow of a measurement process executed by an electroencephalogram determination device and a secretion amount estimation device according to an embodiment of the present invention. [Figure 6] 10 is a flowchart showing the flow of a reference data generation process executed by a secretion amount estimation device according to one embodiment of the present invention. [Figure 7] 1 is a flowchart showing the flow of a secretion amount estimation process executed by a secretion amount estimation device according to one embodiment of the present invention. [Figure 8] 1 is a graph showing the results of a test according to an embodiment of the present invention, illustrating that the amount of female hormone secretion varies with the menstrual cycle. [Figure 9] 1 is a graph showing verification results according to an embodiment of the present invention, illustrating reference data corresponding to estradiol and reference data corresponding to progesterone. DETAILED DESCRIPTION OF THE INVENTION

[0010] An example of an embodiment of the present invention will now be described with reference to the accompanying drawings.

[0011] [System Configuration] Fig. 1 is a block diagram showing the overall configuration of a secretion amount estimation system S according to this embodiment. As shown in Fig. 1, the secretion amount estimation system S includes an electroencephalogram (EEG) measuring device 10 and a secretion amount estimation device 20. Fig. 1 also shows users who wish to know their own hormone secretion amounts using the secretion amount estimation system S, and users who provide predetermined data for that purpose.

[0012] The electroencephalogram measuring device 10 and the secretion amount estimation device 20 are communicably connected in accordance with any communication method. This communication may be performed directly between the devices or may be performed via a network including a relay device. When communication is performed via a network, this network is realized by, for example, a network such as the Internet or a LAN (Local Area Network), or a network combining these.

[0013] Here, the secretion amount estimation system S is an example of an embodiment of the present invention, and makes it possible to grasp the secretion amount of hormones in a simpler manner without the need to collect a sample each time and perform a sample test. As a result of repeated testing and research into hormone secretion, the inventors of the present invention discovered a correlation between hormone secretion and the frequency components of electroencephalograms. This led to the idea that it would be possible to estimate hormone secretion based on the frequency components of electroencephalograms, and this led to the creation of the present invention. Therefore, the secretion amount estimation system S estimates the amount of hormone secretion based on the frequency components of the electroencephalogram.

[0014] Although there are a wide variety of hormones, in the following description, the hormone to be estimated in this embodiment will be referred to as the “estimated target hormone.” The estimated target hormone may be, for example, the so-called female hormones “estradiol” or “progesterone,” the so-called male hormones “testotetron” or “androstenedione,” or other hormones secreted in the brain such as “oxytocin” or “serotonin.”

[0015] The electroencephalogram (EEG) measuring device 10 is a device that measures fluctuations in the electric potential in the user's head as the user's brain waves. The EEG measuring device 10 is configured as a headset-type electroencephalograph that is provided with a pair of electrodes or a larger number of electrodes for measuring the user's brain waves, and each of these electrodes is in electrical contact with a predetermined part of the user.

[0016] The EEG measuring device 10 measures the user's EEG using these electrodes to generate data corresponding to the user's EEG (hereinafter referred to as "EEG data"). The EEG measuring device 10 also transmits the generated EEG data to the secretion amount estimation device 20.

[0017] The secretion amount estimation device 20 is a device that estimates the amount of hormone secretion based on the electroencephalogram data generated by the electroencephalogram measurement device 10. The secretion amount estimation device 20 is configured by an information processing device such as a server device, a personal computer, or a smartphone, for example.

[0018] The secretion amount estimation device 20 detects frequency components of electroencephalogram data (i.e., electroencephalograms) in a specific brain region in order to estimate the secretion amount of a hormone. The secretion amount estimation device 20 also estimates the secretion amount of a hormone to be estimated in a subject to be estimated based on a comparison result between the frequency components detected by a detection means from the subject to be estimated and the frequency components that serve as a reference for estimating the secretion amount of a hormone to be estimated.

[0019] In this way, the secretion amount estimation system S estimates the secretion amount of the hormone to be estimated in the estimation subject based on the frequency components of the detected electroencephalogram. Therefore, although the secretion amount estimation system S requires that the reference frequency components be identified by subjecting the subject to a sample test, the secretion amount of the hormone to be estimated can then be immediately determined by the subject simply measuring their electroencephalogram. Furthermore, while common techniques require the subject to undergo a sample test, the secretion amount estimation system S does not require the subject to undergo a sample test. Therefore, the secretion amount estimation system S can solve the problem of being able to grasp the amount of hormone secretion in a simpler way without the need to collect a sample and perform a sample test each time.

[0020] Next, the configurations and functions of the electroencephalogram measuring device 10 and the secretion amount estimation device 20 for realizing such processing will be described in more detail. For clarity of explanation, the people involved in the secretion amount estimation system S will be referred to separately as follows.

[0021] First, a user whose EEG data is measured in order for the secretion amount estimation system S to identify the reference frequency component is referred to as a "subject." The subject may be, for example, an employee of the business that operates the secretion amount estimation system S or a collaborator who cooperates with the business. Furthermore, a user who receives an estimation of the secretion amount of a hormone to be estimated based on the reference frequency component identified by the secretion amount estimation system S is referred to as an “estimation subject.” An example of an estimation subject is a woman who wants to understand her own menstrual status. The subject and the estimation target may be different persons, or may be the same person. That is, a certain user may be the subject and identify a reference frequency component, and then be the estimation target and receive estimation. Also, a third party such as a medical professional may refer to the estimation result of the secretion amount and use the estimation result for medical treatment, etc.

