Pulse wave measurement system, pulse wave measurement method and pulse wave measurement program

Abstract

To provide a pulse wave measurement system, a pulse wave measurement method, and a pulse wave measurement program, each enabling acquisition of a physiological state feature quantity.SOLUTION: A pulse wave measurement system 1 according to the present disclosure comprises: a pulse wave detection unit comprising a photoelectric pulse wave sensor 10 outputting a pulse wave signal; and a processing unit 20 calculating pulsation information on the basis of the pulse wave signal from the pulse wave detection unit. The processing unit 20 comprises: a high-pass filter 21 through which the pulse wave signal having a frequency equal to or higher than a predetermined frequency passes; and a low-pass filter 22 through which the pulse wave signal having a frequency lower than the predetermined frequency passes.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a pulse wave measurement system, a pulse wave measurement method, a pulse wave measurement program, and the like for acquiring features representing a physiological state of the autonomic nervous system. [Background technology]

[0002] Electronic devices such as pulse rate monitors are widely used. A pulse rate monitor is a device for detecting pulsation resulting from a human heartbeat. A pulse rate monitor is a device that detects a signal resulting from a heartbeat based on a signal from a pulse wave sensor attached to, for example, an arm, a palm, or a finger.

[0003] As a pulse wave sensor, for example, a photoelectric sensor using green light is used. The photoelectric sensor detects the reflection of light irradiated onto a living body. The irradiated light is absorbed by hemoglobin in the blood depending on the blood flow rate in the blood vessels. Therefore, the reflected light decreases as the blood flow rate increases. The photoelectric sensor can obtain information related to the pulsation by analyzing sensor information (pulse wave sensor signal) detected as a change in the reflected light.

[0004] However, the amount of reflected light absorbed according to the blood flow is very small compared to the amount of light emitted by the photoelectric sensor. Therefore, the DC component of the signal from the light receiving part of the photoelectric sensor is removed by, for example, a 0.2 Hz high-pass filter, and only the AC component is amplified several thousand times to observe the pulse waveform. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 6160022 [Patent Document 2] Patent Publication No. 2022-84231 [Non-patent literature]

[0006] [Non-Patent Document 1] W Zong, et al. “An Opensource Algorithm to Detect Onset of Arterial Blood Pressure Pulses” IEEE Computers in Cardiology 2003;30:259-262. Summary of the Invention [Problem to be solved by the invention]

[0007] It is generally known that pulsation information of the autonomic nervous system appears in a frequency band of 0.4 Hz or less. Therefore, pulse waveform information measured by pulse wave sensors available on the market hardly includes information of the autonomic nervous system below 0.1 Hz. Therefore, in order to obtain information of the autonomic nervous system from the measurement data of a pulse wave sensor, it is necessary to calculate the rising position of the pulse waveform to obtain pulse interval data, and obtain information of the autonomic nervous system from the time series data (see Patent Document 2).

[0008] It is believed that pulse wave waveform information in the frequency band of 0.4 Hz or less contains biological information that is not present in pulse interval time series data. The heartbeat interval is controlled by issuing commands from the autonomic nerve control center in the brainstem to the sinoatrial node via the sympathetic and parasympathetic nerves. On the other hand, the autonomic nerve controls the degree of tension in the muscles surrounding the blood vessels to regulate the amount of blood flowing through the blood vessels. The former physiological information can be obtained from the pulse interval time series data. However, the latter physiological information can only be obtained from the pulse wave waveform of 0.1 Hz or less. In general, commercially available pulse wave sensors discard this frequency band information via a high-pass filter. For this reason, there is currently no means of obtaining the latter physiological information.

[0009] Patent Document 1 proposes a technology in which a photoelectric sensor is equipped with both a high-pass filter and a low-pass filter to acquire a pulse wave signal in a frequency band of 0.1 Hz or less. However, the pulse wave signal in that band is only used to optimize the pressure on the sensor, and the problem is not solved. It is difficult to acquire physiological state features from the pulse waveform signal even by using machine learning or the like.

[0010] The present disclosure provides a means for acquiring pulse wave waveform signals in a frequency band of 0.1 Hz or less, which cannot be acquired by general pulse wave sensors, and acquiring physiological state characteristics of the autonomic nervous system, particularly those due to contraction and relaxation of vascular walls, as well as providing a pulse wave measurement system, pulse wave measurement method, and pulse wave measurement program that can collectively measure characteristics of the autonomic nervous system related to pulse intervals and blood flow rate. [Means for solving the problem]

[0011] One aspect of the present disclosure is a pulse wave measurement system comprising a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal, and a processing unit that calculates pulse information based on the pulse wave signal from the pulse wave detection unit, the processing unit having a high-pass filter that passes the pulse wave signal of a predetermined frequency or above, and a low-pass filter that passes the pulse wave signal of a frequency below the predetermined frequency.

