Determination device, learning device, determination method, method for producing learning information, and program

Abstract

To solve the problem that it is conventionally impossible to determine depression with high accuracy by using brain wave information.SOLUTION: A determination device 1 comprising: a reception unit 12 that receives brain wave information; a parameter acquisition unit 131 that acquires one or more parameters from the brain wave information; a determination unit 132 that refers to a storage unit 11 in which pieces of learning data based on two or more pieces of training data including one or more positive examples having one or more parameters indicating a feature of brain wave information of a patient who is depressed and one or more negative examples having one or more parameters indicating a feature of brain wave information of a patient who is not depressed are stored, and acquires a determination result that was received by the reception unit 12 and that indicates whether the patient of brain wave information is depressed, by using the one or more parameters acquired by the parameter acquisition unit 131 and the learning information; and an output unit 14 that outputs the determination result.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a determination device or the like that determines whether or not a person has a mental illness such as depression by using one or more parameters acquired from electroencephalogram information. [Background technology]

[0002] Conventionally, there has been a method for assessing a user's mental state by using questionnaire data and measurement data obtained by measuring changes in the high frequency (HF) and low frequency (LF) components of the user's heart rate variability (HRV) index (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-16369 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in conventional techniques, it has not been possible to accurately diagnose depression using electroencephalogram information. [Means for solving the problem]

[0005] The judgment device of the first invention is a judgment device comprising: a reception unit that receives EEG information; a parameter acquisition unit that acquires one or more parameters from the EEG information; a judgment unit that refers to a storage unit in which learning information based on two or more teacher data having one or more positive examples having one or more parameters that indicate characteristics of the EEG information of patients with depression and one or more negative examples having one or more parameters that indicate characteristics of the EEG information of patients who are not depressed, and acquires a judgment result indicating whether or not the patient of the EEG information received by the reception unit is depressed using the one or more parameters and the learning information acquired by the parameter acquisition unit; and an output unit that outputs the judgment result.

[0006] With this configuration, depression can be diagnosed with high accuracy using electroencephalogram information.

[0007] In addition, in the judgment device of the second invention, compared to the first invention, the parameter acquisition unit acquires one or more parameters including a non-REM representative power value from the EEG information, and the judgment unit uses the one or more parameters and learning information to acquire a judgment result indicating whether or not the patient of the EEG information accepted by the reception unit is depressed, and the parameter acquisition unit acquires a non-REM representative power value which is a value acquired using the overall power value of the frequency in epochs judged to be non-REM sleep in the EEG information and the number of epochs, the non-REM representative power value being a larger value the greater the power value of the frequency and a smaller value the greater the number of epochs.

[0008] With this configuration, depression can be diagnosed with greater accuracy using useful parameters that can be obtained from electroencephalogram information.

[0009] Furthermore, compared to the second invention, the determination device of the third invention is a determination device in which the parameter acquisition unit adds up power values ​​of frequencies between approximately 8 Hz and approximately 14 Hz calculated by fast Fourier transform in epochs determined to be non-REM sleep to obtain an overall power value, and divides the overall power value by the number of non-REM epochs to obtain a non-REM representative power value, which is the average power value per epoch.

[0010] With this configuration, depression can be diagnosed with greater accuracy using useful parameters that can be obtained from electroencephalogram information.

[0011] In addition, the judgment device of the fourth invention is a judgment device according to any one of the first to third inventions, in which the learning information is a learning model obtained by performing a machine learning learning process on two or more teacher data, and the judgment unit performs a machine learning prediction process using one or more parameters and the learning model, and obtains a judgment result.

[0012] With this configuration, depression can be diagnosed with high accuracy using electroencephalogram information.

[0013] Furthermore, the judgment device of the fifth invention is a judgment device in which, compared to any one of the first to third inventions, a positive example is a positive example vector having as its elements two or more parameters which indicate characteristics of the EEG information of a patient who is depressed, and a negative example is a negative example vector having as its elements two or more parameters which indicate characteristics of the EEG information of a patient who is not depressed, and the judgment unit acquires a positive example centroid vector which is a vector of the centroids of one or more positive example vectors, acquires a negative example centroid vector which is a vector of the centroids of one or more negative example vectors, acquires a positive example distance which is the distance between the vector having as elements each of the two or more parameters acquired by the parameter acquisition unit and the positive example centroid vector, acquires a negative example distance which is the distance between the vector having as elements each of the two or more parameters acquired by the parameter acquisition unit and the negative example centroid vector, and acquires a judgment result using the positive example distance and the negative example distance.

[0014] With this configuration, depression can be diagnosed with high accuracy using electroencephalogram information.

[0015] Furthermore, the judgment device of the sixth invention is a judgment device according to any one of the first to third inventions, wherein the positive example is a positive example vector having one or more parameters as elements which are indicative of characteristics of EEG information of patients who are depressed, the negative example is a negative example vector having one or more parameters as elements which are indicative of characteristics of EEG information of patients who are not depressed, the learning information is a correspondence table having correspondence information having one or more positive example vectors and correspondence information having one or more negative example vectors, the judgment unit constructs a vector having one or more parameters acquired by the parameter acquisition unit as elements, acquires a similarity between the vector and one or more positive example vectors and a similarity between the vector and one or more negative example vectors, and acquires a judgment result corresponding to the positive example vector or the negative example vector whose similarity satisfies the adoption condition.

[0016] With this configuration, depression can be diagnosed with high accuracy using electroencephalogram information.

[0017] Furthermore, the judgment device of the seventh invention is a judgment device in which, compared to any one of the first to third inventions, the positive example is a positive example vector whose elements are one or more parameters that indicate characteristics of the EEG information of patients who are depressed, the negative example is a negative example vector whose elements are one or more parameters that indicate characteristics of the EEG information of patients who are not depressed, the learning information is an arithmetic formula that outputs a positive example value that is a value corresponding to a positive example for the positive example vector, and outputs a negative example value that is a value corresponding to a negative example for the negative example vector, and the judgment unit substitutes the one or more parameters acquired by the parameter acquisition unit into the arithmetic formula to acquire a score, and acquires a judgment result corresponding to the score.

[0018] With this configuration, depression can be diagnosed with high accuracy using electroencephalogram information.

[0019] In addition, the learning device of the eighth invention is a learning device comprising a parameter acquisition unit that acquires one or more parameters from two or more pieces of electroencephalogram information corresponding to a judgment result of whether or not the patient is depressed, a learning unit that acquires teacher data for each of the two or more pieces of electroencephalogram information, the teacher data having one or more parameters acquired by the parameter acquisition unit and a judgment result corresponding to each piece of electroencephalogram information, and acquires learning information using the two or more teacher data, and a storage unit that stores the learning information acquired by the learning unit.

[0020] With this configuration, it is possible to create learning information for accurately diagnosing depression using electroencephalogram information. Effect of the Invention

[0021] According to the diagnosis device of the present invention, depression can be diagnosed with high accuracy using electroencephalogram information. [Brief description of the drawings]

[0022] [Figure 1] Conceptual diagram of determination system A in embodiment 1. [Diagram 2] Block diagram of the judgment system A [Diagram 3] A flowchart illustrating an example of the operation of the determination device 1. [Figure 4] A flowchart illustrating an example of the parameter acquisition process. [Diagram 5] The parameter management table is shown in FIG. [Figure 6] The parameter management table is shown in FIG. [Figure 7] The parameter management table is shown in FIG. [Figure 8] The parameter management table is shown in FIG. [Figure 9] FIG. 11 is a block diagram of a learning device 2 according to the second embodiment. [Figure 10] A flowchart for explaining an example of the operation of the learning device 2. [Figure 11] Overview of the computer system [Figure 12] Block diagram of the computer system DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, an embodiment of a determination device and the like will be described with reference to the drawings. Note that, in the embodiments, components with the same reference numerals perform similar operations, and therefore repeated description may be omitted.

[0024] (Embodiment 1) In this embodiment, a determination device for determining mental illness using one or more parameters acquired from electroencephalogram information will be described. Note that the determination of mental illness here is usually a determination of whether or not the patient has depression, but it may also be a determination of whether or not the patient has manic depression, or a determination of the presence or absence of a mental illness in a broader sense. The determination of mental illness may also be a determination of the severity of the mental illness. It is preferable to interpret mental illness broadly to include, for example, not only depression, but also bipolar disorder, schizophrenia, adjustment disorder, and the like. The determination of whether or not the patient has depression may be considered to be equivalent to the determination of whether or not the patient has a mental illness.

[0025] In this embodiment, the determination method will be described using, for example, machine learning, using center of gravity information, using a correspondence table, or using an arithmetic expression.

