Sleep estimation system, sleep estimation method, and computer-readable recording medium

Abstract

The invention provides a sleep estimation system, a sleep estimation method, and a computer-readable recording medium. A sleep stage can be easily estimated. A sleep estimation device is provided with: a first acquisition unit that acquires blood flow data; a generation unit that generates a frequency spectrum of the blood flow data by performing frequency analysis processing on the blood flow data; and a first determination unit that determines the sleep stage of the subject on the basis of the frequency spectrum.

Description

[0001] This application is a divisional application of the patent application with application number 202180057578.9, filed on August 6, 2021, and with the title of “Sleep Estimation Device, Sleep Estimation System, Wearable Device, and Sleep Estimation Method”. TECHNICAL FIELD

[0002] The present disclosure relates to estimation of a sleep stage of a subject. BACKGROUND

[0003] A technique of detecting a sleep stage is described in Patent Literature 1.

[0004] PRIOR ART DOCUMENTS

[0005] PATENT LITERATURE

[0006] Patent Literature 1: Japanese Patent Application Publication No. 2018-161432 SUMMARY

[0007] A sleep estimation device according to an aspect of the present disclosure includes: a first acquisition unit that acquires blood flow data representing blood flow of a subject; a generation unit that generates a frequency spectrum of the blood flow data by performing frequency analysis processing on the blood flow data; and a first determination unit that determines a sleep stage of the subject based on the frequency spectrum.

[0008] Further, a sleep estimation device according to an aspect of the present disclosure includes: an acquisition unit that acquires blood flow data representing blood flow of a subject; a generation unit that generates processing data representing a result of time-frequency analysis processing of the blood flow data by performing wavelet transform processing in which intensity in a given frequency band is more relatively emphasized than in other frequency bands or short-time Fourier transform processing on the blood flow data; and a determination unit that determines a sleep stage of the subject based on the processing data.

[0009] Further, a sleep estimation method according to an aspect of the present disclosure includes: a first acquisition process of acquiring blood flow data representing blood flow of a subject; a generation process of generating a frequency spectrum of the blood flow data by performing frequency analysis processing on the blood flow data; and a first determination process of determining a sleep stage of the subject based on the frequency spectrum.

[0010] Further, a sleep estimation method according to an aspect of the present disclosure includes: an acquisition process of acquiring blood flow data representing blood flow of a subject; a generation process of generating processing data representing a result of time-frequency analysis processing of the blood flow data by performing wavelet transform processing in which intensity in a given frequency band is more relatively emphasized than in other frequency bands or short-time Fourier transform processing on the blood flow data; and a determination process of determining a sleep stage of the subject based on the processing data. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 is a block diagram showing an outline configuration example of the sleep estimation system of Embodiment 1.

[0012] Figure 2 is a graph showing an example of blood flow waveform data detected by a blood flow meter.

[0013] Figure 3 is a graph showing an example of a frequency spectrum generated by performing Fourier transform processing on blood flow waveform data.

[0014] Figure 4 is a graph showing an example of electrocardiogram waveform data detected by an electrocardiograph.

[0015] Figure 5 is a graph showing an example of a frequency spectrum generated by performing Fourier transform processing on electrocardiogram waveform data.

[0016] Figure 6 is an image showing an example of a result of wavelet transform processing on blood flow waveform data.

[0017] Figure 7 is a flowchart showing an example of a flow of processing performed by the sleep estimation device of Embodiment 1.

[0018] Figure 8 is a block diagram showing an outline configuration example of the sleep estimation system of Embodiment 2. DETAILED DESCRIPTION

[0019] The determination (estimation) of the sleep stage of a subject to which the present disclosure is directed will be described below. First, the principle that serves as the basis for the determination of the sleep stage of a subject to which the present disclosure is directed will be described. Note that, in the present specification, the case where "A to B" is described means "A or more and B or less". Furthermore, in the present specification, blood flow waveform data will be described as an example of blood flow data used in the determination of the sleep stage.

[0020] [Principle]

[0021] Figure 2 is a graph showing an example of blood flow waveform data detected by a blood flow meter. In Figure 2 , the vertical axis represents a value proportional to the blood flow per unit time [unit: dimensionless], and the horizontal axis represents the measurement time [unit: min]. The blood flow meter can acquire Figure 2The raw waveform data W1 and the processed waveform data W2 shown as such are blood flow waveform data. The processed waveform data W2 is waveform data obtained by processing the raw waveform data W1 so as to easily obtain the peak value of the R wave. The processed waveform data W2 is generated, for example, by performing a smoothing process on the raw waveform data W1. In the processed waveform data W2, the time interval between adjacent peak values (blood flow at the positions shown by the inverted triangular shapes in the figure) represents the heart rhythm interval (RRI: R-R Interval). In Figure 4 Also in the raw waveform data W12 of the heart rhythm shown as such, the time interval between adjacent peak values (blood flow at the positions shown by the inverted triangular shapes in the figure) represents the heart rhythm interval (RRI).

[0022] A blood flow meter that detects blood flow waveform data is a sensor that can detect blood flow waveform data representing the blood flow of an object by receiving scattered light generated by irradiating light to the blood vessel of the object. The blood flow meter is provided with: a light emitting portion that irradiates light to the blood vessel of the object; and a light receiving portion that receives the scattered light.

[0023] Generally, if laser light is irradiated to a fluid, the irradiated laser light is scattered to generate scattered light due to (i) a scatterer included in the fluid and moving together with the fluid and (ii) a stationary object such as a tube through which the fluid flows. Generally, the scatterer brings about inhomogeneity of complex refractive index in the fluid.

[0024] To the scattered light generated by the scatterer moving together with the fluid, a wavelength shift is brought about due to the Doppler effect corresponding to the flow rate of the scatterer. On the other hand, to the scattered light generated by the stationary object, no wavelength shift is brought about. These scattered lights cause interference of light, and thus light beats (difference beats) are observed.

[0025] A blood flow meter can be a sensor that utilizes this phenomenon. That is, the blood flow meter can be a laser Doppler blood flow meter that detects light beats brought about to the scattered light generated in the blood as a fluid by irradiating laser light to the blood vessel of the object as the blood flow waveform data.

[0026] More specifically, the acquired light receiving signal can be analyzed by a processor provided to the blood flow meter to calculate frequency analysis data representing the signal intensity of each frequency of the light receiving signal. As an example, the processor can analyze the acquired light receiving signal using a method such as FFT (Fast Fourier Transformation).

