JP2021146061A - Biological information acquisition device, biological information acquisition method and program - Google Patents

Abstract

To reduce the effect on the accuracy of an index even if there is abnormal breathing.SOLUTION: The above-mentioned problem is solved by providing a biological information acquisition device comprising: a biological signal acquisition unit for acquiring a biological signal; a heartbeat interval acquisition unit for acquiring a heartbeat interval which is an interval in the time of heartbeat indicated by the biological signal; a first determination unit for determining whether or not the heartbeat interval is an abnormal interval longer than a first predetermined value; and a processing unit for processing data indicating the biological signal or generated based on the biological signal when it is determined that there is the abnormal interval.SELECTED DRAWING: Figure 1

Description

The present invention relates to a biometric information acquisition device, a biometric information acquisition method and a program.

Conventionally, there is known a technique of analyzing an pulse wave or the like that can be obtained from a living body and evaluating an index indicating various states of the living body. For example, the index is a pulse rate or a pulse fluctuation index.

Specifically, the pulse fluctuation index is an index for evaluating fluctuations that occur at peak intervals of the pulse. The pulse fluctuation index includes an LF (Low Frequency) value indicating a low frequency component of about 0.04 Hz (Hertz) to 0.15 Hz, and an HF (High Frequency) value indicating a high frequency component of about 0.15 Hz to 0.40 Hz. , And there are indicators such as LF / HF, which is the ratio of LF to HF. Since these indexes are related to the function of the autonomic nerves of the living body, the state of the autonomic nerves of the living body can be evaluated from the pulse fluctuation index. In this way, fatigue or illness of the living body can be grasped from the state of the autonomic nerves of the living body.

As described above, a technique for analyzing biological information is known. For example, the analyzer first identifies the next heartbeat interval based on the subject's heartbeat signal. The analyzer also calculates the variance value. Then, the representative value indicating the next heartbeat interval and the variance value are compared to determine whether or not noise is generated in the heartbeat signal. In this way, the analyzer is known to have a technique for determining whether or not there is a frequency change due to drowsiness of the subject without imposing a burden on the subject (for example, Patent Document 1 and the like).

However, in the conventional technique, the section of normal breathing may be over-detected as respiratory abnormality. Therefore, abnormal respiration may affect the accuracy of indicators such as LF, HF, and LF / HF.

One aspect of the present invention is aimed at reducing the effect on the accuracy of the index even if there is abnormal breathing.

In order to solve the above-mentioned problems, the biological information acquisition device, which is one aspect of the present invention, is
A biological signal acquisition unit that acquires biological signals,
An interval acquisition unit that acquires an interval in the heartbeat or pulse time indicated by the biological signal, and an interval acquisition unit.
A first determination unit that determines whether or not the interval parameter determined from the interval is an abnormal interval longer than the first predetermined value, and
When it is determined that there is the abnormal interval, it includes a processing unit that indicates the biological signal or processes data generated based on the biological signal.

Even if there is abnormal breathing, the effect on the accuracy of the index can be reduced.

It is a figure which shows the example of the biological information acquisition apparatus. It is a figure which shows the configuration example of hardware. It is a figure which shows the imaging example. It is a figure which shows the functional structure example. It is a figure which shows the whole processing example. It is a figure which shows the example of the procedure of acquiring a biological signal. It is a figure which shows the example of the biological signal. It is a figure which shows the example of the heartbeat interval. It is a figure which shows the example of LF and HF included in the heartbeat interval. It is a figure which shows the example of the influence by breathing. It is a figure which shows the judgment example of abnormal breathing. It is a figure which shows the judgment example by time data. It is a figure which shows the 1st example of processing. It is a figure which shows the 2nd example of processing. It is a figure which shows the example of the 2nd Embodiment. It is a figure which shows the example of the 3rd Embodiment.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the present specification and the drawings, the components having substantially the same functional configuration are designated by the same reference numerals, and duplicate description will be omitted.

<Example of biometric information acquisition device>
FIG. 1 is a diagram showing an example of a biological information acquisition device. For example, the terminal device 100, which is an example of the biological information acquisition device, has an image pickup device 100H1 such as a camera. Then, the terminal device 100 uses the image pickup device 100H1 to image the living body 1 for which the biological information is to be acquired. Next, based on the image captured by the image pickup device 100H1, the terminal device 100 acquires a biological signal indicating a pulse wave or the like of the living body 1.

<Hardware configuration example>
FIG. 2 is a diagram showing an example of hardware configuration. For example, the terminal device 100 includes an image pickup device 100H1, a CPU (Central Processing Unit, hereinafter referred to as “CPU 100H2”), a storage device 100H3, and an input device 100H4. Further, the terminal device 100 has an output device 100H5 and an I / F (interface, hereinafter referred to as "I / F100H6"). These hardware are connected by bus 100H7.

The image pickup apparatus 100H1 is, for example, a camera, an optical sensor, or a combination thereof. Hereinafter, an example in which the image pickup apparatus 100H1 is a camera will be described. Further, in this example, the color filter configuration of the camera may indicate each color by, for example, R (Red), G (Green) and B (Blue) (hereinafter, “R”, “G” and “B”. ) Is a Bayer configuration capable of outputting a color signal of 3 channels. As described above, the color filter configuration of the camera is a configuration that outputs color signals of one or more channels.

Further, it is desirable that the camera has a channel capable of acquiring the brightness change based on the pulse. Luminance changes based on pulse can be easily obtained from, for example, "G" or near infrared (NIR, Near-infrared) light. Therefore, the camera may have a near infrared channel. Near-infrared light mainly has a wavelength of about 750 nm (nanometers) to 1.4 μm (micrometers).

The image sensor included in the image sensor 100H1 is, for example, an optical sensor such as a CCD (Charge Coupled Device) or CMOS (Complementary Metal Oxide Sensor) sensor.

Further, the image pickup apparatus 100H1 is not limited to a camera or the like. For example, the imaging device may be an optical sensor or the like that measures light. That is, the optical sensor is a sensor or the like that can measure the light reflected from the living body.

Hereinafter, an example in which the image pickup apparatus 100H1 is a camera and outputs an RGB image will be described.