[0026] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is not important. Information X and information Y may be linked, may exist in the same buffer, information X may be included in information Y, or information Y may be included in information X, etc.

[0027] 1 is a conceptual diagram of a determination system A in this embodiment. The determination system A includes, for example, a determination device 1, a learning device 2, and one or more terminal devices 3.

[0028] The determination device 1 is a device that determines whether or not a person has depression using electroencephalogram information. The determination device 1 is usually a so-called server. The determination device 1 is, for example, a cloud server or an ASP server, but the type is not important. The determination device 1 may operate as a standalone device. When the determination device 1 operates as a standalone device, the terminal device 3 is not necessary.

[0029] The learning device 2 is a device that acquires learning information used for judgment by the judgment device 1. Details of the learning device 2 will be described in the second embodiment.

[0030] The terminal device 3 is a terminal used by a user. The terminal device 3 is, for example, a device that provides electroencephalogram information to the determination device 1, receives a determination result from the determination device 1, and outputs the result. The terminal device 3 is, for example, a so-called personal computer, a smartphone, a tablet terminal, or an electroencephalograph, but the type is not limited.

[0031] It is preferable that the determination device 1 and each of the one or more terminal devices 3, and the learning device 2 and each of the one or more terminal devices 3, are capable of communicating with each other via a network such as the Internet.

[0032] 2 is a block diagram of the determination system A in this embodiment. The determination device 1 includes a storage unit 11, a reception unit 12, a processing unit 13, and an output unit 14. The processing unit 13 includes a parameter acquisition unit 131 and a determination unit 132.

[0033] Various types of information are stored in the storage unit 11. The various types of information are, for example, learning information and brain wave information, which will be described later.

[0034] The learning information is information for determining whether a patient is depressed using electroencephalogram information. The learning information is, for example, a learning model, but may also be center of gravity information, a correspondence table, or an arithmetic formula. It is preferable that the learning information is information acquired by the learning device 2 described in the second embodiment.

[0035] A learning model is a model obtained by performing a machine learning learning process on two or more pieces of training data. The training data is, for example, a positive example obtained from electroencephalogram information of a patient with depression, or a negative example obtained from electroencephalogram information of a patient without depression. The training data is, for example, a positive example including one or more parameters obtained from electroencephalogram information of a patient with depression, or a negative example including one or more parameters obtained from electroencephalogram information of a patient without depression.

[0036] The center of gravity information is a positive example representative vector and a negative example representative vector. The positive example representative vector is a vector whose elements are representative values ​​of one or more parameters obtained from electroencephalogram information of one or more patients who are depressed. The negative example representative vector is a vector whose elements are representative values ​​of one or more parameters obtained from electroencephalogram information of one or more patients who are not depressed.

[0037] The positive example representative vector is, for example, a positive example centroid vector. The positive example centroid vector is a vector of the centroid of one or more positive example vectors. The positive example representative vector is, for example, a vector whose elements are the representative values ​​of each element of two or more positive example vectors. The negative example representative vector is, for example, a negative example centroid vector. The negative example centroid vector is a vector of the centroid of one or more negative example vectors. The negative example representative vector is, for example, a vector whose elements are the representative values ​​of each element of two or more negative example vectors. The representative value is, for example, an average value or a median value.

[0038] A correspondence table is a table having two or more pieces of correspondence information. The correspondence information is information that associates a positive example vector having one or more parameters obtained from the electroencephalogram information of a patient with depression with depression. The correspondence information is information that associates a negative example vector having one or more parameters obtained from the electroencephalogram information of a patient who is not depressed with depression. The correspondence information has, for example, a positive example vector or a negative example vector, and a flag indicating whether or not the patient is depressed. The flag is, for example, "1" indicating depression, or "0" indicating not depression.

[0039] The arithmetic expression is an arithmetic expression that outputs a positive example value (e.g., “1”) that is a value corresponding to a positive example for a positive example vector, and outputs a negative example value (e.g., “−1”) that is a value corresponding to a negative example for a negative example vector.

[0040] Electroencephalogram information is information on brain waves. Electroencephalogram information is information that records the potential difference between two points on the scalp. Electroencephalogram information is a set of amplitude numbers in a time series. Electroencephalogram information is usually information acquired by an electroencephalograph.

[0041] The reception unit 12 receives one or more pieces of electroencephalogram information. The electroencephalogram information is received, for example, in a pair with a user identifier. The user identifier is information for identifying a user who is to be judged as having depression or not. The user identifier is, for example, a user ID, a name, a telephone number, or an email address.

[0042] Here, "reception" usually means reception of information transmitted via a wired or wireless communication line, but may also be a concept including reception of information input from an input device such as a keyboard, mouse, or touch panel, reception of information read from a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory, etc. Reception is, for example, reception from an electroencephalograph.

[0043] When the determination device 1 is a stand-alone device, the input means for the electroencephalogram information may be any means such as an electroencephalograph, a mouse, a menu screen, etc. The reception unit 12 may be realized by a device driver for an input means such as an electroencephalograph or a mouse, or control software for a menu screen, etc.

[0044] The processing unit 13 performs various types of processing. The various types of processing are, for example, processing performed by a parameter acquisition unit 131 or a determination unit 132.

[0045] The parameter acquiring section 131 acquires one or more parameters from one or more pieces of electroencephalogram information accepted by the accepting section 12 .

[0046] The parameter acquisition unit 131 acquires, for example, one or more epoch attribute values ​​for each of one or more epochs in the electroencephalogram information, and acquires one or more parameters using the one or more epoch attribute values. The epoch attribute value is information indicating characteristics of the electroencephalogram information of one epoch. Note that an epoch is the length of a unit interval, and is, for example, 30 seconds, but may be 20 seconds, 40 seconds, etc. It is preferable that the one or more epoch attribute values ​​are, for example, one or more pieces of information among frequency, average amplitude, duration of a specific range of frequency, and power value of a specific range of frequency.

[0047] Parameter acquisition section 131 acquires parameters using, for example, one or more pieces of raw information. Note that the raw information is, for example, the above-mentioned epoch attribute value, the number of non-REM epochs, and the number of REM epochs.

[0048] It is preferable that the parameter acquiring section 131 acquires, from each piece of electroencephalogram information accepted by the accepting section 12, one or more parameters including a power value ratio parameter (also referred to as a seventh parameter) described later.

[0049] The parameter acquiring unit 131 preferably acquires, for example, a first parameter to a 36th parameter, which will be described later, from one or more pieces of electroencephalogram information accepted by the accepting unit 12. The parameter acquiring unit 131 usually acquires parameters after detecting one or more epochs determined to be non-REM sleep (hereinafter referred to as "non-REM epochs") in the electroencephalogram information. Also, the parameter acquiring unit 131 acquires parameters after detecting, for example, one or more epochs determined to be REM sleep (hereinafter referred to as "REM epochs") in the electroencephalogram information.

[0050] For example, the parameter acquisition unit 131 determines whether each section of the electroencephalogram information to be determined is a non-REM sleep section or a REM sleep section. Such processing is a known technique. The section is, for example, an epoch. Next, the parameter acquisition unit 131 acquires, for example, the following first to 36th parameters using, for example, the determination result of the non-REM sleep section and the REM sleep section.

[0051] It is extremely preferable that the parameter acquiring unit 131 acquires the seventh parameter (7) from among the following 36 parameters.

[0052] Moreover, it is preferable that the parameter acquisition unit 131 acquires one or more of the following 36 parameters: (2), (8), (9), and (15).

[0053] In addition, it is preferable that the parameter acquisition unit 131 acquires one or more parameters from among the following 36 parameters: (1)(3)(4)(5)(6)(10)(11)(12)(13)(14)(16)(17)(18)(19)(20)(21).

[0054] Furthermore, the parameter acquiring unit 131 may acquire one or more parameters from among the following 36 parameters (22) to (36). (1) First parameter

[0055] The parameter acquiring unit 131 acquires the number of first specific spindles in the non-REM epochs. The parameter acquiring unit 131 also acquires the number of non-REM epochs.

[0056] The first specific spindle is a spindle having a first specific frequency, a first specific duration, and a first specific average amplitude in a non-REM epoch. Here, the first specific frequency is, for example, a frequency of about 8 Hz to about 14 Hz, but it goes without saying that some error may be allowed. The first specific duration is, for example, about 0.25 seconds to about 2.5 seconds, but it goes without saying that some error may be allowed. The first specific average amplitude is, for example, about 15 μV or more, but it goes without saying that some error may be allowed.