[0027] The processor can further generate blood flow waveform data representing a variation pattern of the blood flow of the subject based on the frequency resolution data. As an example, the processor can calculate a first moment and X of the acquired frequency resolution data as the blood flow waveform data. More specifically, the processor can calculate the first moment and X of the acquired frequency resolution data using the following equation. The processor can calculate the first moment and X in a part of the frequency band (for example, 1 to 20 kHz) using the following equation.

[0028] X =∑fx×P(fx)

[0029] Here, "fx" is the frequency, and "P(fx)" is a value of the signal intensity at the frequency fx.

[0030] The first moment and X calculated by the processor based on the frequency resolution data can be a value proportional to the blood flow of the subject. The processor can generate pattern data representing a variation pattern of the blood flow of the subject per hour by calculating the first moment and X of each of a plurality of frequency resolution data. In addition, the processor can generate the blood flow waveform data using data included in the frequency resolution data, among data, which is included in a part of the frequency band. The processor can output the generated blood flow waveform data.

[0031] The blood flow waveform data can include, in addition to the blood flow, for example, data related to at least one of a cardiac output and a variation coefficient of a vascular motion (vasomotion). The cardiac output is an amount of blood sent by one heartbeat of the heart. The vasomotion is a contraction and expansion motion of a blood vessel that is naturally generated and rhythmic. The variation coefficient of the vasomotion is a value representing a variation in the blood flow generated based on the vasomotion as a deviation.

[0032] In addition, the blood flow waveform data can include a pulse wave.

[0033] Figure 3 is a graph showing an example of a frequency spectrum generated by performing a Fourier transform process on the blood flow waveform data shown in Figure 2 The vertical axis represents the intensity of the frequency spectrum [unit: dB], and the horizontal axis represents the frequency [unit: Hz]. The Fourier transform process is an example of a frequency resolution process, and is a process of generating a frequency spectrum of waveform data that does not include a change in time.

[0034] In Figure 3 , the frequency spectrum FW1 corresponding to each sleep stage and the frequency spectrum FW2 are shown. The frequency spectrum FW1 is a frequency spectrum generated as a result of performing a Fourier transform process on the original waveform data W1. The frequency spectrum FW2 is a frequency spectrum generated as a result of performing a Fourier transform process on a heart rate interval (RRI) of the processed waveform data W2.

[0035] The above-described sleep stage is classified into three stages of wakefulness, rapid eye movement sleep, and non-rapid eye movement sleep. The non-rapid eye movement sleep can be further classified into stage 1 (N1), stage 2 (N2), and stage 3 (N3) in order from a light sleep stage. The rapid eye movement sleep is sleep accompanied by rapid eye movement (REM). The non-rapid eye movement sleep is sleep not accompanied by rapid eye movement.

[0036] The classification is performed on the basis of brain wave data detected by a brain wave meter equipped to the subject. The brain waves are classified into four types of β wave, α wave, θ wave, and δ wave in order from long to short wavelength. The β wave is, for example, a brain wave of a frequency of about 38 to 14 Hz. The α wave is, for example, a brain wave of a frequency of about 14 to 8 Hz. The θ wave is, for example, a brain wave of a frequency of about 8 to 4 Hz. The δ wave is, for example, a brain wave of a frequency of about 4 to 0.5 Hz.

[0037] In a case where the θ wave and the δ wave are dominant compared to the β wave and the α wave, a person is asleep. Here, the so-called "dominant" means that the proportion of a certain wave becomes large in the measured brain waves. It is known that the dominant brain wave periodically changes in the range of the θ wave and the δ wave at the time of sleep. Further, in a case where the proportion of the θ wave contained in the brain waves is less than a given value, the person becomes a state of rapid eye movement sleep, in a case where the proportion of the θ wave is the given value or more, and in a case where the δ wave is dominant, the person is in a state of non-rapid eye movement sleep. The stage 1 is, for example, a state in which the α wave is 50% or less and various frequencies are mixed at low amplitude. The stage 2 is, for example, a state in which the θ wave and the δ wave are irregularly present at low amplitude but a slow wave is not present at high amplitude. The stage 3 is, for example, a state in which a slow wave of 2 Hz or less and 75 μV is 20% or more. A state in which a slow wave of 2 Hz or less and 75 μV is 50% or more can also be referred to as stage 4.

[0038] In Figure 3 , as the sleep stage, wakefulness is denoted as "WK", rapid eye movement sleep is denoted as "RM", stage 1 of non-rapid eye movement sleep is denoted as "N1", stage 2 of non-rapid eye movement sleep is denoted as "N2", and stage 3 of non-rapid eye movement sleep is denoted as "N3".

[0039] As Figure 3In the spectrum FW1 and the spectrum FW2 corresponding to the period 3, a significant intensity change can be seen in the frequency band of 0.2 to 0.3 Hz (a given frequency band) in the spectrum obtained from the subject in the sleep stage of the period 2 and the period 3. In other words, in the spectrum, the intensity in a first range R1 of a part of the frequency band of 0.2 to 0.3 Hz is larger than the intensity in a second range R2 other than the first range R1 by a given value or more. Hereinafter, the intensity in the first range R1 is referred to as a first intensity, and the intensity in the second range R2 is referred to as a second intensity.

[0040] The first intensity can be, for example, the maximum intensity in the frequency band of 0.2 to 0.3 Hz. The second intensity can be, for example, the maximum intensity in the second range R2 other than the first range R1 (example: ±0.02 Hz or so centered on the maximum intensity, which is within the frequency band of 0.2 to 0.3 Hz) including the maximum intensity. The given value is set, for example, by experiment, to be the size of the degree at which it can be determined that the characteristic waveform Sh included in the frequency band of 0.2 to 0.3 Hz is included when there is no characteristic waveform Sh in the adjacent frequency band. The characteristic waveform Sh can be, for example, a waveform that is convex upward and is a certain degree wide (example: a waveform with a full width at half maximum of 0.03 Hz or more). In the spectrum, the characteristic waveform Sh can be, for example, a waveform that is convex upward and is a certain degree wide (example: a waveform with a full width at half maximum of 0.03 Hz or more) in the frequency band of 0.2 to 0.3 Hz. Figure 3 In the spectrum FW1 and the spectrum FW2 corresponding to the period 3, a significant intensity change can be seen in the frequency band of 0.2 to 0.3 Hz (a given frequency band) in the spectrum obtained from the subject in the sleep stage of the period 2 and the period 3. In other words, in the spectrum, the intensity in a first range R1 of a part of the frequency band of 0.2 to 0.3 Hz is larger than the intensity in a second range R2 other than the first range R1 by a given value or more. Hereinafter, the intensity in the first range R1 is referred to as a first intensity, and the intensity in the second range R2 is referred to as a second intensity.

