Physiological state estimation system, physiological state estimation method, and physiological state estimation program

Abstract

To provide a physiological state estimation system, a physiological state estimation method, and a physiological state estimation program for estimating a physiological state with a high degree of precision.SOLUTION: A physiological state estimation system includes: a waveform information acquisition unit 51 for acquiring a brain wave and a pulse wave; a filtering unit 52 for filtering the acquired brain wave and pulse wave in at least one predetermined frequency band; a conversion unit 53 for performing Hilbert transformation for the brain wave and the pulse wave subjected to filtering in the frequency band; an instantaneous value calculation unit 54 for calculating an instantaneous value including an instantaneous logarithmic amplitude equivalent to a logarithm of an amplitude term of a complex waveform formula subjected to the Hilbert transformation, and an instantaneous frequency equivalent to a time differential value of a phase term of the complex waveform formula; a distribution calculation unit 55 for calculating the probability density distribution of the instantaneous value, and calculating a feature amount including an average value and a dispersion value of the probability density distribution; an extraction unit 56 for extracting an instantaneous value when a predetermined condition is satisfied; and a physiological state estimation unit 57 for estimating a physiological state from the feature amount of the extracted instantaneous value.SELECTED DRAWING: Figure 27

Description

[Technical Field]

[0001] This invention relates to a physiological state estimation system, a physiological state estimation method, and a physiological state estimation program. [Background technology]

[0002] Patent Document 1 describes a biosignal analysis method for obtaining quantitative information about living organisms by calculating features using Lyapunov exponents, assuming chaos properties, from biological signals such as electroencephalograms, pulse waves, and electromyograms. The biosignal analysis method in Patent Document 1 calculates features at high speed by utilizing the high-frequency range. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2002-017687 [Non-patent literature]

[0004] [Non-Patent Document 1] W Zong, T Heldt, GB Moody and RG Mark, “An Open-source Algorithm to Detect Onset of Arterial Blood Pressure Pulses”, Computers in Cardiology 2003; 30:259-262. [online], [Retrieved December 14, 2021], Internet<https: / / lcp.mit.edu / pdf / Zong03a.pdf> [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] There is a need for methods that can estimate physiological states with greater accuracy from biological signals.

[0006] This invention was made to solve such problems and provides a physiological state estimation system, a physiological state estimation method, and a physiological state estimation program that estimate physiological states with high accuracy. [Means for solving the problem]

[0007] The physiological state estimation system according to this embodiment includes: a waveform information acquisition unit that acquires electroencephalograms and pulse waves; a filtering unit that filters the acquired electroencephalograms and pulse waves in at least one predetermined frequency band; a conversion unit that performs a Hilbert transform on the filtered electroencephalograms and pulse waves in the frequency band; an instantaneous value calculation unit that calculates an instantaneous value including an instantaneous logarithmic amplitude corresponding to the logarithm of the amplitude term of the Hilbert-transformed complex wave form and an instantaneous frequency corresponding to the time derivative of the phase term of the complex wave form; a distribution calculation unit that calculates the probability density distribution of the instantaneous value and calculates a feature quantity including the mean and variance of the probability density distribution; and the mean value of the instantaneous frequency in the predetermined frequency band of the electroencephalogram by μ e σ is the variance value of the instantaneous frequency in the predetermined frequency band of the electroencephalogram. e The average value of the instantaneous frequency in the predetermined frequency band of the pulse wave is μ b σ is the variance value of the instantaneous frequency in the predetermined frequency band of the pulse wave. b When η is an integer parameter of 3 or greater, |μ e -μ b |<(σ e +σ b The system includes an extraction unit that extracts the instantaneous value when the condition ) / η is satisfied, and a physiological state estimation unit that estimates the physiological state from the feature quantities of the extracted instantaneous value. With this configuration, the physiological state can be estimated with high accuracy.

[0008] In the physiological state estimation system described above, the parameter η may also be 3. With this configuration, the physiological state can be estimated with even greater accuracy.

[0009] The physiological state estimation method according to this embodiment includes a waveform information acquisition step of acquiring brain waves and pulse waves, a filtering step of filtering the acquired brain waves and pulse waves in at least one predetermined frequency band, a conversion step of performing a Hilbert transform on the filtered brain waves and pulse waves in the frequency band, an instantaneous value calculation step of calculating instantaneous values including an instantaneous logarithmic amplitude corresponding to the logarithm of the amplitude term in the complex wave form obtained by the Hilbert transform and an instantaneous frequency corresponding to the time differential value of the phase term in the complex wave form, a distribution calculation step of calculating the probability density distribution of the instantaneous values and calculating feature quantities including the average value and variance value of the probability density distribution, and the average value of the instantaneous frequency in the predetermined frequency band of the brain waves is μ e The variance value of the instantaneous frequency in the predetermined frequency band of the brain waves is σ e The average value of the instantaneous frequency in the predetermined frequency band of the pulse waves is μ b The variance value of the instantaneous frequency in the predetermined frequency band of the pulse waves is σ b When a parameter of 3 or more integers is η, |μ e -μ b |<(σ e +σ b ) / η, an extraction step of extracting the instantaneous values in this case, and a physiological state estimation step of estimating the physiological state from the feature quantities of the extracted instantaneous values. With such a configuration, the physiological state can be estimated with high accuracy.

[0010] The physiological state estimation program according to this embodiment includes: a waveform information acquisition step for acquiring electroencephalograms and pulse waves; a filtering step for filtering the acquired electroencephalograms and pulse waves in at least one predetermined frequency band; a conversion step for performing a Hilbert transform on the filtered electroencephalograms and pulse waves in the frequency band; an instantaneous value calculation step for calculating an instantaneous value including an instantaneous logarithmic amplitude corresponding to the logarithm of the amplitude term of the Hilbert-transformed complex wave form and an instantaneous frequency corresponding to the time derivative of the phase term of the complex wave form; a distribution calculation step for calculating the probability density distribution of the instantaneous value and calculating a feature quantity including the mean and variance of the probability density distribution; and the mean value of the instantaneous frequency in the predetermined frequency band of the electroencephalogram by μ e σ is the variance value of the instantaneous frequency in the predetermined frequency band of the electroencephalogram. e The average value of the instantaneous frequency in the predetermined frequency band of the pulse wave is μ b σ is the variance value of the instantaneous frequency in the predetermined frequency band of the pulse wave. b When η is an integer parameter of 3 or greater, |μ e -μ b |<(σ e +σ b The computer is made to perform an extraction step to extract the instantaneous value when the condition ) / η is satisfied, and a physiological state estimation step to estimate the physiological state from the feature quantities of the extracted instantaneous value. With this configuration, the physiological state can be estimated with high accuracy. [Effects of the Invention]