[0057] Next, parameter acquisition unit 131 acquires a first parameter that has a larger value as the number of first specific spindles increases and a larger value as the number of non-REM epochs decreases. Parameter acquisition unit 131 acquires the first parameter using a first arithmetic expression that is, for example, an increasing function with the number of first specific spindles as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the first arithmetic expression is, for example, "number of first specific spindles / number of non-REM epochs". In this case, the first parameter is the average number of the first specific spindles per non-REM epoch. (2) Second parameter

[0058] The parameter acquiring section 131 acquires the time lengths of one or more first specific spindles, and acquires a total time length by adding up the one or more time lengths. The parameter acquiring section 131 acquires the number of non-REM epochs.

[0059] Next, the parameter acquiring unit 131 acquires a second parameter that increases as the total time length increases and increases as the number of non-REM epochs decreases. The parameter acquiring unit 131 acquires the second parameter using, for example, a second arithmetic expression that is an increasing function with the total time length as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the second arithmetic expression is, for example, "total time length / number of non-REM epochs." In this case, the second parameter is the average time length of the first specific spindle per one non-REM epoch. (3) Third parameter

[0060] The parameter acquiring unit 131 detects one or more second specific spindles. Then, the parameter acquiring unit 131 acquires the number of the second specific spindles. The parameter acquiring unit 131 acquires the number of non-REM epochs.

[0061] The second specific spindle is a spindle having a first specific frequency, a first specific duration, and a second specific average amplitude in the non-REM epoch. The second specific average amplitude is, for example, about 20 μV or more, although it goes without saying that some margin of error may be allowed.

[0062] Next, the parameter acquiring unit 131 acquires a third parameter, which has a larger value as the number of the second specific spindles increases and a larger value as the number of the non-REM epochs decreases. The parameter acquiring unit 131 acquires the third parameter using a third arithmetic expression, which is, for example, an increasing function with the number of the second specific spindles as a parameter and a decreasing function with the number of the non-REM epochs as a parameter. Note that the third arithmetic expression is, for example, "the number of the second specific spindles / the number of the non-REM epochs". In this case, the third parameter is the average number of the second specific spindles per one non-REM epoch. (4) Fourth parameter

[0063] The parameter acquisition unit 131 detects one or more second specific spindles. Then, the parameter acquisition unit 131 acquires the duration of each of the one or more second specific spindles, and acquires a total duration by adding up the one or more durations. The parameter acquisition unit 131 acquires the number of non-REM epochs.

[0064] Next, the parameter acquiring unit 131 acquires a fourth parameter, which has a larger value as the total time length increases and a larger value as the number of non-REM epochs decreases. The parameter acquiring unit 131 acquires the fourth parameter using, for example, a fourth arithmetic expression that is an increasing function with the total time length as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the fourth arithmetic expression is, for example, "total time length / number of non-REM epochs". In this case, the fourth parameter is the average time length of the second specific spindle per one non-REM epoch. (5) Fifth parameter

[0065] The parameter acquiring section 131 detects one or more third specific spindles. Then, the parameter acquiring section 131 acquires the number of the third specific spindles. The parameter acquiring section 131 acquires the number of non-REM epochs.

[0066] The third specific spindle is a spindle having a first specific frequency, a first specific duration, and a third specific average amplitude in the non-REM epoch. The third specific average amplitude is, for example, about 30 μV or more, although it goes without saying that some margin of error may be allowed.

[0067] Next, the parameter acquiring unit 131 acquires a fifth parameter, which has a larger value as the number of the third specific spindles increases and a larger value as the number of the non-REM epochs decreases. The parameter acquiring unit 131 acquires the fifth parameter using a fifth arithmetic expression, which is, for example, an increasing function with the number of the third specific spindles as a parameter and a decreasing function with the number of the non-REM epochs as a parameter. Note that the fifth arithmetic expression is, for example, "the number of the third specific spindles / the number of the non-REM epochs". In this case, the fifth parameter is the average number of the third specific spindles per one non-REM epoch. (6) Sixth parameter

[0068] The parameter acquisition unit 131 detects one or more third specific spindles. Then, the parameter acquisition unit 131 acquires the duration of each of the one or more third specific spindles, and acquires a total duration by adding up the one or more durations. The parameter acquisition unit 131 acquires the number of non-REM epochs.

[0069] Next, the parameter acquiring unit 131 acquires a sixth parameter, which has a larger value as the total time length increases and a larger value as the number of non-REM epochs decreases. The parameter acquiring unit 131 acquires the sixth parameter using, for example, a sixth arithmetic expression that is an increasing function with the total time length as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the sixth arithmetic expression is, for example, "total time length / number of non-REM epochs". In this case, the sixth parameter is the average time length of the third specific spindle per one non-REM epoch. (7) Seventh parameter

[0070] Parameter acquisition section 131 acquires a power value of the first specific frequency in each of the one or more non-REM epochs, and acquires a total non-REM power value by adding up the one or more power values. Parameter acquisition section 131 also acquires the number of the non-REM epochs.

[0071] The parameter acquisition unit 131 calculates a power value in each of one or more non-REM epochs by fast Fourier transform (FFT).

[0072] Next, parameter acquisition section 131 acquires a seventh parameter, which increases as the non-REM total power value increases and increases as the number of non-REM epochs decreases. The seventh parameter can be said to be a non-REM representative power value. Parameter acquisition section 131 acquires the seventh parameter using a seventh arithmetic expression, which is, for example, an increasing function with the non-REM total power value as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the seventh arithmetic expression is, for example, "non-REM total power value / number of non-REM epochs". In this case, the seventh parameter is the average power value per non-REM epoch. (8) Eighth parameter

[0073] Parameter acquisition section 131 acquires the total non-REM power value and the number of non-REM epochs.

[0074] Parameter acquisition section 131 acquires a power value of the first specific frequency in one or more REM epochs, and acquires a total REM power value by adding up the one or more power values. Parameter acquisition section 131 also acquires the number of the REM epochs.

[0075] Next, parameter acquisition unit 131 acquires an eighth parameter which becomes a larger value as the non-REM total power value increases, becomes a larger value as the number of non-REM epochs decreases, becomes a smaller value as the REM total power value increases, and becomes a larger value as the number of REM epochs increases.

[0076] Parameter acquisition section 131 acquires the eighth parameter using an eighth arithmetic expression which is an increasing function with parameters being the total NREM power value and the number of REM epochs, and a decreasing function with parameters being the total REM power value and the number of NREM epochs. Note that the eighth arithmetic expression is, for example, the "ratio of (total NREM power value / number of NREM epochs) to (total REM power value / number of REM epochs)." (9) 9th parameter

[0077] Parameter acquisition section 131 detects one or more ninth waveforms in the non-REM epochs, and acquires the number of the ninth waveforms. Parameter acquisition section 131 acquires the number of non-REM epochs.

[0078] The ninth waveform is a waveform of a ninth specific frequency and a ninth specific amplitude. Here, the ninth specific frequency is, for example, a frequency of about 8 Hz to about 12 Hz, but it goes without saying that some error may be allowed. The ninth specific amplitude is, for example, about 20 μV to about 200 μV, but it goes without saying that some error may be allowed.

[0079] Next, parameter acquiring section 131 acquires a ninth parameter that has a larger value as the number of ninth waveforms in a non-REM epoch increases and a larger value as the number of non-REM epochs decreases. Parameter acquiring section 131 acquires the ninth parameter using a ninth arithmetic expression that is, for example, an increasing function with the number of the ninth waveforms as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the ninth arithmetic expression is, for example, "the number of ninth waveforms in a non-REM epoch / the number of non-REM epochs". In this case, the ninth parameter is the average number of ninth waveforms in a non-REM epoch. (10) 10th parameter

[0080] Parameter acquisition section 131 acquires one or more power values ​​of the ninth waveform in one or more non-REM epochs, and acquires a total non-REM power value by adding up the one or more power values. Parameter acquisition section 131 also acquires the number of the non-REM epochs.

[0081] It should be noted that parameter acquiring section 131 calculates the power value of the ninth waveform by FFT in each of one or more non-REM epochs.

[0082] Next, parameter acquisition section 131 acquires a tenth parameter that increases as the non-REM total power value increases and increases as the number of non-REM epochs decreases. Parameter acquisition section 131 acquires the tenth parameter using a tenth arithmetic expression that is, for example, an increasing function with the non-REM total power value as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the tenth arithmetic expression is, for example, "non-REM total power value / number of non-REM epochs". In this case, the tenth parameter is the average power value per one non-REM epoch. (11) 11th parameter

[0083] The parameter acquiring unit 131 applies a filter to a specific band for each of one or more non-REM epochs to remove noise. Then, the parameter acquiring unit 131 performs a process similar to the process for acquiring the tenth parameter to acquire an eleventh parameter.

