Information processing device, information processing method, and program

Abstract

To enable the assessment of the effectiveness of cognitive behavioral therapy using a simple method. [Solution] The information processing device 1 comprises an acquisition unit 131 that acquires electroencephalogram (EEG) information showing the patient's brain waves, a calculation unit 132 that calculates the complexity in the low-frequency band of the EEG information, and an output unit 134 that outputs information indicating that cognitive behavioral therapy is effective when the complexity in the low-frequency band calculated by the calculation unit is above a predetermined first threshold.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] Cognitive behavioral therapy is a form of psychotherapy that integrates cognitive therapy, which modifies the way of interpreting and understanding things, and behavioral therapy, which modifies behavior based on learning theory. It has been considered effective for panic disorder, obsessive-compulsive disorder, eating disorder, anxiety disorder, etc. In recent years, cognitive behavioral therapy has also been recommended for chronic pain disorders (especially chronic low back pain) in which organic findings are absent and psychosocial factors are considered to be involved. Here, it is known that cognitive behavioral therapy is effective when verbal IQ is maintained and there is no tendency to developmental disorder (see Non-Patent Document 1). Also, in obsessive-compulsive disorder, working memory and communication skills affect the success of cognitive behavioral therapy (Non-Patent Document 2), and in depression, an influence of language ability has been reported (Non-Patent Document 3). Further, it has been shown that the cognitive function of a patient affects the treatment result for a disease targeted by cognitive behavioral therapy (Non-Patent Document 4). From these facts, it is considered that there is a common correlation between the effectiveness of cognitive behavioral therapy and the cognitive function of a patient in diseases targeted by cognitive behavioral therapy.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

[0004]

Outdoor Tool2

[0005] [Non-Patent Document 3] Kishon R, Abraham K, Alschuler DM, Keilp JG, Stewart JW, McGrath PJ, Bruder G E. Lateralization for speech predicts therapeutic response to cognitive behavioral therapy for depression. Psychiatry Res., 2015 Aug 30;228(3):606-11. doi: 10.1016 / j.psychres.2015.04.054. Epub 2015 Jun 11. PMID: 26162656; PMICD: PMC4532556.

[0006] [Non-Patent Document 4] McLellan, LF, Peters, L. & Rapee, RM Measuring Suitability for Cognitive Behavior Therapy: A Self-Report Measure. Cogn Ther Res 40, 687‐704 (2016).

[0007] [Non-Patent Document 5] Mizuno T, Takahashi T, Cho RY, et al(2010)Assessment of EEG dynamical complexity in Alzheimer's disease using multiscale entropy. Clin Neurophysiol, 121:1438-1446. [Overview of the Initiative] [Problems that the invention aims to solve]

[0008] However, there are a significant number of cases where cognitive behavioral therapy (CBT) is ineffective, leading to the problem of wasted time and resources when choosing this treatment method. Therefore, there is a need for a simple method to determine the effectiveness of CBT.

[0009] Therefore, the present invention has been made in view of these points, and aims to enable the determination of the effectiveness of cognitive behavioral therapy in a simple manner. [Means for solving the problem]

[0010] An information processing device according to a first aspect of the present invention includes: an acquisition unit that acquires electroencephalogram (EEG) information showing the electroencephalogram of a patient who is the subject of cognitive behavioral therapy; a calculation unit that calculates the complexity in the low-frequency band of the EEG shown in the EEG information; and an output unit that outputs information indicating that cognitive behavioral therapy is effective when the complexity in the low-frequency band calculated by the calculation unit is equal to or greater than a predetermined first threshold.

[0011] The predetermined first threshold may be a threshold determined by performing ROC (Receiver Operating Characteristic) analysis on the complexity in the low-frequency band of the electroencephalograms of multiple subjects having predetermined symptoms, where the cognitive behavioral therapy was effective for the subjects and the cognitive behavioral therapy was not effective for the subjects.