The CPU 100H2 is a central processing unit. That is, the CPU 100H2 is an arithmetic unit that performs arithmetic operations and data processing for realizing processing, and a control device that controls hardware. Further, the CPU 100H2 executes the process based on a program or the like stored in the storage device 100H3 or the like.

The storage device 100H3 is, for example, a RAM (Random Access Memory), a ROM (Read-Only Memory), a hard disk, or a combination thereof. That is, the storage device 100H3 is a main storage device, an auxiliary storage device, and the like.

The input device 100H4 is a device for inputting an operation by the user. For example, the input device 100H4 is a keyboard, a mouse, a combination thereof, or the like.

The output device 100H5 is a device that displays an image, various processing results, a combination thereof, and the like to the user. For example, the output device 100H5 is a liquid crystal display or the like.

The input device 100H4 and the output device 100H5 may be an integrated touch panel or the like.

The I / F 100H6 is an interface for connecting to an external device. For example, the I / F 100H6 is a USB (Universal Serial Bus) or the like. Further, the I / F 100H6 inputs / outputs image data or the like to / from an external device. Further, the I / F 100H6 may transmit / receive data by communicating with an external device via a network or the like.

Further, the I / F 100H6 inputs a program or the like from the recording medium 200 or the like.

As described above, the terminal device 100 is an information processing device such as a smartphone or a PC (Personal Computer), and is a computer. The terminal device 100 may be a combination of the information processing device and an imaging device connected to the information processing device. As described above, the biometric information acquisition device is a combination of an information processing device and a measuring device for acquiring biometric information, or a device in which the information processing device and the measuring device are integrated.

Further, the biological information acquisition device may further include a lighting device that irradiates the living body 1 with light. Then, the biological information acquisition device may control the lighting device to control the amount of light or lighting. Further, the biological information acquisition device may control the combination of the lighting device and the imaging device, and may perform imaging by changing the imaging conditions such as exposure and lighting.

<Example of imaging>
FIG. 3 is a diagram showing an imaging example. For example, it is desirable that the terminal device 100 acquires a biological signal by taking an image so that the illustrated region 11 is the center of the part of the living body 1. Specifically, the region 11 is a region including the nose and cheeks of the living body 1.

This region 11 is a region in which the pixel value is likely to change depending on the pulse. Therefore, when the index is calculated based on the image including the region 11, the pulse wave fluctuation index and the like can be calculated with high accuracy.

Further, the region 11 is a region where the skin is less likely to be hidden by hair, clothes, or the like. Therefore, when the index is calculated based on the image including the region 11, the terminal device 100 can accurately calculate the pulse wave fluctuation index and the like.

The terminal device 100 may image a region other than the region 11 as long as the change in the pixel value due to the pulse can be observed. For example, the terminal device 100 may image a part such as a forehead or a fingertip.

Further, it is desirable that the terminal device 100 or the measuring device is non-contact with the living body 1 such as by using an imaging device.

<Functional configuration example>
FIG. 4 is a diagram showing an example of functional configuration. For example, the terminal device 100 has a functional configuration including a biological signal acquisition unit 100F1, a heartbeat interval acquisition unit 100F2, a first determination unit 100F3, and a processing unit 100F4. Further, it is desirable that the terminal device 100 further includes a second determination unit 100F5, a frequency analysis unit 100F6, and an index calculation unit 100F7. Hereinafter, the illustrated functional configuration will be described as an example.

For example, the biological signal acquisition unit 100F1 and the like are realized by a measuring device such as an imaging device 100H1 or I / F100H6, an interface, or a combination thereof. Other configurations are realized by operating the CPU 100H2, the arithmetic unit such as the storage device 100H3, the control device, and the storage device in cooperation with each other to perform processing.

<Overall processing example>
FIG. 5 is a diagram showing an example of overall processing.

<Example of procedure for acquiring biological signals> (step S1)
The biological signal acquisition unit 100F1 acquires the biological signal. For example, a signal indicating a pulse wave (hereinafter referred to as "pulse wave signal"), which is an example of a biological signal, is acquired as follows.

FIG. 6 is a diagram showing an example of a procedure for acquiring a biological signal.

<Imaging example> (Step S20)
The biological signal acquisition unit 100F1 images a living body. For example, the biological signal acquisition unit 100F1 images a living body and generates image data showing the living body. For example, the biological signal acquisition unit 100F1 takes an image at about 30 fps (frames per second) and generates moving image data. The image may be a still image. Specifically, the biological signal acquisition unit 100F1 takes an image as shown in FIG.

<Example of procedure for calculating feature point coordinates on face> (step S21)
The biological signal acquisition unit 100F1 calculates the feature point coordinates on the face. Specifically, first, the biological signal acquisition unit 100F1 detects the coordinates of feature points such as eyes, mouth, and nose from the captured image. The detection of each part can be realized by, for example, a known face recognition technique or the like.

<Example of procedure for setting the pixel area used for extracting the pulse wave signal> (step S22)
The biological signal acquisition unit 100F1 sets a pixel region used for extracting a pulse wave signal. That is, the biological signal acquisition unit 100F1 sets so that the pulse wave signal can be extracted from the region 11. Specifically, the biological signal acquisition unit 100F1 sets a region including the nose and cheeks of the living body based on the calculation result in step S21.

Further, in this case, the set area is set so that the eyes and mouth do not enter the area. The area setting is not limited to the face recognition and the like. For example, the start point position, width, height, and the like of the area may be input and set by the operation by the user. Further, the area may be divided into a plurality of areas and set. For example, the region may be divided into a region including the nose, a region including the left cheek, and a region including the right cheek.

<Example of procedure for averaging pixel values in a region> (step S23)
The biological signal acquisition unit 100F1 averages the pixel values in the region. Specifically, the biological signal acquisition unit 100F1 averages the pixel values of R, G, B, etc. of the image generated from the region set in step S22, and calculates the average value of each.

The change in the pixel value due to the pulse of the living body is a minute change. Therefore, the influence of noise is large in units of one pixel. Therefore, by averaging the pixel values of the plurality of pixels, the influence of noise on the pulse wave signal can be reduced.