[0084] That is, parameter acquiring section 131 acquires an eleventh parameter which increases as the non-REM total power value increases and increases as the number of non-REM epochs decreases. Parameter acquiring section 131 acquires the eleventh parameter using, for example, an eleventh arithmetic expression which is an increasing function with the non-REM total power value as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the eleventh arithmetic expression is, for example, "non-REM total power value / number of non-REM epochs". In this case, the eleventh parameter is the average power value per non-REM epoch. (12) 12th parameter

[0085] The parameter acquiring unit 131 acquires the tenth parameter. Furthermore, the parameter acquiring unit 131 acquires a REM tenth parameter, which is the same parameter as the tenth parameter, for one or more REM epochs. Next, the parameter acquiring unit 131 acquires a twelfth parameter, which is the ratio between the tenth parameter and the REM tenth parameter.

[0086] In addition, parameter acquisition unit 131 acquires a 12th parameter which becomes a larger value as the non-REM total power value increases, becomes a larger value as the number of non-REM epochs decreases, becomes a smaller value as the REM total power value increases, and becomes a larger value as the number of REM epochs increases.

[0087] Parameter acquisition section 131 acquires the 12th parameter using a 12th arithmetic expression which is an increasing function with parameters being the total NREM power value and the number of REM epochs, and a decreasing function with parameters being the total REM power value and the number of NREM epochs. As described above, the 12th arithmetic expression is, for example, the "ratio of (total NREM power value / number of NREM epochs) to (total REM power value / number of REM epochs)." (13) 13th parameter

[0088] The parameter acquisition unit 131 removes noise by applying a filter to a specific band. Next, the parameter acquisition unit 131 performs a process similar to the process for acquiring the twelfth parameter to acquire a thirteenth parameter. (14) 14th parameter

[0089] Parameter acquiring section 131 detects one or more fourteenth waveforms and acquires the power values ​​of the fourteenth waveforms. Parameter acquiring section 131 acquires a total NREM power value that is the sum of one or more power values. Parameter acquiring section 131 also acquires the number of NREM epochs.

[0090] The fourteenth waveform is a waveform of the fourteenth specific frequency in the non-REM epoch. Here, the ninth specific frequency is, for example, a frequency of about 0.5 Hz to about 2 Hz, but it goes without saying that some degree of error may be allowed.

[0091] Next, parameter acquisition section 131 acquires a 14th parameter, which increases as the NREM total power value increases and increases as the number of NREM epochs decreases. Parameter acquisition section 131 acquires the 14th parameter using, for example, a 14th arithmetic expression that is an increasing function with the NREM total power value as a parameter and a decreasing function with the number of NREM epochs as a parameter. Note that the 14th arithmetic expression is, for example, "NREM total power value / number of NREM epochs". In this case, the 14th parameter is the average power value per NREM epoch. (15) 15th parameter

[0092] The parameter acquiring unit 131 acquires the 14th parameter. In addition, the parameter acquiring unit 131 acquires a REM 14th parameter, which is the same parameter as the 14th parameter, for one or more REM epochs. Next, the parameter acquiring unit 131 acquires a 15th parameter, which is, for example, a ratio between the 14th parameter and the REM 14th parameter.

[0093] In addition, parameter acquisition unit 131 acquires a 15th parameter that becomes larger as the non-REM total power value increases, becomes larger as the number of non-REM epochs decreases, becomes smaller as the REM total power value increases, and becomes larger as the number of REM epochs increases.

[0094] Parameter acquisition section 131 acquires the 15th parameter using a 15th arithmetic expression which is an increasing function with parameters being the NREM total power value and the number of REM epochs, and a decreasing function with parameters being the REM total power value and the number of NREM epochs. As described above, the 15th arithmetic expression is, for example, the "ratio of (NREM total power value / number of NREM epochs) to (REM total power value / number of REM epochs)." (16) 16th parameter

[0095] The parameter acquiring section 131 detects one or more sixteenth waveforms and acquires the power values ​​of the sixteenth waveforms. The parameter acquiring section 131 acquires a total NREM power value by adding up one or more power values. The parameter acquiring section 131 also acquires the number of NREM epochs.

[0096] The sixteenth waveform is a waveform of a sixteenth specific frequency in a non-REM epoch. Here, the sixteenth specific frequency is, for example, a frequency of about 1 Hz to about 2 Hz, but it goes without saying that some degree of error may be allowed.

[0097] Next, parameter acquisition section 131 acquires a 16th parameter, which increases as the NREM total power value increases and increases as the number of NREM epochs decreases. Parameter acquisition section 131 acquires the 16th parameter using, for example, a 16th arithmetic expression that is an increasing function with the NREM total power value as a parameter and a decreasing function with the number of NREM epochs as a parameter. Note that the 16th arithmetic expression is, for example, "NREM total power value / number of NREM epochs". In this case, the 16th parameter is the average power value per NREM epoch. (17) 17th parameter

[0098] The parameter acquiring unit 131 acquires the 16th parameter. In addition, the parameter acquiring unit 131 acquires a REM 16th parameter, which is the same parameter as the 16th parameter, for one or more REM epochs. Next, the parameter acquiring unit 131 acquires a 17th parameter, which is, for example, a ratio between the 16th parameter and the REM 16th parameter.

[0099] In addition, parameter acquisition unit 131 acquires a 17th parameter which becomes a larger value the larger the non-REM total power value, a larger value the smaller the number of non-REM epochs, a smaller value the larger the REM total power value, and a larger value the larger the number of REM epochs.

[0100] Parameter acquisition section 131 acquires the 17th parameter using a 17th arithmetic expression which is an increasing function with parameters being the total NREM power value and the number of REM epochs, and a decreasing function with parameters being the total REM power value and the number of NREM epochs. As described above, the 17th arithmetic expression is, for example, the "ratio of (total NREM power value / number of NREM epochs) to (total REM power value / number of REM epochs)." (18) 18th parameter

[0101] The parameter acquiring section 131 detects one or more 18th waveforms and acquires the power values ​​of the 18th waveforms. The parameter acquiring section 131 acquires a total NREM power value by adding up one or more power values. The parameter acquiring section 131 also acquires the number of NREM epochs.

[0102] The eighteenth waveform is a waveform of an eighteenth specific frequency in a non-REM epoch. Here, the eighteenth specific frequency is, for example, a frequency of about 18 Hz to about 35 Hz, but it goes without saying that some degree of error may be allowed.

[0103] Next, parameter acquisition section 131 acquires an 18th parameter which increases as the non-REM total power value increases and increases as the number of non-REM epochs decreases. Parameter acquisition section 131 acquires the 18th parameter using, for example, an 18th arithmetic expression which is an increasing function with the non-REM total power value as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the 18th arithmetic expression is, for example, "non-REM total power value / number of non-REM epochs". In this case, the 18th parameter is the average power value per non-REM epoch. (19) 19th parameter

[0104] The parameter acquisition unit 131 removes noise by applying a filter to a specific band. Then, the parameter acquisition unit 131 performs a process similar to the process for acquiring the 18th parameter to acquire a 19th parameter. (20) 20th parameter

[0105] The parameter acquiring unit 131 acquires the 18th parameter. In addition, the parameter acquiring unit 131 acquires a REM 18th parameter, which is the same parameter as the 18th parameter, for one or more REM epochs. Next, the parameter acquiring unit 131 acquires a 20th parameter, which is, for example, a ratio between the 18th parameter and the REM 18th parameter.

[0106] In addition, parameter acquisition unit 131 acquires a 20th parameter which becomes a larger value as the non-REM total power value increases, becomes a larger value as the number of non-REM epochs decreases, becomes a smaller value as the REM total power value increases, and becomes a larger value as the number of REM epochs increases.

[0107] Parameter acquisition section 131 acquires the 20th parameter using a 20th arithmetic expression which is an increasing function with parameters being the total NREM power value and the number of REM epochs, and a decreasing function with parameters being the total REM power value and the number of NREM epochs. As described above, the 20th arithmetic expression is, for example, the "ratio of (total NREM power value / number of NREM epochs) to (total REM power value / number of REM epochs)." (21) 21st parameter

[0108] The parameter acquisition unit 131 removes noise by applying a filter to a specific band, and then performs a process similar to the process for acquiring the 20th parameter to acquire the 21st parameter. (22) 22nd parameter

[0109] Parameter acquisition section 131 detects one or more twenty-second waveforms in a non-REM epoch and acquires the power value of the twenty-second waveform. Parameter acquisition section 131 acquires a total non-REM power value by adding up one or more power values. Parameter acquisition section 131 also acquires the number of non-REM epochs.