[0022] [Configuration of EEG measuring device] Next, the configuration of the electroencephalogram measuring device 10 will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of the electroencephalogram measuring device 10. 2, the electroencephalogram measuring device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a communication unit 14, a storage unit 15, an input unit 16, an output unit 17, and a measuring unit 18. These units are connected by signal lines and send and receive signals to and from each other.

[0023] The CPU 11 executes various processes (for example, measurement processes described later) according to a program recorded in the ROM 12 or a program loaded from the storage unit 15 into the RAM 13. The RAM 13 also stores data and the like necessary for the CPU 11 to execute various processes.

[0024] The communication unit 14 controls communication so that the CPU 11 can communicate with other devices (for example, the secretion amount estimation device 20). The storage unit 15 is configured with a semiconductor memory such as a DRAM (Dynamic Random Access Memory), and stores various data.

[0025] The input unit 16 is made up of various buttons and the like, and is used to input various information in response to user operations. The output unit 17 is composed of a display, a speaker, etc., and outputs images and sounds.

[0026] The measurement unit 18 measures fluctuations in the potential of the head of the user (here, a test subject or a presumed subject) as the user's brain waves. In this embodiment, as an example of a measurement method, it is assumed that the measurement unit 18 measures brain waves from a specific region of the brain using a reference electrode derivation method. In this case, one end of a pair of electrodes provided in the measurement unit 18 is brought into contact with a point where the potential is close to zero (for example, the user's earlobe) to serve as the reference electrode. The other end is brought into contact with a predetermined position on the user's head (for example, a position corresponding to Fp1 in the left prefrontal cortex defined in the International 10-20 System) to serve as the probe electrode. Then, the measurement unit 18 measures fluctuations in the potential difference between the reference electrode and the probe electrode over time at a predetermined sampling frequency (for example, 512 Hz) as brain waves in the predetermined region of the user's brain.

[0027] As described above, the EEG measuring device 10 has a headset-type configuration, and the pair of electrodes provided in the measuring unit 18 are arranged at positions suitable for measurement (for example, a position in contact with the earlobe and a position in contact with a part corresponding to Fp1) when the user wears the EEG measuring device 10. In this regard, if a typical EEG device is used in which an electrode net is placed over the user's head, the user feels pressured, and noise due to tension is generated in the EEG. In contrast, the EEG measuring device 10 has a headset-type configuration, so that such pressure is not felt and measurement can be performed while the user's tension is suppressed. Therefore, the EEG measuring device 10 can suppress the generation of noise due to the user's tension and perform accurate measurement.

[0028] In the electroencephalogram measuring device 10, these units cooperate to perform the "measurement process." Here, the measurement process is a series of processes in which the electroencephalogram measuring device 10 and the secretion amount estimation device 20 measure the electroencephalogram of the user and perform predetermined pre-processing on the measured electroencephalogram.

[0029] When the measurement process is executed, as shown in FIG. 2, a measurement control section 111, a preprocessing section 112, and an electroencephalogram data transmission section 113 function in the CPU 11. Including cases not specifically mentioned below, data required to realize processing is transmitted and received between these functional blocks at appropriate times.

[0030] The measurement control unit 111 controls the measurement of electroencephalograms by the measurement unit 18 based on the user's instruction operation accepted by the input unit 16 (or the user's instruction operation received via the communication unit 14). For example, based on these instructions, the measurement control unit 111 controls the timing of the start and end of measurement by the measurement unit 18, and controls the sampling period in the measurement, etc. Then, the measurement control unit 111 outputs the user's electroencephalograms obtained by measurement by the measurement unit 18 to the preprocessing unit 112.

[0031] The measurement of the brain waves by the measurement control unit 111 may be performed in any situation, but is preferably performed in a regular situation, for example, when the user wakes up every day. Furthermore, since it is believed that giving the user a task during measurement would prevent imaging of the brain's normal state, the measurement is performed in a state where no task is given, such as when the user is at rest with their eyes closed. Furthermore, the length of a single measurement is also arbitrary, but may be, for example, about 15 minutes.

[0032] The preprocessing unit 112 performs preprocessing such as removing noise components on the electroencephalogram input from the measurement control unit 111 to generate electroencephalogram data.

[0033] For example, taking into consideration the fact that there are individual differences in the magnitude of the amplitude of the electroencephalogram data, the preprocessing unit 112 normalizes the amplitude of the electroencephalogram to absorb these individual differences. In this case, for example, the preprocessing unit 112 normalizes the measured electroencephalogram so that the average amplitude value becomes 0 and the standard deviation becomes 1.

[0034] Furthermore, for example, the pre-processing unit 112 uses a band-pass filter to pass only a predetermined frequency band (e.g., 1 to 40 Hz) from the normalized electroencephalogram. In this case, if only the frequency band of 4 to 24 Hz, which has a particularly high correlation with the secretion amount of the hormone to be estimated, is passed through, it becomes possible to estimate the secretion amount of the hormone to be estimated with higher accuracy. In this way, the pre-processing unit 112 realizes the removal of noise (artifacts) corresponding to the user's body movements during electroencephalogram measurement.

[0035] Furthermore, for example, the preprocessing unit 112 removes noise (artifacts) corresponding to myoelectric potentials using existing methods such as Ensemble Empirical Mode Decomposition (EEMD) and Canonical Correlation Analysis (CCA).

[0036] In this way, the preprocessing unit 112 performs preprocessing such as normalization and removal of various noises (artifacts) to generate electroencephalogram data from the measured electroencephalograms of the user. Then, the preprocessing unit 112 outputs the generated electroencephalogram data to the electroencephalogram data transmitting unit 113. Note that a part or all of the pre-processing described as being performed by the pre-processing unit 112 may be performed by the frequency component detection unit 212 of the secretion amount estimation device 20 instead of the pre-processing unit 112 .