[0011] According to this embodiment, a physiological state estimation system, a physiological state estimation method, and a physiological state estimation program can be provided that estimate physiological states with high accuracy. [Brief explanation of the drawing]

[0012] [Figure 1] This figure illustrates a method for measuring bio-waves according to Embodiment 1. [Figure 2] This graph illustrates the bio-waves according to Embodiment 1, with the horizontal axis representing time and the vertical axis representing pulse interval and pulse amplitude. [Figure 3] This graph illustrates the bio-waves according to Embodiment 1, with the horizontal axis representing time and the vertical axis representing intensity. [Figure 4] This graph illustrates the bio-waves according to Embodiment 1, with the horizontal axis representing time and the vertical axis representing intensity. [Figure 5] This graph illustrates the bio-waves according to Embodiment 1, with the horizontal axis representing time and the vertical axis representing intensity. [Figure 6] This graph illustrates the waveforms obtained by Fourier transforming the electroencephalogram and pulse wave according to Embodiment 1, with the horizontal axis representing frequency and the vertical axis representing intensity. [Figure 7] This graph illustrates the waveforms obtained by Fourier transforming the electroencephalogram and pulse wave according to Embodiment 1, with the horizontal axis representing frequency and the vertical axis representing intensity. [Figure 8] This figure illustrates waveforms obtained by filtering bio-waves according to Embodiment 1 across multiple frequency bands, with the horizontal axis representing time and the vertical axis representing intensity. [Figure 9] This figure illustrates waveforms obtained by filtering bio-waves according to Embodiment 1 across multiple frequency bands, with the horizontal axis representing time and the vertical axis representing intensity. [Figure 10] This figure illustrates waveforms obtained by filtering bio-waves according to Embodiment 1 across multiple frequency bands, with the horizontal axis representing time and the vertical axis representing intensity. [Figure 11] This figure shows the complex wave form obtained by Hilbert transforming the bio-wave according to Embodiment 1, in the complex plane. [Figure 12] This graph illustrates the probability density distribution of instantaneous brainwave frequencies in the alpha frequency band during eye-opening according to Embodiment 1, with the horizontal axis representing frequency and the vertical axis representing density. [Figure 13] This graph illustrates the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band during eye opening according to Embodiment 1, with the horizontal axis representing frequency and the vertical axis representing density. [Figure 14] This graph illustrates the probability density distribution of the instantaneous phase difference between electroencephalograms and pulse waves in the alpha frequency band during eye opening according to Embodiment 1, with the horizontal axis representing the phase difference and the vertical axis representing the density. [Figure 15] This graph illustrates the probability density distribution of instantaneous brainwave frequencies in the alpha frequency band during eye-opening according to Embodiment 1, with the horizontal axis representing frequency and the vertical axis representing density. [Figure 16] This graph illustrates the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band during eye opening according to Embodiment 1, with the horizontal axis representing frequency and the vertical axis representing density. [Figure 17] This graph illustrates the probability density distribution of the instantaneous phase difference between electroencephalograms and pulse waves in the alpha frequency band during eye opening according to Embodiment 1, with the horizontal axis representing the phase difference and the vertical axis representing the density. [Figure 18] This graph illustrates the probability density distribution of instantaneous electroencephalogram (EEG) frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. [Figure 19] This graph illustrates the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band when the eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. [Figure 20] This graph illustrates the probability density distribution of the instantaneous phase difference between electroencephalogram and pulse wave in the alpha frequency band when the eyes are closed, according to Embodiment 1. The horizontal axis represents the phase difference, and the vertical axis represents the density. [Figure 21] This graph illustrates the probability density distribution of instantaneous electroencephalogram (EEG) frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. [Figure 22] This graph illustrates the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band when the eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. [Figure 23] This graph illustrates the probability density distribution of the instantaneous phase difference between electroencephalogram and pulse wave in the alpha frequency band when the eyes are closed, according to Embodiment 1. The horizontal axis represents the phase difference, and the vertical axis represents the density. [Figure 24] This graph illustrates the characteristic quantities for eye-open and eye-closed states according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents the characteristic quantity of instantaneous frequency, the characteristic quantity of instantaneous logarithmic amplitude, and the concentration level. [Figure 25]This figure illustrates the complex wave forms in each frequency band according to Embodiment 1 in three dimensions. [Figure 26] This figure illustrates the complex wave forms in each frequency band according to Embodiment 1 in three dimensions. [Figure 27] This is a block diagram illustrating a physiological state estimation device according to Embodiment 1. [Figure 28] This is a flowchart illustrating the physiological state estimation method according to Embodiment 1. [Modes for carrying out the invention]

[0013] The present invention will be described below through embodiments, but the claims are not limited to the following embodiments. Furthermore, not all of the configurations described in the embodiments are necessarily essential for solving the problem. For clarity of explanation, the following descriptions and drawings have been omitted and simplified as appropriate. In each drawing, the same elements are denoted by the same reference numerals, and redundant explanations have been omitted where necessary.

[0014] (Embodiment 1) First, the waveform information relating to a living organism used in this embodiment will be described. The living organism is, for example, a subject. The physiological state of the subject is estimated from the waveform information relating to the living organism. The waveform information relating to a living organism includes biological waves. Biological waves include, for example, pulse-related waves and brain-related waves. Pulse-related waves may include pulse waves and pulse interval waves. Brain-related waves may include electroencephalograms, carotid artery waves, and cerebral blood flow waves. Note that biological waves may include waveform information other than pulse-related waves and brain-related waves, as long as they relate to a living organism. Pulse-related waves may include waveform information other than pulse waves and pulse interval waves, as long as they relate to pulse. Brain-related waves may include waveform information other than electroencephalograms, carotid artery waves, and cerebral blood flow waves, as long as they relate to the brain. Electroencephalograms, pulse waves, and pulse interval waves will be described below as biological waves.

[0015] <Measurement of bio-waves> This section describes methods for measuring bio-waves, such as electroencephalograms (EEGs), pulse waves, and pulse interval waves. Figure 1 is an example of a bio-wave measurement method according to Embodiment 1. As shown in Figure 1, examples of measuring instruments for bio-waves include an EEG measuring instrument 10 and a pulse wave measuring instrument 20.

[0016] The electroencephalogram (EEG) measuring device 10 acquires the subject's brainwaves as waveform information related to the body. The EEG measuring device 10 includes a sensor 11 and a main unit 12. The sensor 11 is placed in multiple locations using methods such as the 10-20 method, and the main unit 12 measures simultaneously. The sensor 11 is attached, for example, to the scalp of the subject's head, and senses the subject's brainwave information from outside the body. The subject's brainwave information is, for example, voltage. In addition to voltage, the sensor 11 may also sense electric current, magnetic fields, etc., as information from the subject's brainwaves. The sensor 11 is attached to the body in a non-invasive manner.