[0012] The calculation unit may calculate the complexity of multiple F / τ frequencies included in the low-frequency band for brain waves by roughening the time-series data generated by sampling the measured voltage values ​​of the brain waves indicated by the brainwave information at a sampling rate F, for each of several different time scale factors τ.

[0013] The complexity in the aforementioned low-frequency band may be represented by MFSE (Multiscale Fuzzy Sample Entropy).

[0014] The complexity in the low-frequency band may be expressed by MFSE (Multiscale Fuzzy Sample Entropy), MSE (Multiscale Sample Entropy), approximate entropy, sample entropy, refined multiscale entropy, or inherent fuzzy entropy.

[0015] The aforementioned low-frequency band may include a band between 10 Hz and 13 Hz.

[0016] The electroencephalogram (EEG) information indicates EEG detected in the frontal lobe, the calculation unit calculates the complexity of the EEG in the low-frequency band in the frontal lobe, and the output unit may output information indicating that psychotherapy is effective if the complexity of the EEG in the low-frequency band in the frontal lobe is equal to or greater than the first threshold.

[0017] The calculation unit may further calculate the complexity in the high-frequency band of the brainwaves indicated by the electroencephalogram information, and the output unit may output information indicating that there is a suspicion of psychogenic pain if the complexity in the high-frequency band is less than a predetermined second threshold.

[0018] In a second aspect of the present invention, the information processing method includes: an acquisition step in which a computer acquires electroencephalogram (EEG) information showing the brain waves of a patient who is the subject of cognitive behavioral therapy; a calculation step in which the complexity in the low-frequency band of the EEG information is calculated; and an output step in which, if the complexity of the EEG in the low-frequency band calculated in the calculation step is equal to or greater than a predetermined first threshold, information indicating that psychotherapy is effective is output.

[0019] In the program according to the third aspect of the present invention, the computer is caused to execute an acquisition step of acquiring electroencephalogram information indicating the electroencephalogram of a patient targeted for cognitive behavioral therapy, a calculation step of calculating the complexity in the low-frequency band of the electroencephalogram indicated by the electroencephalogram information, and an output step of outputting information indicating that psychotherapy is effective when the complexity of the electroencephalogram in the low-frequency band calculated in the calculation step is equal to or greater than a predetermined first threshold value.

Advantages of the Invention

[0020] According to the present invention, the effectiveness of cognitive behavioral therapy can be determined by a simple method.

Brief Description of the Drawings

[0021] [Figure 1] It is a diagram for explaining the outline of the information processing system S according to the embodiment. [Figure 2] It is a block diagram showing the configuration of the information processing apparatus 1. [Figure 3] It is a diagram showing an example of the screen to be displayed by the output unit 134. [Figure 4] It is a flowchart showing the processing flow in the information processing apparatus 1. [Figure 5] It is a diagram for explaining the experiment conducted by the inventors of the present application. [Figure 6] It is a diagram for explaining the experiment conducted by the inventors of the present application. [Figure 7] It is a diagram for explaining the results of the experiment conducted by the inventors of the present application. [Figure 8] It is a diagram for explaining the results of the experiment conducted by the inventors of the present application. [Figure 9] It is a diagram for explaining the results of the experiment conducted by the inventors of the present application. [Figure 10] It is a diagram for explaining the results of the experiment conducted by the inventors of the present application. [Figure 11] It is a diagram for explaining the results of the experiment conducted by the inventors of the present application. [Figure 12]This figure illustrates the results of the experiments conducted by the present inventors. [Modes for carrying out the invention]