<Example of procedure for generating a pulse wave signal> (step S24)
The biological signal acquisition unit 100F1 generates a pulse wave signal. For example, the biological signal acquisition unit 100F1 calculates the following equation (1) based on the average value calculated in step S23, and is the value of the pulse wave signal (p0 (n) in the following equation (1)). ) Is generated.

p0 (n) = a _r × r (n) + a _g × g (n) + a _b × b (n) (1)

In the above equation (1), "n" is a value indicating a frame number. Further, "r (n)" is a pixel value of R indicated by the image in the "n" frame. Similarly, "g (n)" is a pixel value of G indicated by the image in the "n" frame. Further, "b (n)" is a pixel value of B indicated by the image in the "n" frame.

Further, in the above equation (1), " _ar " is a coefficient that is a weight with respect to R. Similarly, " _ag " is a coefficient that is a weight with respect to G. Further, " _ab " is a coefficient that is a weight with respect to B.

For example, _{"a r", "a g"} and _{"a b"} in advance _"a r = 0", _"a g = 1", when set as _"a b = 0", the biological signal obtaining part 100F1 Can generate a pulse wave signal from which only the G component is extracted. The change in pixel value due to the pulse of the living body can be observed from the component of G. Therefore, with the above settings, the biological signal acquisition unit 100F1 can generate a pulse wave signal that makes it easy to observe changes in pixel values due to the pulse of the living body.

In addition, “ _ar ”, “ _ag ” and “ _ab ” may be set in advance as “ _ar = −k”, “ _ag = 1”, “ _ab = 0” and the like. With such a setting, the pulse wave signal is generated by subtracting the R component corrected by "k" from the G component.

In this way, the biological signal acquisition unit 100F1 can reduce noise caused by body movement or the like included in the G component. The noise may be caused by, for example, a change in the amount of light in the surroundings or a change in the surrounding environment such as flickering of a light source.

Therefore, the setting coefficient "k" is set so that the component that becomes noise is reduced. In addition, "k" is a positive value. Further, "k" may be set for each frame, for example.

In addition, a plurality of areas may be set. In such a case, the terminal device first generates a pulse wave signal for each region. Then, the terminal device may synthesize a plurality of pulse wave signals into one pulse wave signal. Specifically, the terminal device synthesizes a plurality of pulse wave signals by addition averaging or the like. That is, the terminal device may average and synthesize the pulse wave signals for each region. In addition, the terminal device may weight and synthesize the pulse wave signal for each region.

By performing the above processing, for example, the following pulse wave signal can be obtained as a biological signal.

<Example of biological signal>
FIG. 7 is a diagram showing an example of a biological signal. For example, the biological signal is a pulse wave signal as shown in the figure. In the figure, the horizontal axis indicates the measured time. On the other hand, the vertical axis indicates the signal strength of the pulse wave signal.

A pulse wave is a signal that captures a change in the volume of a blood vessel due to a pulse as a waveform. Then, as the pulse wave signal, a lighting device such as an LED (Light Emitting Diode) is pointed at the skin surface, green, red, infrared light, or a combination of these is applied, and the reflected light or transmitted light is photographed. It can also be obtained by the photoelectric pulse wave method that measures with a transistor.

In addition, the pulse wave signal can also be obtained by a contact method or the like in which a sensor such as an acceleration sensor or a pressure sensor is attached to the skin directly above the artery to measure the pulse wave. Alternatively, the pulse wave signal can also be obtained by a non-contact method of extracting a heartbeat by reading a change in blood flow flowing through a blood vessel from a change in skin color in a moving image of a part of a living body such as a face. Further, as for the pulse wave, a periodic waveform is measured according to the pulsation of the heart, similarly to the electrocardiogram.

Hereinafter, a pulse wave signal as shown will be described as an example.

<Example of procedure for acquiring heart rate interval> (step S2)
The heartbeat interval acquisition unit 100F2 acquires the heartbeat interval.

The heartbeat interval is an interval in the time of the heartbeat, and is an interval between a peak of a signal generated at a certain time interval and an interval of the next peak. For example, the heartbeat interval is indicated by an index such as RRI (RR Interval), which is the interval between the R wave including the sharpest peak of the heartbeat and the next R wave measured by an electrocardiograph.

The heartbeat is the beating of the heart. On the other hand, a pulse is a pulsation of blood flow in an artery. Generally, the heartbeat and the pulse match, but in the case of an arrhythmia or the like, the heartbeat and the pulse do not match. Then, based on the pulse wave signal, a PPI (Peal-Peek Interval) indicating the interval between the pulse waves is acquired.

As mentioned above, the heart rate and pulse may not match. On the other hand, the heartbeat and the pulse correspond to the normal state of the living body (that is, a healthy state without arrhythmia or the like). When the heartbeat and the pulse match, the pulse interval that can be obtained from the pulse wave signal and the heartbeat interval that can be obtained based on the electrocardiogram acquired by the electrocardiograph match. Hereinafter, in order to explain an example in which the heartbeat and the pulse match, the “pulse interval” and the “heartbeat interval” are unified and referred to as the “heartbeat interval”.

Hereinafter, in the pulse wave signal, it is assumed that the peak detected in the “m” th position from the start of the signal _{occurs at the peak time “T m”.} Then, the heartbeat interval is defined as the heartbeat interval "I ( _Tm )". That is, the heartbeat interval "I (T _m )" indicates the interval between the "m" th peak and the time of the immediately preceding ("m-1" th peak) peak. Therefore, the heartbeat interval "I ( _Tm )" can be expressed as the following equation (2).

I (T _m ) = T _m −T _m-1 (2)

_{The peak time “T m} ” in the above equation (2) is, for example, the maximum value of the pulse signal or the maximum value of the acceleration pulse wave calculated by differentiating the pulse wave signal twice. The heartbeat interval “I (T _m )” may be calculated at an interval of time that is the minimum value of the pulse signal. Further, the heartbeat interval may be calculated based on a signal obtained by correcting the pulse wave signal.

For example, the heart rate interval "I (T _m )" is acquired as follows.

FIG. 8 is a diagram showing an example of a heartbeat interval. In the figure, the horizontal axis represents time. On the other hand, the vertical axis indicates the heartbeat interval. For example, for the heartbeat interval, the result shown in the figure can be obtained by plotting the result of calculating the above equation (2) based on the pulse wave signal.