[0017] The sensor 11 outputs the detected brainwave information to the main unit 12 of the electroencephalogram (EEG) measuring device 10. The main unit 12 of the EEG measuring device 10 measures the time-dependent changes in voltage, etc., output from the sensor 11. The sensor 11 is connected to the main unit 12 by a wired or wireless communication line.

[0018] The pulse wave measuring device 20 measures the pulse wave of a subject as waveform information related to the body. The pulse wave is waveform information of the body formed by the pulse interval, blood output, and the physical characteristics of the blood vessels. The pulse wave measuring device 20 includes a sensor 21 and a main unit 22. The pulse wave measuring device 20 may be a photoelectric type such as a photoplethysmometer, or a piezoelectric type. In the case of a photoelectric type, it is preferable to use near-infrared light with a wavelength of 800 to 1000 nm. The sensor 21 is attached, for example, to the skin on the subject's neck (carotid artery bifurcation) or cervical spine (vertebral artery) to measure pulse wave information flowing into the subject's brain from outside the body.

[0019] Specifically, the sensor 21 may be positioned near at least one of the carotid artery bifurcations on the left and right sides, or near the vertebral arteries on the left and right sides. The pulse wave information is, for example, pulse pressure. In addition to pulse pressure, the sensor 21 may also detect blood flow, etc., as pulse wave information. The sensor 21 is attached to the body in a non-invasive manner.

[0020] The sensor 21 outputs information about the pulse wave it has detected to the main unit 22 of the pulse wave measuring device 20. The main unit 22 of the pulse wave measuring device 20 measures the time-dependent changes in pulse pressure, etc., output from the sensor 21. The sensor 21 is connected to the main unit 22 by a wired or wireless communication line.

[0021] <Time-series data of bio-waves> Next, we will explain the time-series data of bio-waves. Figure 2 is a graph illustrating bio-waves according to Embodiment 1, where the horizontal axis represents time and the vertical axis represents pulse interval and pulse amplitude. Figure 3 is a graph illustrating bio-waves according to Embodiment 1, where the horizontal axis represents time and the vertical axis represents intensity. Figure 2 shows the pulse interval and pulse amplitude of pulse waves obtained from the left and right carotid artery branches as an example of bio-waves. Figure 3 shows the electroencephalogram (EEG) obtained from the frontal lobe as an example of bio-waves. As shown in Figures 2 and 3, the EEG and pulse waves form time-series data. In Figures 2 and 3, the measurement results from 0 to 300 seconds are shown with eyes open. For example, these are the measurement results when taking a quiz with eyes open. The measurement results from 300 to 600 seconds are shown with eyes closed. For example, these are the measurement results when listening to music with eyes closed.

[0022] Figures 4 and 5 are graphs illustrating bio-waves according to Embodiment 1, where the horizontal axis represents time and the vertical axis represents intensity. Figure 4 shows pulse waves for defining pulse interval waves and pulse amplitude from pulse waves. Figure 5 shows pulse interval waves as an example of bio-waves. Pulse interval waves are obtained by the following procedure. First, the pulse interval (PPI: Peak-Peak Interval) is calculated from the time-series data of the pulse wave shown in Figure 4 using the pulse wave rise position detection algorithm presented in Non-Patent Literature 1. Next, by plotting the rise time of the pulse wave and the pulse interval and performing spline interpolation, a pulse interval wave as shown in Figure 5 can be obtained. As shown in Figure 5, pulse interval waves also form time-series data. Although other bio-waves, not just electroencephalograms, pulse waves, and pulse interval waves, also form time-series data, only electroencephalograms, pulse waves, and pulse interval waves are shown here. This embodiment estimates the physiological state of a living organism from the time-series data of bio-waves.

[0023] Figures 6 and 7 are graphs illustrating the waveforms obtained by Fourier transforming the electroencephalogram (EEG) and pulse wave according to Embodiment 1, with the horizontal axis representing frequency and the vertical axis representing intensity. Figure 6 shows the EEG and pulse wave with eyes open as in Figures 2 and 3, and Figure 7 shows the EEG and pulse wave with eyes closed as in Figures 2 and 3.

[0024] <Frequency Band> In this embodiment, the bio-wave is filtered through multiple frequency bands over a predetermined period. The predetermined period is, for example, a period during which the physiological state can be considered constant. The frequency bands are, for example, VLF2, VLF1, LF, HF, δ1, δ2, θ, α, β, γ, etc. Figures 8 to 10 illustrate waveforms obtained by filtering the bio-wave according to Embodiment 1 through multiple frequency bands, with the horizontal axis representing time and the vertical axis representing intensity. Figure 8 shows, for example, an electroencephalogram filtered through the frequency bands of δ1 and δ2. Figure 9 shows, for example, an electroencephalogram filtered through the frequency bands of θ and α. Figure 10 shows, for example, an electroencephalogram filtered through the frequency bands of β and γ.

[0025] As shown in Figures 8 to 10, the bio-waves filtered by each frequency band form time-series data. In addition to the above, electroencephalograms (EEGs) may also be filtered by the VLF2, VLF1, LF, and HF frequency bands. While bio-waves, not just EEGs, may be filtered by multiple frequency bands, only EEGs are shown here. In Figures 8 to 10, the measurement results from 0 to 300 seconds show the results with eyes open, and the measurement results from 300 to 600 seconds show the results with eyes closed. Each frequency band will be explained below.

[0026] VLF2 is a frequency band, for example, between 0.004 and 0.015 Hz. The center frequency of VLF2 is, for example, 0.01 Hz. VLF2 is a frequency band that indicates the functions of the autonomic nervous system, such as thermoregulation, digestion, excretion, reproduction, and immunity.

[0027] VLF1 is a frequency band, for example, between 0.015 and 0.04 Hz. The center frequency of VLF1 is, for example, 0.0300 Hz. VLF1 is also a frequency band that indicates the functions of the autonomic nervous system, such as thermoregulation, digestion, excretion, reproduction, and immunity.

[0028] LF is, for example, a frequency band of 0.04 to 0.15 Hz. The center frequency of LF is, for example, 0.1 Hz. LF is the frequency band of the blood pressure fluctuation band that indicates the function of blood pressure.

[0029] HF is a frequency band, for example, between 0.15 and 0.40 Hz. The center frequency of HF is, for example, 0.30 Hz. HF is the frequency band of the respiratory fluctuation range, which indicates the function of respiration.

[0030] δ1 is, for example, a frequency band of 0.4 to 1.5 Hz. The center frequency of δ1 is, for example, 1.0 Hz.

[0031] δ2 is, for example, a frequency band of 1.5 to 4 Hz. The center frequency of δ2 is, for example, 3.0 Hz.

[0032] θ is, for example, a frequency band of 4.0 to 8.0 Hz. The center frequency of θ is, for example, 5.0 Hz.