[0110] The twenty-second waveform is a waveform of a twenty-second specific frequency. Here, the twenty-second specific frequency is, for example, a frequency of about 2 Hz to about 4 Hz, although it goes without saying that some degree of error may be allowed.

[0111] Next, parameter acquisition section 131 acquires a 22(1) parameter that increases as the non-REM total power value increases and increases as the number of non-REM epochs decreases. Parameter acquisition section 131 acquires the 22(1) parameter using, for example, a 22(1) arithmetic expression that is an increasing function with the non-REM total power value as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the 22(1) arithmetic expression is, for example, "non-REM total power value / number of non-REM epochs". In this case, the 22(1) parameter is the average power value per non-REM epoch.

[0112] Furthermore, parameter acquiring unit 131 acquires a 22(2) parameter that is the same as the 22(1) parameter for one or more REM epochs. Next, parameter acquiring unit 131 acquires a 22 parameter that is, for example, a ratio between the 22(1) parameter and the 22(2) parameter.

[0113] In addition, parameter acquisition unit 131 acquires a 22nd parameter which becomes larger as the non-REM total power value increases, becomes larger as the number of non-REM epochs decreases, becomes smaller as the REM total power value increases, and becomes larger as the number of REM epochs increases.

[0114] Parameter acquisition section 131 acquires the 22nd parameter using a 22nd arithmetic expression which is an increasing function with parameters being the NREM total power value and the number of REM epochs, and a decreasing function with parameters being the REM total power value and the number of NREM epochs. As described above, the 22nd arithmetic expression is, for example, the "ratio of (NREM total power value / number of NREM epochs) to (REM total power value / number of REM epochs)." (23) 23rd Parameter

[0115] The parameter acquiring unit 131 acquires the power value of the twenty-third waveform instead of the twenty-second waveform in the process described in (22). Then, the parameter acquiring unit 131 performs the same process as the process described in (22) to acquire the twenty-third parameter.

[0116] The twenty-third waveform is a waveform of a twenty-third specific frequency. Here, the twenty-third specific frequency is, for example, a frequency of about 4 Hz to about 8 Hz, although it goes without saying that some degree of error may be allowed. (24) 24th parameter

[0117] The parameter acquisition unit 131 removes noise by applying a filter to a specific band. Then, the parameter acquisition unit 131 performs a process similar to the process for acquiring the 23rd parameter to acquire the 24th parameter. (25) 25th parameter

[0118] The parameter acquiring unit 131 acquires the power value of the twenty-fifth waveform instead of the twenty-third waveform in the process described in (23). Then, the parameter acquiring unit 131 performs the same process as the process described in (23) to acquire the twenty-fifth parameter.

[0119] The twenty-fifth waveform is a waveform of a twenty-fifth specific frequency. Here, the twenty-fifth specific frequency is, for example, a frequency of about 4 Hz to about 6 Hz, but it goes without saying that some degree of error may be allowed. (26) 26th Parameter

[0120] The parameter acquiring unit 131 acquires the power value of the twenty-sixth waveform instead of the twenty-fifth waveform in the process described in (25). Then, the parameter acquiring unit 131 performs the same process as the process described in (25) to acquire the twenty-sixth parameter.

[0121] The twenty-sixth waveform is a waveform of a twenty-sixth specific frequency. Here, the twenty-sixth specific frequency is, for example, a frequency of about 6 Hz to about 8 Hz, although it goes without saying that some degree of error may be allowed. (27) 27th parameter

[0122] The parameter acquisition unit 131 removes noise by applying a filter to a specific band. Then, the parameter acquisition unit 131 performs a process similar to the process for acquiring the 26th parameter to acquire the 27th parameter. (28) 28th Parameter

[0123] The parameter acquiring unit 131 acquires the power value of the 28th waveform instead of the 26th waveform in the process described in (26). Then, the parameter acquiring unit 131 performs the same process as the process described in (25) to acquire the 28th parameter.

[0124] The 28th waveform is a waveform of a 28th specific frequency. Here, the 28th specific frequency is, for example, a frequency of about 8 Hz to about 10 Hz, but it goes without saying that some degree of error may be allowed. (29) 29th parameter

[0125] The parameter acquisition unit 131 removes noise by applying a filter to a specific band. Then, the parameter acquisition unit 131 performs a process similar to the process of acquiring the 28th parameter to acquire the 29th parameter. (30) 30th parameter

[0126] Parameter acquisition section 131 acquires a power value of the 30th specific frequency in each of the one or more non-REM epochs, and acquires a total non-REM power value by adding up the one or more power values. Parameter acquisition section 131 also acquires the number of the non-REM epochs.

[0127] Incidentally, the 30th specific frequency is, for example, a frequency of approximately 10 Hz to approximately 12 Hz, but it goes without saying that some degree of error may be allowed.

[0128] Note that the parameter acquisition unit 131 calculates a power value by FFT in each of one or more non-REM epochs.

[0129] Next, parameter acquisition section 131 acquires a seventh parameter, which increases as the non-REM total power value increases and increases as the number of non-REM epochs decreases. Parameter acquisition section 131 acquires the thirtieth parameter using a thirtieth arithmetic expression, which is, for example, an increasing function with the non-REM total power value as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the thirtieth arithmetic expression is, for example, "non-REM total power value / number of non-REM epochs". In this case, the thirtieth parameter is the average power value per one non-REM epoch. (31) 31st parameter

[0130] The parameter acquiring unit 131 acquires the power value of the 30th waveform instead of the 28th waveform in the process described in (28). Then, the parameter acquiring unit 131 performs the same process as the process described in (28) to acquire the 31st parameter. Note that the 30th waveform is a waveform of a 30th specific frequency. (32) 32nd parameter

[0131] The parameter acquiring unit 131 acquires the power value of the thirty-second waveform instead of the thirty-tenth waveform in the process described in (31). Then, the parameter acquiring unit 131 performs the same process as the process described in (31) to acquire the thirty-second parameter. The thirty-second waveform is a waveform of a thirty-second specific frequency. The thirty-second specific frequency is, for example, a frequency of about 0.5 Hz to about 45 Hz, although it goes without saying that some error may be allowed. (33) 33rd Parameter

[0132] The parameter acquisition unit 131 removes noise by applying a filter to a specific band. Then, the parameter acquisition unit 131 performs a process similar to the process of acquiring the 32nd parameter to acquire the 33rd parameter. (34) 34th parameter

[0133] Parameter acquisition unit 131 applies a filter to a specific band to remove noise. Then, parameter acquisition unit 131 detects one or more ninth waveforms. Then, parameter acquisition unit 131 acquires the time length of each of the one or more ninth waveforms, and acquires a total time length by adding up the one or more time lengths. Parameter acquisition unit 131 acquires the number of non-REM epochs.

[0134] Next, parameter acquiring section 131 acquires a 34th parameter, which has a larger value as the total time length increases and a larger value as the number of non-REM epochs decreases. Parameter acquiring section 131 acquires the 34th parameter using, for example, a 34th arithmetic expression which is an increasing function with the total time length as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the 34th arithmetic expression is, for example, "total time length / number of non-REM epochs". In this case, the 34th parameter is the average time length of the 34th waveform per one non-REM epoch. (35) 35th parameter

[0135] The parameter acquiring section 131 detects one or more thirty-fifth waveforms. Then, the parameter acquiring section 131 acquires the time length of each of the one or more thirty-fifth waveforms, and acquires a total time length by adding up the one or more time lengths. The parameter acquiring section 131 acquires the number of non-REM epochs.

[0136] The thirty-fifth waveform is a waveform having a thirty-tenth specific frequency in the non-REM epoch and a ninth specific amplitude.

[0137] Next, parameter acquiring section 131 acquires a 35th parameter, which has a larger value as the total time length increases and a larger value as the number of non-REM epochs decreases. Parameter acquiring section 131 acquires the 35th parameter using, for example, a 35th arithmetic expression which is an increasing function with the total time length as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the 35th arithmetic expression is, for example, "total time length / number of non-REM epochs". In this case, the 35th parameter is the average time length of the 35th waveform per one non-REM epoch. (36) 36th Parameter

[0138] The parameter acquiring section 131 detects one or more thirty-fifth waveforms and acquires the number of the thirty-fifth waveforms. The parameter acquiring section 131 acquires the number of non-REM epochs.

[0139] Next, parameter acquiring section 131 acquires a 36th parameter, which has a larger value as the number of the 35th waveforms increases and a larger value as the number of non-REM epochs decreases. Parameter acquiring section 131 acquires the 36th parameter using, for example, a second arithmetic expression that is an increasing function with the number of the 35th waveforms as a parameter and a decreasing function with the number of non-REM epochs as a parameter. Note that the 36th arithmetic expression is, for example, "the number of the 35th waveforms / the number of non-REM epochs." In this case, the 36th parameter is the average number of the 35th waveforms in the non-REM epochs.