The heartbeat interval is a value that fluctuates reflecting the balance of nerve activity performed by the sympathetic nerve and parasympathetic nerve of the heart, which is the autonomic nervous system. Such fluctuations become "heart rate variability (HRV)". That is, the heart rate variability is a time-series change of _{the heart rate interval “I (T m)”.} For example, by calculating such an index, an index showing physical and mental stress can be generated.

<Example of procedure for determining whether or not the interval parameter is longer than the first predetermined value> (step S3)
The first determination unit 100F3 determines whether or not the interval parameter is longer than the first predetermined value. Hereinafter, the threshold value used for determining whether or not the interval parameter is long, that is, whether or not the interval is abnormal is referred to as a "first predetermined value". The interval parameter will be described with reference to FIG. 10 and the like.

First, the heartbeat interval includes two components, LF and HF, as follows.

FIG. 9 is a diagram showing an example of LF and HF included in the heartbeat interval. Hereinafter, the case where the heartbeat interval as shown in FIG. 9A is acquired in step S2 will be described as an example.

The heartbeat interval as shown in FIG. 9 (A) includes a low frequency component as shown in FIG. 9 (B) and a high frequency component as shown in FIG. 9 (C).

FIG. 9B shows an example of a low frequency fluctuation component included in the heartbeat interval. LF is derived from blood pressure fluctuations. Further, LF is an integral value of the low frequency band of the power spectrum of the heartbeat interval. The LF then reflects the activity of both the sympathetic and parasympathetic nervous systems.

FIG. 9C shows an example of a high-frequency fluctuation component included in the heartbeat interval. HF is derived from respiration by the living body 1. Further, HF is an integral value of the high frequency band of the power spectrum of the heartbeat interval. The HF then decreases as the activity of the parasympathetic nervous system decreases.

In addition, the index calculated by calculating the ratio of LF and HF is an index for evaluating the degree of fatigue or stress of the living body. Therefore, if LF and HF can be extracted, an index for evaluating the degree of fatigue or stress of the living body can be generated.

LF and HF are extracted, for example, as follows.

First, the heart rate interval is resampled. As mentioned above, the heartbeat intervals are often not evenly spaced. Therefore, in order to perform frequency analysis of the heartbeat interval, it is desirable to perform resampling with respect to the heartbeat interval so that the intervals are evenly spaced. For example, the heart rate interval is resampled to 0.25 seconds.

Resampling, for example, interpolates signal values. The interpolation is, for example, linear interpolation or spline interpolation.

Second, the power spectrum is calculated based on the time-series data of the resampled heartbeat intervals, that is, the time-series data of the evenly spaced heartbeat intervals. The power spectrum is calculated by frequency analysis. Specifically, the maximum entropy method calculates the power spectrum at a specified frequency.

Third, indicators such as LF and HF can be calculated based on the power spectrum. Specifically, the LF is calculated by integrating the power spectrum of "0.04 Hz" to "0.15 Hz". Further, the HF is calculated by integrating the power spectrum of "0.15 Hz" to "0.4 Hz". In addition, "LF / HF" is determined by calculating the calculated ratio of LF and HF.

As described above, in order to calculate LF, HF, etc., it is desirable that the heartbeat interval is calculated accurately. On the other hand, the following abnormalities may occur in the heartbeat interval, for example.

<Examples of abnormal breathing>
When the living body 1 performs "abnormal breathing", the heartbeat interval is affected.

Abnormal breathing is, for example, deep breathing, sighing, yawning, sneezing, etc. As described above, the abnormal breathing is a standard breathing (for example, a breathing performed at rest. For example, in a healthy adult at rest, the number of times is about 12 to 20 times per minute, and once. The depth is 450 to 500 ml. Therefore, the standard number and depth of breathing depends on the age, gender, condition, etc.), the interval between breaths, the depth of breathing, or this. Both are different breaths. Then, depending on the presence or absence of abnormal breathing, the heartbeat interval differs as follows, for example.

FIG. 10 is a diagram showing an example of the effect of respiration. Hereinafter, a “normal” heartbeat interval as shown in FIG. 10 (A) will be described as an example. That is, FIG. 10A shows an example of a heartbeat interval that can be obtained in the absence of abnormal respiration. As described above, when it is "normal", the interval parameter is often almost constant. Hereinafter, an example of the interval parameter in "normal" is shown in "first period CY1".

The interval parameter is, for example, a substantially periodic structure that occurs in the heartbeat interval.

The heartbeat interval does not take a constant value even in a resting state, and increases or decreases according to the breathing cycle. This increase or decrease has a substantially periodic structure of the heartbeat interval. The method of specifying the interval parameter will be described with reference to FIG.

On the other hand, when the living body 1 breathes abnormally, the interval parameter becomes, for example, as shown in FIG. 10 (B).

Compared with FIG. 10A, in the example shown in FIG. 10B, the interval parameter is different in that it has a longer period (hereinafter referred to as “second period CY2”) as compared with the first period CY1.

In this way, when there is abnormal respiration, one cycle of the substantially periodic structure becomes longer as in the second cycle CY2 (hereinafter, as shown in FIG. 10B, the abnormally long substantially periodic structure). One cycle of is called "abnormal interval"). With such data, the periodic structure of HF disappears. Such a peculiar pattern is caused by abnormal breathing.

When the data including the abnormal interval as shown in FIG. 10B is used, the HF value is calculated to be smaller and the LF value is calculated to be larger than in the “normal” case. Therefore, the index such as "LF / HF" is also affected.

Further, in the structure having a substantially periodic structure, the amplitude may be determined. Even when the amplitude of the substantially periodic structure is increased, the HF value is decreased and the LF value is increased. Therefore, it is desirable to determine whether the amplitude of the substantially periodic structure is normal or abnormal. For example, in the case shown in FIG. 10B, the substantially periodic structure has a large amplitude. In such a case, it may be determined that the amplitude of the substantially periodic structure is large, and it may be determined that the structure is abnormal.

That is, the interval parameter is the length of one cycle of a substantially periodic structure, the magnitude of the amplitude, or both.

As described above, when the data affected by the abnormal respiration, that is, the data including the abnormal interval is used, the accuracy of the LF, HF and the index deteriorates. Therefore, the first determination unit 100F3 determines whether or not the data includes such an abnormal interval.

The first determination unit 100F3 determines whether or not the data includes an abnormal interval by comparing whether or not one cycle having a substantially periodic structure of the pulse interval is longer than the first predetermined value.