[0041] On the other hand, in the spectrum obtained from the subject in the sleep stage of the period 1, the wakefulness, and the rapid eye movement sleep, no significant intensity change can be seen in the frequency band of 0.2 to 0.3 Hz, and in addition, the characteristic waveform Sh described above cannot be seen.

[0042] As a result of diligent research, the inventors found that when the spectrum in which a significant intensity change (a characteristic waveform Sh) can be seen in the frequency band of 0.2 to 0.3 Hz is obtained, the subject is highly likely to be in the sleep stage of the period 2 or the period 3. That is, the inventors found that when the spectrum having the characteristic waveform Sh can be obtained, the subject is highly likely to be in the sleep stage of the period 2 or the period 3. In addition, the inventors found that, in particular, in the spectrum of the blood flow waveform data detected by the blood flow meter (example: a laser Doppler blood flow meter), a significant intensity change can be seen in the frequency band of 0.2 to 0.3 Hz. The inventors arrived at these insights to develop the sleep estimation device that can improve the accuracy of the determination of the sleep stage of the subject.

[0043] (Comparison with electrocardiogram waveform data)

[0044] In addition, the following differences are described between the electrocardiogram waveform data (electrocardiogram) detected by the electrocardiograph and the blood flow waveform data detected by the blood flow meter.

[0045] Figure 4 This is a chart representing an example of electrocardiogram (ECG) waveform data detected by an ECG monitor. In Figure 4 In the graph, the vertical axis represents the intensity of the heart rhythm [unit: dB], and the horizontal axis represents the measurement time [unit: min]. Figure 4 In the middle, as electrocardiogram waveform data, the original waveform data W11 and W12 of the heart rhythm are shown.

[0046] Figure 5 This is a graph representing an example of the spectrum generated by performing a Fourier transform on electrocardiogram waveform data. The vertical axis represents the intensity of the spectrum [unit: dB], and the horizontal axis represents the frequency [unit: Hz].

[0047] exist Figure 5 The spectra FW11 and FW12 corresponding to each sleep stage are shown. Spectrum FW11 is the spectrum generated by performing a Fourier transform on the original waveform data W11. Spectrum FW12 is the spectrum generated by performing a Fourier transform on the heart rhythm interval (RRI). The sleep stage corresponding to spectra FW11 and FW12 is determined based on brainwave data obtained from an EEG meter equipped on the subject.

[0048] like Figure 5 As shown, in the spectra FW11 and FW12 obtained by transforming the electrocardiogram waveform data detected from subjects in sleep stages 2 and 3, no significant intensity changes (characteristic waveform Sh) are observed in the 0.2–0.3 Hz band. The FW12 spectrum corresponding to sleep stages 2 and 3 exhibits an upward convex shape in the 0.2–0.3 Hz band. However, the FW12 spectrum corresponding to sleep stage 1 also exhibits the same shape as the FW12 spectra corresponding to stages 2 and 3. Therefore, no significant intensity changes are observed in the 0.2–0.3 Hz band for the FW12 spectrum corresponding to stages 2 and 3.

[0049] Through their in-depth research, the inventors discovered that significant intensity variations in the 0.2–0.3 Hz frequency band are a phenomenon unique to the spectrum of blood flow waveform data. Therefore, the inventors found that by using a spectrum derived from blood flow waveform data rather than electrocardiogram waveform data, they can determine the sleep stage of a subject, particularly accurately identifying whether the subject is in stage 2 or stage 3 sleep.

[0050] (Regarding frequency bands)

[0051] According to the blood flow meter used and individual differences of the subject, and the like, there is a possibility that the band in which the above-described significant intensity change can be seen is expanded. If this is taken into consideration, in the band of, for example, 0.15 to 0.4 Hz of the frequency spectrum FW1 and FW2 corresponding to the period 2 or the period 3, the possibility that a significant intensity change that cannot be seen from the electrocardiogram waveform data is sufficiently high. In the following description, it is described that the band in which the above-described significant intensity change can be seen is 0.2 to 0.3 Hz.

[0052] [Wavelet transform processing]

[0053] In the above-described principle, it is described that the frequency spectrum obtained by performing the Fourier transform processing as the frequency analysis processing is used. The sleep stage of the subject can be determined based on the frequency spectrum obtained by performing the wavelet transform processing as the frequency analysis processing. The wavelet transform processing is an example of the time-frequency analysis processing. The time-frequency analysis processing is processing of generating a frequency spectrum of waveform data including a change in time. The wavelet transform processing is processing of generating a frequency spectrum of waveform data using a mother wavelet as an arbitrary reference waveform.

[0054] The mother wavelet used in the wavelet transform processing is defined as follows. In the following equation, "t" represents a time variable, "a" represents a scale parameter (a parameter that expands or contracts the mother wavelet in the time axis direction), and "b" represents a translation parameter (a parameter that moves the mother wavelet in parallel with the time axis direction).

[0055] [Mathematical equation 1]

[0056]

[0057] Further, a function for performing the wavelet transform processing is defined as follows. In the following equation, "f(t)" represents waveform data, and "*" represents a conjugate complex number. By substituting the mother wavelet in which the values of the above-described "a" and "b" are adjusted into the following equation, a frequency spectrum of the waveform data can be generated.

[0058] [Mathematical equation 2]

[0059]

[0060] By using the wavelet transform processing, the intensity (hereinafter referred to as an object intensity) in the band of 0.2 to 0.3 Hz can be relatively more emphasized than other bands. As described in the above-described principle, when the frequency spectrum of the waveform Sh characteristic in the band of 0.2 to 0.3 Hz is obtained, the possibility that the subject is in the sleep stage of the period 2 or the period 3 is high. Therefore, by emphasizing the object intensity using the wavelet transform processing, it is possible to improve the possibility of determining whether the subject is in the sleep stage of the period 2 or the period 3 more accurately and favorably.

