WO2010073908A1

WO2010073908A1 - Biological information signal processing apparatus, biological information signal processing method and biological information measuring apparatus

Info

Publication number: WO2010073908A1
Application number: PCT/JP2009/070616
Authority: WO
Inventors: 謙治蛤
Original assignee: コニカミノルタセンシング株式会社
Priority date: 2008-12-26
Filing date: 2009-12-09
Publication date: 2010-07-01
Also published as: JPWO2010073908A1; JP4613261B2

Abstract

A biological information signal processing apparatus comprises: a signal generating unit that generates, from first and second time series signals, a signal including a first signal component, based on the first time series signal including both the first signal component having a periodicity and a first noise component, based on a second time series signal including both a second signal component having a predetermined relationship with the first signal component and a second noise component having a predetermined relationship with the first noise component, and based on the predetermined relationship between the first and second noise components; and an estimating unit that uses the periodicity of the generated signal within a first predetermined time range to estimate the predetermined relationship between the first and second noise components within the first predetermined time range.

Description

Biological information signal processing device, biological information signal processing method, and biological information measuring device

The present invention relates to a biological information signal processing device, a biological information signal processing method, and a biological information measuring device that remove noise components from time-series signals.

Conventionally, techniques related to signal processing for removing noise components from time-series data on which noise components are superimposed have been applied to various signal processing apparatuses. In particular, when the time-series data includes information related to biological information, the above signal processing device is called a biological information measuring device. The biological information measuring device is a device that non-invasively detects biological information from biological tissue, specifically, a measuring device called a pulse wave meter and a pulse oximeter that measures the pulse waveform and pulse rate of a living body called a photoelectric pulse wave meter. It is a measuring device or the like that measures the arterial blood oxygen saturation concentration. The principle of these measuring devices is that a living body such as the concentration of a light-absorbing substance in blood is obtained based on a signal component obtained by receiving light transmitted or reflected through a living tissue and corresponding to fluctuations due to pulsation of the living tissue. It seeks information.

Generally, various noise components are superimposed on data necessary for detection of biological information obtained by receiving light transmitted or reflected through biological tissue. FIG. 1 is a diagram illustrating an example of data necessary for detection of biological information obtained by receiving light transmitted or reflected through a biological tissue. The horizontal axis in FIG. 1 is time, and the vertical axis is the intensity of light transmitted or reflected through the living tissue. The noise component is mainly due to body movement such as movement of the body when the biological information measuring device is used. In the example shown in FIG. 1, noise components due to body movement or the like are not superimposed on the signal components at the beginning, but the noise components are superimposed on the signal components from a predetermined time. Thus, if a noise component is superimposed on a signal component, it becomes an error factor in calculation of biological information. For this reason, it is desired to remove noise components.

A technique has been proposed in which biological information is calculated based on a direct current to alternating current ratio in the intensity of light transmitted or reflected through a living tissue when a living body is irradiated with a plurality of lights having different wavelengths. In particular, when a noise component is superimposed on a signal component, the DC / AC ratio for each wavelength is represented by the signal component and the noise component. As a technique for calculating the noise component represented in this way, there is a technique using a cross-correlation between a signal component and a noise component. For example, in the removal of noise components, in Patent Document 1, the DC / AC ratio for each wavelength is obtained, the noise component is included above a predetermined frequency, and the ratio of the noise component according to the wavelength is constant over the entire frequency range. Under the assumption, the ratio of the noise component depending on the wavelength is calculated, and the noise component removal waveform is calculated by using the cross-correlation between the signal component and the noise component. Further, for example, in Patent Document 2, the ratio by the wavelength of the signal component and the ratio by the wavelength of the noise component that obtain the maximum power of the signal component are obtained under the condition that the correlation between the signal component and the noise component is small. Thus, the noise component is removed.