As shown in FIG. 10B, the abnormal interval is one cycle having a substantially periodic structure of the pulse interval. Therefore, the first determination unit 100F3 determines that the abnormal interval is included if there is one cycle having a substantially periodic structure with a pulse interval longer than the first predetermined value (YES in step S3).

As described above, the first predetermined value is preset with a value that can determine whether or not it is a long cycle such as the second cycle CY2, that is, whether or not it is an abnormal interval. For example, the first predetermined value is set to about "6.7 seconds". The first predetermined value may be other than "6.7 seconds" depending on the setting of which frequency band LF and HF are set to.

Assuming that the first predetermined value is "6.7" seconds, the abnormality interval can be accurately determined.

The first predetermined value is not limited to "6.7" seconds. The setting that sets the first predetermined value to "6.7" seconds is a setting for separating LF and HF by the first predetermined value. In this example, the period is "6.7" seconds, that is, the frequency "0.15 Hz" is set as the boundary between LF and HF. Therefore, the first predetermined value does not have to have a period of "6.7" seconds, that is, a frequency of "0.15 Hz", depending on the definitions of LF and HF.

For example, LF may be defined as a frequency component near "0.1 Hz". Specifically, LF may be defined as "0.08 Hz" to "0.12 Hz". On the other hand, HF may be defined as a frequency in the ± 0.05 Hz range with respect to the respiratory frequency (GA).

Therefore, the first predetermined value may be set to determine the frequency "0.10 Hz" to "0.35 Hz", including the case where the above definition is applied. That is, the first predetermined value may be set in the range of the period "10 seconds" to "2.9 seconds". Preferably, the first predetermined value is set to determine the frequency "0.12 Hz" to "0.30 Hz" (in the cycle, it is "8.3 seconds" to "3.3 seconds"). When such a value is set to the first predetermined value, LF and HF can be accurately determined.

The determination of the abnormal interval, that is, the determination of whether or not one cycle of the substantially periodic structure of the pulse interval is longer than the first predetermined value may be determined by the value of the frequency instead of the cycle. That is, since the period of "6.7" seconds is about "0.15 Hz", one cycle having a substantially periodic structure of the pulse interval is the first cycle depending on whether the frequency is lower than "0.15 Hz". It may be determined whether or not it is longer than a predetermined value.

Specifically, for example, the abnormal interval is determined as follows.

FIG. 11 is a diagram showing an example of determining abnormal breathing. Hereinafter, the same heartbeat intervals as those in FIGS. 10 (A) and 10 (B) are shown in FIGS. 11 (A) and 11 (B), and these two examples will be described.

FIG. 11C is an example of a heart rate interval differential value obtained by differentiating FIG. 11A. Therefore, FIG. 11C shows an example of the heart rate interval differential value in the “normal” case. On the other hand, FIG. 11 (D) shows an example of a heart rate interval differential value obtained by differentiating FIG. 11 (B). Therefore, FIG. 11D shows an example of the heart rate interval differential value in the case of data including abnormal intervals.

When the differential calculation is performed on the data showing the heartbeat interval as shown in FIGS. 11A and 11B, the heartbeat interval differential value can be calculated. Then, when the point where the heartbeat interval differential value becomes "0" is detected, the peak can be calculated. If such a peak can be calculated, the approximate periodic structure in the waveform indicating the heartbeat interval can be analyzed.

For example, one cycle of the substantially periodic structure of the heartbeat interval, that is, the interval parameter, is the time from one peak to the next. As for the interval parameter, it suffices if one cycle having a substantially periodic structure of the heartbeat interval can be grasped. Therefore, the peak may be detected and grasped at a point other than the point where the heart rate interval differential value becomes “0”.

As described above, the interval parameter is a differential value from an arbitrary predetermined value (for example, “0” as in the above example) based on the differential value obtained by differentiating the heartbeat interval with respect to time. Is a value determined by the elapsed time until the next predetermined value is reached. The predetermined value is a preset value.

Further, the interval parameter may take into account the amplitude of the substantially periodic structure of the heartbeat interval.

Specifically, when the differential is calculated, the heartbeat interval differential values shown in FIGS. 11 (C) and 11 (D) can be obtained. Then, the first determination unit 100F3 detects the time when the heartbeat interval differential value becomes “0”.

In addition, the calculation of the derivative may be the calculation of the difference discretely. That is, in processing by a computer, the calculation of the differential may be treated as a differential value by calculating the difference between the data to be calculated and the data at different time points in the time series from the data to be calculated.

Hereinafter, the time during which the heart rate interval differential value becomes "0", that is, the straight line indicating the extreme value (the vertical axis of FIGS. 11 (C) and 11 (D) is "0", and the curve showing the heart rate interval differential value). the intersection is. take the time) and "t _i". The subscript "i" is a number indicating the order in which the extremums were detected. Let "i = 0, 1, 2, ...".

For example, "t _i" used as a reference for judgment, the difference between a "t _i" from two previous extremes "t _{i + 2",} i.e., the "t _i" to "t _{i + 2"} The elapsed time of is used as an interval parameter. Then, the first determination unit 100F3 compares with the first predetermined value using the interval parameter calculated in this way as a threshold value, and determines whether or not the interval is abnormal.

For example, in the example shown in FIG. 11D, the first determination unit 100F3 determines that _{the interval from “t 6} ” to “t ₈ ” is longer than the first predetermined value and is an abnormal interval (YES in step S3). ).

It should be noted that, among the extreme values, either the maximum value or the minimum value may be used to determine whether or not the interval parameter is an abnormal interval.

Further, the first determination unit 100F3 may divide the data into fixed sections and make a judgment for each section.

Based on the above determination, the data determined to be the abnormal interval becomes the target of processing in the subsequent step S6.

Next, when it is determined that the heartbeat interval is longer than the first predetermined value (YES in step S3), the first determination unit 100F3 proceeds to step S4. On the other hand, if it is determined that the heartbeat interval is not longer than the first predetermined value (NO in step S3), the first determination unit 100F3 proceeds to step S7.

To detect abnormal data, "time data" is obtained by statistically processing a value indicating the length (amplitude) of one cycle of a substantially periodic structure generated in a heartbeat interval calculated from time series data of a plurality of heartbeat intervals. It is desirable that the configuration further uses. Hereinafter, a configuration using amplitude (time data) will be described as an example. For example, the amplitude is used as follows.