[0061] In the wavelet transform processing, as described above, a mother wavelet set to emphasize the object intensity can be used. The mother wavelet with increased object intensity can be set by adjusting the values of "a" and "b" described above. In addition, Morlet can also be used as the mother wavelet. In this case, the scale parameter "a" has a relationship of "ω = 2π / a" and represents a local angular frequency. Since the angular frequency "ω" is represented as "ω = 2πf", the portion of "f (frequency)" can be set to 0.2 to 0.3 Hz to perform the wavelet transform processing. The object intensity can be the intensity of the entire frequency band of 0.2 to 0.3 Hz, or the intensity in a partial range (for example, the first range R1) of the frequency band.

[0062] In addition, as a result of the wavelet transform processing of the blood flow waveform data, intensity change data indicating the change in intensity in each frequency band over time within a given time can be generated. The given time can be set to a time at which the sleep stage of the subject can be accurately determined by experiment, for example. In the present embodiment, the given time can be set to 2.5 minutes, for example.

[0063] The wavelet transform processing can generate a frequency spectrum including the change in intensity over time, unlike the Fourier transform processing, and thus can increase the number of data compared to the Fourier transform processing. Generally, in the generation of the learned model described below, the more the number of data (the more the features learned), the more a learned model that can output output data with high accuracy can be generated. Therefore, in the case of generating a learned model, it is effective to use the intensity change data.

[0064] Figure 6 is an image indicating an example of a result of the wavelet transform processing of the blood flow waveform data. In Figure 6 , an image generated by performing the wavelet transform processing on the blood flow waveform data (original waveform data W1) serving as the basis of the frequency spectrum FW1 of the period 3 shown in Figure 3 is shown. This image is an example of intensity change data that emphasizes the object intensity. Hereinafter, the image indicating the intensity change data will be referred to as a wavelet image.

[0065] In Figure 6 , the vertical axis represents the frequency [unit: Hz] and the horizontal axis represents the time [unit: min]. Figure 6 The shades in the wavelet image of represent the intensity [unit: dB]. That is, the wavelet image is data that shows the intensity distribution of the frequency spectrum in a plane defined by the frequency and the time.

[0066] In the wavelet image of this embodiment, frequency bands (intensities) can be represented by color gradients. For example, in a wavelet image, low frequency bands can be represented by cool colors, and high frequency bands by warm colors. Specifically, frequency bands can be represented from low to high using dark blue, blue, light blue, yellowish-green, light yellowish-green, yellow, orange, and red. As long as the intensity distribution is visually discernible in the wavelet image, frequency bands can also be represented by other colors, or even by grayscale levels. Figure 6 This represents an example of a wavelet image represented by the aforementioned colors and converted to grayscale.

[0067] exist Figure 6 In the wavelet image, region AR1, distributed along the time axis at and around 0.2 Hz, shows an intensity higher than that in the frequency band adjacent to region AR1. Specifically, within the frequency band of approximately 0.2 Hz ± approximately 0.05 Hz of region AR1, an intensity band shown in red is distributed along the time axis, centered on this intensity band, with intensity regions shown in orange, yellow, pale yellow-green, and yellow-green. Figure 6 In the diagram, a portion of the intensity band shown in red is represented by symbol 101. Furthermore, portions of the intensity regions shown in orange, yellow, pale yellow-green, and yellow-green are represented by symbol 102. On the other hand, in adjacent frequency bands, intensity regions shown in pale blue, blue, and dark blue are predominantly distributed, but intensity regions shown in red, orange, and yellow are not present. Figure 6 In the diagram, a portion of the intensity areas shown in light blue, blue, and dark blue will be represented by the symbol 103.

[0068] In addition, such as Figure 6 As shown, in the wavelet image, a sawtooth-shaped intensity band forms along the time axis in frequency bands higher than the 0.2–0.3 Hz band. Figure 6 In the wavelet image, a sawtooth-shaped intensity band is formed in region AR2 (the frequency band above approximately 0.7 Hz). Region AR2 has a lower intensity than region AR1 in the frequency band of approximately 0.9 to 1.0 Hz, and the intensity gradually decreases towards the frequency band below 0.9 Hz and the frequency band above 1.0 Hz.

[0069] The aforementioned saw-shaped intensity band represents the intensity distribution corresponding to the heart rhythm. This intensity distribution forms a band shape along the time axis during sleep; the lighter the sleep, the more fragmented the band shape becomes. The intensity distribution corresponding to the heart rhythm is an intensity distribution that cannot be obtained through Fourier transform processing. By using wavelet images in the generation of the learned model described below, a learned model incorporating the heart rhythm can be generated.

[0070] Figure 6One example of a wavelet image in which the object intensity is emphasized is shown, but it is to be noted that even a wavelet image in which the object intensity is not emphasized shows a higher intensity in the first region AR1 than in the adjacent frequency band.

[0071] Generation of a learned model

[0072] In the determination of the sleep stage of the subject, a learned model (an approximator) for determining the sleep stage of the subject can be used. The learned model is a mathematical model of a neuron that mimics a human brain nervous system (a neural network including an input layer, a hidden layer, and an output layer) that is learned to determine the sleep stage of the user. The mathematical model can be, for example, a convolutional neural network (CNN), a recurrent neural network (RNN), or an LSTM (Long Short Term Memory).

[0073] The learning refers to adjusting the strength and bias of the coupling between units so that the correct operation result is output from the output layer. In the present embodiment, in the case of learning, the learning data is input to the input layer. In the hidden layer, the operation based on the operation data is performed on the learning data, and the operation result in the hidden layer is output as the output data from the output layer. The operation data is adjusted by comparing the teaching data and the output data so that the error becomes small. By repeatedly performing this processing on a plurality of learning data, the learned model in which the operation data is adjusted is generated. That is, in the present embodiment, the learned model can be generated by so-called supervised learning using the learning data and the teaching data. The sleep estimation device 51 described later can determine the sleep stage of the subject by using the learned model thus generated.

[0074] The learning data is data that becomes an example for generating the learned model. The learning data can be a spectrum generated from the blood flow waveform data. In the present embodiment, a wavelet image is used. As the wavelet image, the object intensity can be emphasized or not. The learning data can be data in which the behavior differs between the time of wakefulness and the time of sleep, or data in which the behavior changes depending on the depth of sleep. As the learning data, various kinds of data (example: a spectrum of blood flow waveform data showing mutually different waveforms) can be used.

