JP7787669B2 - Secretion amount estimation system and secretion amount estimation program - Google Patents

Description

The present invention relates to a secretion amount estimation system and a secretion amount estimation program.

Traditionally, the amount of hormone secreted in the body has been measured for a variety of purposes. Measuring hormone secretion allows medical professionals to select appropriate medical treatment and allows the person being measured to accurately understand their own physical condition.

An example of such technology for measuring hormone secretion levels is disclosed in Patent Document 1. The technology disclosed in Patent Document 1 measures hormone secretion levels at medical institutions or testing facilities. Medical advice corresponding to the measured hormone secretion levels is then provided to the person being measured, allowing the measurement results to be used more effectively.

Japanese Patent Application Laid-Open No. 2021-108177

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.

The present invention was made in light of these circumstances. The object of the present invention is to make it possible to grasp hormone secretion levels in a simpler manner, without the need to collect and test samples each time.

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:

The present invention eliminates the need to collect and test samples each time, making it possible to grasp hormone secretion levels in a simpler manner.

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. 1 is a block diagram showing an example of the configuration of an electroencephalogram measuring device according to one embodiment of the present invention. 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. 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. 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. 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. 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. 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. 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.

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

[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.

The EEG measurement device 10 and the secretion amount estimation device 20 are connected to each other so that they can communicate with each other in accordance with any communication method. This communication may be performed directly between the devices, or may be performed via a network that includes a relay device. When communication is performed via a network, this network may be realized by, for example, a network such as the Internet or a LAN (Local Area Network), or a combination of these.

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.

Note that while there are a wide variety of hormones, in the following explanation, 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" and "progesterone," the so-called male hormones "testotetron" and "androstenedione," or other hormones secreted in the brain such as "oxytocin" and "serotonin."

The EEG measurement device 10 is a device that measures fluctuations in the electrical potential in the user's head as the user's brain waves. The EEG measurement device 10 is configured as a headset-type electroencephalograph equipped with a pair of electrodes or a larger number of electrodes for measuring the user's brain waves, with each of these electrodes electrically contacting a specific part of the user's body.

The EEG measurement device 10 measures the user's brain waves using these electrodes and generates data corresponding to the user's brain waves (hereinafter referred to as "brain wave data"). The EEG measurement device 10 also transmits the generated brain wave data to the secretion amount estimation device 20.

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

The secretion amount estimation device 20 detects frequency components of electroencephalogram data (i.e., electroencephalograms) in a specific brain region 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 based on the results of a comparison between the frequency components detected by a detection means from the subject and the reference frequency components for estimating the secretion amount 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 each time and perform a sample test.

Next, we will explain in more detail the configurations and functions of the EEG measurement device 10 and the secretion amount estimation device 20 that are used to realize this processing. For clarity, the individuals involved in the secretion amount estimation system S will be referred to separately as follows:

First, a user whose electroencephalogram 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 become 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 in medical treatment, etc.

[Configuration of the electroencephalogram 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.

The CPU 11 executes various processes (for example, measurement processes described below) 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.

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 by a semiconductor memory such as a DRAM (Dynamic Random Access Memory), and stores various data.

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

The measurement unit 18 measures fluctuations in the electrical potential of the user's (here, a test subject or a predicted subject) head 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 by the measurement unit 18 is brought into contact with a point where the electrical 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 as defined by the International 10-20 System) to serve as the probing electrode. The measurement unit 18 then measures fluctuations in the electrical potential difference between the reference electrode and the probing electrode over time at a predetermined sampling frequency (for example, 512 Hz) as brain waves in the predetermined region of the user's brain.