<Example of procedure for generating time data> (step S4)
The second determination unit 100F5 generates time data.

<Example of procedure for determining whether or not the time data value is larger than the second predetermined value> (step S5)
The second determination unit 100F5 determines whether or not the value of the time data is larger than the second predetermined value. For example, the second determination unit 100F5 determines as follows.

FIG. 12 is a diagram showing an example of determination based on time data. Hereinafter, the time set shorter than the time obtained by measuring the entire pulse signal is referred to as “t _L ”.

As shown in the figure, _{the section of "t L} " includes a plurality of heartbeat intervals. For example, time data is a statistic calculated by statistical processing based on a plurality of heart rate intervals included in the _{"t L" interval.} Specifically, the time data is the mean, variance, standard deviation, multiple of the standard deviation, the combination of the mean and the standard deviation, or the mean and the mean of the length of one cycle of the substantially periodic structure that occurs in multiple heartbeat intervals. This is data showing the value of a combination of multiples of the standard deviation.

In the illustrated example, the second determination unit 100F5 statistically processes the length of one cycle of the substantially periodic structure occurring in the plurality of heartbeat intervals included in the section of _{“t L ” to obtain the standard deviation “δ 0} ”. calculate. Next, the second determination unit 100F5 is shifted "Δt" from the start of the "t _L", it sets the section of the same length as the "t _L".

Hereinafter, _{the sections of "t L} " are numbered in order and referred to as "nth section". In the figure, the _{section indicated by “t L} ” is the “0th section” (n = 0). On the other hand, the section set next to the 0th section, that is, the section whose start time deviates from the 0th section by "Δt" is called "first section" (n = 1).

In this way, the second determination unit 100F5 shifts by "Δt", such as "0th section", "1st section", "2nd section", ..., "Nth section", and so on. Set the section of "t _L".

Then, the time data is _{generated by calculating the standard deviation "δ n} " (n = 0, 1, 2, ...) For each interval.

Next, the second determination unit 100F5 determines whether or not the abnormal interval is included based on the statistic for each section.

If the abnormal interval is included, the value of the time data becomes large. Specifically, when an abnormal interval occurs, the variation becomes large, so that the values of the variance and the standard deviation become large values. Therefore, the second determination unit 100F5 determines whether or not the value of the time data is a large value by comparing the value of the time data with the second predetermined value. Hereinafter, the threshold value for determining whether or not the value of the time data is large in this way is referred to as a "second predetermined value".

Therefore, it is desirable that the second predetermined value is set to a value that can determine whether or not the value of the time data is a large value. For example, the second predetermined value is a value obtained by averaging the time data calculated based on the data acquired in the "normal" state. That is, the average, variance, standard deviation, etc. of the heartbeat interval under the "normal" state are set to the second predetermined values and used as the reference. Then, when there is a variation larger than the reference second predetermined value, the second determination unit 100F5 determines that the data includes the abnormal interval (YES in step S5).

As described above, it is desirable that the time data is determined based on the second predetermined value calculated based on the statistic.

The depth of respiration varies depending on the living body 1. That is, for some people, shallow breathing may be "normal", while deep breathing may be "normal". Therefore, different people have different criteria for judging "shallow", "deep", "fast" and "slow" breathing. Therefore, rather than setting a uniform standard for any living body 1, it is desirable to calculate a second predetermined value that can be estimated to be "normal" for each living body 1 by statistical processing. In this way, when the statistic is used as the second predetermined value, the abnormal interval can be accurately determined according to the respiratory characteristics of each living body 1.

The standard deviation may be a multiple multiplied by an integer. So-called "σ", "2σ", "3σ" and the like may be used as the second predetermined value. That is, the magnification of the standard deviation may be set according to the criteria for determining an abnormality. Based on the value of the combination of the mean and the multiple of the standard deviation in this way, an extremely long or extremely short heartbeat interval that deviates from the mean by more than "σ", "2σ", or "3σ" is abnormal. Can be judged.

The determination based on the interval parameter and the determination based on the time data may be performed in parallel or in an order different from the order shown in the figure. That is, the configuration may be such that "AND" can be taken by both judgments. As described above, when two or more judgments of the judgment based on the interval parameter and the judgment based on the time data are used, the data including the abnormal interval can be accurately judged.

However, as shown in the figure, the judgment based on the interval parameter may be more important than the judgment based on the time data. In the overall process shown in the figure, the determination based on the interval parameter is performed before the determination based on the time data. Therefore, the judgment based on the interval parameter is prioritized over the judgment based on the time data. In this way, if the judgment based on the interval parameter is prioritized, the data including the abnormal interval can be accurately determined.

Next, when it is determined that the value of the time data is larger than the second predetermined value (YES in step S5), the second determination unit 100F5 proceeds to step S6. On the other hand, if it is determined that the value of the time data is not larger than the second predetermined value (NO in step S5), the second determination unit 100F5 proceeds to step S7.

<Example of procedure for processing data> (step S6)
The processing unit 100F4 processes the data. Hereinafter, the data generated by the processing in step S6 is referred to as "post-processing data". For example, the processing is the following processing.

<First example>
FIG. 13 is a diagram showing a first example of processing. For example, the processing unit 100F4 processes so as to exclude the data including the abnormal interval as follows.

Hereinafter, among the pulse wave signals shown in FIG. 13 (A), the case where the data including the abnormal interval (hereinafter referred to as “abnormal data D1”) is determined to be the target of processing by the determination of the interval parameter or the like is an example. Explain to. In such a case, for example, data including the abnormal data D1 (hereinafter referred to as “processing target data D2”) is processed, and for example, post-processing data as shown in FIG. 13B is generated. Therefore, in the subsequent processing, the post-processing data as shown in FIG. 13B excluding the processing target data D2 is used for the processing. When the abnormal data D1 is excluded in this way, the index can be calculated accurately.

The unit to be excluded does not have to be the processing target data D2. For example, processing may be performed to exclude the entire pulse wave signal shown in FIG. 13A or only the portion of the abnormal data D1. Further, by reducing the abnormal data D1 and the like to reduce the data in this way, the processing load in the subsequent stage can be reduced.