[0022] [Regarding the ability to make distinctions based on the complexity of electroencephalograms] As previously reported (Non-Patent Literature 1-3), the suitability of cognitive behavioral therapy (CBT) is known to be influenced by the patient's cognitive function, and it is possible to predict the effectiveness of CBT by evaluating cognitive function. However, evaluating cognitive function is a highly specialized and time-consuming task, and there has been a need to establish a simpler evaluation method. Functional connectivity in the brain indicates the synchronization of information networks between multiple brain regions, and it is thought that when functional connectivity in the brain is strong, it is possible to maintain high cognitive function and respond flexibly to environmental changes. Therefore, it is thought that the cognitive function and flexibility of the brain can be evaluated by evaluating functional connectivity in the brain. Accordingly, the inventors of this application focused on the complexity of electroencephalograms (EEGs) as an indicator that reflects the functional connectivity of the brain, and in a study targeting patients with chronic low back pain, an example of a disease targeted by CBT, they revealed that EEG complexity, cognitive function, and the effectiveness of CBT are correlated, and that the effectiveness of CBT can be predicted using EEG complexity as an indicator.

[0023] In the experiment, the inventors of the present invention measured electroencephalograms (EEGs) of 25 patients with chronic low back pain who responded well to cognitive behavioral therapy (CBT), 25 patients who did not respond to CBT, and 20 healthy individuals without chronic low back pain symptoms, and calculated the complexity of the EEGs. The inventors evaluated the EEG complexity of the 25 patients in the response group and the 25 patients in the response group. As will be described in detail later, the inventors of the present invention found a significant difference in the complexity of the EEG in the low-frequency band between the response group and the response group, and discovered that the complexity of the EEG in the low-frequency band can function as a biomarker for differentiating the effectiveness of cognitive behavioral therapy. Below, based on the above experiment, an overview of the information processing system S, which is a system for determining the effectiveness of cognitive behavioral therapy based on the complexity of the EEG, will be described.

[0024] [Overview of Information Processing System S] Figure 1 is a diagram illustrating the overview of an information processing system S according to an embodiment. The information processing system S is a system for determining the effectiveness of cognitive behavioral therapy for patients with predetermined symptoms. One example of a predetermined symptom is chronic lower back pain. The information processing system S includes an information processing device 1 and a measuring device 2. The information processing device 1 may further include an information terminal (not shown) for use by a physician.

[0025] Information processing device 1 is a device for determining the effectiveness of cognitive behavioral therapy based on the complexity of the patient's electroencephalogram (EEG). Measurement device 2 is a device for measuring the patient's EEG. Measurement device 2 is an electroencephalograph that can be attached to the patient's head. Measurement device 2 measures the voltage detected at electrodes placed on the patient's scalp.

[0026] The processing in the information processing device 1 will now be explained. The measuring device 2 measures the subject's brainwaves and generates brainwave information (Figure 1 (1)). Brainwave information is information that shows the patient's brainwaves in time series. More specifically, brainwave information is time series data of voltage values ​​detected at a predetermined sampling rate by electrodes placed on the patient's scalp. The measuring device 2 transmits the brainwave information to the information processing device 1 (Figure 1 (2)).

[0027] The information processing device 1 calculates the complexity of the subject's electroencephalogram (EEG) based on the EEG information (Figure 1, (3)). EEG complexity is considered one of the indicators that reflect the functional connectivity of the brain (Non-Patent Literature 5). One example of EEG complexity is MFSE (Multiscale Fuzzy Sample Entropy). Details of the calculation of complexity will be described later.

[0028] The information processing device 1 determines the effectiveness of cognitive behavioral therapy based on the complexity of the calculated electroencephalogram (EEG) (Figure 1, (4)). As an example, the information processing device 1 determines the effectiveness of cognitive behavioral therapy by comparing the complexity of the EEG with a predetermined threshold. The information processing device 1 determines that cognitive behavioral therapy is effective if the complexity in the low-frequency band of the EEG is above a predetermined threshold.

[0029] If the information processing device 1 determines that cognitive behavioral therapy is effective based on the complexity of the calculated electroencephalogram, it outputs information indicating that cognitive behavioral therapy is effective (Figure 1, (5)).