[0037] The electroencephalogram data transmitting unit 113 transmits the electroencephalogram data input from the preprocessing unit 112 to the secretion amount estimation device 20. In this case, the electroencephalogram data generated in parallel with the measurement may be temporarily stored (i.e., buffered) in the memory unit 15, and the electroencephalogram data stored in the memory unit 15 may be transmitted all at once at the timing such as when the measurement is completed, or may be transmitted in real time every time the electroencephalogram data is generated by the preprocessing unit 112.

[0038] [Configuration of the secretion volume estimation device] Next, the configuration of the secretion amount estimation device 20 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the configuration of the secretion amount estimation device 20. As shown in Fig. 3, the secretion amount estimation device 20 includes a CPU 21, a ROM 22, a RAM 23, a communication unit 24, a storage unit 25, an input unit 26, an output unit 27, and a drive 28. These units are connected by signal lines and send and receive signals to and from each other.

[0039] The CPU 21 executes various processes (for example, a reference data generation process and a secretion amount estimation process, which will be described later) according to a program recorded in the ROM 22 or a program loaded from the storage unit 25 to the RAM 23. The RAM 23 also stores data and the like necessary for the CPU 21 to execute various processes.

[0040] The communication section 24 controls communication so that the CPU 21 can communicate with other devices (for example, the electroencephalogram measuring device 10). The storage unit 25 is configured with a semiconductor memory such as a DRAM (Dynamic Random Access Memory), and stores various data.

[0041] The input unit 26 is composed of various buttons and a touch panel, or an external input device such as a mouse and a keyboard, and inputs various information in response to user instructions. The output unit 27 is composed of a display, a speaker, etc., and outputs images and sounds.

[0042] Removable media (not shown) such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory are appropriately loaded into the drive 28. The program read from the removable media by the drive 28 is installed in the storage unit 25 as needed.

[0043] In the secretion amount estimation device 20, these units cooperate to perform the "measurement process," the "reference data generation process," and the "secretion amount estimation process."

[0044] As described above, the measurement process is a series of processes in which the electroencephalogram measuring device 10 and the secretion amount estimation device 20 measure the electroencephalogram of the user and perform predetermined pre-processing on the measured electroencephalogram. In addition, the reference data generation process is a series of processes in which the secretion amount estimation device 20 identifies a reference frequency component for estimating the secretion amount of the hormone to be estimated based on the electroencephalogram data of the subject measured by the electroencephalogram measurement device 10, and generates data of this reference frequency component (hereinafter referred to as "reference data"). Furthermore, the reference data generation process is a series of processes in which the secretion amount estimation device 20 estimates the secretion amount of the hormone to be estimated based on the electroencephalogram data of the subject measured by the electroencephalogram measurement device 10 and the reference data. That is, in this embodiment, the secretion amount estimation device 20 generates reference data and estimates the secretion amount of the hormone to be estimated by using the generated reference data.

[0045] When these reference data generation processes and secretion amount estimation processes are executed, as shown in Figure 3, the CPU 21 functions as follows: an electroencephalogram data acquisition unit 211, a frequency component detection unit 212, a measured secretion amount acquisition unit 213, a reference data generation unit 214, and an estimation unit 215. In addition, a subject data storage unit 251 and a reference data storage unit 252 are provided in one area of ​​the storage unit 25. Including cases not specifically mentioned below, data required to realize processing is transmitted and received between these functional blocks at appropriate times.

[0046] The electroencephalogram data acquiring section 211 acquires the electroencephalogram data by receiving it transmitted from the electroencephalogram measuring device 10. Then, the electroencephalogram data acquiring section 211 outputs the acquired electroencephalogram data to the frequency component detecting section 212.

[0047] The EEG measuring device 10 may be provided with a drive similar to the drive 28 so that the EEG measuring device 10 stores the EEG data in removable media. The EEG data acquiring section 211 may acquire the EEG data from this removable media via the drive 28, rather than acquiring the data through communication.

[0048] The frequency component detection section 212 detects frequency components of each of a plurality of frequencies from the electroencephalogram data of the subject or the electroencephalogram data of the estimated target person acquired by the electroencephalogram data acquisition section 211. In this case, the frequency component detection section 212 divides the electroencephalogram data into predetermined time units (for example, 30 seconds). Furthermore, frequency component detection unit 212 detects frequency components of each of a plurality of frequencies from each of the divided electroencephalogram data. For example, frequency component detection unit 212 performs a Fourier transform (for example, a fast Fourier transform (FFT) using a 512-point Hamming window and 50% overlap processing) on ​​each of the divided electroencephalogram data, and then performs averaging to generate a power spectrum indicating the power value of each of a plurality of frequencies (for example, 1 to 40 Hz) as the frequency components in the electroencephalogram data. Then, the frequency component detection section 212 detects the power values ​​of each of the multiple frequencies in the power spectrum generated in this way as frequency components.

[0049] When frequency component detection section 212 detects a frequency component from the electroencephalogram data of the subject, it stores the frequency component of the subject in subject data storage section 251. On the other hand, when frequency component detection section 212 detects a frequency component from the electroencephalogram data of the estimated subject, it outputs the frequency component of the estimated subject to estimation section 215.