The data exclusion may be performed by targeting data generated based on a biological signal such as an index calculated using data including an abnormal section instead of the biological signal.

<Second example>
FIG. 14 is a diagram showing a second example of processing. For example, the processing unit 100F4 processes the data including the abnormal interval so as to interpolate as follows.

Hereinafter, as in FIG. 13, a case where the abnormal data D1 among the pulse wave signals shown in FIG. 14A is determined to be the target of processing by determination of the heartbeat interval or the like will be described as an example.

As shown in FIG. 14 (B), as compared with FIG. 14 (A), in FIG. 14 (B), the abnormal data D1 is interpolated and becomes another data (hereinafter referred to as “interpolated data D3”). different.

The interpolated data D3 is generated by estimating from the tendency before or after the abnormal data D1, for example. That is, the interpolated data D3 is generated by applying the data determined not to include the abnormal interval. In this way, the interpolated data D3 is generated by copying features such as fluctuations in normal data. Further, the interpolated data D3 may be generated by repeating normal data.

As shown in the figure, the interpolation is not limited to the process of applying the interpolation data D3 to the abnormal data D1. For example, the interpolation may be a process of erasing the abnormal data D1 and connecting the front and back of the abnormal data D1.

When the data is processed in this way, the influence of the data including the abnormal interval can be reduced.

In addition, it is desirable that the data becomes data of 90 seconds or more by processing. In calculating the index, the data is often clinically valid if it has a length of 90 seconds or more. Therefore, it is desirable that the processing is performed so that a large amount of data has a length of 90 seconds or more.

<Example of procedure for determining normal data> (step S7)
The terminal device determines that the data is normal. That is, the terminal device adopts normal data for the process of calculating the index performed in the subsequent stage.

<Example of procedure for calculating an index based on data> (step S8)
The index calculation unit 100F7 calculates an index based on the data. For example, the index calculation unit 100F7 calculates an index such as “LF / HF”. In addition, the whole acquired data may be used for the calculation of the index, or the index may be calculated for each section by dividing into a certain section. Further, when the data is divided into sections, the data used for the calculation of the index may partially overlap between the section and the next section.

When step S6 is performed, the post-processing data is used for the calculation of the index. On the other hand, when processing is not performed, the data acquired in step S1 is used.

For example, LF, HF, and the like are used as indexes for evaluating autonomic nerve function. The time-series data of heart rate variability produces two substantially periodic fluctuations under the control of two autonomic nervous systems, a sympathetic nerve that actively works during tension and a parasympathetic nerve that works actively during relaxation.

The sympathetic nerve produces a substantially periodic fluctuation at a low frequency (0.04 to 0.15 Hz), and the parasympathetic nerve produces a substantially periodic fluctuation at a high frequency (0.04 to 0.15 Hz). Therefore, the LF is calculated by the integral value of the frequency band of the low frequency component (0.04 to 0.15 Hz) of the power spectrum of the heartbeat interval data. Further, the HF is calculated by the integral value of the frequency band of the high frequency component (0.15 to 0.40 Hz) of the power spectrum of the heartbeat interval data. Then, LF / HF, which is an index obtained by taking the ratio of LF and HF, is an index for evaluating the degree of fatigue and stress of the living body 1.

In calculating such indicators, abnormal respiration during the acquisition of biological signals is compared to a state in which heart rate variability is steady (ie, no abnormal respiration). The period of the high-frequency substantially periodic structure tends to be long. In many cases, the effects of such abnormal breathing occur. Therefore, abnormal respiration is detected, and data including abnormal intervals due to abnormal respiration is processed. By processing the data in this way, it is possible to reduce the influence of abnormal respiration on the index and the like calculated using the data.

<Modification using frequency analysis>
For the detection of the above abnormal data, the analysis result of frequency analysis of the pulse wave signal may be used. For example, the frequency analysis unit 100F6 first divides the pulse wave signal into fixed sections.

Next, the frequency analysis unit 100F6 performs frequency analysis on the pulse wave signal divided into a certain section. That is, the frequency analysis unit 100F6 converts the data into the frequency space at regular intervals. In this way, the spectral intensity for each frequency band can be calculated by frequency analysis.

Then, when the intensity of the predetermined frequency band in the pulse wave signal is equal to or less than the threshold value (hereinafter, the value that becomes the threshold value in the frequency analysis is referred to as "third predetermined value"), the frequency analysis unit 100F6 does not include the abnormal interval. Judge that.

The predetermined frequency is set to, for example, "0.04 Hz" to "0.15 Hz". That is, the abnormal interval has a longer period than in the "normal" case, and therefore has a lower frequency. Therefore, if the data includes an abnormal interval, the intensity of the frequency band lower than the frequency that can be determined to be "normal" becomes stronger. Therefore, the intensity of the predetermined frequency band becomes weak. It is desirable to determine the presence or absence of abnormal intervals based on whether or not such a phenomenon occurs. Therefore, whether the intensity of the predetermined frequency band is strong or weak is based on the third predetermined value, and whether or not there is an abnormal interval based on whether the intensity of the predetermined frequency band included in the heartbeat is equal to or less than the third predetermined value. To judge.

In this way, when frequency analysis is used, data including abnormal intervals can be accurately determined.

It is desirable that the judgment by frequency analysis is used in combination with the judgment based on the heartbeat interval and the judgment based on the time data. Specifically, in both the judgment based on the heartbeat interval and the judgment based on the frequency analysis, the case where the judgment is made as an abnormal interval is finally judged as the abnormal interval (that is, the judgment based on the heartbeat interval and the judgment based on the frequency analysis " AND ").

In addition, three judgments of the judgment based on the interval parameter, the judgment based on the time data, and the judgment based on the frequency analysis may be combined. In this case, in any of the judgments based on the interval parameter, the time data, and the frequency analysis, the case where the abnormal interval is determined is finally determined as the abnormal interval (that is, the three judgments "AND". "). By combining the judgments in this way, it is possible to accurately judge the data including the abnormal interval.

<Second Embodiment>
The hardware for acquiring the biological signal is different in the second embodiment as compared with the first embodiment. Hereinafter, the differences will be mainly described, and duplicate description will be omitted.