[0075] The teaching data is data in which a correct answer label and learning data are associated. For example, data in which a sleep stage of a person from whom blood flow waveform data is acquired is associated with a spectrum as a correct answer label and as learning data can be used as teaching data. As described above, the sleep stage of the subject can be determined based on brain wave data detected by an electroencephalograph. As the correct answer label, a symbol indicating each sleep stage can be used. Alternatively, as the correct answer label, a symbol indicating a correct answer can be used for a certain specific sleep stage (e.g., stage 2 or stage 3), and a symbol indicating an incorrect answer can be used for sleep stages other than these. In the present embodiment, as an example of the teaching data, data in which a wavelet image (a wavelet image determined to be stage 2 or stage 3 by brain wave data) corresponding to the case where stage 2 or stage 3 is known as a correct answer label can be used.

[0076] The operation data is data related to operations for generating the learned model, including operation expressions, variables (e.g., biases and weights) of the operation expressions, and activation functions. The biases and weights define the strength of the combination between units. By adjusting the biases and weights, the accuracy of the learned model can be improved. As an adjustment method of the operation data, for example, the error backpropagation method and the gradient descent method can be used.

[0077] [Embodiment 1]

[0078] An example of a sleep estimation system 1 capable of determining a sleep stage of a subject, which is constructed based on the above-described principle, will be described below. The sleep estimation system 1 of the present embodiment can be a system capable of determining a sleep stage of a subject using the above-described learned model.

[0079] [Sleep Stage Estimation System]

[0080] Figure 1 is a block diagram showing an example of the schematic configuration of the sleep estimation system 1 of Embodiment 1. As shown in Figure 1 The sleep estimation system 1 includes an accelerometer 2, a blood flow meter 3, and a portable terminal 5. In the portable terminal 5, for example, a sleep estimation device 51 that determines a sleep stage of a subject by executing an application capable of determining the sleep stage is constructed as a part of the functions of a control unit that comprehensively controls each component of the portable terminal 5.

[0081] [Accelerometer]

[0082] The accelerometer 2 is a sensor capable of detecting acceleration generated by the activity of a subject. The accelerometer 2 can transmit the detected acceleration as acceleration data to the sleep estimation device 51 through wireless or wired communication. The accelerometer 2 is attached to a part of the body of the subject, such as the head or a finger, for example. As the accelerometer 2, a publicly known sensor such as a frequency change type, a piezoelectric type, a piezoresistance type, or an electrostatic capacity type can be used.

[0083] <Blood flow meter>

[0084] The blood flow meter 3 can be a blood flow meter explained in the above principle. The blood flow meter 3 can be, for example, a laser Doppler blood flow meter. In the present embodiment, the blood flow meter 3 can transmit the raw waveform data W1 to the sleep estimation device 51 as the blood flow waveform data. The blood flow meter 3 can also transmit the processed waveform data W2 to the sleep estimation device 51 instead of the raw waveform data W1. The blood flow meter 3 can not generate the processed waveform data W2, and the sleep estimation device 51 can generate the processed waveform data W2. The blood flow meter 3 is equipped, for example, to a part of the body of the subject such as an ear, a finger, a wrist, a forearm, a forehead, a nose, or a neck.

[0085] <Portable terminal>

[0086] The portable terminal 5 can be at least a terminal capable of data communication with the accelerometer 2 and the blood flow meter 3. The portable terminal 5 can be, for example, a smartphone or a tablet. The sleep estimation device 51 is built in the portable terminal 5, and has a storage 52 and a notification 53.

[0087] The storage 52 can store programs and data used by the control section (particularly, the sleep estimation device 51). The storage 52 stores, for example, the learned model generated as described above, and a threshold value for determining whether the subject is at rest.

[0088] The notification 53 can notify various information to the surroundings (example: the subject) of the portable terminal 5. In the present embodiment, the notification 53 can notify various information following the notification instruction from the sleep estimation device 51. The notification 53 can be at least any one of a sound output device that outputs sound, a vibration device that vibrates the portable terminal 5, and a display device that displays an image.

[0089] (Sleep estimation device)

[0090] The sleep estimation device 51 can determine the sleep stage of the subject equipped with the accelerometer 2 and the blood flow meter 3. The sleep estimation device 51 can have the 2nd acquisition section 11, the 2nd determination section 12, the 1st acquisition section 13 (acquisition section), the generation section 14, the 1st determination section 15 (determination section), and the notification section 16.

[0091] The 2nd acquisition section 11 can acquire acceleration data from the accelerometer 2. The 2nd determination section 12 can determine whether the subject is at rest based on the acceleration data acquired by the 2nd acquisition section 11. The 2nd determination section 12 determines, for example, that the subject is at rest in a case where the acceleration indicated by the acceleration data is less than the threshold value stored in the storage 52. The 2nd determination section 12 can transmit determination result data to the generation section 14.

[0092] The first acquisition unit 13 can acquire the blood flow waveform data (raw waveform data W1) from the blood flow meter 3. The generation unit 14 can generate a frequency spectrum of the blood flow waveform data by performing a frequency analysis process on the blood flow waveform data acquired by the first acquisition unit 13. The first determination unit 15 can determine the sleep stage of the subject based on the frequency spectrum generated by the generation unit 14. In other words, the first determination unit 15 can determine the transition of the depth of sleep of the subject based on the frequency spectrum.

[0093] In the present embodiment, the generation unit 14 can generate a wavelet image by performing a wavelet transform process in which the target frequency band is relatively emphasized more than other frequency bands as the frequency analysis process. The wavelet image is process data indicating the result of the time-frequency analysis process of the blood flow waveform data. The first determination unit 15 can determine the sleep stage of the subject based on the wavelet image generated by the generation unit 14.

[0094] In the present embodiment, the first determination unit 15 can determine that the sleep stage of the subject is the period 2 or the period 3 when it is determined that the characteristic waveform Sh is contained in the frequency band of 0.2 to 0.3 Hz of the frequency spectrum. In this case, the first determination unit 15 can determine that the sleep stage of the subject is transitioning from the period 1 to the period 2 or the period 3. On the other hand, the first determination unit 15 can determine that the sleep stage of the subject is a sleep stage other than the period 2 or the period 3 when it is determined that the characteristic waveform Sh is not contained in the frequency band of 0.2 to 0.3 Hz. The first determination unit 15 can determine that the sleep stage of the subject is transitioning from the period 2 or the period 3 to the period 1 when it is determined that the characteristic waveform Sh is not contained in the frequency band of 0.2 to 0.3 Hz after determining that the subject is in the sleep stage of the period 2 or the period 3. The determination of whether or not the above-described characteristic waveform Sh is contained can be performed, for example, by determining whether or not the first intensity is larger than the second intensity by a given value or more.