[0012] In the above-described pulse wave measuring system, the processing unit may remove the DC component of the pulse wave signal waveform from the pulse wave sensor that has passed through the high-pass filter, amplify the AC component, and obtain the pulse rate from the rising position of the pulse wave waveform.

[0013] In the above pulse wave measuring system, the processing unit may obtain time series data of the pulse interval waveform divided into a plurality of frequency bands by passing the pulse interval waveform generated from the pulse rate information through a bandpass filter.

[0014] In the above pulse wave measuring system, the processing unit may obtain time series data of the pulse wave waveform divided into a plurality of frequency bands by passing the pulse wave signal waveform from the pulse wave sensor, which has passed through the low-pass filter, through a band-pass filter.

[0015] In the above pulse wave measuring system, the processing unit may perform a Hilbert transform of the pulse interval waveform divided into the multiple frequency bands, calculate the instantaneous amplitude, the instantaneous phase, and the instantaneous frequency of the time derivative of the instantaneous phase, calculate each distribution over a predetermined period of time, and obtain each average value and each standard deviation value.

[0016] In the above pulse wave measuring system, the processing unit may perform a Hilbert transform on the pulse wave waveform divided into the multiple frequency bands, calculate the instantaneous amplitude, the instantaneous phase, and the instantaneous frequency of the time derivative of the instantaneous phase, calculate the respective distributions over a predetermined period of time, and obtain the respective average values ​​and respective standard deviation values.

[0017] In the above pulse wave measuring system, the processing unit may pass the pulse interval waveform generated from the pulse rate information through a bandpass filter to obtain time series data of the pulse interval waveform divided into a plurality of frequency bands, pass the pulse wave signal waveform from the pulse wave sensor that has passed through the lowpass filter through the bandpass filter to obtain time series data of the pulse wave waveform divided into a plurality of frequency bands, perform a Hilbert transform on the pulse interval waveform divided into the plurality of frequency bands, perform a Hilbert transform on the pulse wave waveform divided into the plurality of frequency bands, calculate a difference between an instantaneous phase of the pulse interval waveform and an instantaneous phase of the pulse wave waveform as an instantaneous phase difference, and calculate a distribution of the instantaneous phase differences over a predetermined period to obtain each average value and each concentration degree.

[0018] In the above pulse wave measuring system, the processing unit may obtain a feature quantity representing a physiological state of the autonomic nervous system from at least one of the distributions.

[0019] One aspect of the present disclosure is a pulse wave measurement method in which, when calculating pulse information based on a pulse wave signal output from a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal, the pulse wave signal having a frequency equal to or greater than a predetermined frequency is passed through a high-pass filter, and the pulse wave signal having a frequency less than the predetermined frequency is passed through a low-pass filter.

[0020] One aspect of the present disclosure is a pulse wave measurement program that causes a computer to execute the following steps when calculating pulse information based on a pulse wave signal output from a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal: pass the pulse wave signal having a frequency equal to or greater than a predetermined frequency through a high-pass filter, and pass the pulse wave signal having a frequency less than the predetermined frequency through a low-pass filter.

[0021] For example, one aspect of the present disclosure, as described in JP 2022-84231 A, includes a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave sensor signal, a processing unit that calculates pulsation information based on a signal from the pulse wave detection unit, a display unit that displays the processing result in the processing unit, and a storage unit that stores the processing result in the processing unit, and the processing unit includes an autonomic nervous system pulse wave processing unit that processes an autonomic nervous system pulse wave signal waveform obtained by passing a signal from the pulse wave detection unit through a low-pass filter, a pulse interval waveform processing unit that processes pulse interval time series data by passing a signal from the pulse wave detection unit through a high-pass filter, a phase difference processing unit that detects the phase difference between the autonomic nervous system pulse wave waveform and the pulse wave interval waveform, and a distribution calculation unit. Here, the cutoff frequency of the low-pass filter and the high-pass filter is in the range of 0.1 to 0.5 Hz, for example, 0.4 Hz.

[0022] The autonomic nervous system pulse wave processing unit amplifies the pulse wave signal that has been passed through a low-pass filter to an appropriate voltage, passes the signal through band-pass filters of four frequency bands, HF (0.15 to 0.4 Hz), LF (0.04 to 0.15 Hz), VLF1 (0.015 to 0.04 Hz), and VLF2 (0.004 to 0.015 Hz), to generate four time series signals, performs a Hilbert transform on each of these signals to calculate the instantaneous amplitude, instantaneous phase, and instantaneous frequency obtained by time-differentiating the instantaneous phase, and stores the processing results in a memory unit.

[0023] The pulse interval waveform processing unit amplifies the AC pulse wave signal that has been passed through a high-pass filter to an appropriate voltage and generates a time-series waveform of the pulse interval using the algorithm described in Non-Patent Document 1, passes this waveform through band-pass filters of four frequency bands, HF (0.15 to 0.4 Hz), LF (0.04 to 0.15 Hz), VLF1 (0.015 to 0.04 Hz), and VLF2 (0.004 to 0.015 Hz), to generate four time-series signals, performs a Hilbert transform on each of these to calculate the instantaneous amplitude, instantaneous phase, and instantaneous frequency obtained by time-differentiating the instantaneous phase, and stores the processing results in a memory unit.