[0033] α is, for example, a frequency band of 8.0 to 12.0 Hz. The center frequency of α is, for example, 10.0 Hz.

[0034] β is, for example, a frequency band of 12.0 to 30.0 Hz. The center frequency of β is, for example, 20.0 Hz.

[0035] γ represents a frequency band greater than, for example, 30 Hz.

[0036] <Complex number conversion> The filtered bio-waves in each frequency band are converted into a complex wave format, for example, by a Hilbert transform. Specifically, the time-series data φ of the bio-waves in each frequency band is obtained. k (n) (t) is transformed by a Hilbert transform into the complex wave form shown in equation (1) below.

[0037] φ k (n) (t) = exp(a k (n) (t) + iψ k (n) (t)) (1)

[0038] Here, k represents each frequency band. That is, k=1, 2, ... represents frequency bands such as VLF2, VLF1, LF, HF, δ1, δ2, θ, α, β, γ, ..., etc. n represents each biowave. That is, n=1, 2, ... represents either pulse-related waves or brain-related waves. In particular, in the case of electroencephalograms, pulse waves, and pulse interval waves, n=e, n=b, and n=r are used respectively, as shown in equations (2) to (4), φ k (e) (t), φ k (b) (t) and φ k (r) It is represented by (t).

[0039] φ k (e) (t) = exp(a k (e) (t)+iψk (e) (t)) (2) φ k (b) (t) = exp(a k (b) (t)+iψ k (b) (t)) (3) φ k (r) (t) = exp(a k (r) (t)+iψ k (r) (t)) (4)

[0040] From equation (1), we can obtain equations (5) to (7) below.

[0041] a k (n) (t) (5) ψ k (n) (t) (6) ω k (n) (t) = dψ k (n) (t) / dt (7)

[0042] Furthermore, the bio-waves before filtering in each frequency band can be expressed as a linear combination of frequency bands k, as shown in equation (8).

[0043] φ (n) (t) = Σexp(a k (n) (t) + iψ k (n) (t)) (8)

[0044] Figure 11 is a diagram showing the complex wave form obtained by Hilbert transforming the bio-wave according to Embodiment 1 on the complex plane. As shown in Figure 11, the bio-wave φ is filtered in the k-frequency band and shown in complex wave form. k (n) (t) can be represented on the complex plane. The horizontal axis on the complex plane is Re(lnφ). k (n) Let (t) be the vertical axis, and Im(lnφ) k(n) When it is set as a(t)), a k (n) a(t) represents the radius centered at the origin, and ψ k (n) ψ(t) represents the angle with the horizontal axis. Here, k = 1, 2, 3, 4 ··· represents each frequency band.

[0045] <Definition of Instantaneous Value> Next, the instantaneous value will be explained. As in equation (5), the logarithm a of the amplitude term in the complex wave form k (n) a(t) is called the instantaneous logarithmic amplitude of the biological wave in the k - frequency band, and ψ in equation (6) k (n) ψ(t) is called the instantaneous phase of the biological wave in the k - frequency band. The time derivative value ω of the instantaneous phase in equation (7) k (n) ω(t) is called the instantaneous frequency of the biological wave in the k - frequency band. The instantaneous logarithmic amplitude corresponds to the logarithm of the amplitude term of the complex wave form after Hilbert transform. The instantaneous phase corresponds to the phase term of the complex wave form after Hilbert transform. The instantaneous frequency corresponds to the time derivative value of the phase term of the complex wave form.

[0046] Also, as shown in equation (9), the difference between the instantaneous phases of two different biological waves is called the instantaneous phase difference θ k (x、y) θ(t). Here, (x, y) indicates that the instantaneous phases of two different biological waves (n = x) and (n = y) are used.

[0047] θ k (x、y) θ(t)=ψ k (x) ψ(t)-ψ k (y) (t) (9)

[0048] Here, two different biological waves are represented by equations (10) and (11).

[0049] φ k (x) φ(t)=exp(a k (x) a(t)+iψ k(x) (t)) (10) φ k (y) (t) = exp(a k (y) (t)+iψ k (y) (t)) (11)

[0050] In particular, as shown in (12) to (14), the instantaneous phase difference between the instantaneous phase of the pulse wave and the instantaneous phase of the pulse interval wave is θ. k (b、r) (t) is called the instantaneous phase difference between the instantaneous phase of the electroencephalogram and the instantaneous phase of the pulse wave, and θ is called the instantaneous phase difference. k (e、b) (t) is called the instantaneous phase difference between the instantaneous phase of the electroencephalogram and the instantaneous phase of the pulse interval wave, and θ is called the instantaneous phase difference. k (e、r) It is called (t).

[0051] θ k (b、r) (t)=ψ k (b) (t)-ψ k (r) (t) (12) θ k (e、b) (t)=ψ k (e) (t)-ψ k (b) (t) (13) θ k (e、r) (t)=ψ k (e) (t)-ψ k (r) (t) (14)

[0052] The instantaneous value is the instantaneous logarithmic amplitude a k (n) (t), instantaneous phase ψ k (n) (t), instantaneous frequency ω k (n) (t), and instantaneous phase difference θ k (x、y) (t) is included.

[0053] <Instantaneous distribution of values> By measuring instantaneous values ​​at predetermined time intervals, the probability density distribution of instantaneous values ​​can be obtained. Therefore, the instantaneous amplitude a k (n) (t) Probability density distribution, instantaneous phase ψ k (n) Probability density distribution of (t), instantaneous frequency ω k (n) Probability density distribution of (t), instantaneous phase difference θ k (x、y) The probability density distribution of (t) can be obtained.

[0054] Figure 12 is a graph illustrating the probability density distribution of instantaneous electroencephalogram (EEG) frequencies in the alpha frequency band during eye opening according to Embodiment 1, where the horizontal axis represents frequency and the vertical axis represents density. Figure 13 is a graph illustrating the probability density distribution of instantaneous pulse wave frequencies in the alpha frequency band during eye opening according to Embodiment 1, where the horizontal axis represents frequency and the vertical axis represents density. Figure 14 is a graph illustrating the probability density distribution of instantaneous phase difference between EEG and pulse wave frequencies in the alpha frequency band during eye opening according to Embodiment 1, where the horizontal axis represents phase difference and the vertical axis represents density.

[0055] Figure 15 is a graph illustrating the probability density distribution of instantaneous electroencephalogram (EEG) frequencies in the alpha frequency band during eye opening according to Embodiment 1, where the horizontal axis represents frequency and the vertical axis represents density. Figure 16 is a graph illustrating the probability density distribution of instantaneous pulse wave frequencies in the alpha frequency band during eye opening according to Embodiment 1, where the horizontal axis represents frequency and the vertical axis represents density. Figure 17 is a graph illustrating the probability density distribution of instantaneous phase difference between EEG and pulse wave frequencies in the alpha frequency band during eye opening according to Embodiment 1, where the horizontal axis represents phase difference and the vertical axis represents density.