[0030] With the information processing system S configured in this way, the effectiveness of cognitive behavioral therapy can be determined in a simple manner, and healthcare professionals can understand whether or not cognitive behavioral therapy is effective. As a result, it is possible to prevent wasting time and money by performing cognitive behavioral therapy even when it is ineffective. In the above description, the effectiveness of cognitive behavioral therapy is determined based on electroencephalogram data of patients with predetermined symptoms, but the symptoms of patients targeted by the present invention are not limited to these, as long as they are symptoms for which cognitive behavioral therapy can be an effective treatment.

[0031] [Configuration of Information Processing Device 1] Figure 2 is a block diagram showing the configuration of the information processing device 1. The information processing device 1 includes a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 includes an acquisition unit 131, a calculation unit 132, a determination unit 133, and an output unit 134.

[0032] The communication unit 11 is a communication interface for sending and receiving data with other devices via a network. The storage unit 12 is a storage medium including ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drive), hard disk drive, etc. The storage unit 12 pre-stores programs to be executed by the control unit 13.

[0033] The control unit 13 is a processor, such as a CPU (Central Processing Unit). The control unit 13 functions as an acquisition unit 131, a calculation unit 132, a determination unit 133, and an output unit 134 by executing a program stored in the storage unit 12.

[0034] The acquisition unit 131 acquires electroencephalogram (EEG) information showing the brainwaves of patients who are subjects of cognitive behavioral therapy. The acquisition unit 131 acquires EEG information from the measurement device 2.

[0035] The calculation unit 132 calculates the complexity of the brainwaves in the low-frequency band as indicated by the electroencephalogram (EEG) information. As an example, the calculation unit 132 calculates Multiscale Fuzzy Sample Entropy as the complexity of the EEG. Details of the calculation of EEG complexity are described below. The time-series data of voltage values ​​indicated by the EEG information acquired by the acquisition unit 131 is shown as shown in Equation 1.

[0036]

number

[0037] The calculation unit 132 calculates the complexity of the brainwaves for multiple F / τ frequencies included in the low-frequency band by roughening the time-series data generated by sampling the measured voltage values ​​of the brainwaves indicated by the EEG information at a sampling rate F, for each of several different time scale factors τ. The calculation unit 132 calculates the time-series data X of the brainwaves roughened by the time scale factor τ. The roughened time-series data X of the brainwaves is shown in Equation 2.

[0038]

number

[0039] As an example, when the sampling rate of EEG information is 200 Hz and the time scale factor τ is 10, the time series data x of the roughened EEG jThis shows time-series data corresponding to a 20Hz electroencephalogram (EEG). In this specification, when the sampling rate of EEG information is 200Hz, 1≦τ≦7 is defined as the high-frequency band and 15≦τ≦20 as the low-frequency band. Defined by frequency, 10Hz to 13Hz is the low-frequency band, and 28Hz to 200Hz is the high-frequency band. The calculation unit 132 may also calculate complexity using the alpha wave band as the low-frequency band.

[0040] Next, the calculation unit 132 calculates a template vector of the roughened electroencephalogram time-series data based on the following formula. The template vector is shown in Equation 3, where m is the number of dimensions of the template vector.

[0041]

number

[0042] Next, the calculation unit 132 calculates the infinity norm distance between the template vectors using the formula shown in Equation 4 below. The infinity norm distance represents the distance between the vectors and is determined by the element with the largest difference among the elements that make up the vectors being compared.

[0043]

number

[0044] Next, the calculation unit 132 calculates the Multiscale Fuzzy Sample Entropy as the complexity of the electroencephalogram based on the equations shown in Equations 5 and 6 below. Here, c and r are arbitrary constants. For example, c is 0.01. For r, a value within the range expected to be used as the standard deviation may be used. For example, r is 0.15.

[0045]

number

[0046]

number

[0047] The calculation unit 132 calculates complexity based on a template vector of low-frequency electroencephalograms (i.e., time-series data of electroencephalograms roughened by a time scale factor τ between 15 and 20). The calculation unit 132 may also calculate the average value of the complexity calculated for each time scale factor τ between 15 and 20.