[0140] The judgment unit 132 acquires a judgment result using one or more parameters acquired by the parameter acquisition unit 131 and the learning information in the storage unit 11. The judgment result is information indicating whether or not the person of the electroencephalogram information accepted by the reception unit 12 has a mental illness, or information indicating the degree of the mental illness. The judgment result is, for example, information indicating whether or not the patient of the electroencephalogram information accepted by the reception unit 12 is depressed, or information indicating the degree of depression. The judgment result is, for example, "1 for depression" or "0 for not depression". The judgment result may have a score. It can be said that the score is the certainty, likelihood, or probability of the judgment result.

[0141] The learning information is information based on two or more teacher data sets having one or more positive examples having one or more parameters that indicate characteristics of the electroencephalogram information of patients with depression and one or more negative examples having one or more parameters that indicate characteristics of the electroencephalogram information of patients without depression. The learning information is stored in the storage unit 11.

[0142] It is highly preferable that the determination section 132 obtains a seventh parameter (parameter (7)) from the electroencephalogram information accepted by the acceptance section 12, and obtains a determination result using the seventh parameter.

[0143] Moreover, it is preferable that the judgment unit 132 acquires one or more parameters among the parameters (2), (8), (9), and (15) and acquires a judgment result using the one or more parameters.

[0144] In addition, it is preferable that the judgment unit 132 obtains a judgment result using one or more parameters from among (1)(3)(4)(5)(6)(10)(11)(12)(13)(14)(16)(17)(18)(19)(20)(21).

[0145] Moreover, the determination unit 132 may obtain the determination result using one or more parameters among (22) to (36).

[0146] Furthermore, the determination unit 132 may obtain the determination result using all of the parameters (1) to (36).

[0147] The determination unit 132 acquires the determination result by, for example, any one of the following methods (A), (B), (C), or (D). (A) Machine learning method

[0148] The determination unit 132 performs machine learning prediction processing using one or more parameters acquired by the parameter acquisition unit 131 and the learning model, and acquires a determination result.

[0149] More specifically, the determination unit 132 provides one or more parameters and a learning model to a machine learning prediction module, executes the prediction module, and obtains a determination result.

[0150] The machine learning algorithm may be any algorithm, such as deep learning, random forest, decision tree, SVM, etc. For machine learning, various machine learning functions such as the TensorFlow (registered trademark) library, the random forest module of the R language, TinySVM, and various existing libraries can be used. (2) Method using center of gravity information

[0151] The determination unit 132 obtains a determination result using a vector having elements each of the two or more parameters obtained by the parameter obtaining unit 131, and the centroid information of the positive examples and the centroid information of the negative examples.

[0152] More specifically, the determination unit 132 obtains a vector having elements each of the two or more parameters obtained by the parameter obtaining unit 131. Next, the determination unit 132 obtains a positive example distance, which is the distance between the vector and centroid information of a positive example. Furthermore, the determination unit 132 obtains a negative example distance, which is the distance between the vector and centroid information of a negative example. Next, the determination unit 132 obtains a determination result using the positive example distance and the negative example distance. The determination unit 132 normally obtains a determination result of "not depressed" if "positive example distance<negative example distance", and obtains a determination result of "depressed" if "positive example distance>negative example distance". Furthermore, the determination unit 132 may obtain a degree of depression (score, etc.) using a difference between the positive example distance and the negative example distance.

[0153] The centroid information of a positive example is a vector whose elements are two or more parameters contained in the teacher data of a positive example. The centroid information of a negative example is a vector whose elements are two or more parameters contained in the teacher data of a negative example. The centroid information of a positive example may be called a positive example centroid vector, and the centroid information of a negative example may be called a negative centroid vector. (3) Method using the correspondence table

[0154] The determination unit 132 constructs a vector having elements each of the one or more parameters acquired by the parameter acquisition unit 131. Next, the determination unit 132 acquires a similarity with a vector contained in each of two or more pieces of correspondence information in the correspondence table. The vector contained in the correspondence information is a positive example vector or a negative example vector. Next, the determination unit 132 acquires a determination result corresponding to each of the one or more vectors whose similarity satisfies the adoption condition. The adoption condition is that the similarity is maximum, that the similarity is equal to or greater than a threshold, and that the ranking of the similarity is equal to or greater than a threshold.

[0155] When the determination unit 132 acquires a determination result corresponding to one vector that satisfies the adoption condition, the determination unit 132 acquires the determination result as the final determination result. When the determination unit 132 acquires determination results corresponding to two or more vectors that satisfy the adoption condition, the determination unit 132 acquires the final determination result using the two or more determination results. The determination unit 132 acquires the final determination result using the two or more determination results, for example, by majority vote. (4) Method using an arithmetic formula

[0156] The judgment unit 132 substitutes one or more parameters acquired by the parameter acquisition unit 131 into an arithmetic expression to acquire a score, and acquires a judgment result corresponding to the score. For example, the judgment unit 132 acquires a judgment result of "depressed '1'" when the score is equal to or greater than a threshold value, and acquires a judgment result of "not depressed '0'" when the score is less than or equal to the threshold value.

[0157] The output unit 14 outputs the determination result acquired by the determination unit 132. The output unit 14 transmits the determination result to, for example, the terminal device 3. The output unit 14 displays the determination result on a display, for example.

[0158] Here, output means, for example, transmission to terminal device 3, but it may also be a concept including display on a display, projection using a projector, printing on a printer, sound output, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0159] The storage unit 11 is preferably a non-volatile recording medium, but may also be realized as a volatile recording medium.

[0160] There is no restriction on the process by which information is stored in the storage unit 11. For example, information may be stored in the storage unit 11 via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 11, or information inputted via an input device may be stored in the storage unit 11.

[0161] The reception unit 12 is preferably realized by a wireless or wired communication means, but may also be realized by a means for receiving broadcasts, a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen.

[0162] The processing unit 13, the parameter acquisition unit 131, and the determination unit 132 can usually be realized by a processor, a memory, etc. The processing procedure of the processing unit 13, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type is not important.

[0163] The output unit 14 is preferably realized by a wireless or wired communication means, but may also be realized by an output device such as a display or a speaker.

[0164] Next, an example of the operation of the determination device 1 will be described with reference to the flowchart of FIG.

[0165] (Step S301) The accepting unit 12 judges whether or not electroencephalogram information has been accepted. If electroencephalogram information has been accepted, the process proceeds to step S302, and if electroencephalogram information has not been accepted, the process returns to step S301.

[0166] (Step S302) The parameter acquiring section 131 acquires one or more parameters from the electroencephalogram information accepted in step S301. An example of such parameter acquiring processing will be described with reference to the flowchart of FIG.

[0167] (Step S303) The determination unit 132 acquires the learning information from the storage unit 11.

[0168] (Step S304) The determination unit 132 performs a determination process. That is, the determination unit 132 obtains a determination result by using one or more parameters obtained in step S302 and the learning information obtained in step S303.

[0169] (Step S305) The output unit 14 outputs the determination result acquired in step S304. The process returns to step S301.

[0170] In the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt occurs to end the process.

[0171] Next, an example of the parameter acquisition process in step S302 will be described with reference to the flowchart in FIG.

[0172] (Step S401) The parameter acquiring unit 131 acquires accepted electroencephalogram information.

[0173] (Step S402) The parameter acquiring unit 131 assigns 1 to a counter i.

[0174] (Step S403) The parameter acquisition unit 131 judges whether or not the i-th epoch is present in the electroencephalogram information acquired in step S401. If the i-th epoch is present, the process proceeds to step S404, and if the i-th epoch is not present, the process proceeds to step S407. Note that an epoch is, for example, 30 seconds of electroencephalogram information.

[0175] (Step S404) The parameter acquiring unit 131 acquires state information of the i-th epoch using the electroencephalogram information corresponding to the i-th epoch, and associates the state information with the i-th epoch. The state information is information indicating a sleep state. The state information is, for example, "non-REM sleep" or "REM sleep".

[0176] (Step S405) The parameter acquiring section 131 acquires one or more epoch attribute values ​​of the i-th epoch by using the electroencephalogram information corresponding to the i-th epoch.

[0177] (Step S406) The parameter acquiring unit 131 increments the counter i by 1. The process returns to step S403.

[0178] (Step S407) The parameter acquiring unit 131 assigns 1 to a counter j.