As described above, the EEG measurement device 10 has a headset-type configuration, and when the user wears the EEG measurement device 10, the pair of electrodes provided in the measurement unit 18 are positioned at positions suitable for measurement (for example, a position in contact with the earlobe and a position in contact with the area corresponding to Fp1). In this regard, if a typical EEG device is used in which an electrode net is placed over the user's head, the user will feel a sense of pressure, and noise caused by tension will be generated in the EEG. In contrast, the EEG measurement device 10 has a headset-type configuration, so it is possible to perform measurements without causing such a sense of pressure and while reducing tension in the user. Therefore, the EEG measurement device 10 can reduce the generation of noise caused by tension in the user and perform accurate measurements.

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 EEG measurement device 10 and the secretion amount estimation device 20 measure the user's EEG and perform predetermined pre-processing on the measured EEG.

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.

The measurement control unit 111 controls the measurement of brain waves by the measurement unit 18 based on user instruction operations received by the input unit 16 (or user instruction operations 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 during measurement, etc. Then, the measurement control unit 111 outputs the user's brain waves obtained through measurement by the measurement unit 18 to the pre-processing unit 112.

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, every day when the user wakes up.
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 time for one measurement is also arbitrary, but may be, for example, about 15 minutes.

The pre-processing unit 112 generates EEG data by performing pre-processing such as removing noise components on the EEG input from the measurement control unit 111.

For example, the preprocessing unit 112 takes into account individual differences in the amplitude of EEG data and normalizes the amplitude of the EEG to absorb these individual differences. In this case, for example, the preprocessing unit 112 normalizes the measured EEG amplitude so that the average amplitude value is 0 and the standard deviation is 1.

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 EEG. In this case, passing 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, makes it possible to estimate the secretion amount of the hormone to be estimated with greater accuracy. In this way, the pre-processing unit 112 can remove noise (artifacts) corresponding to the user's body movements during EEG measurement.

Furthermore, for example, the preprocessing unit 112 removes noise (artifacts) corresponding to myoelectric signals using existing methods such as ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA).

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. The preprocessing unit 112 then outputs the generated electroencephalogram data to the electroencephalogram data transmitting unit 113.
Note that some 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 .

The brain wave data transmission unit 113 transmits the brain wave data input from the pre-processing unit 112 to the secretion amount estimation device 20. In this case, the brain wave data generated in parallel with the measurement may be temporarily stored (i.e., buffered) in the memory unit 15, and the brain wave data stored in the memory unit 15 may be transmitted all at once at the end of the measurement, or the brain wave data may be transmitted in real time each time it is generated by the pre-processing unit 112.

[Configuration of the secretion amount 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.

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.

The communication unit 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 by a semiconductor memory such as a DRAM (Dynamic Random Access Memory), and stores various data.

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.

Removable media (not shown), such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is appropriately attached to drive 28. Programs read from the removable media by drive 28 are installed in storage unit 25 as needed.

In the secretion amount estimation device 20, these components work together to perform the "measurement process," "reference data generation process," and "secretion amount estimation process."

As described above, the measurement process is a series of processes in which the EEG measurement device 10 and the secretion amount estimation device 20 measure the user's EEG and perform predetermined pre-processing on the measured EEG.
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.

When these reference data generation processes and secretion amount estimation processes are executed, as shown in Figure 3, the CPU 21 functions with an EEG 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.

The brain wave data acquisition unit 211 acquires the brain wave data by receiving it transmitted from the EEG measurement device 10. The brain wave data acquisition unit 211 then outputs the acquired brain wave data to the frequency component detection unit 212.

The EEG measurement device 10 may be provided with a drive similar to the drive 28, so that the EEG measurement device 10 stores the EEG data on removable media. The EEG data acquisition unit 211 may then acquire the EEG data from this removable media via the drive 28, rather than acquiring the data via communication.

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 (e.g., 30 seconds).
Furthermore, the frequency component detection unit 212 detects frequency components of each of a plurality of frequencies from each of the divided electroencephalogram data. For example, the frequency component detection unit 212 performs a Fourier transform (e.g., a fast Fourier transform (FFT) using a 512-point Hamming window and 50% overlap processing) on each of the divided electroencephalogram data and averages the results to generate a power spectrum indicating the power values of each of a plurality of frequencies (e.g., 1 to 40 Hz) as 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.