[0056] Figure 18 is a graph illustrating the probability density distribution of instantaneous electroencephalogram (EEG) frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. Figure 19 is a graph illustrating the probability density distribution of instantaneous pulse wave frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. Figure 20 is a graph illustrating the probability density distribution of instantaneous phase differences between EEG and pulse wave frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents phase difference, and the vertical axis represents density.

[0057] Figure 21 is a graph illustrating the probability density distribution of instantaneous electroencephalogram (EEG) frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. Figure 22 is a graph illustrating the probability density distribution of instantaneous pulse wave frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents frequency, and the vertical axis represents density. Figure 23 is a graph illustrating the probability density distribution of instantaneous phase differences between EEG and pulse wave frequencies in the alpha frequency band when eyes are closed, according to Embodiment 1. The horizontal axis represents phase difference, and the vertical axis represents density.

[0058] As shown in Figures 12 to 23, instantaneous values ​​measured at predetermined time intervals form a probability density distribution. Note that the probability density distribution is formed not only by instantaneous values ​​of electroencephalograms (EEGs) and pulse waves, but also by instantaneous values ​​of other pulse-related waves and brain-related waves. Here, however, we show the probability density distribution of the instantaneous frequency of the EEG, the instantaneous frequency of the pulse wave, and the instantaneous phase difference between the EEG and pulse wave.

[0059] <Fitting> By fitting the probability density distribution of each instantaneous value with a predetermined function, the feature quantities of the probability density distribution of the instantaneous values ​​can be calculated. For example, by fitting the probability density distribution of instantaneous frequencies with a Gaussian distribution, feature quantities such as the mean and variance can be obtained.

[0060] Furthermore, by fitting the probability density distribution of the instantaneous phase difference with a von Mises distribution, feature quantities such as the mean and concentration can be obtained. Since the value of the instantaneous phase difference is limited to the range of -π to +π, the von Mises distribution is applied. The von Mises distribution is given by equation (15) below. Here, I j (κ) is the j-th order modified Bessel function of the first kind in equation (16).

[0061] f(θ)=exp(κcos(θ-μ)) / 2πI0(κ) (15) I j (κ) = (κ / 2) j Σ(κ 2 / 4) i / i!Γ(j+i+1) (16)

[0062] As shown in Figure 12, when the probability density distribution of the instantaneous frequency of the electroencephalogram (EEG) in the alpha frequency band with eyes open is fitted with a Gaussian distribution, the mean μ is 10.41 Hz and the variance is 1.28. The measurement period is 70-90 seconds. As shown in Figure 13, when the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band with eyes open is fitted with a Gaussian distribution, the mean μ is 9.71 Hz and the variance is 1.07. The measurement period is 70-90 seconds. As shown in Figure 14, when the probability density distribution of the instantaneous phase difference of the EEG and pulse wave in the alpha frequency band with eyes open is fitted with a von Mises distribution, the mean is 0.13 Hz and the concentration level is 0. The measurement period is 70-90 seconds.

[0063] As shown in Figure 15, when the probability density distribution of the instantaneous frequency of the electroencephalogram (EEG) in the alpha frequency band with eyes open is fitted with a Gaussian distribution, the mean value is 10.26 Hz and the variance is 1.19. The measurement period is 210-230 seconds. As shown in Figure 16, when the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band with eyes open is fitted with a Gaussian distribution, the mean value is 9.5 Hz and the variance is 0.92. The measurement period is 210-230 seconds. As shown in Figure 17, when the probability density distribution of the instantaneous phase difference of the EEG and pulse wave in the alpha frequency band with eyes open is fitted with a von Mises distribution, the mean value is 0.78 Hz and the concentration level is 0.13. The measurement period is 210-230 seconds.

[0064] As shown in Figure 18, when the probability density distribution of the instantaneous frequency of the electroencephalogram (EEG) in the alpha frequency band with eyes closed is fitted with a Gaussian distribution, the mean value is 9.1 Hz and the variance is 0.96. The measurement period is 340-360 seconds. As shown in Figure 19, when the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band with eyes closed is fitted with a Gaussian distribution, the mean value is 8.99 Hz and the variance is 1.07. The measurement period is 340-360 seconds. As shown in Figure 20, when the probability density distribution of the instantaneous phase difference of the EEG and pulse wave in the alpha frequency band with eyes closed is fitted with a von Mises distribution, the mean value is 0.28 Hz and the concentration level is 0.29. The measurement period is 340-360 seconds.

[0065] As shown in Figure 21, when the probability density distribution of the instantaneous frequency of the electroencephalogram (EEG) in the alpha frequency band with eyes closed is fitted with a Gaussian distribution, the mean value is 9.39 Hz and the variance is 0.97. The measurement period is 410-430 seconds. As shown in Figure 22, when the probability density distribution of the instantaneous frequency of the pulse wave in the alpha frequency band with eyes closed is fitted with a Gaussian distribution, the mean value is 9.03 Hz and the variance is 0.98. The measurement period is 410-430 seconds. As shown in Figure 23, when the probability density distribution of the instantaneous phase difference of the EEG and pulse wave in the alpha frequency band with eyes closed is fitted with a von Mises distribution, the mean value is 0.2 and the concentration level is 0.33. The measurement period is 410-430 seconds.

[0066] <Tuning> Next, we will explain the tuning process. In the tuning process, features that satisfy predetermined conditions are extracted from the relationship between the features of two different bio-waves. Below, we will explain using electroencephalograms (EEGs) and pulse waves as examples. The tuning process extracts cases where the instantaneous frequency distribution of the EEG and the instantaneous frequency distribution of the pulse wave are similar. The case where the instantaneous frequency distribution of the EEG and the instantaneous frequency distribution of the pulse wave are similar is when the following equation (17) is satisfied.

[0067] (|μ e -μ b |<(σ e +σ b ) / η) (17)

[0068] Here, η is, for example, equation (18) below.

[0069] η=3 (18)

[0070] In the cases of Figures 12 and 13, the left side of equation (17) is |10.41-9.71|=0.7, and the right side of equation (17) is (1.28+1.07) / 3=0.783, so it holds true. In the cases of Figures 15 and 16, the left side of equation (17) is |10.26-9.5|=0.76, and the right side of equation (17) is (1.19+0.92) / 3=0.703, so it does not hold true.

[0071] In the cases of Figures 18 and 19, the left side of equation (17) is |9.1-8.99|=0.11, and the right side of equation (17) is (0.96+1.07) / 3=0.676. In the cases of Figures 21 and 22, the left side of equation (15) is |9.39-9.03|=0.36, and the right side of equation (17) is (0.97+0.98) / 3=0.65. Extracting instantaneous EEG and pulse wave values ​​that satisfy equation (17) in this way is called tuning.