[0048] Furthermore, if the electroencephalogram (EEG) information already represents time-series data of EEG in the low-frequency band, the calculation unit 132 may calculate a template vector based on that time-series data and then calculate the complexity of the low-frequency band based on the calculated template vector.

[0049] The determination unit 133 compares the complexity of the calculated electroencephalogram with a predetermined threshold (hereinafter sometimes referred to as the "first threshold") established to determine the effectiveness of cognitive behavioral therapy, and determines the effectiveness of cognitive behavioral therapy.

[0050] The first threshold is determined by performing ROC (Receiver Operating Characteristic) analysis on the complexity in the low-frequency band of the electroencephalogram (EEG) of multiple subjects with predetermined symptoms for whom cognitive behavioral therapy was effective, and for subjects for whom cognitive behavioral therapy was not effective. For example, the first threshold is 1.25. The determination unit 133 determines that cognitive behavioral therapy is effective if the calculated complexity in the low-frequency band is equal to or greater than the first threshold.

[0051] The determination unit 133 may determine whether the complexity is greater than or equal to the first threshold based on the complexity calculated for any of the time scale factor values ​​corresponding to the low frequency band, or it may be configured to determine whether each of the complexity values ​​calculated for multiple values ​​of the time scale factor corresponding to the low frequency band is greater than or equal to the first threshold. The determination unit 133 may also be configured to determine whether the average value of each of the complexity values ​​calculated for multiple values ​​of the time scale factor corresponding to the low frequency band is greater than or equal to the first threshold.

[0052] The output unit 134 outputs information indicating that cognitive behavioral therapy is effective when the complexity in the low-frequency band calculated by the calculation unit 132 is above a predetermined first threshold. For example, if the complexity in the low-frequency band is above a predetermined first threshold, the output unit 134 may display a screen on the information terminal used by the physician indicating that cognitive behavioral therapy is effective for the patient corresponding to the electroencephalogram (EEG) information. Figure 3 shows an example of a screen output by the output unit 134. In the screen S1 shown in Figure 3, the EEG complexity S11 calculated by the calculation unit 132 and information S12 indicating the effectiveness of cognitive behavioral therapy are arranged.

[0053] The output unit 134 may, if the complexity in the low-frequency band is below a predetermined first threshold, display a screen on the information terminal used by the physician indicating that cognitive behavioral therapy is ineffective.

[0054] With the information processing device 1 configured in this way, the effectiveness of cognitive behavioral therapy can be determined in a simple manner.

[0055] Furthermore, the complexity of the electroencephalogram (EEG) may be calculated using methods other than Multiscale Fuzzy Sample Entropy. For example, the complexity of the EEG may be calculated using known approximate entropy, sample entropy, multiscale sample entropy, refined multiscale entropy, or inherent fuzzy entropy.

[0056] Incidentally, it has been shown that areas of the frontal lobe involved in emotion and cognition are activated in cases of chronic lower back pain. Therefore, the information processing device 1 may be configured to determine the appropriateness of cognitive behavioral therapy based on complexity calculated from electroencephalograms detected in the frontal lobe.

[0057] The electroencephalogram (EEG) information shows the brain waves detected in the frontal lobe. The measuring device 2 generates EEG information showing the voltage detected at electrodes placed on the patient's frontal lobe and transmits the generated EEG information to the information processing device 1. As an example, the EEG information generated by the measuring device 2 shows the brain waves detected at positions AF7 and AF8 in the international 10-10 system.

[0058] The calculation unit 132 calculates complexity in the low-frequency band based on electroencephalogram (EEG) information showing the EEG in the frontal lobe. The determination unit 133 determines that cognitive behavioral therapy is effective if the complexity in the low-frequency band of the EEG in the frontal lobe calculated by the calculation unit 132 is equal to or greater than a first threshold. The output unit 134 outputs information indicating that cognitive behavioral therapy is effective when the determination unit 133 determines that cognitive behavioral therapy is effective.