[0050] The measured secretion amount acquisition unit 213 acquires the secretion amount of the hormone to be estimated for each subject. In this case, the secretion amount of the hormone to be estimated acquired by the measured secretion amount acquisition unit 213 is not estimated by the secretion amount estimation device 20, but is measured by an existing method involving a sample test. That is, in this embodiment, a sample test is performed on the subject to generate reference data. However, after the reference data is created, it is possible to estimate the secretion amount of the hormone to be estimated without performing a sample test on the subject. For clarity of explanation, the secretion amount of the hormone to be estimated measured by a sample test on the subject and acquired by the measured secretion amount acquisition unit 213, rather than the secretion amount of the hormone to be estimated by this embodiment, will be referred to as the "measured secretion amount" hereinafter.

[0051] The measured secretion amount acquiring unit 213 acquires the estimated secretion amount for each of the acquired subjects, for example, based on an input operation from the user accepted by the input unit 26 (or an input operation from the user received via the communication unit 14). Then, the measured secretion amount acquiring unit 213 stores the acquired measured secretion amount in the subject data storage unit 251.

[0052] The subject data storage unit 251 stores subject data. The subject data is data in which frequency components and measured secretion amounts are linked for each measurement of each subject. Fig. 4 is a table showing an example of subject data stored in the subject data storage unit 251. As shown in Fig. 4, the subject data storage unit 251 stores the subject data in table format, for example. In this table, a column (row) is provided for each measurement of each subject. Also, for each measurement of each subject, a record (column) is provided for the subject ID, measurement date, frequency component, and measured secretion amount. Then, in a field (cell) where a column and a record intersect, corresponding information is stored.

[0053] Here, the subject ID is unique information assigned to each subject, and may be, for example, an identifier that combines information such as numbers and letters. The measurement date is the date on which the electroencephalogram data was measured by the electroencephalogram measuring device 10. Furthermore, the frequency components are the power values ​​of each of a plurality of frequencies (for example, 1 to 40 [Hz]) in the power spectrum generated as described above by the measured secretion amount acquisition unit 213. Note that in the drawing, the power values ​​that are frequency components are indicated as "***". Furthermore, the measured secretion amount is the measured secretion amount when each measurement is performed for each subject, obtained by the frequency component detection unit 212 as described above, and is expressed as a numerical value corresponding to a unit such as [pmol / L]. By storing the subject data in this format, the frequency components and measured secretion amounts for which subjects and on which measurement dates are stored in a manner that allows identification.

[0054] Returning to Figure 3, the reference data generating unit 214 generates reference data that serves as a reference for the estimation unit 215, described below, to estimate the secretion amount of the hormone to be estimated. The reference data is frequency components detected by the frequency component detecting unit 212 from a subject in which the secretion amount of the hormone to be estimated is in a predetermined state. As an example for the purposes of explanation below, it is assumed that the predetermined state in which the secretion amount of the hormone to be estimated is in a state in which the secretion amount of the hormone to be estimated is relatively high when the secretion amount of the hormone to be estimated fluctuates periodically.

[0055] As described above, the secretion amount of the hormone to be estimated in each subject is stored in the subject data storage unit 251 as the measured secretion amount in the subject data. The reference data generation unit 214 then reads the measured secretion amount in this subject data and identifies the measurement day on which the measured secretion amount was the largest for each subject. The reference data generation unit 214 also extracts the frequency component of the measurement day on which the measured secretion amount was the largest from the subject data. That is, the reference data generation unit 214 extracts frequency components for N subjects, such as the frequency component of the measurement day on which the measured secretion amount was the largest for the first subject, ..., the frequency component of the measurement day on which the measured secretion amount was the largest for the Nth subject (N is an integer value equal to or greater than 1).

[0056] Then, the reference data generating unit 214 calculates, for each frequency, the average value of the power values ​​of the frequency components of the N subjects, thereby calculating, for each frequency, the average value of the power values ​​when the secretion amount of the hormone to be estimated is relatively high.

[0057] The reference data generating unit 214 stores the average value of the power values ​​of each frequency calculated in this manner as reference data in the reference data storage unit 252. That is, the reference data storage unit 252 functions as a storage unit that stores reference data. In this way, the reference data generating unit 214 realizes generation of the reference data.

[0058] The above-described method of generating the reference data is merely an example, and the reference data generating unit 214 may alternatively generate the reference data by calculating other values, such as the median or the mode, instead of the average value. Alternatively, the reference data generating unit 214 may generate the reference data by extracting frequency components from measurement days on which the secretion amount of the hormone to be estimated is relatively low, rather than from measurement days on which the secretion amount is relatively high. Furthermore, the reference data generating unit 214 may alternatively set a threshold value based on an absolute value for the secretion amount of the hormone to be estimated, and generate the reference data by extracting frequency components from measurement days on which the measured secretion amount is equal to or greater than this threshold (or below the threshold).

[0059] The estimation unit 215 estimates the secretion amount of the hormone to be estimated in the estimated subject based on the comparison result between the frequency components of the estimated subject input from the frequency component detection unit 212 and the reference data stored in the reference data storage unit 252. The comparison by the estimation unit 215 is realized, for example, by calculating the similarity between the frequency components of the estimated subject and the reference data. In this case, the similarity can be calculated by any method. For example, the similarity can be calculated by comparing power values ​​(i.e., frequency components) and calculating a difference value, or by using an existing method such as pattern matching.

[0060] In this case, the similarity may be calculated by comparing the power values ​​of all frequencies included in each data, or by comparing only the power values ​​of a predetermined frequency depending on the type of hormone to be estimated.For example, if a predetermined frequency at which a particular difference occurs in that type of hormone to be estimated is known based on past estimation results, the similarity may be calculated by comparing only the power values ​​of this predetermined frequency.Alternatively, the power values ​​of all frequencies may be compared, but the power values ​​of this predetermined frequency may be weighted to be prioritized as a judgment criterion, and the similarity may be calculated by comparison.