[0072] In the tuning process, features are extracted when the electroencephalogram (EEG) and pulse wave are correlated. For example, pulse waves measured from blood vessels that flow into the brain, such as the carotid bifurcation and vertebral artery, are closely related to EEG activity. Therefore, by extracting features when equation (17) holds, it is possible to extract EEG and pulse waves that are related to each other.

[0073] <Estimation of physiological state> Next, we will explain the estimation of physiological state. Here, we will describe a method for estimating physiological state using electroencephalography (EEG) and pulse wave analysis. Physiological state is estimated from the instantaneous feature values ​​of EEG and pulse wave analysis extracted through tuning. Several examples of estimating physiological state are shown below.

[0074] The features shown in Figures 12-14 and 18-20 satisfy equation (17) and were extracted through tuning. As shown in Figures 12 and 13, in the probability density distribution of instantaneous frequencies in the alpha frequency band when eyes are open, the average value of the electroencephalogram (EEG) is 10.41 and the average value of the pulse wave is 9.71, both of which are approximately 10 Hz. On the other hand, as shown in Figures 18 and 19, in the probability density distribution of instantaneous frequencies in the alpha frequency band when eyes are closed, the average value of the EEG is 9.1 and the average value of the pulse wave is 8.99, both of which are approximately 9 Hz. Thus, the average values ​​of the EEG and pulse wave shift from approximately 10 Hz to approximately 9 Hz when eyes are changed from open to closed.

[0075] Furthermore, as shown in Figure 14, the concentration level in the probability density distribution of the instantaneous phase difference between electroencephalograms and pulse waves in the alpha frequency band when eyes are open is 0. On the other hand, as shown in Figure 20, the concentration level in the probability density distribution of the instantaneous phase difference between electroencephalograms and pulse waves in the alpha frequency band when eyes are closed is 0.29. Thus, the concentration level of the instantaneous phase difference between electroencephalograms and pulse waves increases from 0 to 0.29 when eyes are changed from open to closed.

[0076] Figure 24 is a graph illustrating the characteristic quantities for open-eyed and closed-eyed states according to Embodiment 1. The horizontal axis represents time, and the vertical axis represents the characteristic quantity of instantaneous frequency, the characteristic quantity of instantaneous logarithmic amplitude, and the concentration level. As described above, when the state changes from open-eyed to closed-eyed, the average value of the instantaneous frequency of the electroencephalogram (EEG) and the average value of the instantaneous frequency of the pulse wave shift from approximately 10 Hz to approximately 9 Hz. As a result, the probability density distributions of the instantaneous frequencies of the EEG and pulse wave overlap. Therefore, as shown in Figure 24, the concentration level κ becomes greater than 0. This indicates that when the eyes are open, multiple centers are active, so the pulse wave measured from the carotid bifurcation does not synchronize with the EEG, but when the eyes are closed, the number of active centers decreases, so the pulse wave measured from the carotid bifurcation synchronizes with the EEG. In this way, the state of the centers active in the brain can be estimated by the changes in characteristic quantities.

[0077] Figures 25 and 26 are diagrams illustrating the complex wave forms in each frequency band according to Embodiment 1 in three dimensions, where the horizontal and vertical axes in the complex plane represent the complex transformation formula Re(lnφ k (n) (t)) and Im(lnφ k (n)(t)) is shown, and the axis orthogonal to the complex plane represents the frequency band (energy axis). For example, frequency bands longer than the pulse interval are represented as follows: k=4 as HF (respiration), k=3 as LF (blood pressure), k=2 as VLF1 (possibly related to the autonomic nervous system), and k=1 as VLF2 (possibly related to the autonomic nervous system). For the electroencephalogram bands, k=5 is δ1, k=6 as δ2, k=7 as θ, k=8 as α, and so on. Then, the radial movement in the complex plane becomes the instantaneous logarithmic amplitude of the bio-wave in each frequency band. The direction of rotation around the energy axis is the instantaneous phase of the bio-wave in each frequency band. The energy axis becomes the frequency band axis. And, a Gaussian-like distribution is observed around each axis, rotating around the energy axis. In this way, a picture is obtained in which electrons at each energy level orbit around the atomic nucleus as an electron cloud represented by the probability of existence. The Gaussian-like distributions in each frequency band, which resemble electron clouds, are formed along the 1 / F equi-energy surface.

[0078] Therefore, if there are instantaneous values ​​(features) in a frequency band that deviate from a Gaussian distribution along the 1 / F isoenergy surface, it can be estimated that an abnormality is occurring in a physiological state characteristic of that frequency band. For example, if the instantaneous values ​​(features) of VLF1 and VLF2 deviate from a Gaussian distribution along the 1 / F isoenergy surface, it can be estimated that an abnormality is occurring in a physiological state related to the autonomic nervous system.

[0079] <Physiological State Estimation System> Next, the physiological state estimation system of this embodiment will be described. The physiological state estimation system of this embodiment includes a physiological state estimation device. In the following, the physiological state estimation device will be described as the physiological state estimation system.

[0080] Figure 27 is a block diagram illustrating a physiological state estimation device according to Embodiment 1. As shown in Figure 27, the physiological state estimation device 50 comprises a control unit 50a, a communication unit 50b, a storage unit 50c, an interface unit 50d, a waveform information acquisition unit 51, a filtering unit 52, a conversion unit 53, an instantaneous value calculation unit 54, a distribution calculation unit 55, an extraction unit 56, and a physiological state estimation unit 57. The control unit 50a, communication unit 50b, storage unit 50c, interface unit 50d, waveform information acquisition unit 51, filtering unit 52, conversion unit 53, instantaneous value calculation unit 54, distribution calculation unit 55, extraction unit 56, and physiological state estimation unit 57 each function as a control means, communication means, storage means, interface means, waveform information acquisition means, filtering means, conversion means, instantaneous value calculation means, distribution calculation means, extraction means, and physiological state estimation means, respectively.

[0081] The physiological state estimation device 50 is an information processing device including a computer. The control unit 50a includes, for example, a processor such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), ECU (Electronic Control Unit), FPGA (Field-Programmable Gate Array), or ASIC (Application Specific Integrated Circuit). The control unit 50a has the function of an arithmetic unit that performs control processing and calculation processing. The control unit 50a also controls the operation of each component of the communication unit 50b, storage unit 50c, interface unit 50d, waveform information acquisition unit 51, filtering unit 52, conversion unit 53, instantaneous value calculation unit 54, distribution calculation unit 55, extraction unit 56, and physiological state estimation unit 57.