[0059] By configuring the information processing device 1 to determine the effectiveness of cognitive behavioral therapy based on frontal lobe electroencephalograms, it becomes possible to make a determination based on electroencephalograms detected by a simple measuring device, thereby reducing the burden on patients during examinations.

[0060] [Processing flow in information processing device 1] Figure 4 is a flowchart showing the processing flow in the information processing device 1. The flowchart shown in Figure 4 starts from the point when the information processing device 1 is ready to acquire electroencephalogram (EEG) information.

[0061] The acquisition unit 131 acquires electroencephalogram (EEG) information (S01). The calculation unit 132 coarses the time-series data shown by the EEG information and generates time-series data in the low-frequency band (S02). The calculation unit 132 calculates the complexity of the EEG in the low-frequency band based on the time-series data in the low-frequency band (S03). The determination unit 133 determines whether the complexity of the EEG in the low-frequency band calculated by the calculation unit 132 is equal to or greater than a first threshold (S04).

[0062] If the complexity in the low-frequency band of the electroencephalogram is above the first threshold (YES in S04), the output unit 134 outputs information indicating that cognitive behavioral therapy is effective (S05), and the information processing device 1 terminates processing. If the complexity in the low-frequency band of the electroencephalogram is below the first threshold (NO in S04), the output unit 134 outputs information indicating that cognitive behavioral therapy is not effective (S06), and the information processing device 1 terminates processing.

[0063] [Effects of Information Processing Device 1] The information processing device 1 is configured to output information indicating the effectiveness of cognitive behavioral therapy based on the complexity of brain waves, thereby enabling the determination of the effectiveness of cognitive behavioral therapy in a simple manner.

[0064] <Variation> Furthermore, the inventors of this application have experimentally confirmed that when comparing the complexity of brainwaves of patients complaining of chronic lower back pain with that of healthy individuals, the complexity of broadband brainwaves in patients complaining of chronic lower back pain tends to be lower than that of healthy individuals. Therefore, the information processing device 1 may be configured to determine the presence or absence of psychogenic chronic lower back pain based on the complexity of brainwaves in the high-frequency band.

[0065] The calculation unit 132 calculates the complexity of the high-frequency band of the electroencephalogram (EEG) as indicated by the EEG information. As already explained, the high-frequency band is the band where the time scale factor τ takes 1 ≤ τ ≤ 7 when the sampling rate is 200 Hz. The calculation unit 132 may also calculate the complexity of the gamma wave band as the complexity of the high-frequency band.

[0066] The determination unit 133 determines whether psychogenic pain is present based on whether the complexity in the high-frequency band calculated by the calculation unit 132 is below a predetermined threshold (hereinafter sometimes referred to as the "second threshold"). The second threshold is, for example, a threshold determined by performing ROC analysis on the complexity in the high-frequency band of the electroencephalograms of multiple subjects with predetermined symptoms and multiple healthy subjects.

[0067] The output unit 134 outputs information indicating that psychogenic pain is suspected when the complexity in the high-frequency band is less than the second threshold. The output unit 134 may also output information indicating that cognitive behavioral therapy is ineffective, along with information indicating that psychogenic pain is suspected, when the complexity in the low-frequency band calculated by the calculation unit 132 is less than a predetermined first threshold and the complexity in the high-frequency band is less than the second threshold. With the output unit 134 configured in this way, it is possible to prevent ineffective cognitive behavioral therapy from being performed when psychogenic pain is present.

[0068] The modified information processing device 1 is configured to output information indicating the presence or absence of psychogenic pain based on the complexity in the high-frequency band of the electroencephalogram, thereby providing information that can be used as a reference during diagnosis.