[0095] In the present embodiment, the first determination unit 15 can perform the determination of the sleep stage of the subject by using the learned model. In this case, the first determination unit 15 can output the determination result of the sleep stage of the subject from the output layer of the learned model by assigning the wavelet image generated by the generation unit 14 as input data to the input layer of the learned model.

[0096] As described above, the learning completed model is generated using, as an example of the teaching data, data associated with the wavelet image whose correct answer label and the case corresponding to period 2 or period 3 are known. For this reason, the first determination section 15 is able to determine that the sleep stage of the subject is period 2 or period 3, and is also able to determine that the transition from period 1 to period 2 or period 3 is made, by giving the wavelet image generated by the generation section 14 to the learning completed model. The first determination section 15 particularly has a possibility of being able to determine that the sleep stage of the subject is period 2 or period 3 with good accuracy, in a case where the spectrum including the characteristic waveform Sh in the frequency band of 0.2 to 0.3 Hz is given to the learning completed model.

[0097] Here, in a case where the sleep stage is estimated using the blood flow waveform data, there is a possibility that the sleep estimation device is not able to determine whether or not the subject is asleep, with only the blood flow waveform data. For example, in a case of a subject whose blood flow is stable even when awake, it can be that no significant difference is seen between the blood flow waveform data when awake and the blood flow waveform data when asleep, and in this case, there is a possibility that the sleep estimation device is not able to determine whether or not the subject is asleep. In the present embodiment, the generation section 14 is able to perform the determination processing based on the first determination section 15 in a state where the possibility that the subject is asleep is high, by performing the frequency analysis processing on the blood flow waveform data in a case where it is determined by the second determination section 12 that the subject is at rest.

[0098] The notification section 16 is able to perform the notification processing based on the determination result of the first determination section 15. The notification section 16 can transmit a notification instruction according to the notification processing to the notification section 53. Thereby, the notification section 53 is able to notify the information according to the notification processing to the surroundings of the portable terminal 5. The notification section 16 can have a first notification section 161 and a second notification section 162.

[0099] As the above-described notification processing, the first notification section 161 is able to perform the first notification processing after a given time elapses from when the transition from period 1 to period 2 or period 3 is detected by the first determination section 15. The first notification processing is the notification processing accompanying the detection of the transition, and for example, can be an alarm processing for prompting the subject to wake up, or a processing that notifies of the detection of the transition. The given time can be appropriately set according to the purpose of the notification, for example, by experiment. In the present embodiment, for example, when the time is counted from the time when the transition from period 1 to period 2 or period 3 is made, the given time is set to a time at which it is estimated that the subject is likely to wake up. Through the first notification processing, for example, the subject is able to wake up at a good wake-up timing after falling asleep. The first notification section 161 can also perform the first notification processing when the transition from period 1 to period 2 or period 3 is detected by the first determination section 15.

[0100] The second notification section 162 can execute the second notification process as the notification process described above, after a given time elapses from the detection of the transition to the period 2 or the period 3 by the first determination section 15. The second notification process is a notification process accompanying the detection of the transition, and can be, for example, an alarm process for prompting the subject to wake up, or a process of notifying the detection of the transition. The given time can be appropriately set according to the purpose of the notification, for example, by experiment. In the present embodiment, for example, the given time can be set to a time at which it is presumed that the subject is likely to wake up, when the time is counted from the time of the transition from the period 2 or the period 3 to the period 1. Through the second notification process, the subject can wake up at a good wake-up timing after falling asleep, for example. The second notification section 162 can also execute the second notification process when the transition from the period 2 or the period 3 to the period 1 is detected by the first determination section 15.

[0101] 〔Flow of processing〕

[0102] Figure 7 is a flowchart showing an example of the flow of processing based on the sleep estimation device 51 (sleep estimation method). In a case where the sleep stage of the subject is determined by the sleep estimation device 51, the blood flow meter 3 can start the detection of the blood flow waveform data after the blood flow meter 3 is attached to the subject.

[0103] As shown in Figure 7 , in the sleep estimation device 51, the first acquisition section 13 can acquire the blood flow waveform data from the blood flow meter 3 (S1: first acquisition process, acquisition process). The generation section 14 can generate a frequency spectrum of the blood flow waveform data by performing a frequency analysis process on the blood flow waveform data. In the present embodiment, the generation section 14 can generate a wavelet image in which the intensity of the object is emphasized by performing a wavelet transform process on the blood flow waveform data (S2: generation process). The first determination section 15 can determine the sleep stage of the subject based on the frequency spectrum. In the present embodiment, the first determination section 15 can determine the sleep stage of the subject by inputting the wavelet image in which the intensity of the object is emphasized to the learned model (S3) (S4: first determination process, determination process).

[0104] The first determination section 15 can determine whether the transition from the period 1 to the period 2 or the period 3 is made based on the determination result of the sleep stage (S5). In a case where the first determination section 15 determines that the transition from the period 1 to the period 2 or the period 3 is made (YES in S5), the first notification section 161 can determine whether a given time elapses from the determination (S6). The first notification section 161 can execute, as the first notification process, an alarm process in which an alarm sound is notified from the notification section 53, for example, in a case where it is determined that the given time elapses (YES in S6) (S7). The notification section 53 can receive a notification instruction from the first notification section 161, and notify the alarm sound.

[0105] In the case where "No" in S5, the processing can return to S1. In the case where "No" in S6, the processing of S6 can be repeated. Further, in S5, in the case where the first determination section 15 determines that the period 2 or the period 3 is transitioned to the period 1, the second notification section 162 can perform the second notification processing, for example, after a given time elapses.

[0106]

[0107] In Patent Literature 1, a method of detecting non-rapid eye movement sleep including steps 1 to 4 below is disclosed.

[0108] • Step 1: a step of generating time series data of an interbeat interval of a heart of a subject.

[0109] • Step 2: a step of setting a window of a given time length moving along a time axis of the time series data, and performing spectral analysis on the time series data in the window with respect to each of a plurality of determination time points on the time axis.

[0110] • Step 3: a step of calculating a concentration degree of a power of a heart rate variability high frequency component from the spectrum of each window.

[0111] • a step of determining whether or not it is non-rapid eye movement sleep based on the calculated concentration degree.

[0112] In the above method, as a detection device of the time series data of step 1, a pulse wave meter or an electrocardiograph is used, for example.