When the frequency component detection unit 212 detects a frequency component from the EEG data of a subject, it stores the frequency component of this subject in the subject data storage unit 251. On the other hand, when the frequency component detection unit 212 detects a frequency component from the EEG data of an estimated subject, it outputs the frequency component of this estimated subject to the estimation unit 215.

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 using an existing method involving sample testing. That is, in this embodiment, sample testing 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 sample testing on the subject. For clarity of explanation, the secretion amount of the hormone to be estimated measured by sample testing on the subject and acquired by the measured secretion amount acquisition unit 213 will be referred to below as the "measured secretion amount", rather than the secretion amount of the hormone to be estimated by this embodiment.

The measured secretion amount acquisition unit 213 acquires the estimated secretion amount for each acquired subject, 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). The measured secretion amount acquisition unit 213 then stores the acquired measured secretion amount in the subject data storage unit 251.

The subject data storage unit 251 stores subject data, which is data linking frequency components with measured secretion amounts for each measurement of each subject.
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. Furthermore, for each measurement of each subject, a record (column) is provided for the subject ID, measurement date, frequency component, and measured secretion amount. Corresponding information is stored in a field (cell) where a column and a record intersect.

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 figure, 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.

Returning to Figure 3, the reference data generation 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 detection 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, the predetermined state of the secretion amount of the hormone to be estimated is assumed to be 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.

As described above, the secretion amount of the estimated target hormone in the 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 each subject had the greatest measured secretion amount. The reference data generation unit 214 also extracts the frequency component of the measurement day on which this greatest measured secretion amount occurred from the subject data. That is, the frequency component of the measurement day on which the greatest measured secretion amount occurred for the first subject, ..., the frequency component of the measurement day on which the greatest measured secretion amount occurred for the Nth subject (N is an integer value equal to or greater than 1), and so on, extracting frequency components for N subjects.

Then, the reference data generation unit 214 calculates the average power value of the frequency components of N subjects for each frequency. This allows the average power value to be calculated for each frequency when the secretion amount of the hormone to be estimated is relatively high.

The reference data generation unit 214 stores the average value of the power values for each frequency calculated in this manner as reference data in the reference data storage unit 252. In other words, the reference data storage unit 252 functions as a storage unit that stores reference data. This allows the reference data generation unit 214 to generate reference data.

The above-described method of generating reference data is one example, and the reference data generating unit 214 may alternatively generate reference data by calculating other values, such as the median or mode, instead of the average value. Alternatively, the reference data generating unit 214 may generate 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 an absolute threshold value for the secretion amount of the hormone to be estimated, and generate 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).

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 using any method. For example, it can be calculated by comparing power values (i.e., frequency components) and calculating the difference value, or by using existing methods such as pattern matching.

In this case, the power values for all frequencies included in each data set may be compared to calculate the similarity, but the similarity may also be calculated 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 for 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 for all frequencies may be compared, but the power value of this predetermined frequency may be weighted to give it priority as a judgment criterion, and the similarity may be calculated by comparing them.

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 estimation subject.
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 the value obtained by multiplying the similarity by the reference value as the secretion amount of the hormone to be estimated in the estimation subject. For example, if the similarity is 100%, the value 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 value 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.

The estimation unit 215 then presents this estimation result (i.e., the estimated value) to the user (here, a third party such as the person being estimated, the 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 paper media 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.

As described above, in the secretion amount estimation system S, the EEG measuring device 10 measures EEG 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 EEG.
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 each time and perform a sample test.

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 the first hormone to be estimated (e.g., estradiol) and the second hormone to be estimated (e.g., progesterone). Therefore, the reference data generation 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.

[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.

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 measurement of the brain waves by the measurement unit 18. Then, the measurement control unit 111 outputs the user's brain waves obtained by measurement by the measurement unit 18 to the preprocessing unit 112.