[0061] The estimation unit 215 then estimates that the higher the calculated similarity, the closer the secretion amount of the hormone to be estimated corresponding to the reference data is to a predetermined state. Here, the predetermined state is assumed to be a state in which the secretion amount of the hormone to be estimated is relatively high, so the estimation unit 215 estimates that the higher the similarity, the greater the secretion amount of the hormone to be estimated in the person to be estimated. To this end, the estimation unit 215 further calculates the average, median, and mode of the measured secretion amount of each subject on the measurement day with the highest measured secretion amount, which was identified to generate the reference data, as the ``reference value.'' The estimation unit 215 then estimates that the value obtained by multiplying the similarity by the reference value is the secretion amount of the hormone to be estimated in the estimation subject. For example, if the similarity is 100%, the result is "reference value x 1.00", so the value of the reference value itself is estimated to be the secretion amount of the hormone to be estimated in the estimation subject. Alternatively, if the similarity is 50%, the result is "reference value x 0.50", so half the value of the reference value is estimated to be the secretion amount of the hormone to be estimated in the estimation subject.

[0062] Then, the estimation unit 215 presents this estimation result (i.e., estimated value) to a user (here, a third party such as a person to be estimated, a test subject, or a medical professional). This presentation may be, for example, a display on a display included in the output unit 27, an audio output from a speaker included in the output unit 27, printing on a paper medium from a printing device via the communication unit 24, or transmission to another device (not shown) used by the user via the communication unit 24.

[0063] As described above, in the secretion amount estimation system S, the electroencephalogram measuring device 10 measures electroencephalogram data. Also, in the secretion amount estimation system S, the secretion amount estimation device 20 estimates the secretion amount of the hormone to be estimated in the estimation subject based on the frequency components of the detected electroencephalogram. Therefore, the secretion amount estimation system S can solve the problem of being able to grasp the amount of hormone secretion in a simpler way without the need to collect a sample and perform a sample test each time.

[0064] Naturally, the relationship between the hormone to be estimated and the frequency component differs depending on the type of hormone to be estimated, for example, it differs between a first hormone to be estimated (e.g., estradiol) and a second hormone to be estimated (e.g., progesterone). Therefore, the reference data generating unit 214 generates reference data according to each type of hormone to be estimated. Furthermore, the estimation unit 215 estimates the secretion amount of the hormone to be estimated using the reference data corresponding to the type of hormone to be estimated.

[0065] [Measurement processing] Next, the flow of the measurement process executed by the EEG measurement device 10 and the secretion amount estimation device 20 will be described with reference to Fig. 5. Fig. 5 is a flowchart illustrating the flow of the measurement process executed by the EEG measurement device 10 and the secretion amount estimation device 20. The measurement process is executed in response to an instruction operation from the user to start measurement.

[0066] First, the flow of processing on the electroencephalogram measuring device 10 side will be described. In step S11, the measurement control unit 111 controls the measurement of the user's brain waves by the measurement unit 18, thereby starting the measurement of the brain waves by the measurement unit 18. Then, the measurement control unit 111 outputs the user's brain waves obtained by the measurement by the measurement unit 18 to the preprocessing unit 112.

[0067] In step S12, the preprocessing unit 112 generates electroencephalogram data by performing preprocessing on the electroencephalogram input from the measurement control unit 111. Then, the preprocessing unit 112 temporarily stores (i.e., buffers) the generated electroencephalogram data in the storage unit 15.

[0068] In step S13, the measurement control unit 111 determines whether or not to end the measurement of electroencephalograms by the measurement unit 18. For example, the measurement control unit 111 determines to end the measurement of electroencephalograms when a predetermined time has elapsed since the start of measurement or when the user issues an instruction to end the measurement. If the measurement of electroencephalograms is to be ended, the determination in step S13 is Yes, and the process proceeds to step S14. On the other hand, if the measurement of electroencephalograms is not to be ended, the determination in step S13 is No, and the process is repeated again from step S11.

[0069] In step S14, the electroencephalogram data transmitting section 113 transmits all of the electroencephalogram data that the preprocessing section 112 has temporarily stored in the storage section 15 to the secretion amount estimation device 20. This completes the process on the electroencephalogram measuring device 10 side.

[0070] In this flowchart, it is assumed that the EEG data is temporarily stored (i.e., buffered) in the memory unit 15, and that the EEG data stored in the memory unit 15 is transmitted all at once at the end of measurement, etc. However, as mentioned above, it is also possible to transmit the EEG data in real time each time it is generated.

[0071] Next, the flow of processing on the side of the secretion amount estimation device 20 will be described. In step S21, the electroencephalogram data acquiring section 211 acquires the electroencephalogram data transmitted from the electroencephalogram measuring device 10 by receiving the data. In step S22, the frequency component detection section 212 detects frequency components of each of a plurality of frequencies from the electroencephalogram data.

[0072] In step S23, the frequency component detection unit 212 determines whether or not a frequency component has been detected from the electroencephalogram data of the subject in step S22. If a frequency component has been detected from the electroencephalogram data of the subject, the determination in step S23 is Yes. The process proceeds to step S24.On the other hand, if no frequency component is detected from the electroencephalogram data of the subject (i.e., if a frequency component is detected from the electroencephalogram data of the estimated subject), the result of step S23 is determined as No, On the secretion amount estimation device 20 side, this process ends.

[0073] In step S24, the measured secretion amount acquisition section 213 acquires the secretion amount of the hormone to be estimated for the subject from whom the frequency component was detected in step S22. In step S25, the frequency component detection unit 212 and the measured secretion amount acquisition unit 213 store the subject data for the subject from which the frequency components were detected in step S22 in the subject data storage unit 251. This completes the process on the secretion amount estimation device 20 side.