[0113] However, a high level of expertise is required in the handling of the electroencephalograph and the acquisition of the brain waves. Further, the installation of the electroencephalograph is cumbersome. Therefore, it is difficult for the subject to acquire the brain waves simply and to grasp his or her sleep stage simply.

[0114] Further, as described above, the inventors found that, in the spectrum of the blood flow waveform data, in the case where a significant intensity change is seen in the frequency band of 0.2 to 0.3 Hz, the subject is highly likely to be in the sleep stage of the period 2 or the period 3.

[0115] The sleep estimation device 51 of the present disclosure can determine the sleep stage of the subject using the blood flow waveform data. A high level of expertise than the electroencephalograph is not required in the handling of the blood flow meter and the acquisition of the blood flow waveform data. Further, the installation of the blood flow meter is easier than the electroencephalograph. That is, the sleep estimation device 51 can determine the sleep stage of the subject relatively simply because it can acquire the blood flow waveform data of the subject relatively simply. Further, according to the sleep estimation device 51, the subject can grasp his or her sleep stage simply.

[0116] ​Furthermore, when the sleep estimation device 51 of this disclosure obtains a spectrum with the aforementioned significant intensity changes that are not visible in electrocardiogram waveform data, it can determine that the subject's sleep stage is period 2 or period 3. Therefore, the sleep estimation device 51 can improve the likelihood of accurately estimating that the subject's sleep stage is period 2 or period 3.

[0117] [Implementation Method 2]

[0118] Other embodiments of this disclosure are described below. Furthermore, for ease of explanation, components having the same function as those described in the above embodiments are marked with the same symbols and their descriptions will not be repeated. Figure 8 This is a block diagram illustrating a schematic structural example of the sleep estimation system 1A according to Embodiment 2.

[0119] In the sleep estimation system 1 of Embodiment 1, the portable terminal 5 can acquire blood flow waveform data from the blood flow meter 3 equipped on the subject, for example, via wireless communication. Furthermore, the sleep estimation device 51 built into the portable terminal 5 can determine the sleep stage of the subject based on the blood flow waveform data.

[0120] On the other hand, such as Figure 8 As shown, in the sleep estimation system 1A of Embodiment 2, a flowmeter 3 can be installed in the wearable device 20. In addition, a sleep estimation device 51 can be constructed in the wearable device 20 as part of the function of a control unit that comprehensively controls the various components of the wearable device 20. That is, in the sleep estimation system 1A, the sleep estimation device 51 and the flowmeter 3 can be installed together in the wearable device 20. Therefore, the acquisition processing of blood flow waveform data and the determination processing of sleep stages based on the blood flow waveform data can be performed with a single device. Furthermore, various devices or components required for wireless or wired communication between two devices are no longer needed. The wearable device 20 can be mounted in the same position as the flowmeter 3 is mounted on the subject.

[0121] [Variation Example]

[0122] The invention disclosed herein has been described above based on the accompanying drawings and embodiments. However, the invention disclosed herein is not limited to the embodiments described above. That is, the invention disclosed herein can be modified in various ways within the scope shown in this disclosure, and embodiments obtained by appropriately combining the disclosed technical means with different embodiments are also included in the technical scope of the invention disclosed herein. It is important to note that those skilled in the art can easily make various modifications or alterations based on this disclosure. Furthermore, it is important to note that these modifications or alterations are included within the scope of this disclosure.

[0123] (A variation of frequency analysis processing)

[0124] For example, the generation section 14 can generate the wavelet transform image by performing the wavelet transform process on the blood flow waveform data, and does not necessarily have to perform the wavelet transform process so as to emphasize the object intensity.

[0125] Further, the generation section 14 can perform a time-frequency analysis process other than the wavelet transform process on the blood flow waveform data. The generation section 14 can perform, for example, a short-time Fourier transform process on the blood flow waveform data. The short-time Fourier transform process performs the Fourier transform process on each of a plurality of waveform data cut out along the time axis using a window function. In this case, the generation section 14 can generate the same image (intensity variation data) as the wavelet image by performing the short-time Fourier transform process that relatively emphasizes the object intensity more than other frequency bands, as the process data.

[0126] Further, the generation section 14 can perform a process other than the time-frequency analysis process as the frequency analysis process. The generation section 14 can perform, for example, a Fourier transform process. In a case where the generation section 14 performs the Fourier transform process, the generation section 14 can generate, for example, the frequency spectrum FW1 or FW2 illustrated. Figure 3 Further, the generation section 14 can generate, for example, the frequency spectrum (waveform) by performing the short-time Fourier transform process. Figure 3

[0127] As such, the generation section 14 can generate various frequency spectra. Therefore, the first determination section 15 can determine the sleep stage of the subject by inputting the various frequency spectra to the learned model. The frequency spectrum of the same kind as the frequency spectrum generated by the generation section 14 is used as the learning data and the teaching data to generate the learned model.

[0128] (Modified example of determination process of first determination section)

[0129] The learned model can be stored in the storage section 52 of the portable terminal 5 or the wearable device 20. That is, the sleep estimation system 1 or 1A can determine the sleep stage of the subject without using the learned model.

[0130] For example, the first determination section 15 can determine that the sleep stage of the subject is the period 2 or the period 3 in a case where it is determined that the first intensity is greater than the second intensity by a given value or more in the frequency spectrum generated by the generation section 14. The given value can be stored in the storage section 52 instead of the learned model.

[0131] ​Further, in a case where the first determination section 15 determines the sleep stage of the subject using the spectrum FW1, it can determine that the sleep stage of the subject is period 2 or period 3 when a characteristic waveform Sh can be extracted in the frequency band of 0.2 to 0.3 Hz. In this case, in the storage section 52, instead of the learned model, a reference waveform of the characteristic waveform Sh that can be extracted in the spectrum FW1 can be stored. The first determination section 15 can determine that the characteristic waveform Sh can be extracted in the frequency band of 0.2 to 0.3 Hz of the spectrum FW1 in a case where it is determined that a waveform that coincides with the reference waveform exists in the frequency band. The first determination section 15 can determine that a waveform that coincides with the reference waveform exists in the frequency band of 0.2 to 0.3 Hz in a case where the degree of coincidence of the waveform contained in the frequency band of 0.2 to 0.3 Hz with the reference waveform is equal to or higher than a threshold value set through experiments, for example. Further, the first determination section 15 can determine whether or not the characteristic waveform Sh is contained in the frequency band of 0.2 to 0.3 Hz through other indexes (example: the degree of change in the inclination of the waveform). Furthermore, as for the spectrum FW2, the same determination as the spectrum FW1 can be performed.