[0024] The phase difference processing unit calculates the phase difference between the instantaneous phase data calculated by the autonomic nervous system pulse wave processing unit and the instantaneous phase data calculated by the pulse interval waveform processing unit for each frequency band, and stores the processing results in a memory unit.

[0025] The distribution calculation unit calculates the probability density distribution of each data for a predetermined period that can be considered as a physiological steady state using the data of instantaneous amplitude, instantaneous frequency, and instantaneous phase difference of each band, which are the processing results stored in the storage unit. If the physiological steady state is present, the distribution will be a Gaussian-like distribution, so the average value and standard deviation of each distribution are calculated by approximating the Gaussian distribution, and the processing results are stored. The average value and standard deviation of each distribution calculated here are important feature quantities for indexing the physiological state.

[0026] The display unit not only displays the raw pulse waveform during measurement, but also displays the pulse waveform after it has passed through a low-pass filter, the pulse interval waveform, and the distribution shapes of the instantaneous amplitude, instantaneous frequency, and instantaneous phase difference for each band. Effect of the Invention

[0027] The present disclosure provides a means for acquiring pulse waveform signals in a frequency band of 0.1 Hz or less, which cannot be acquired by ordinary pulse wave sensors, and for acquiring physiological state features of the autonomic nervous system, particularly those caused by contraction and relaxation of vascular walls, as well as providing an apparatus and system capable of collectively measuring features of the autonomic nervous system related to pulse intervals and blood flow rate. [Brief description of the drawings]

[0028] [Figure 1] 1 is a block diagram illustrating a pulse wave measuring system according to an embodiment. [Diagram 2] 1 is a graph illustrating a pulse waveform obtained by inverting and amplifying a pulse wave signal that has passed through a high-pass filter according to an embodiment. [Diagram 3] 3 is a graph showing an example of a pulse interval waveform obtained by calculating the rising edge position of the pulse waveform in FIG. 2. [Figure 4]1 is a graph illustrating a pulse waveform obtained by non-inverting and amplifying a pulse wave signal that has passed through a low-pass filter according to an embodiment. [Diagram 5] 1 is a graph illustrating the distribution of instantaneous amplitudes when a pulse interval waveform according to an embodiment is divided into four frequency bands and subjected to a Hilbert transform. [Figure 6] 1 is a graph illustrating an example of the distribution of instantaneous amplitude when a pulse waveform according to an embodiment is divided into four frequency bands and subjected to a Hilbert transform. [Figure 7] 1 is a graph illustrating an example of the distribution of instantaneous frequencies when a pulse waveform according to an embodiment is divided into four frequency bands and subjected to a Hilbert transform. [Figure 8] 11 is a graph illustrating the distribution of instantaneous phase differences between pulse interval waveforms and pulse waveforms in four frequency bands according to an embodiment. [Figure 9] FIG. 2 is a diagram illustrating a physiological state model according to the embodiment. [Figure 10] 1 is a configuration diagram illustrating a pulse wave measuring system for acquiring a feature quantity representing a physiological state of the autonomic nervous system according to an embodiment. [Figure 11] 1 is a circuit diagram illustrating a full-band pulse wave sensor circuit according to an embodiment of the present invention; [Figure 12] 4 is a graph illustrating a waveform measured by a full-band pulse wave sensor according to an embodiment of the present invention. [Figure 13] 11 is a graph illustrating the results of FFT analysis of a low-pass pulse wave waveform measured by a full-band pulse wave sensor according to an embodiment. [Figure 14] 11 is a graph illustrating the results of FFT analysis of a high-pass pulse wave waveform measured by a full-band pulse wave sensor according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Hereinafter, specific embodiments to which the present disclosure is applied will be described in detail with reference to the drawings. However, the present disclosure is not limited to the following embodiments. In addition, in order to clarify the explanation, the following description and drawings are appropriately simplified. In this embodiment, the pulse wave measuring system may be replaced with a pulse wave measuring device, and the pulse wave measuring device may be replaced with a pulse wave measuring system. In addition, the pulse wave measuring system of this embodiment may include a pulse wave measuring device.

[0030] Fig. 1 is a block diagram illustrating a pulse wave measuring system according to an embodiment. As shown in Fig. 1, the pulse wave measuring system 1 includes a pulse wave detection unit and a processing unit 20. A photoelectric pulse wave sensor 10 will be used as an example of the pulse wave detection unit. The photoelectric pulse wave sensor 10 is a pulse wave sensor that detects a pulse wave and outputs a pulse wave signal. Therefore, the pulse wave detection unit has a pulse wave sensor that outputs a pulse wave signal.