In step S12, the preprocessing unit 112 generates EEG data by performing preprocessing on the EEG input from the measurement control unit 111. The preprocessing unit 112 then temporarily stores (i.e., buffers) the generated EEG data in the memory unit 15.

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

In step S14, the EEG data transmission unit 113 transmits all of the EEG data that the preprocessing unit 112 has temporarily stored in the memory unit 15 to the secretion amount estimation device 20. This completes the process on the EEG measurement device 10 side.

Note that this flowchart assumes 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, such as at the end of measurement. However, as mentioned above, it is also possible to transmit EEG data in real time as it is generated.

Next, the flow of processing on the side of the secretion amount estimation device 20 will be described.
In step S21, the electroencephalogram data acquisition section 211 receives and acquires electroencephalogram data transmitted from the electroencephalogram measurement device 10.
In step S22, the frequency component detection section 212 detects frequency components of each of a plurality of frequencies from the electroencephalogram data.

In step S23, the frequency component detection unit 212 determines whether or not a frequency component was detected from the electroencephalogram data of the subject in step S22. If a frequency component was detected from the electroencephalogram data of the subject, step S23 returns Yes, and the process proceeds to step S24. On the other hand, if a frequency component was not detected from the electroencephalogram data of the subject (i.e., if a frequency component was detected from the electroencephalogram data of the estimation target), step S23 returns No, and the process ends on the secretion amount estimation device 20 side.

In step S24, the measured secretion amount acquisition unit 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.

Through the measurement process described above, the EEG measurement device 10 can measure the EEG from the user and transmit EEG data generated based on the measured EEG to the secretion amount estimation device 20. The secretion amount estimation device 20 can also detect frequency components and store subject data about the subject.

[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 process, it is assumed that the subject data generated by the measurement process is stored in the subject data storage unit 251.

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.

Through the reference data generation process described above, the secretion amount estimation device 20 can generate reference data that serves as a basis for estimating the secretion amount of the hormone to be estimated.

[Secretion amount estimation process]
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 is stored in the reference data storage unit 252.

In step S41, the estimation unit 215 acquires electroencephalogram data of the estimation subject detected by the measurement process.
In step S<b>42 , the estimation unit 215 reads the reference data stored in the reference data storage unit 252 .

In step S43, 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 electroencephalogram data of the estimated subject 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.

Through the secretion amount estimation process described above, the secretion amount estimation system S can solve the problem of being able to grasp hormone secretion amounts in a simpler way, without the need to collect samples and test them each time.

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

As a premise, 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 greatly throughout the four phases of the menstrual cycle. During the menstrual phase, the secretion of estradiol and progesterone is lowest among the four phases. During the subsequent follicular phase, the secretion of estradiol gradually increases while the secretion of progesterone decreases. During the ovulation phase, the secretion of estradiol temporarily decreases, while the secretion of progesterone gradually increases. The secretion of estradiol, which has the function of lowering body temperature, is at its highest in the first half of the ovulation phase. During the luteal phase, the secretion of estradiol and progesterone both increases. In particular, the secretion of progesterone is highest during the luteal phase. At the end of the luteal phase, the secretion of estradiol and progesterone both decreases, and the menstrual phase begins again. Thus, the secretion of hormones fluctuates with each phase during the menstrual cycle.

Figure 8(A) is a graph showing fluctuations in estradiol secretion, with the horizontal axis representing the number of days since the onset 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. Figure 8(B) is a graph similar to Figure 8(A), but showing fluctuations in progesterone secretion instead of estradiol secretion.
As shown in FIGS. 8(A) and 8(B), it can be seen that the amounts of estradiol and progesterone actually secreted by the subject fluctuate with the menstrual cycle.

In parallel with the measurement of 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 the estimated target hormones. This reference data is shown in Figure 9.

Figure 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 for multiple subjects for the frequency component (i.e., power value) on the measurement day when the measured secretion amount of the hormone to be estimated is the highest. Therefore, for example, in the examples of Figures 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.