[0082] Each component of the physiological state estimation device 50 can be realized, for example, by executing a program controlled by the control unit 50a. More specifically, each component can be realized by the control unit 50a executing a program stored in the memory unit 50c. Alternatively, each component may be realized by recording the necessary programs on any non-volatile recording medium and installing them as needed. Furthermore, each component is not limited to being realized by software programs, but may also be realized by any combination of hardware, firmware, and software.

[0083] The communication unit 50b receives waveform information of biological waves measured by waveform measuring instruments such as the electroencephalogram (EEG) measuring instrument 10 and the pulse wave measuring instrument 20 from the waveform measuring instruments. The waveform information of biological waves includes, for example, time-series data of biological waves.

[0084] The storage unit 50c may have a storage device such as memory or a hard disk. The storage device may be, for example, ROM (Read Only Memory) or RAM (Random Access Memory). The storage unit 50c has a function for storing control programs and calculation programs executed by the control unit 50a. The storage unit 50c also has a function for temporarily storing processing data. Furthermore, the storage unit 50c stores the correspondence between characteristic quantities in the instantaneous values ​​of the subject's bio-waves, which have been performed in advance, and the subject's physiological state. The storage unit 50c may also store waveform information of the bio-waves received by the communication unit 50b.

[0085] The interface unit 50d is, for example, a user interface. The interface unit 50d has an input device such as a keyboard, touch panel, or mouse, and an output device such as a display or speaker. The interface unit 50d accepts data input operations from the user (operator, etc.) and outputs information to the user.

[0086] The waveform information acquisition unit 51 acquires waveform information obtained by the communication unit 50b from the waveform measuring instrument. The waveform information is, for example, time-series data of biological waves. The waveform information acquisition unit 51 acquires, for example, electroencephalograms and pulse waves. It may also include a pulse-related wave acquisition unit for acquiring pulse-related waves and a brain-related wave acquisition unit for acquiring brain-related waves.

[0087] The filtering unit 52 filters the acquired waveform information within at least one predetermined frequency band.

[0088] The conversion unit 53 performs a transformation to convert the waveform information of the filtered frequency band into a complex number. The transformation to convert to a complex number is, for example, the Hilbert transform.

[0089] The instantaneous value calculation unit 54 calculates the instantaneous value. As mentioned above, the instantaneous value is the instantaneous logarithmic amplitude a k (n) (t), instantaneous phase ψ k (n) (t), instantaneous frequency ω k (n) (t), instantaneous phase difference θ k (x、y) (t) is included. The instantaneous value calculation unit 54 calculates, for example, the instantaneous logarithmic amplitude a which corresponds to the logarithm of the amplitude term of the complex wave form obtained by Hilbert transform. k (n) (t), and the instantaneous frequency ω, which corresponds to the time derivative of the phase term in complex wave form. k (n) Calculate the instantaneous value including (t).

[0090] The distribution calculation unit 55 calculates the probability density distribution of the instantaneous value. The distribution calculation unit 55 then calculates features including the mean and variance of the probability density distribution of the instantaneous value. Depending on the instantaneous value, the distribution calculation unit 55 may fit a Gaussian distribution to the probability density distribution. In that case, the mean and variance of the probability density distribution are the mean and variance of the probability density distribution when fitted with a Gaussian distribution. Furthermore, depending on the instantaneous value, the distribution calculation unit 55 may fit a von Mises distribution to the probability density distribution. In that case, the mean of the probability density distribution is the mean of the probability density distribution when fitted with a von Mises distribution.

[0091] The extraction unit 56 extracts instantaneous values ​​when predetermined conditions are met. The predetermined conditions are, for example, the following conditions: the average value of the instantaneous frequency in a predetermined frequency band of the electroencephalogram is μ e σ is the variance of instantaneous frequencies in a given frequency band of electroencephalograms. e μ is the average value of the instantaneous frequency in a given frequency band of the pulse wave. b σ is the variance of the instantaneous frequency in a given frequency band of the pulse wave. b When the parameter η is an integer greater than or equal to 3, the extraction unit 56 extracts the instantaneous value that satisfies equation (17) described above. For example, the parameter η is 3.

[0092] The physiological state estimation unit 58 estimates the physiological state from the extracted instantaneous value features. For example, features calculated in advance for the subject's biowaves are associated with the subject's subjective evaluation RAS and stress scale PSS, etc. Then, the physiological state of the subject is estimated from the extracted instantaneous value features.

[0093] <Method for estimating physiological state> Next, a method for estimating the physiological state will be described. Figure 28 is a flowchart illustrating a physiological state estimation method according to Embodiment 1. As shown in Figure 28, the physiological state estimation method comprises a waveform information acquisition step (step S11) for acquiring waveform information related to a living organism, a filtering step (step S12) for filtering the acquired waveform information in at least one frequency band, a conversion step (step S13) for converting the waveform information of the filtered frequency band into a complex wave format, an instantaneous value calculation step (step S14) for calculating an instantaneous value that includes at least one of instantaneous logarithmic amplitude, instantaneous frequency, instantaneous phase, and instantaneous phase difference, a distribution calculation step (step S15) for calculating the distribution of instantaneous values, an extraction step (step S16) for extracting instantaneous values ​​that satisfy predetermined conditions, and a physiological state estimation step (step S17) for estimating the physiological state.

[0094] <Waveform Information Acquisition Step> First, in the waveform information acquisition step (step S11), the waveform information acquisition unit 51 acquires waveform information of biological waves such as brain waves and pulse waves. Specifically, the waveform information acquisition unit 51 acquires brain waves measured by the brain wave measuring device 10 and pulse waves measured by the pulse wave measuring device 20.

[0095] <Filtering Step> Next, in the filtering step (step S12), the filtering unit 52 filters the acquired waveform information in at least one predetermined frequency band.

[0096] <Conversion Steps> Next, in the conversion step (step S13), the conversion unit 53 processes the time-series data φ of the filtered frequency band. k (n) (t) is transformed into complex wave form using the Hilbert transform.

[0097] <Instantaneous Value Calculation Step> Next, in the instantaneous value calculation step (step S14), the instantaneous value calculation unit 54 calculates the instantaneous value described above. Specifically, the instantaneous value calculation unit 54 calculates an instantaneous value that includes at least one of the following: the instantaneous logarithmic amplitude corresponding to the logarithm of the amplitude term of the Hilbert-transformed complex wave form; the instantaneous phase corresponding to the phase term of the complex wave form; the instantaneous frequency corresponding to the time derivative of the phase term of the complex wave form; and the instantaneous phase difference corresponding to the difference between the instantaneous phases of two different bio-waves.