[0132] (Modified example of sleep estimation system 1 or 1A)

[0133] It can not be that the acceleration generated by the activity of the subject is detected by the accelerometer 2 and the sleep estimation device 51 determines the sleep state of the subject based on the acceleration. In this case, the sleep estimation system 1 or 1A can not be provided with the accelerometer 2, and the sleep estimation device 51 can not be provided with the second acquisition section 11 and the second determination section 12.

[0134] (Example of software-based implementation)

[0135] The control block of the sleep estimation device 51 can be implemented by a logic circuit (hardware) formed as an integrated circuit (IC chip) or the like, or can be implemented by software.

[0136] In the latter case, the sleep estimation device 51 has a computer that executes a command of a program that is software that realizes each function. The computer has, for example, at least one processor (control device) and at least one recording medium that is readable by the computer and stores the program. In the computer, the program is read from the recording medium by the processor and executed, thereby achieving the object of the present disclosure. As the processor, a CPU (Central Processing Unit) can be used, for example. As the recording medium, a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used in addition to a "non-transitory tangible medium" such as a ROM (Read Only Memory) or the like. Further, a RAM (Random Access Memory) or the like that expands the program can also be provided. Further, the program can be provided to the computer via any transmission medium (communication network, carrier wave, or the like) that can transmit the program. In addition, one embodiment of the present disclosure can also be realized in the form of a data signal embedded in a carrier wave by electronically transmitting the program.

[0137] Symbol explanation

[0138] 1, 1A Sleep estimation system

[0139] 3 Blood flow meter

[0140] 11 Second acquisition unit

[0141] 12 Second determination unit

[0142] 13 First acquisition unit (acquisition unit)

[0143] 14 Generation unit

[0144] 15 First determination unit (determination unit)

[0145] 20 Wearable device

[0146] 51 Sleep estimation device

[0147] 161 First notification unit

[0148] 162 Second notification unit

Claims

1. A sleep estimation system, comprising: The determination unit, based on the spectrum of blood flow data of the subject, determines the sleep stage of the subject; and The notification department performs notification processing based on the determination result of the determination department. The notification unit provides a notification to awaken the subject when the determination unit determines that at least the spectrum contains a predetermined shape in a region including a frequency band of 0.2 Hz or higher and 0.3 Hz or lower, or after a predetermined time has elapsed since the determination. The defined shape contained within the region is an upwardly convex waveform shape containing the maximum intensity, i.e., the first intensity, within the region.

2. The sleep estimation system according to claim 1, wherein, In the spectrum, if the first intensity in a first range of a portion of the frequency band is greater than the second intensity in a second range outside the first range, the determination unit determines that the specified shape is included.

3. The sleep estimation system according to claim 1 or 2, wherein, The sleep estimation system has the following features: A flowmeter detects blood flow data by receiving scattered light generated when light is irradiated onto the blood vessels of the subject.

4. The sleep estimation system according to claim 1 or 2, wherein, The sleep estimation system is equipped with wearable devices. The wearable device has the following features: A flowmeter detects blood flow data by receiving scattered light generated when light is irradiated onto the blood vessels of the subject.

5. A computer-readable recording medium storing a program for enabling a computer to function as the sleep estimation system of claim 1, and for enabling the computer to function as the determination unit and the notification unit.

6. A sleep estimation system, comprising: The determination unit, based on the spectrum of blood flow data of the subject, determines the sleep stage of the subject; and The notification department performs notification processing based on the determination result of the determination department. The determination unit, upon assigning the spectrum of the blood flow data to the learned model, determines whether it conforms to the correct solution. The learned model is obtained using teaching data, which is obtained by associating correct solution labels based on biological information different from the blood flow data with a spectrum of a defined shape in a frequency band including frequencies above 0.2 Hz and below 0.3 Hz. The notification unit sends a notification to the subject to awaken when the determination unit determines that the solution is correct, or after a predetermined time has elapsed since the determination. The defined shape contained within the region is an upwardly convex waveform shape that contains the maximum intensity, i.e., the first intensity, within the region. The biological information is brainwave data detected by an EEG device.

7. The sleep estimation system according to claim 6, wherein, The sleep estimation system has the following features: A flowmeter detects blood flow data by receiving scattered light generated when light is irradiated onto the blood vessels of the subject.

8. The sleep estimation system according to claim 6, wherein, The sleep estimation system is equipped with wearable devices. The wearable device has the following features: A flowmeter detects blood flow data by receiving scattered light generated when light is irradiated onto the blood vessels of the subject.

9. A computer-readable recording medium storing a program for enabling a computer to function as the sleep estimation system of claim 6, and for enabling the computer to function as the determination unit and the notification unit.

10. A sleep estimation method, executed in a sleep estimation system, the sleep estimation method comprising: In the determination process, the determination unit of the sleep estimation system determines the sleep stage of the subject based on the spectrum of the subject's blood flow data; and In the notification process, the notification unit of the sleep estimation system determines in the determination process that at least the spectrum contains a predetermined shape in a region including a frequency band of 0.2 Hz or higher and 0.3 Hz or lower, or after a predetermined time has elapsed since the determination, that it will notify the subject to wake up. The defined shape contained within the region is an upwardly convex waveform shape containing the maximum intensity, i.e., the first intensity, within the region.

11. A sleep estimation method, executed in a sleep estimation system, the sleep estimation method comprising: In the determination process, the determination unit of the sleep estimation system determines the sleep stage of the subject based on the spectrum of the subject's blood flow data; and In the notification process, the notification unit of the sleep estimation system performs notification processing based on the determination result in the determination process. In the determination process, after assigning the spectrum of the blood flow data to the learned model, it is determined whether it conforms to the correct solution. The learned model is learned using teaching data, which is obtained by associating correct solution labels based on biological information different from the blood flow data with a spectrum of a defined shape in a frequency band including 0.2 Hz and 0.3 Hz. In the notification process, a notification is issued to awaken the subject when the judgment process determines that the answer is correct, or after a predetermined time has elapsed since the judgment. The defined shape contained within the region is an upwardly convex waveform shape that contains the maximum intensity, i.e., the first intensity, within the region. The biological information is brainwave data detected by an EEG device.

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