[0074] By the measurement process described above, the EEG measuring device 10 can measure the EEG from the user and transmit the EEG data generated based on the measured EEG to the secretion amount estimation device 20. Furthermore, the secretion amount estimation device 20 can detect frequency components and store subject data about the subject.

[0075] [Reference data generation process] Next, the flow of the reference data generation process executed by the secretion amount estimation device 20 will be described with reference to Fig. 6. Fig. 6 is a flowchart illustrating the flow of the reference data generation process executed by the secretion amount estimation device 20. The reference data generation process is executed in response to an instruction operation from the user to start reference data generation. As a prerequisite for the processing, it is assumed that the subject data generated by the measurement processing is stored in the subject data storage unit 251.

[0076] In step S31, the reference data generating unit 214 reads the subject data stored in the subject data storage unit 251. In step S32, the reference data generating unit 214 generates reference data based on the subject data read in step S31. In step S33, the reference data generating unit 214 stores the reference data generated in step S32 in the reference data storage unit 252. This completes the process.

[0077] By the above-described reference data generation process, the secretion amount estimation device 20 can generate reference data that serves as a standard for estimating the secretion amount of the hormone to be estimated.

[0078] [Secretion amount estimation processing] Next, the flow of the secretion amount estimation process executed by the secretion amount estimation device 20 will be described with reference to Fig. 7. Fig. 7 is a flowchart illustrating the flow of the secretion amount estimation process executed by the secretion amount estimation device 20. The secretion amount estimation process is executed in response to an instruction operation from the user to start secretion amount estimation. Note that, as a prerequisite for the process, it is assumed that electroencephalogram data of the estimation subject has been detected by the measurement process. Furthermore, it is assumed that the reference data generated by the reference data generation process has been stored in the reference data storage unit 252.

[0079] In step S41, the estimation unit 215 acquires electroencephalogram data of the estimation subject detected by the measurement process. In step S42, the estimation unit 215 reads the reference data stored in the reference data storage unit 252.

[0080] In step S43, the estimation unit 215 estimates the secretion amount of the hormone to be estimated in the subject to be estimated based on the comparison result between the electroencephalogram data of the subject to be estimated acquired in step S41 and the reference data read in step S42. In step S44, the estimation unit 215 presents the estimation result of step S43 to the user.

[0081] By the secretion amount estimation process described above, the secretion amount estimation system S can solve the problem of being able to grasp the secretion amount of hormones in a simpler way, without the need to collect samples and perform sample testing each time.

[0082] [Verification example] The above describes an embodiment of the present invention. Next, with reference to Fig. 8 and Fig. 9, the results of a verification conducted on actual subjects to verify that there is a correlation between hormone secretion amounts and frequency components of electroencephalograms will be described. Fig. 8 is a graph showing fluctuations in the secretion amounts of female hormones (estradiol and progesterone in this case) along with the menstrual cycle. Fig. 9 is a graph showing reference data corresponding to estradiol and reference data corresponding to progesterone.

[0083] As a general rule, the menstrual cycle is divided into four phases: the luteal phase, menstrual phase, follicular phase, and ovulation phase. It is generally known that the secretion of female hormones also fluctuates cyclically. In particular, the secretion of estradiol fluctuates significantly throughout the four phases of the menstrual cycle. During the menstrual phase, estradiol and progesterone secretion are at their lowest among the four phases. During the subsequent follicular phase, estradiol secretion gradually increases while progesterone secretion decreases. During ovulation, estradiol secretion temporarily decreases, while progesterone secretion gradually increases. The secretion of estradiol, which functions to lower body temperature, is at its highest during the first half of the ovulation phase. During the luteal phase, both estradiol and progesterone secretion increase. Progesterone, in particular, is secreted at its highest during the luteal phase. At the end of the luteal phase, estradiol and progesterone secretion both decrease, and menstruation resumes. Thus, hormone secretion fluctuates with each phase of the menstrual cycle.

[0084] FIG. 8(A) is a graph showing fluctuations in estradiol secretion, with the horizontal axis representing the number of days since the start of menstruation and the vertical axis representing the estradiol secretion (here, the concentration [pmol / L] in the saliva, which is the sample). Note that this estradiol secretion was actually measured by a specimen test using the subject's saliva as the sample, and corresponds to the measured secretion amount in the above-mentioned embodiment. FIG. 8(B) is a graph similar to FIG. 8(A), showing fluctuations in progesterone secretion instead of estradiol secretion. As shown in FIGS. 8(A) and (B), it can be seen that the amounts of estradiol and progesterone actually secreted by the subject fluctuate with the menstrual cycle.

[0085] In parallel with the measurement of the secretion amounts shown in Figures 8(A) and (B), the measurement process shown in Figure 5 was performed by actually measuring the electroencephalogram of the subject. Based on these measurement results, the reference data generation process shown in Figure 6 was performed to generate reference data for estradiol and progesterone as estimated target hormones. The reference data is shown in Figure 9.

[0086] 9(A) is a graph showing reference data generated when the hormone to be estimated is estradiol, with the horizontal axis representing frequency and the vertical axis representing the frequency component (i.e., power value) of each frequency. Here, the reference data is the average value of the frequency component (i.e., power value) of multiple subjects on the measurement day when the measured secretion amount of the hormone to be estimated is the highest. Therefore, for example, in the example of FIGS. 8(A) and (B), the reference data corresponds to the measurement day about one week after the start of menstruation, when the measured secretion amount of estradiol is the highest and the measured secretion amount of progesterone is normal in the menstrual cycle.