[0069] [Regarding the experiments conducted by the inventors of this application] Figures 5 to 12 illustrate the results of the experiments conducted by the inventors of this invention. In the inventors' experiments, the Visual Analogue Scale (VAS) was administered to 50 patients at the initial consultation and after eight CBT sessions to evaluate changes in pain. The Autism-Spectrum Quotient (AQ), the Wechsler Adult Intelligence Scale-IV, and electroencephalogram (EEG) complexity were evaluated only at the initial consultation. For the 20 healthy control subjects, only EEG complexity was measured.

[0070] One-way ANOVA was used to compare mean differences in AQ and WAIS-IV between two groups, a response group and a non-response group, classified according to the improvement rate of pain VAS scores. Tukey's method was used for post-hoc testing. Repeated ANOVA was performed on four electrodes placed at positions AF7, AF8, TP9, and TP10 for 20 time-scale factors to evaluate differences between groups. The Greenhouse-Geisser test was used for degrees of freedom, with a significance level of 0.05. The ability of MFSE to differentiate the applicability of CBT was evaluated by ROC analysis. Discriminative accuracy was evaluated by measuring the area under the ROC curve (AUC). Three clinical psychologists with more than 10 years of experience conducted the CBT. The CBT techniques incorporated psychoeducation, cognitive restructuring, relaxation (abdominal breathing, progressive muscle relaxation), stress management, pacing, and behavioral activation, which are commonly used in the treatment of chronic low back pain. Due to the limited number of available slots, the sessions were limited to one 50-minute session per week, for a total of eight sessions.

[0071] Figures 5 to 7 show the average EEG complexity of the response group, non-responder group, and healthy control group in the experiment described above, respectively. In the graphs shown in Figures 5 to 7, the vertical axis represents EEG complexity, and the horizontal axis represents the time scale factor τ. Comparing the EEG complexity of the response group and non-responder group from Figures 5 and 6, it can be seen that there is a significant difference in EEG complexity in the low-frequency band.

[0072] Furthermore, comparing the graphs shown in Figures 5 to 7, it can be confirmed that the complexity of the electroencephalogram (EEG) in the high-frequency band of the responding and non-responding groups is lower than that of the healthy control group. From this, it can be seen that the complexity of the EEG in the high-frequency band can function as an indicator of the presence or absence of psychogenic pain.

[0073] Figure 8 shows the results of ROC analysis based on electroencephalogram (EEG) complexity in the response and non-response groups in the above experiment. In the graph shown in Figure 8, the horizontal axis represents the false-positive rate, and the vertical axis represents the sensitivity. In the results shown in Figure 8, when the cutoff value for EEG complexity in the low frequency band is set to 1.25, the AUC is 0.825, indicating high discriminative ability. Through the above experiment, the inventors of this application discovered that EEG complexity in the low frequency band can function as a biomarker for discriminating the effectiveness of cognitive behavioral therapy.

[0074] Figures 9 to 12 are tables and graphs showing the correlation between cognitive ability and electroencephalogram complexity in the above experiment. In Figures 9 to 12, VCI, Imagination, and Local details are used as indicators of cognitive ability. Here, VCI (Verbal Comprehension Index) is an index included in WAIS-IV and indicates the level of comprehension of language, amount of knowledge, or the ability to express one's own thoughts using language or to infer what others are trying to say. Imagination and Local details are indicators that make up AQ. Imagination and Local details indicate the level of creativity and the strength of attention to detail, respectively.

[0075] Figure 9 is the correlation matrix between cognitive ability and MFSE for the response group in the above experiment. Figure 10 is a graph plotting the correlation between cognitive ability and MFSE for the response group in the above experiment. Figure 11 is the correlation matrix between cognitive ability and MFSE for the non-responder group in the above experiment. Figure 12 is a graph plotting the correlation between cognitive ability and MFSE for the non-responder group in the above experiment.

[0076] Figures 9 to 12 show that cognitive ability and electroencephalogram (EEG) complexity correlate in both the response group and the non-response group, and that the response group tends to have higher EEG complexity and cognitive ability compared to the non-response group. Therefore, it can be said that EEG complexity, cognitive function, and the effectiveness of cognitive behavioral therapy are correlated, and that EEG complexity can be used as an indicator to predict the effectiveness of cognitive behavioral therapy.