[0031] The photoplethysmography sensor 10 irradiates the skin of a living body with light emitted from an LED and converts the amount of reflected light into a voltage value using a photosensor. LEDs that emit green, red, infrared, etc. can be used, but the main type is green light. A photodiode or phototransistor is used as the photosensor. The light irradiated to the skin is reflected by the blood in the blood vessels. However, some of the light is absorbed by hemoglobin in the blood. A voltage waveform signal in which the amount of light absorbed changes according to the blood flow rate is output from the photosensor.

[0032] Processing unit 20 has a high-pass filter 21, a low-pass filter 22, amplifiers 23 and 24, a pulse interval waveform generator + band-pass filter 25, a band-pass filter 26, Hilbert transform units 27 and 28, and a distribution calculation unit 29. Processing unit 20 calculates pulse information based on the pulse wave signal from the pulse wave detection unit.

[0033] High-pass filter 21 passes the pulse wave signal with a predetermined frequency or higher. For example, high-pass filter 21 filters the voltage waveform signal in the photosensor output voltage output from the photosensor with, for example, a cutoff frequency of 0.4 Hz. Inverting amplifier 23 with a gain of several thousand times amplifies the photosensor output voltage that has passed through high-pass filter 21. As a result, a pulse wave waveform as shown in FIG. 2 is observed. If the pulse wave waveform thus obtained is subjected to, for example, an algorithm described in Non-Patent Document 1, a pulse interval waveform that shows a very good agreement with the heartbeat interval obtained from an electrocardiogram is obtained as shown in FIG. 3.

[0034] On the other hand, commercially available pulse wave sensors discard pulse wave waveform signals of 0.1 Hz or less. Low-pass filter 22 in this embodiment passes pulse wave signals below a predetermined frequency. To obtain a pulse waveform signal of 0.1 Hz or less, processing unit 20 filters the photosensor output voltage with low-pass filter 22 at a cutoff frequency of 0.4 Hz. Non-inverting amplifier 24, which has a gain of several times, amplifies the photosensor output voltage that has passed through low-pass filter 22. As a result, a pulse waveform of 0.4 Hz or less is obtained, as shown in FIG. 4.

[0035] The bandpass filter 25 filters the pulse interval waveform shown in Fig. 3 in four frequency bands, HF (0.15-0.4 Hz), LF (0.04-0.15 Hz), VLF1 (0.015-0.04 Hz), and VLF2 (0.004-0.015 Hz). The bandpass filter 26 filters the pulse waveform of 0.4 Hz or less shown in Fig. 4 in four frequency bands, HF (0.15-0.4 Hz), LF (0.04-0.15 Hz), VLF1 (0.015-0.04 Hz), and VLF2 (0.004-0.015 Hz). As a result, the processing unit 20 generates eight series of time series data. The Hilbert transform units 27 and 28 perform Hilbert transform on the eight series of time series data. As a result, the processing unit 20 obtains the instantaneous amplitudes, instantaneous phases, and instantaneous frequencies obtained by time-differentiating the instantaneous phases of the eight series. Furthermore, the processing unit 20 obtains the phase differences of the four series by calculating the phase differences between the pulse interval waveform series and the pulse wave waveform series for the instantaneous phases of the same frequency band.

[0036] The pulse interval waveform is Ψ(t) and the pulse waveform is ψ(t). The waveforms passed through the bandpass filters of four frequency bands are denoted as Ψ (k) (t), ψ (k) (t), k=HF, LF, VLF1, VLF2. HF is related to breathing. LF is related to blood pressure. VLF1 and VLF2 are related to the autonomic nervous system. By transforming them with the Hilbert transform, we get Ψ (k) (t) and ψ (k) (t) is expressed by the following equations (1) and (2).

[0037] Psi (k) (t) = Σexp(A j (k) + iΩ j (k) t) (1) ψ (k) (t) = Σexp(a j (k) + iω j (k) t) (2)

[0038] Here, i is an imaginary number, t is time, and A j (k) and Ω j (k) is Ψ (k) (t) shows the amplitude and frequency when it is Fourier-expanded in the k frequency band, j (k) , and ω j (k) is ψ (k) This shows the amplitude and frequency when (t) is Fourier expanded in the k frequency band.

[0039] Psi (k) (t) and ψ (k) (t) is a function of complex numbers. Therefore, Ψ (k) (t) and ψ (k) Calculating the absolute value and phase of (t) at a certain time gives the instantaneous amplitude and instantaneous phase. Also, differentiating the instantaneous phase with respect to time gives the instantaneous frequency.