FIG. 15 is a diagram showing an example of the second embodiment. In the second embodiment, as shown in the figure, the biological information acquisition device acquires a biological signal with an electrocardiograph using an electrode 301, an electrometer 302, and the like.

For example, the electrode 301 is placed on the chest of the living body 1.

The electrometer 302 measures the potential measured by the electrode 301 and generates time-series data of the potential. In this way, the biological signal may be acquired.

<Third Embodiment>
The hardware for acquiring the biological signal is different in the third embodiment as compared with the first embodiment. Hereinafter, the differences will be mainly described, and duplicate description will be omitted.

FIG. 16 is a diagram showing an example of the third embodiment. In the third embodiment, the biological information acquisition device acquires a biological signal with the heart rate sensor 401 as shown in the figure.

The heart rate sensor 401 is a sensor having a light source and a light receiving device. Then, the heart rate sensor 401 measures the amount of light by bringing the light source and the light receiving device into contact with the skin of the living body 1. Time-series data of the amount of light measured in this way is generated. In this way, the biological signal may be acquired.

The electrocardiograph, heart rate sensor, and the like may be wirelessly connected to the terminal device 100.

<Other Embodiments>
In addition to the processing shown above, the biometric information acquisition device may perform filtering processing for reducing noise contained in the signal, processing for amplifying the signal, and the like. Further, in performing these processes, the biometric information acquisition device may perform FFT (Fast Fourier Transform) for frequency analysis of the signal, calculation of the S / N ratio, or the like.

Further, the embodiment according to the present invention may be realized by executing the process according to the embodiment of the present invention by an information processing apparatus or an information processing system based on a program. That is, the embodiment according to the present invention may be realized by a program or the like for causing a computer to execute a biometric information acquisition method. The program is stored in a computer-readable recording medium or the like and installed in the computer.

Further, the embodiment according to the present invention may be realized by a biometric information acquisition system having one or more information processing devices. Then, the biometric information acquisition system may perform the processing redundantly, distributed or in parallel.

Although the preferred embodiments of the present invention have been described in detail above, the present invention is not limited to the specific embodiment. That is, various modifications or changes are possible within the scope of the gist of the present invention described in the claims.

1 Living body 11 Area 100 Terminal device 100F1 Biological signal acquisition unit 100F2 Heart rate interval acquisition unit 100F3 First judgment unit 100F4 Processing unit 100F5 Second judgment unit 100F6 Frequency analysis unit 100F7 Index calculation unit 301 Electrode 302 Electrode meter 401 Heart rate sensor D1 Abnormal data D2 Data to be processed D3 Interpolated data CY1 1st cycle CY2 2nd cycle

Japanese Patent No. 5982910

Claims (18)

A biological signal acquisition unit that acquires biological signals,
An interval acquisition unit that acquires an interval in the heartbeat or pulse time indicated by the biological signal, and an interval acquisition unit.
A first determination unit that determines whether or not the interval parameter determined from the interval is an abnormal interval longer than the first predetermined value, and
A biological information acquisition device including a processing unit that indicates the biological signal or processes data generated based on the biological signal when it is determined that there is an abnormal interval. The biometric information acquisition device according to claim 1, further comprising a second determination unit for determining whether or not there is the abnormal interval based on whether or not the section including the plurality of intervals is larger than the second predetermined value. The biometric information acquisition device according to claim 2, wherein the section is a substantially periodic structural amplitude generated at the interval. The second predetermined value is calculated based on one cycle of the substantially periodic structure occurring at the plurality of intervals, and is the average, the deviation, and the deviation of the lengths of the substantially periodic structures occurring at the intervals at the time of one cycle. The biometric information acquisition device according to claim 2 or 3, which is a standard deviation, a multiple of the standard deviation, a combination of the average and the standard deviation, or a combination of the average and a multiple of the standard deviation. The biometric information acquisition device according to any one of claims 2 to 4, wherein when both the first determination unit and the second determination unit determine that there is an abnormal interval, it is determined that there is an abnormal interval. .. Any of claims 1 to 5, further comprising a frequency analysis unit for determining whether or not there is an abnormal interval based on whether or not the intensity of a predetermined frequency band included in the biological signal is equal to or less than a third predetermined value. The biological information acquisition device according to item 1. The biometric information acquisition device according to any one of claims 1 to 6, wherein the interval parameter is one cycle of a substantially periodic structure that occurs at the interval. The interval acquisition unit detects the heartbeat or the peak of the pulse, and
Based on the peak interval, get the interval parameter and
The peak is a point at which the differential value with respect to the time of the interval becomes a predetermined value.
The biometric information acquisition device according to any one of claims 1 to 7, wherein the interval parameter is an elapsed time from when the differential value becomes the predetermined value to when the differential value becomes the predetermined value next. The biometric information acquisition device according to any one of claims 1 to 8, wherein the processing unit excludes data including the abnormal interval. The biometric information acquisition device according to any one of claims 1 to 8, wherein the processing unit interpolates data including the abnormal interval. The biometric information acquisition device according to any one of claims 1 to 10, wherein the first predetermined value is 10 seconds to 2.9 seconds. The biometric information acquisition device according to claim 11, wherein the first predetermined value is 8.3 seconds to 3.3 seconds. The biometric information acquisition device according to claim 12, wherein the first predetermined value is 6.7 seconds. The biological information acquisition device according to any one of claims 1 to 13, wherein the biological signal acquisition unit acquires the biological signal based on an image which is a still image or a moving image of a living body. The biometric information acquisition device according to any one of claims 1 to 14, wherein an index is calculated based on the post-processing data generated by the processing unit. The biometric information acquisition device according to claim 15, wherein the index includes at least one of LF and HF. This is a biometric information acquisition method performed by a biometric information acquisition device.
The biological signal acquisition procedure in which the biological information acquisition device acquires the biological signal,
An interval acquisition procedure in which the biological information acquisition device acquires an interval in the heartbeat or pulse time indicated by the biological signal, and an interval acquisition procedure.
The first determination procedure in which the biological information acquisition device determines whether or not the interval parameter determined from the interval is an abnormal interval longer than the first predetermined value.
A biological information acquisition method including a processing procedure for displaying the biological signal or processing data generated based on the biological signal when the biological information acquisition device is determined to have the abnormal interval. A program for causing a computer to execute the biometric information acquisition method according to claim 17.

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