[0077] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of its gist. For example, all or part of the apparatus can be configured by functionally or physically distributing and integrating in any unit. Furthermore, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effects of the new embodiments resulting from the combinations are combined with the effects of the original embodiments. [Explanation of symbols]

[0078] 1. Information Processing Device 2. Measuring device 11 Communications Department 12 Storage section 13 Control Unit 131 Acquisition Department 132 Calculation Section 133 Judgment section 134 Output section

Claims

1. An acquisition unit that acquires electroencephalogram (EEG) information showing the brainwaves of patients who are candidates for cognitive behavioral therapy, A calculation unit that calculates the complexity in the low-frequency band of the electroencephalogram (EEG) as indicated by the EEG information, An output unit that outputs information indicating that cognitive behavioral therapy is effective when the complexity in the low-frequency band calculated by the calculation unit is equal to or greater than a predetermined first threshold, An information processing device having

2. The predetermined first threshold is a threshold determined by performing ROC (Receiver Operating Characteristic) analysis on the complexity in the low-frequency band of the electroencephalogram (EEG) of subjects who had a predetermined symptom and for whom the cognitive behavioral therapy was effective, and for subjects for whom the cognitive behavioral therapy was not effective. The information processing apparatus according to claim 1.

3. The calculation unit calculates the complexity of multiple F / τ frequencies included in the low-frequency band for brain waves by roughening the time-series data generated by sampling the measured voltage values ​​of the brain waves indicated by the brainwave information at a sampling rate F, for each of several different time scale factors τ. The information processing apparatus according to claim 1.

4. The complexity in the aforementioned low-frequency band is represented by MFSE (Multiscale Fuzzy Sample Entropy). The information processing apparatus according to claim 1.

5. The complexity in the aforementioned low-frequency band is represented by MFSE (Multiscale Fuzzy Sample Entropy), MSE (Multiscale Sample Entropy), approximate entropy, sample entropy, Refined Multiscale Entropy, or Inherent fuzzy entropy. The information processing apparatus according to claim 1.

6. The aforementioned low-frequency band includes a band between 10 Hz and 13 Hz. The information processing apparatus according to claim 1.

7. The aforementioned electroencephalogram (EEG) information shows EEGs detected in the frontal lobe. The calculation unit calculates the complexity of the electroencephalogram in the low-frequency band in the frontal lobe, The output unit outputs information indicating that psychotherapy is effective when the complexity of the electroencephalogram in the low-frequency band in the frontal lobe is equal to or greater than the first threshold. The information processing apparatus according to any one of claims 1 to 6.

8. The calculation unit further calculates the complexity in the high-frequency band of the brainwaves indicated by the brainwave information, The output unit outputs information indicating that psychogenic pain is suspected to be occurring when the complexity in the high-frequency band is less than a predetermined second threshold. The information processing apparatus according to any one of claims 1 to 6.

9. A computer executes The acquisition step involves obtaining electroencephalogram (EEG) information showing the brainwave patterns of patients who are candidates for cognitive behavioral therapy, and A calculation step for calculating the complexity in the low-frequency band of the electroencephalogram (EEG) as indicated by the EEG information, An output step that outputs information indicating that psychotherapy is effective if the complexity of the low-frequency brainwaves calculated in the calculation step is equal to or greater than a predetermined first threshold, An information processing method having

10. On the computer, The acquisition step involves obtaining electroencephalogram (EEG) information showing the brainwave patterns of patients who are candidates for cognitive behavioral therapy, and A calculation step for calculating the complexity in the low-frequency band of the electroencephalogram (EEG) as indicated by the EEG information, An output step that outputs information indicating that psychotherapy is effective if the complexity of the low-frequency brainwaves calculated in the calculation step is equal to or greater than a predetermined first threshold, A program to execute.

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