[0040] By carrying out the above data processing, the instantaneous amplitudes of the eight series, the instantaneous frequencies of the eight series, and the phase differences of the four series are obtained. The distribution calculation unit 29 calculates the probability density distribution of the instantaneous amplitudes, instantaneous frequencies, and instantaneous phase differences in a predetermined period. In order to avoid multicollinearity, for example, Ψ (k) (t) Time series data of 12 series of instantaneous amplitude, instantaneous frequency and instantaneous phase difference of four frequency bands observed from the side, or ψ (k) For 12 series of time series data of instantaneous amplitude, instantaneous frequency, and instantaneous phase difference of four frequency bands observed from the (t) side, the probability density distribution of the instantaneous amplitude, instantaneous frequency, and instantaneous phase difference is calculated at a time interval that can be considered as a physiological steady state. In a physiological steady state, the distribution is distributed in a Gaussian manner. Therefore, the distribution calculation unit 29 calculates the average value and standard deviation of the distribution by fitting the probability density distribution of the instantaneous amplitude, instantaneous frequency, and instantaneous phase difference to a Gaussian distribution. For example, the distribution calculation unit 29 calculates the average value and standard deviation of the distribution, and expresses the physiological state in a 24-dimensional data space of 4 bands x 3 physical quantities x 2 items (average and standard deviation).

[0041] Next, the results of calculating a specific distribution shape using data that can be considered as a physiologically steady state are shown below. (k) 6 is a graph showing an example of a distribution shape of instantaneous amplitude when (t) is divided into four frequency bands and Hilbert transformed. (k) 7 is a graph showing an example of a distribution shape of instantaneous amplitude when (t) is divided into four frequency bands and Hilbert transformed. (k) 8 is a graph showing an example of a distribution shape of instantaneous frequency when (t) is divided into four frequency bands and Hilbert transformed. (k) (t) and pulse waveform ψ (k) 1 is a graph showing an example of the distribution shape of the instantaneous phase difference between the input and output signals (t). The instantaneous phase difference is calculated using the average angle μ and the concentration κ as the Von Meses distribution shown in the following formula (3). Here, I0(κ) is the 0th order Bessel function.

[0042] f(θk)=exp(κcos(θk-μ) / I0(κ) (3)

[0043] It is not necessary to use all 24 features, but rather to select features that seem effective as needed. For example, when analyzing a physiological state that appears only in heart rate fluctuations, it is sufficient to analyze only features related to the amplitude and frequency of the beat-to-beat waveform. Also, when analyzing a physiological state that appears only in respiration (HF) and blood pressure (LF), it is not necessary to analyze the VLF band.

[0044] FIG. 9 is a diagram illustrating a physiological state model according to an embodiment. As shown in FIG. 9, the physiological state model can be expressed using a polar coordinate plane and an imaginary axis. The polar coordinate plane is formed with the instantaneous amplitude as the radius and the instantaneous phase as the rotation angle. The imaginary axis is an axis perpendicular to the polar coordinate plane. If the imaginary axis is taken as the energy axis obtained by multiplying the frequency of each band by the Planck constant, the physiological state model has a Gaussian-like distribution of the instantaneous amplitude (radial direction), instantaneous phase (rotation direction), and instantaneous frequency (energy direction) of the above-mentioned four bands. As shown in FIG. 9, the physiological state model is a spheroid like an electron cloud, and exhibits an aspect similar to an atomic orbital model.

[0045] Such a physiological state model is called a biological state trajectory model. By performing the above-mentioned data processing, the physiological state can be represented in a 24-dimensional data space. If the correlation between the physiological mechanisms among the respective features is not strong, the 24 physiological features can be treated as mathematically almost independent features. In this way, the processing unit 20 may obtain features representing the physiological state of the autonomic nervous system from at least one distribution.

[0046] The frequency bands of the autonomic nervous system are not limited to the four bands of HF, LF, VLF1, and VLF2. The frequency bands of the autonomic nervous system include frequencies lower than the above-mentioned four bands, such as ULF1 (1.5-4 mHz), ULF2 (0.4-1.5 mHz), UULF1 (0.15-0.4 mHz), UULF2 (0.04-0.15 mHz), etc. Since the autonomic nervous system may have diurnal, weekly, monthly, and seasonal variations, it may be possible to perform a similar analysis for these bands as well.

[0047] In order to calculate the characteristic quantities of physiological states in VLF2 (4-15mHz), a data length of at least three wavelengths is required (three or more wavelengths are required to calculate variance), so a data length of 3 / 4mHz = 750 seconds = about 13 minutes is required. In order to calculate the characteristic quantities of physiological states in ULF2 (0.4-1.5mHz), a data length of 3 / 0.4mHz = 7500 seconds = about 2 hours is required. Therefore, in order to deal with physiological states in frequency bands lower than VLF, it is necessary to set up an experimental system in which the physiological state does not change over several hours. Therefore, it is quite difficult to deal with physiological states in frequency bands lower than VLF.

[0048] Fig. 10 is a configuration diagram illustrating a pulse wave measuring system for acquiring features representing a physiological state of the autonomic nervous system according to an embodiment. As shown in Fig. 10, the pulse wave measuring system 1 includes a photoelectric pulse wave sensor 10 and a processing unit 20. The processing unit 20 includes a signal processing unit 20a, a data processing unit 20b, a data storage unit 20c, a data display unit 20d, and a control unit 20e.