[0087] On the other hand, Figure 9(B) is a graph similar to Figure 9(A), showing reference data generated when progesterone, rather than estradiol, is used as the estimated target hormone. For example, in the examples of Figures 8(A) and (B), the reference data corresponds to a measurement date about two weeks after the start of menstruation, when the measured secretion amount of progesterone is the highest and the measured secretion amount of estradiol is normal in the menstrual cycle.

[0088] 9(A) and (B), the relationship between the strength of the frequency components (i.e., power values) of each frequency in the reference data and the degree of strength are clearly different depending on the hormone to be estimated. Therefore, it is clear that there is a correlation between the secretion amount of the hormone to be estimated and the frequency components of the electroencephalogram. Therefore, it can be said that by generating reference data from the electroencephalogram of the subject as in the above-described embodiment and estimating the secretion amount of the hormone to be estimated in the subject based on this reference data.

[0089] [Variations] Although the embodiments of the present invention have been described above, these embodiments are merely illustrative and do not limit the technical scope of the present invention. The present invention can take on various other embodiments and can undergo various modifications such as omissions and substitutions without departing from the spirit of the present invention. In such cases, these embodiments and their modifications are included in the scope and spirit of the invention described in this specification, etc., and are also included in the scope of the invention described in the claims and their equivalents. As an example, the embodiment of the present invention described above may be modified as illustrated below.

[0090] <First Modification> The device configuration of the secretion amount estimation system S in the above-described embodiment is merely an example and can be changed as appropriate. For example, in the above-described embodiment, the EEG measurement device 10 and the secretion amount estimation device 20 are realized as separate devices. However, this is not limiting, and for example, the EEG measurement device 10 and the secretion amount estimation device 20 may be realized as an integrated device.

[0091] Additionally, in the above-described embodiment, the EEG measuring device 10 and the secretion amount estimation device 20 are each realized by a single device. However, without being limited to this, each of the EEG measuring device 10 and the secretion amount estimation device 20 may be realized by a plurality of devices by utilizing technology such as cloud computing.

[0092] Additionally, in the above-described embodiment, it is assumed that the EEG measuring device 10 is used by a plurality of subjects. However, the present invention is not limited to this, and for example, an EEG measuring device 10 may be provided for each subject, and each subject may use the EEG measuring device 10 on a daily basis in a private environment such as their own home.

[0093] Additionally, in the above-described embodiment, the EEG measuring device 10 measures the brain waves at Fp1 using a pair of electrodes. However, the present invention is not limited to this, and the EEG measuring device 10 may measure brain waves at a location other than Fp1. Alternatively, the EEG measuring device 10 may be provided with a larger number of electrodes and measure brain waves at a plurality of locations. Then, the above-described series of processes may be performed based on each of the brain waves at a plurality of locations.

[0094] <Second Modification> In the above-described embodiment, the estimation unit 215 presented the estimated secretion amount of the hormone to be estimated as the estimation result. However, other information related to the estimation result may also be presented. For example, as described above in the description of the verification example, there is a predetermined pattern between the menstrual cycle and the increase and decrease in the secretion amounts of estradiol and progesterone. Therefore, based on the estimated secretion amounts of estradiol and progesterone, it is possible to determine which phase of the menstrual cycle the estimation subject is currently in. Therefore, the estimation unit 215 may also present the determination result of which phase of the menstrual cycle the estimation subject is currently in. For example, by notifying the user that the subject is currently in the luteal phase, the cause of the user's poor mental or physical condition can be easily identified by electroencephalogram (EEG) without hormone testing. This is expected to avoid many cases where the luteal phase, whose symptoms are similar to those of depression, is mistakenly mistaken for depression and the subject visits a psychiatrist.

[0095] Additionally, it is known that when a person feels angry, the secretion amounts of noradrenaline and adrenaline increase. Therefore, the estimation unit 215 estimates the secretion amounts of noradrenaline and adrenaline as the hormones to be estimated. Based on the estimation results of the secretion amounts of noradrenaline and adrenaline, a determination result of the degree of anger felt may also be presented.

[0096] <Third Modification> In the above-described embodiment, the electroencephalogram (EEG) is measured by the electroencephalogram measuring device 10, and a series of processes are performed based on the electroencephalogram data of the measured electroencephalogram. However, other biological information may be measured, the electroencephalogram may be estimated from the other measured information, and a series of processes may be performed based on the electroencephalogram data of the estimated electroencephalogram. In other words, the electroencephalogram may not be a measured value, but may be an estimated value from other biological information.

[0097] As a technology for estimating brain waves without using a head-mounted electroencephalograph, for example, the technology disclosed in JP 2015-109964 A, which shares some inventors with the present invention, can be used. This makes it possible to realize the series of processes in the above-described embodiment based on other biological information without actually measuring electroencephalograms.

[0098] [Configuration example] As described above, the secretion amount estimation system S according to this embodiment includes the frequency component detection unit 212 and the estimation unit 215. The frequency component detection section 212 detects the frequency components of the electroencephalogram in a specific brain region. The estimation unit 215 estimates the amount of secretion of the hormone to be estimated in the subject to be estimated based on the comparison result between the frequency components detected from the subject to be estimated by the frequency component detection unit 212 and the frequency components that serve as a reference for estimating the amount of secretion of the hormone to be estimated. In this way, the secretion amount estimation system S estimates the secretion amount of the hormone to be estimated in the estimation subject based on the frequency components of the detected electroencephalogram. Therefore, although the secretion amount estimation system S requires that the reference frequency components be identified by subjecting the subject to a sample test, the secretion amount of the hormone to be estimated can then be immediately determined by the subject simply measuring their electroencephalogram. Furthermore, while common techniques require the subject to undergo a sample test, the secretion amount estimation system S does not require the subject to undergo a sample test. Therefore, the secretion amount estimation system S can solve the problem of being able to grasp the amount of hormone secretion in a simpler way without the need to collect a sample and perform a sample test each time.