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 highest and the measured secretion amount of estradiol is normal during the menstrual cycle.

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.

[Modification]
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.

<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.

Furthermore, in the above-described embodiment, the EEG measurement device 10 and the secretion amount estimation device 20 were each realized as a single device. However, this is not limiting, and each of the EEG measurement device 10 and the secretion amount estimation device 20 may be realized as multiple devices by using technology such as cloud computing.

Furthermore, in the above-described embodiment, it was assumed that the EEG measuring device 10 would be used by multiple subjects. However, this is not limiting, 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.

In addition, in the above-described embodiment, the EEG measurement device 10 measures brain waves at Fp1 using a pair of electrodes. However, this is not limiting, and the EEG measurement device 10 may also measure brain waves at locations other than Fp1. Alternatively, the EEG measurement device 10 may be equipped with a larger number of electrodes and measure brain waves at multiple locations. The above-described series of processes may then be performed based on each of the brain waves at multiple locations.

<Second Modification>
In the above-described embodiment, the estimation unit 215 presented an estimated value of the 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 or 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 from 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 identified as depression and the subject visits a psychiatrist.

It is also known that when a person feels angry, for example, the secretion of noradrenaline and adrenaline increases. Therefore, the estimation unit 215 estimates the secretion amounts of noradrenaline and adrenaline as the hormones to be estimated. Then, based on the estimated secretion amounts of these noradrenaline and adrenaline, a determination result as to the degree of anger felt may also be presented.

<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.

As a technology for estimating brain waves without using an electroencephalograph worn on the head, for example, the technology disclosed in Japanese Patent Application Laid-Open No. 2015-109964, 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.

[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 component detected from the subject to be estimated by the frequency component detection unit 212 and the frequency component that serves 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 each time and perform a sample test.

The estimation unit 215 uses, as a reference frequency component, the frequency component detected by the frequency component detection unit 212 from a subject whose 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.

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 by the frequency component detection section 212 from the estimation subject and the reference frequency component, the greater the amount of hormone secreted by 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.

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.

[Realizing 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.

For example, if 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 may be composed of various types of stand-alone processing devices such as single processors, multiprocessors, and multi-core processors, as well as devices that combine these types of processing devices with processing circuits such as ASICs (Application Specific Integrated Circuits) or FPGAs (Field-Programmable Gate Arrays).

For example, if the function of executing the series of processes described above is realized by software, the program that constitutes the software is installed on a computer via a network or a recording medium. In this case, the computer may be a computer with dedicated hardware built in, or a general-purpose computer (e.g., a general-purpose electronic device such as a general-purpose personal computer) that can execute predetermined functions by installing a program. The steps of writing the program may include only processes that are executed chronologically in accordance with the order, but may also include processes that are executed in parallel or individually. The steps of writing the program may be executed in any order without departing from the spirit of the present invention.

A recording medium on which such a program is recorded may be provided to the user by being distributed separately from the computer itself, or may be provided to the user pre-installed in the computer itself. In this case, the storage medium distributed separately from the computer itself may be composed of a magnetic disk (including a floppy disk), an optical disk, or a magneto-optical disk. Optical disks may be composed, for example, of CD-ROMs (Compact Disc-Read Only Memory), DVDs (Digital Versatile Discs), or Blu-ray (registered trademark) Discs. Magneto-optical disks may be composed, for example, of MDs (Mini Discs). These storage media may be installed in drive 28 in Figure 3 and incorporated into the computer itself. Furthermore, the recording medium provided to the user in a state where it is pre-installed in the computer main unit 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.

10 EEG measurement device, 20 EEG 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 EEG secretion amount estimation system

Claims (4)

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: 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 . 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. the detection function detects, as frequency components, power values of each of a plurality of frequencies when the electroencephalogram is represented as a power spectrum;
4. The secretion amount estimation program according to claim 3.