[0098] <Instantaneous Value Distribution Calculation Step> Next, in the instantaneous value distribution calculation step (step S15), the distribution calculation unit 55 calculates the distribution of instantaneous values. Specifically, the distribution calculation unit 55 calculates the probability density distribution of instantaneous logarithmic amplitude, instantaneous phase, instantaneous frequency, and instantaneous phase difference over a desired time interval. Then, the distribution calculation unit 55 calculates, for example, the mean and variance as feature quantities of the above-mentioned probability density distribution.

[0099] <Extraction Step> Next, in the extraction step (step S16), the extraction unit 56 calculates the average value of the instantaneous frequencies in a predetermined frequency band of the electroencephalogram by μ e σ is the variance of instantaneous frequencies in a given frequency band of electroencephalograms. e μ is the average value of the instantaneous frequency in a given frequency band of the pulse wave. b σ is the variance of the instantaneous frequency in a given frequency band of the pulse wave. b When the parameter η is an integer greater than or equal to 3, we extract the instantaneous value that satisfies equation (17).

[0100] <Physiological State Estimation Step> Next, in the physiological state estimation step (step S17), the physiological state estimation unit 57 estimates the physiological state from the extracted instantaneous value features.

[0101] Next, the effects of this embodiment will be explained. In this embodiment, instantaneous values ​​satisfying predetermined conditions for correlation between electroencephalograms and pulse waves are extracted, and the physiological state is estimated from the features of the extracted instantaneous values. Therefore, the physiological state can be estimated with high accuracy. Furthermore, since the electroencephalograms and pulse waves are filtered by a specific frequency band correlated with the physiological state, the physiological state related to that specific frequency band can be estimated with high accuracy. Since the probability density distributions of instantaneous logarithmic amplitude, instantaneous phase, and instantaneous frequency are fitted with a Gaussian distribution, the mean and variance can be obtained as features. Furthermore, since the probability density distribution of instantaneous phase difference is fitted with a von Mises distribution, the mean and concentration can be obtained as features. As a result, the physiological state can be estimated with high accuracy.

[0102] It should be noted that the present invention is not limited to the embodiments described above, and can be modified as appropriate without departing from the spirit of the invention. For example, a physiological state estimation program that causes a computer to execute a physiological state estimation method is also included within the scope of the technical concept of the embodiments. [Explanation of Symbols]

[0103] 10. Electroencephalogram (EEG) measuring device 11 Sensor 12 Main body 20. Pulse wave measuring device 21 Sensor 22 Main body 50 Physiological State Estimation Device 50a Control Unit 50b Communication Department 50c storage section 50d Interface Section 51 Waveform information acquisition section 52 Filtering section 53 Conversion section 54 Instantaneous Value Calculation Unit 55 Distribution calculation part 56 Extraction part 57 Physiological state estimation unit

Claims

1. A waveform information acquisition unit that acquires electroencephalograms and pulse waves, A filtering unit that filters the acquired electroencephalogram and pulse wave in at least one predetermined frequency band, A conversion unit that performs a Hilbert transform on the filtered electroencephalogram and pulse wave in the aforementioned frequency band, An instantaneous value calculation unit calculates an instantaneous value including an instantaneous logarithmic amplitude corresponding to the logarithm of the amplitude term of the Hilbert-transformed complex wave form, and an instantaneous frequency corresponding to the time derivative of the phase term of the complex wave form. A distribution calculation unit calculates the probability density distribution of the instantaneous value and calculates feature quantities including the mean and variance of the probability density distribution. The average value of the instantaneous frequency in the predetermined frequency band of the electroencephalogram is μ ｅ , The variance of the instantaneous frequency in the predetermined frequency band of the electroencephalogram is σ ｅ , The average value of the instantaneous frequency in the predetermined frequency band of the pulse wave is μ ｂ , The variance of the instantaneous frequency in the predetermined frequency band of the pulse wave is σ ｂ , When the parameter η is an integer greater than or equal to 3, |m ｅ -m ｂ |<(s ｅ +s ｂ ) / or An extraction unit that extracts the instantaneous value when the condition is met, A physiological state estimation unit that estimates the physiological state from the extracted instantaneous value feature quantities, A physiological state estimation system equipped with the following features.

2. The parameter η is 3. The physiological state estimation system according to claim 1.

3. A waveform information acquisition step to acquire electroencephalogram and pulse wave, A filtering step of filtering the acquired electroencephalogram and pulse wave in at least one predetermined frequency band, A conversion step of performing a Hilbert transform on the filtered electroencephalogram and pulse wave in the frequency band, An instantaneous value calculation step that calculates an instantaneous value including an instantaneous logarithmic amplitude corresponding to the logarithm of the amplitude term of the Hilbert-transformed complex wave form, and an instantaneous frequency corresponding to the time derivative of the phase term of the complex wave form, A distribution calculation step involves calculating the probability density distribution of the instantaneous value and calculating a feature quantity including the mean and variance of the probability density distribution. Let μ be the average value of the instantaneous frequency in the predetermined frequency band of the electroencephalogram ｅ , The variance of the instantaneous frequency in the predetermined frequency band of the electroencephalogram is σ ｅ , The average value of the instantaneous frequency in the predetermined frequency band of the pulse wave is μ ｂ , The variance of the instantaneous frequency in the predetermined frequency band of the pulse wave is σ ｂ , When the parameter η is an integer greater than or equal to 3, |m ｅ -m ｂ |<(s ｅ +s ｂ ) / or An extraction step to extract the instantaneous value when the condition is met, A physiological state estimation step in which the physiological state is estimated from the feature quantities of the extracted instantaneous values, A method for estimating physiological state, equipped with the following features.

4. A waveform information acquisition step to obtain electroencephalogram and pulse wave data, A filtering step of filtering the acquired electroencephalogram and pulse wave in at least one predetermined frequency band, A conversion step of performing a Hilbert transform on the filtered electroencephalogram and pulse wave in the frequency band, An instantaneous value calculation step that calculates an instantaneous value including an instantaneous logarithmic amplitude corresponding to the logarithm of the amplitude term of the Hilbert-transformed complex wave form, and an instantaneous frequency corresponding to the time derivative of the phase term of the complex wave form, A distribution calculation step that calculates the probability density distribution of the instantaneous value and calculates feature quantities including the mean and variance of the probability density distribution, The average value of the instantaneous frequency in the predetermined frequency band of the electroencephalogram is μ ｅ , The variance of the instantaneous frequency in the predetermined frequency band of the electroencephalogram is σ ｅ , The average value of the instantaneous frequency in the predetermined frequency band of the pulse wave is μ ｂ , The variance of the instantaneous frequency in the predetermined frequency band of the pulse wave is σ ｂ , When the parameter η is an integer greater than or equal to 3, |m ｅ -m ｂ |<(s ｅ +s ｂ ) / or An extraction step to extract the instantaneous value when the condition is met, A physiological state estimation step in which the physiological state is estimated from the feature quantities of the extracted instantaneous values, A physiological state estimation program that is executed by a computer.

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