[0099] The estimation unit 215 uses, as a reference frequency component, a frequency component detected by the frequency component detection unit 212 from a subject in which the secretion amount of the hormone to be estimated is in a predetermined state. This makes it possible to identify the reference frequency component based on an objective index, namely, the frequency component of the subject's electroencephalogram.

[0100] The state in which the secretion amount of the estimated target hormone is a state in which the secretion amount of the estimated target hormone is relatively high when the secretion amount of the estimated target hormone fluctuates periodically, The estimation section 215 estimates that the higher the similarity between the frequency component detected from the estimation subject by the frequency component detection section 212 and the reference frequency component, the greater the amount of secretion of the hormone to be estimated in the estimation subject. This makes it possible to estimate the secretion amount of the hormone to be estimated based on an objective index, namely, the degree of similarity with the reference frequency component.

[0101] The frequency component detection section 212 detects, as frequency components, the power values ​​of each of a plurality of frequencies when the electroencephalogram is expressed as a power spectrum. This makes it possible to detect frequency components through information processing, that is, calculation.

[0102] [Realization of functions through hardware and software] The function of executing the series of processes according to the above-described embodiment can be realized by hardware, software, or a combination of these. In other words, it is sufficient that the function of executing the series of processes described above is realized in any one of the secretion amount estimation systems S, and there are no particular limitations on how this function is realized.

[0103] For example, when the function of executing the above-mentioned series of processes is realized by a processor that executes arithmetic processing, the processor that executes this arithmetic processing includes processors that are composed of various processing devices alone, such as single processors, multiprocessors, and multicore processors, as well as processors that combine these various processing devices with processing circuits such as ASICs (Application Specific Integrated Circuits) or FPGAs (Field-Programmable Gate Arrays).

[0104] Furthermore, for example, when the function of executing the above-described series of processes is realized by software, the program constituting the software is installed on a computer via a network or a recording medium. In this case, the computer may be a computer incorporating dedicated hardware, or may be a general-purpose computer (e.g., a general electronic device such as a general-purpose personal computer) that can execute predetermined functions by installing a program. Furthermore, the steps of writing the program may include only processes that are executed chronologically according to the order, but may also include processes that are executed in parallel or individually. Furthermore, the steps of writing the program may be executed in any order within the scope of the present invention.

[0105] A recording medium on which such a program is recorded may be provided to a user by being distributed separately from the computer main body, or may be provided to a user in a state where it is pre-installed in the computer main body. In this case, the recording medium distributed separately from the computer main body may be a magnetic disk (including a floppy disk), an optical disk, a magneto-optical disk, or the like. The optical disk may be, for example, a CD-ROM (Compact Disc-Read Only Memory), a DVD (Digital Versatile Disc), or a Blu-ray (registered trademark) Disc. The magneto-optical disk may be, for example, an MD (Mini Disc). These recording media may be loaded into, for example, drive 28 in FIG. 3 and installed in the computer main body. Furthermore, a recording medium provided to a user in a state where it is pre-installed in the computer main body may be, for example, ROM 12 in FIG. 2, ROM 22 in FIG. 3, memory unit 15 in FIG. 2, or a solid-state drive (SSD) or hard disk included in memory unit 25 in FIG. 3, on which the program is recorded. [Explanation of symbols]

[0106] 10 EEG measuring device, 20 secretion amount estimation device, 11, 21 CPU, 12, 22 ROM, 13, 23 RAM, 14, 24 communication unit, 15, 25 memory unit, 16, 26 input unit, 17, 27 output unit, 18 measurement unit, 28 drive, 111 measurement control unit, 112 preprocessing unit, 113 EEG data transmission unit, 211 EEG data acquisition unit, 212 frequency component detection unit, 213 measured secretion amount acquisition unit, 214 reference data generation unit, 215 estimation unit, 251 subject data storage unit, 252 reference data storage unit, S secretion amount estimation system

Claims

1. a detection means for detecting frequency components of electroencephalograms in a specific brain region; an estimation means for estimating that the secretion amount of the hormone to be estimated in the subject to be estimated is greater the higher the similarity between the frequency component detected by the detection means from the subject to be estimated and the frequency component serving as a reference for estimating the secretion amount of the hormone to be estimated, detected by the detection means from a subject in a state where the secretion amount of the hormone to be estimated is relatively large, when the secretion amount of the hormone to be estimated fluctuates periodically; A secretion amount estimation system comprising:

2. the detection means detects, as frequency components, power values ​​of each of a plurality of frequencies when the electroencephalogram is represented as a power spectrum; The secretion amount estimation system according to claim 1 .

3. a detection function for detecting frequency components of electroencephalograms in a specific brain region; an estimation function that estimates that the higher the similarity between a frequency component detected by the detection function from a subject to be estimated and a reference frequency component for estimating the secretion amount of the hormone to be estimated, detected by the detection function from a subject in a state where the secretion amount of the hormone to be estimated is relatively high, when the secretion amount of the hormone to be estimated fluctuates periodically, the higher the secretion amount of the hormone to be estimated in the subject to be estimated; A secretion amount estimation program characterized by causing a computer to realize the above.

4. The detection function detects the power values ​​of each of a plurality of frequencies as frequency components when the electroencephalogram is represented as a power spectrum.

4. The secretion amount estimation program according to claim 3.

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