[0179] (Step S408) The parameter acquisition unit 131 judges whether or not the j-th parameter to be acquired exists. If the j-th parameter exists, the process proceeds to step S409, and if the j-th parameter does not exist, the process returns to the upper process. It is preferable that the parameters to be acquired are determined in advance.

[0180] (Step S409) The parameter acquiring unit 131 assigns 1 to a counter k.

[0181] (Step S410) The parameter acquisition unit 131 judges whether or not the kth raw information used in acquiring the jth parameter exists. If the kth raw information exists, the process proceeds to step S411, and if the kth raw information does not exist, the process proceeds to step S413.

[0182] (Step S411) The parameter acquiring unit 131 acquires the k-th piece of raw information. The parameter acquiring unit 131 acquires the k-th piece of raw information, for example, by using one or more epoch attribute values ​​acquired in step S405.

[0183] (Step S412) The parameter acquiring unit 131 increments the counter k by 1. The process returns to step S410.

[0184] (Step S413) The parameter acquiring unit 131 acquires the j-th parameter by using one or more pieces of raw information acquired in step S411, and temporarily stores the j-th parameter in a buffer (not shown).

[0185] (Step S414) The parameter acquiring unit 131 increments the counter j by 1. The process returns to step S408.

[0186] A specific example of the determination device 1 in this embodiment will be described below. The parameter acquisition unit 131 of the determination device 1 acquires, for example, 36 parameters shown in the parameter management tables of Fig. 5 to Fig. 8. The parameter management table has 36 records each having an "ID" and "parameter description." "ID" is the ID of the parameter. "Parameter description" is information indicating the content of the parameter.

[0187] Then, the determination unit 132 obtains a determination result by using one or more parameters and the learning information among the 36 parameters obtained by the parameter obtaining unit 131. Next, the output unit 14 outputs the determination result.

[0188] As described above, according to this embodiment, depression can be diagnosed with high accuracy using electroencephalogram information.

[0189] Furthermore, according to this embodiment, depression can be diagnosed with higher accuracy by using useful parameters that can be acquired from electroencephalogram information.

[0190] (Embodiment 2) In this embodiment, a learning device will be described for acquiring learning information used for judgment by the judgment device 1. Note that in this embodiment, a case will be described in which the learning information is, for example, a learning model acquired by a learning process of machine learning, center of gravity information, a correspondence table, or an arithmetic formula.

[0191] 9 is a block diagram of the learning device 2. The learning device 2 includes a learning storage unit 21, a parameter acquisition unit 131, a learning unit 22, and an accumulation unit .

[0192] Various types of information are stored in the learning storage unit 21 constituting the learning device 2. The various types of information are, for example, two or more pieces of electroencephalogram information, two or more pieces of teacher data, and learning information. The electroencephalogram information in the learning storage unit 21 is associated with a judgment result indicating whether or not the subject of the electroencephalogram information (such as a patient) is depressed. The teacher data in the learning storage unit 21 is associated with a judgment result indicating whether or not the subject of the teacher data is depressed. Note that such a judgment result is the result of a person (usually a doctor). Furthermore, the teacher data is, for example, a vector having two or more parameters acquired from the electroencephalogram information as elements. The teacher data is, for example, a vector having 36 parameters acquired from the electroencephalogram information as elements.

[0193] When electroencephalogram information exists in the learning storage unit 21 but teacher data does not exist, the parameter acquisition unit 131 acquires one or more parameters for each of two or more pieces of electroencephalogram information by the operation described in Fig. 4, configures teacher data having the one or more parameters and the judgment result, and accumulates it in the learning storage unit 21. It is preferable that the two or more pieces of teacher data are one or more positive examples and one or more negative examples.

[0194] The learning unit 22 acquires learning information by using two or more pieces of teacher data in the learning storage unit 21. The learning unit 22 acquires learning information by performing, for example, any one of the following processes (A), (B), (C), and (D). (A) When the learning information is a learning model

[0195] The learning unit 22 performs a machine learning learning process using two or more pieces of teacher data in the learning storage unit 21 to acquire a learning model. The machine learning algorithm may be deep learning, random forest, decision tree, SVM, or the like. For the machine learning, various machine learning functions such as the TensorFlow (registered trademark) library, the random forest module of the R language, TinySVM, and various existing libraries may be used. (B) When the learning information is center of gravity information

[0196] The learning unit 22 acquires one or more positive examples from the two or more pieces of teacher data in the learning storage unit 21. Here, the positive example is a vector having two or more parameters acquired from the electroencephalogram information of a patient suffering from depression as elements. The learning unit 22 acquires the centroid vector of the one or more positive examples and associates it with information indicating that it is a positive example vector. Such a vector is a positive example centroid vector.

[0197] The learning unit 22 acquires one or more negative examples from the two or more pieces of teacher data in the learning storage unit 21. Here, the negative example is a vector having two or more parameters acquired from the electroencephalogram information of a patient with depression as elements. The learning unit 22 acquires the centroid vector of the one or more negative examples and associates it with information indicating that it is a negative example vector. Such a vector is a negative example centroid vector. (C) When the learning information is a correspondence table (C-1) The learning unit 22 acquires, for each of two or more teacher data, correspondence information having a vector whose elements are one or more parameters contained in the teacher data and a judgment result. The learning unit 22 creates a correspondence table having two or more pieces of correspondence information. (C-2) The learning unit 22 obtains an average vector of vectors whose elements are one or more parameters that pair with the "depressed" judgment result, and obtains correspondence information having the average vector and the "depressed" judgment result. The learning unit 22 obtains an average vector of vectors whose elements are one or more parameters that pair with the "not depressed" judgment result, and obtains correspondence information having the average vector and the "not depressed" judgment result. Next, the learning unit 22 constructs a correspondence table having two pieces of correspondence information. (D) When the learning information is an arithmetic expression

[0198] The learning unit 22 performs multiple regression analysis on each of the two or more teacher data, for example, to obtain an arithmetic formula. More specifically, the learning unit 22 obtains an arithmetic formula that receives one or more parameters contained in each of the two or more teacher data as input and outputs a judgment result (here, typically a score).

[0199] The accumulation unit 23 accumulates the learning information acquired by the learning unit 22. For example, the accumulation unit 23 accumulates the learning information acquired by the learning unit 22 in the learning storage unit 21 or the storage unit 11 of the determination device 1. There is no restriction on where the accumulation unit 23 accumulates the learning information.

[0200] The learning storage unit 21 is preferably a non-volatile recording medium, but may also be realized as a volatile recording medium.

[0201] There is no restriction on the process by which information is stored in the learning storage unit 21. For example, information may be stored in the learning storage unit 21 via a recording medium, information transmitted via a communication line or the like may be stored in the learning storage unit 21, or information inputted via an input device may be stored in the learning storage unit 21.

[0202] The learning unit 22 and the storage unit 23 can usually be realized by a processor, a memory, etc. The processing procedure of the learning unit 22, etc. is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type is not important.

[0203] Next, an example of the operation of the learning device 2 will be described with reference to the flowchart of FIG.

[0204] (Step S1001) The parameter acquiring unit 131 assigns 1 to a counter i.

[0205] (Step S1002) Parameter acquiring section 131 determines whether or not the i-th electroencephalogram information exists in learning storage section 21. If the i-th electroencephalogram information exists, the process proceeds to step S1003, and if the i-th electroencephalogram information does not exist, the process proceeds to step S1007.

[0206] (Step S1003) The parameter acquiring section 131 acquires one or more parameters from the i-th electroencephalogram information. An example of such parameter acquiring processing has been described with reference to the flowchart of FIG.

[0207] (Step S1004) The learning section 22 acquires the determination result paired with the i-th electroencephalogram information.

[0208] (Step S1005) The learning unit 22 configures teacher data using the one or more parameters acquired in step S1003 as explanatory variables and the determination result acquired in step S1004 as a response variable, and accumulates the teacher data in the learning storage unit 21.

[0209] (Step S1006) The parameter acquiring unit 131 increments the counter i by 1. The process returns to step S1002.

[0210] (Step S1007) The learning unit 22 acquires learning information using the two or more pieces of teacher data accumulated in step S1005.

[0211] (Step S1008) The storage unit 23 stores the learning information acquired in step S1007, and the process ends.

[0212] As described above, according to this embodiment, learning information can be constructed for accurately diagnosing depression using electroencephalogram information.