[0049] The photoplethysmographic sensor 10 irradiates the skin surface with the emitted light of an LED. The photoplethysmographic sensor 10 receives the reflected or transmitted light after the emitted light is absorbed by hemoglobin in the bloodstream with a photosensor. The photoplethysmographic sensor 10 converts the received reflected or transmitted light into a voltage waveform. The emitted light is generally green light, but may be red or infrared light. Green light has a high absorption rate by hemoglobin in the bloodstream. Therefore, the photoplethysmographic sensor 10 using green light is preferably of the reflection type. However, the absorption rate of red light and infrared light is low, so information on the bloodstream in the depths can be obtained. Therefore, the photoplethysmographic sensor 10 may be of the transmission type using red light and infrared light.

[0050] Signal processing unit 20a inputs the voltage waveform of photoplethysmographic sensor 10 in parallel to high-pass filter 21 and low-pass filter 22. The cutoff frequencies of high-pass filter 21 and low-pass filter 22 are designed to be between 0.1 and 0.5 Hz.

[0051] The pulse wave voltage waveform that has passed through high-pass filter 21 has its DC components removed, leaving only the AC components. The AC components are amplified several thousand times by inverting amplifier 13 and output to data processing unit 20b after AD conversion. In this way, signal processing unit 20a removes the DC components from the pulse wave signal waveform from the pulse wave sensor that has passed through high-pass filter 21 and amplifies the AC components. The pulse wave voltage waveform that has passed through low-pass filter 22 is amplified several times by non-inverting amplifier 14 and output to data processing unit 20b after AD conversion.

[0052] Signal processing unit 20a calculates the pulse wave rising edge position from the pulse wave signal waveform passed through high-pass filter 21. Then, signal processing unit 20a generates time series data of the pulse interval waveform based on the calculated pulse wave rising edge position. Note that signal processing unit 20a may obtain the pulse rate from the rising edge position of the pulse wave waveform. Furthermore, signal processing unit 20a generates time series data of the pulse waveform from the pulse wave signal passed through low-pass filter 22.

[0053] Signal processing unit 20a performs bandpass filter processing for four frequency bands, HF (0.15-0.4 Hz), LF (0.04-0.15 Hz), VLF1 (0.015-0.04 Hz), and VLF2 (0.004-0.015 Hz). As a result, signal processing unit 20a generates eight time series data. Specifically, data processing unit 20b passes a pulse interval waveform generated from the rising position time of the pulse wave through bandpass filter 25 to obtain time series data of the pulse interval waveform divided into a plurality of frequency bands. Data processing unit 20b also passes a pulse wave signal waveform from the pulse wave sensor, which has passed through lowpass filter 22, through bandpass filter 26 to obtain time series data of the pulse wave waveform divided into a plurality of frequency bands.

[0054] The data processor 20b performs a Hilbert transform on each of the eight generated time series data. The data processor 20b calculates the instantaneous amplitude and instantaneous phase from each Hilbert transformed time series data, and the instantaneous frequency obtained by time-differentiating the instantaneous phase. The data processor 20b calculates the instantaneous amplitude, instantaneous frequency, and the phase difference between the pulse interval and the low-pass pulse wave for each frequency band at a time interval that can be considered as a physiological steady state, and calculates a probability density distribution. In the physiological steady state, the probability density distribution is distributed in a Gaussian-like manner (wherein the phase difference is a Von-Meses distribution-like manner). Therefore, the data processor 20b calculates the average value and standard deviation of each probability density distribution (wherein the phase difference is the concentration degree).

[0055] In this way, the data processor 20b performs a Hilbert transform on the time series data of the pulse interval waveform divided into a plurality of frequency bands, calculates the instantaneous amplitude, the instantaneous phase, and the instantaneous frequency of the time differential value of the instantaneous phase, calculates the distribution of each in a predetermined period, and obtains the average value and the standard deviation value. The data processor 20b also performs a Hilbert transform on the time series data of the pulse waveform divided into a plurality of frequency bands, calculates the instantaneous amplitude, the instantaneous phase, and the instantaneous frequency of the time differential value of the instantaneous phase, calculates the distribution of each in a predetermined period, and obtains the average value and the standard deviation value. Furthermore, the data processor 20b calculates the difference between the instantaneous phase of the pulse interval waveform and the instantaneous phase of the pulse waveform as the instantaneous phase difference, calculates the distribution of the instantaneous phase difference in a predetermined period, and obtains the average value and the concentration degree.

[0056] The control unit 20e controls the start and end of measurement in the signal processing unit 20a and the setting of drive conditions for the photoplethysmographic sensor 10. The control unit 20e controls the setting of processing conditions in the data processing unit 20b and the selection of data series to be processed. The control unit 20e controls the selection of data to be stored in the data storage unit 20c. The control unit 20e controls the display of data in the data display unit 20d. The processing unit 20 including the signal processing unit 20a, data processing unit 20b, data storage unit 20c, data display unit 20d and control unit 20e can be constructed on a computer.