[0213] The process in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software for realizing the determination device 1 in this embodiment is a program as follows. That is, this program causes a computer to function as a reception unit that receives electroencephalogram information, a parameter acquisition unit that acquires one or more parameters from the electroencephalogram information, a determination unit that refers to a storage unit in which learning information based on two or more teacher data having one or more positive examples having one or more parameters that indicate characteristics of the electroencephalogram information of a patient with depression and one or more negative examples having one or more parameters that indicate characteristics of the electroencephalogram information of a patient without depression is stored, and uses the one or more parameters acquired by the parameter acquisition unit and the learning information to obtain a determination result indicating whether or not the patient of the electroencephalogram information received by the reception unit is depressed, and an output unit that outputs the determination result.

[0214] Moreover, the software for realizing the learning device 2 in this embodiment is the following program. That is, this program causes a computer to function as a parameter acquisition unit that acquires one or more parameters from two or more pieces of electroencephalogram information associated with a judgment result of whether or not the user is depressed, a learning unit that acquires teacher data having one or more parameters acquired by the parameter acquisition unit and a judgment result associated with each piece of electroencephalogram information for each piece of two or more pieces of electroencephalogram information and acquires learning information using the two or more pieces of teacher data, and a storage unit that stores the learning information acquired by the learning unit.

[0215] 11 shows the appearance of a computer that executes the programs described in this specification to realize the determination device 1, learning device 2, and the like of the various embodiments described above. The above-mentioned embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 11 is an overview of this computer system 300, and FIG. 12 is a block diagram of the system 300.

[0216] In FIG. 11, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0217] 12, computer 301 includes, in addition to CD-ROM drive 3012, MPU 3013, bus 3014 connected to CD-ROM drive 3012 etc., ROM 3015 for storing programs such as a boot-up program, RAM 3016 connected to MPU 3013 for temporarily storing instructions of application programs and providing temporary storage space, and hard disk 3017 for storing application programs, system programs, and data. Although not shown here, computer 301 may further include a network card for providing connection to a LAN.

[0218] A program for causing computer system 300 to execute the functions of the determination device and the like of the above-mentioned embodiments may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and further transferred to hard disk 3017. Alternatively, the program may be transmitted to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 when executed. The program may also be loaded directly from CD-ROM 3101 or the network.

[0219] The program does not necessarily include an operating system (OS) or a third party program that causes the computer 301 to execute the functions of the determination device of the above-mentioned embodiment. The program only needs to include an instruction portion that calls appropriate functions (modules) in a controlled manner to obtain a desired result. How the computer system 300 operates is well known, and a detailed description will be omitted.

[0220] The program may be executed by a single computer or a plurality of computers. That is, the program may be executed by a centralized processing or a distributed processing.

[0221] Furthermore, in each of the above embodiments, it goes without saying that two or more communication means present in one device may be physically realized by one medium.

[0222] In each of the above embodiments, each process may be realized by centralized processing in a single device, or may be realized by distributed processing in a plurality of devices.

[0223] The present invention is not limited to the above-described embodiment, and various modifications are possible, and it goes without saying that these modifications are also included within the scope of the present invention. [Industrial Applicability]

[0224] As described above, the determination device 1 according to the present invention has an effect of being able to perform a highly accurate depression determination using electroencephalogram information, and is useful as a determination device for determining depression, etc. [Explanation of symbols]

[0225] 1 Judgment device 2 Learning device 3 Terminal Equipment 11 Storage area 12 Reception 13 Processing section 14 Output section 21 Learning Storage Unit 22 Learning Department 23 Storage section 131 Parameter Acquisition Unit 132 Judgment section

Claims

1. A reception area that receives brainwave information, A parameter acquisition unit that acquires one or more parameters from the electroencephalogram information, A determination unit refers to a storage unit that stores learning information based on two or more training data sets, each containing one or more positive examples having one or more parameters that represent the characteristics of brainwave information of persons with mental illness and one or more negative examples having one or more parameters that represent the characteristics of brainwave information of persons without mental illness, and uses the one or more parameters acquired by the parameter acquisition unit and the learning information to acquire a determination result indicating whether or not the patient whose brainwave information received by the reception unit has a mental illness. The system comprises an output unit that outputs the aforementioned determination result, The parameter acquisition unit, From the aforementioned electroencephalogram information, one or more parameters, including the non-REM representative power value, are obtained. The determination unit, Using the one or more parameters and the learning information, the receiving unit obtains a determination result indicating whether or not the patient whose electroencephalogram information it receives has a mental illness. The parameter acquisition unit, A determination device that obtains a non-REM representative power value, which is a value obtained using the overall power value of the frequencies in the epoch determined to be non-REM sleep in the electroencephalogram information and the number of the epoch, wherein the value becomes larger as the power value of the frequencies increases and the value becomes smaller as the number of epochs increases.

2. The parameter acquisition unit, The determination device according to claim 1, which, in an epoch determined to be non-REM sleep, sums up power values ​​with frequencies of approximately 8 Hz to approximately 14 Hz calculated by fast Fourier transform to obtain the overall power value, divides the overall power value by the number of non-REM epochs to obtain the non-REM representative power value, which is the average power value per epoch.

3. The aforementioned learning information is The learning model obtained by performing machine learning training on the two or more training data sets mentioned above is: The determination unit, The determination device according to claim 1, which performs machine learning prediction processing using the one or more parameters and the learning model and obtains the determination result.

4. The aforementioned positive example is a positive example vector whose elements are two or more parameters that represent the characteristics of electroencephalogram information of a person with a mental illness. The aforementioned negative example is a negative example vector whose elements are two or more parameters that represent the characteristics of electroencephalogram information of a person without mental illness. The determination unit, The determination device according to claim 1, which obtains a positive centroid vector, which is the centroid vector of one or more positive example vectors; obtains a negative centroid vector, which is the centroid vector of one or more negative example vectors; obtains a positive distance, which is the distance between the positive centroid vector and a vector whose elements are two or more parameters obtained by the parameter acquisition unit; obtains a negative distance, which is the distance between the negative centroid vector and a vector whose elements are two or more parameters obtained by the parameter acquisition unit; and obtains the determination result using the positive distance and the negative distance.

5. The aforementioned positive example is a positive example vector whose elements are one or more parameters that represent the characteristics of electroencephalogram information of a person with a mental illness. The aforementioned negative example is a negative example vector whose elements are one or more parameters that represent the characteristics of electroencephalogram information of a person without mental illness. The aforementioned learning information is A correspondence table having correspondence information for each positive example vector (one or more) and correspondence information for each negative example vector (one or more), The determination unit, The determination device according to claim 1, which constructs a vector using the one or more parameters acquired by the parameter acquisition unit as elements, acquires the similarity between the vector and the one or more positive example vectors, and the similarity between the vector and the one or more negative example vectors, and acquires a determination result corresponding to a positive example vector or negative vector whose similarity satisfies the adoption conditions.

6. The aforementioned positive example is a positive example vector whose elements are one or more parameters that represent the characteristics of electroencephalogram information of a person with a mental illness. The aforementioned negative example is a negative example vector whose elements are one or more parameters that represent the characteristics of electroencephalogram information of a person without mental illness. The aforementioned learning information is This is an arithmetic formula that outputs a positive example value, which is the value corresponding to a positive example, for the aforementioned positive example vector, and a negative example value, which is the value corresponding to a negative example, for the aforementioned negative example vector. The determination unit, The determination device according to claim 1, which substitutes each of the one or more parameters acquired by the parameter acquisition unit into the calculation formula to acquire a score and acquires a determination result corresponding to the score.

7. A parameter acquisition unit that acquires one or more parameters, including non-REM representative power values, from two or more electroencephalogram (EEG) data associated with the determination results of whether or not a person has a mental illness, A learning unit acquires training data for each of two or more electroencephalogram (EEG) data sets, which includes one or more parameters acquired by the parameter acquisition unit and a determination result corresponding to each EEG data set, and uses the two or more training data sets to acquire learning information. A learning device comprising: a learning unit and a storage unit that stores the learning information acquired by the learning unit.

8. A determination method that causes a computer to perform all the processing performed by the determination device described in any one of Claims 1 to 6.

9. A method for producing learning information, which is realized by a parameter acquisition unit, a learning unit, and a storage unit, The parameter acquisition step involves the parameter acquisition unit acquiring one or more parameters, including non-REM representative power values, from two or more electroencephalogram (EEG) data associated with the determination result of whether or not a person has a mental disorder. The learning unit acquires training data for each of the two or more electroencephalogram (EEG) information sets, which includes one or more parameters acquired in the parameter acquisition step and a judgment result corresponding to each EEG information set, and a learning step in which it acquires learning information using the two or more training data sets. A method for producing learning information, comprising: an accumulation unit; and an accumulation step for accumulating the learning information acquired in the learning step.

10. Computers, A program for causing a determination device according to any one of claims 1 to 6, or a learning device according to claim 7.

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