[0057] Next, as an example of a pulse wave detection unit, a full-band pulse wave sensor circuit in the photoelectric pulse wave sensor 10 will be described. FIG. 11 is a circuit diagram illustrating a full-band pulse wave sensor according to an embodiment. The full-band pulse wave sensor of this embodiment may be configured as a circuit as shown in FIG. 11. The full-band pulse wave sensor circuit has a green LED. Green light emitted from the green LED is reflected at a measurement target part of the measurement target person. The measurement target part is, for example, a fingertip. Note that the measurement target part is not limited to the fingertip. A part of the green light reflected at the measurement target part is absorbed by the blood in the blood vessels. Depending on the amount of absorption of the green light, the amount of light incident on the photodiode changes, and the current flowing through the photodiode changes. Therefore, the current flowing through the 100 kΩ resistor changes, and can be measured as a change in the voltage input to the amplifier circuit.

[0058] The circuit on the high-pass filter side in the lower part of Figure 11 has the same function as pulse wave sensors that are generally available on the market. A general pulse wave sensor only acquires signals that have passed through a high-pass filter of a certain frequency or higher, such as 0.1 Hz, and cannot acquire signals below the certain frequency.

[0059] On the other hand, the circuit on the low-pass filter side in the upper part of Fig. 11 has the functions of the low-pass filter 22 and the amplifier 24 shown in Fig. 1. In this way, the pulse wave measuring system of this embodiment can obtain a pulse waveform signal in a band of, for example, 0.1 Hz or less, which cannot be obtained by a general pulse wave sensor. Therefore, it provides a means for obtaining physiological state features of the autonomic nervous system, particularly those due to contraction and relaxation of the vascular wall, and can measure the features of the autonomic nervous system related to the pulse interval and blood flow rate all at once.

[0060] Fig. 12 is a graph illustrating a waveform measured by the full-band pulse wave sensor according to the embodiment. As shown in Fig. 12, the above-mentioned circuit configuration can also obtain a pulse wave signal similar to the pulse wave signal passed through the high-pass filter shown in Fig. 2. Furthermore, the above-mentioned circuit can obtain a pulse wave signal passed through a low-pass filter as well as a pulse wave signal passed through a high-pass filter. Therefore, it is possible to obtain both pulse wave signals having frequencies equal to or higher than a predetermined frequency and pulse wave signals having frequencies lower than the predetermined frequency.

[0061] Fig. 13 is a graph showing an example of the results of FFT analysis of a low-pass pulse wave waveform measured by a full-band pulse wave sensor according to an embodiment. Fig. 14 is a graph showing an example of the results of FFT analysis of a high-pass pulse wave waveform measured by a full-band pulse wave sensor according to an embodiment. As shown in Figs. 13 and 14, by performing FFT analysis on the acquired pulse wave signal, the frequency spectrum intensity of each filter output waveform can be obtained. It can be seen that the low-pass filter side (Fig. 13) has a spectrum intensity distribution of approximately 1 / f in the frequency band of 0.1 Hz or less. It can be seen that the high-pass filter side (Fig. 14) shows that the spectrum intensity of 0.1 Hz or less decreases as the frequency decreases.

[0062] Although the embodiment of the present disclosure has been described above, the present disclosure includes appropriate modifications that do not impair the objects and advantages thereof, and is not limited by the above-mentioned embodiment. In addition, each configuration in the embodiment may be appropriately changed, deleted, or combined. In addition, the following pulse wave measurement method and pulse wave measurement program are also included in the technical concept of the present embodiment.

[0063] When calculating pulsation information based on a pulse wave signal output from a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal, Passing the pulse wave signal having a frequency equal to or higher than a predetermined frequency through a high-pass filter; Passing the pulse wave signal having a frequency below a predetermined frequency through a low-pass filter; Pulse wave measurement method.

[0064] When calculating pulsation information based on a pulse wave signal output from a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal, Passing the pulse wave signal having a frequency equal to or higher than a predetermined frequency through a high-pass filter; Passing the pulse wave signal having a frequency below a predetermined frequency through a low-pass filter; A pulse wave measurement program that causes a computer to perform the above steps.

[0065] The processing unit 20 of this embodiment may be an information processing device such as a PC or a server. The information processing device may include a processor, a memory, and a storage device. The storage device may store the processes performed by each component of the processing unit 20 as a program. The processor may also load the program from the storage device into the memory and execute the program. In this way, the processor realizes the functions of each component in the processing unit 20. The information processing device may realize the processing unit 20 by having the processor execute programs corresponding to the signal processing unit 20a, the data processing unit 20b, the data storage unit 20c, the data display unit 20d, and the control unit 20e while referring to the memory and the storage device.

[0066] Each of the components of the processing unit 20 may be realized by dedicated hardware. Also, some or all of the components may be realized by a general-purpose or dedicated circuit, processor, or a combination of these. These may be configured by a single chip, or may be configured by multiple chips connected via a bus. Some or all of the components may be realized by a combination of the above-mentioned circuits, etc. and programs. Also, as the processor PRC, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field-programmable Gate Array), a quantum processor (quantum computer control chip), etc. can be used.

[0067] Furthermore, when some or all of the components of the processing unit 20 are realized by a plurality of information processing devices, circuits, etc., the plurality of information processing devices, circuits, etc. may be centrally arranged or distributed. For example, the information processing devices, circuits, etc. may be realized in a form in which each is connected via a communication network by a client-server system, a cloud computing system, etc. Furthermore, the functions of the processing unit 20 may be provided in a SaaS (Software as a Service) format.

[0068] The program includes a set of instructions (or software code) for making the computer perform one or more functions described in the embodiment when the program is loaded into a processing unit 20 including a computer. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the computer-readable medium or tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray (registered trademark) disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, the transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagated signals. [Explanation of symbols]

[0069] 1. Pulse wave measurement system 10 Photoplethysmography sensor 20 Processing section 21 High-pass filter 22 Low-pass filter 23, 24 Amplifier 25, 26 Bandpass Filter 27, 28 Hilbert transform section 29 Distribution calculation part 20a Signal processing section 20b Data processing section 20c Data storage section 20d Data display section 20e Control section

Claims

1. a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal; a processing unit that calculates pulsation information based on the pulse wave signal from the pulse wave detection unit; Equipped with The processing unit includes: a high-pass filter that passes the pulse wave signal having a frequency equal to or higher than a predetermined frequency; a low-pass filter that passes the pulse wave signal having a frequency less than a predetermined frequency; having Pulse wave measurement system.

2. The processing unit includes: removing the DC component of the pulse wave signal waveform from the pulse wave sensor that has passed through the high-pass filter, amplifying the AC component, and obtaining the pulse interval from the rising edge of the pulse wave waveform; The pulse wave measuring system according to claim 1 .

3. The processing unit includes: a pulse interval waveform generated from the pulse interval information is passed through a band pass filter to obtain time series data of the pulse interval waveform divided into a plurality of frequency bands; The pulse wave measuring system according to claim 2 .

4. The processing unit includes: the pulse wave signal waveform from the pulse wave sensor, which has passed through the low-pass filter, is passed through a band-pass filter to obtain time series data of the pulse wave waveform divided into a plurality of frequency bands; The pulse wave measuring system according to claim 1 .

5. The processing unit performs a Hilbert transform on the time series data of the pulse interval waveform divided into the plurality of frequency bands, calculates an instantaneous amplitude, an instantaneous phase, and an instantaneous frequency of a time derivative of the instantaneous phase, calculates each distribution in a predetermined period, and obtains each average value and each standard deviation value. The pulse wave measuring system according to claim 3 .

6. The processing unit performs a Hilbert transform on the time series data of the pulse waveform divided into the plurality of frequency bands, calculates an instantaneous amplitude, an instantaneous phase, and an instantaneous frequency of a time derivative of the instantaneous phase, calculates each distribution for a predetermined period, and obtains each average value and each standard deviation value. The pulse wave measuring system according to claim 4.

7. The processing unit includes: A pulse interval waveform generated from the pulse interval information is passed through a band pass filter to obtain time series data of the pulse interval waveform divided into a plurality of frequency bands; the pulse wave signal waveform from the pulse wave sensor that has passed through the low-pass filter is passed through the band-pass filter to obtain the time series data of the pulse wave waveform divided into the plurality of frequency bands; performing a Hilbert transform on the time series data of the pulse interval waveform divided into the plurality of frequency bands; performing a Hilbert transform on the time series data of the pulse waveform divided into the plurality of frequency bands; Calculating an instantaneous phase difference as a difference between an instantaneous phase of the pulse interval waveform and an instantaneous phase of the pulse wave waveform, and calculating each distribution of the instantaneous phase difference for a predetermined period to obtain each average value and each concentration degree. The pulse wave measuring system according to claim 2 .

8. The processing unit acquires a feature quantity representing a physiological state of the autonomic nervous system from at least one of the distributions. The pulse wave measuring system according to any one of claims 5 to 7.

9. When calculating pulsation information based on a pulse wave signal output from a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal, Passing the pulse wave signal having a frequency equal to or higher than a predetermined frequency through a high-pass filter; Passing the pulse wave signal having a frequency below a predetermined frequency through a low-pass filter; Pulse wave measurement method.

10. When calculating pulsation information based on a pulse wave signal output from a pulse wave detection unit having a pulse wave sensor that outputs a pulse wave signal, Passing the pulse wave signal having a frequency equal to or higher than a predetermined frequency through a high-pass filter; Passing the pulse wave signal having a frequency below a predetermined frequency through a low-pass filter; A pulse wave measurement program that causes a computer to perform the above steps.

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