Japanese Patent No. 3627214 US Pat. No. 7,254,433

By the way, in the techniques of Patent Document 1 and Patent Document 2, since the amount of calculation processing is relatively large, power consumption increases. This is a serious problem in terms of power consumption, especially in portable biological information measuring devices, which are usually driven by batteries.

In addition, the biological information measuring device is not only for operating rooms, intensive care units, etc., but also for respiratory failure patients, home oxygen therapy patients in daily life respiratory data collection and management, sleep apnea syndrome screening, It is being used even for sportswear such as mountain climbing that can be worn at all times. Even in view of such a situation, the biological information measuring apparatus is also required to be reduced in size, weight, power saving, price reduction, and the like.

In addition to the biological information measuring device taken up as an example, in general, in signal processing devices that perform signal processing for removing noise components from time-series data, signal processing for removing noise components is a serious problem in terms of power consumption. I can say that.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a biological information signal processing device, a biological information signal processing method, and a biological information measurement device that reduce the amount of signal processing and further reduce power consumption. Is to provide.

According to one aspect of the present invention, a first time-series signal including a first signal component having a periodicity and a first noise component, a second signal component having a predetermined relationship with the first signal component, and the Based on the second time-series signal including the second noise component having a predetermined relationship with the first noise component, and the predetermined relationship between the first noise component and the second noise component, the first A signal including the first signal component is generated from the time-series signal and the second time-series signal, and the first noise in the predetermined time range is generated using periodicity of the generated signal in the predetermined time range. The predetermined relationship between a component and the second noise component is estimated. Thereby, the amount of signal processing required for noise component removal can be reduced as compared with the prior art.

It is a figure which shows an example of the data required for the detection of biological information obtained by receiving the light which permeate | transmitted or reflected the biological tissue. It is a block diagram which shows the structure of the biological information measuring device in one Embodiment of this invention. It is a flowchart which calculates the initial value in the said biological information measuring device. It is a flowchart which calculates the arterial blood oxygen saturation in the said biological information measuring device. It is a flowchart which calculates the pulse rate in the said biological information measuring device. It is a figure which shows the waveform of R-kv * IR by the measurement data shown by FIG.

The principle and embodiment of the present invention will be described. For convenience, a biological information measuring device is taken as an example of signal processing devices, but the present invention is applicable not only to a biological information measuring device but also to a signal processing device that removes noise components.

(Principle of the present invention)
First, the principle of the present invention will be described. In this description, as an example, based on each measurement data obtained by irradiating a living body with a plurality of lights having different wavelengths IR and R, respectively, and receiving or receiving each light transmitted or reflected by the living body. A case where blood oxygen saturation is measured as biological information of a living body will be described.

According to Lambert-Beer's law, the ratio between the alternating current component and the direct current component in the intensity of light having a certain wavelength transmitted or reflected through the living tissue is approximated to be equal to the change in absorbance of the living tissue at that wavelength.

By using the Lambert-Beer law approximation described above, the infrared orthogonal ratio IR_signal, which is the ratio of the direct current component and the alternating current component of the transmitted or reflected light intensity for the infrared wavelength IR, It can be regarded as equal to the change in the absorbance of the tissue. Similarly, the red orthogonal ratio R_signal, which is the ratio of the direct current component to the alternating current component of the intensity of transmitted light or reflected light for the red wavelength R, may also be regarded as equal to the change in the absorbance of the living tissue for the wavelength R. it can.

The infrared orthogonal ratio IR_signal is expressed as in equation (1).

Here, s is a signal component corresponding to a change in absorbance, and n is a noise component superimposed on the signal component.

The red orthogonal ratio R_signal is expressed by equation (2).

Where k_a is the ratio of the signal component s of the absorbance change at the wavelength IR to the signal component of the absorbance change at the wavelength R, and k_v is the noise component n and the wavelength superimposed on the signal component of the wavelength IR. This is the ratio of the noise component superimposed on the R signal component.

It is known that k_a in formula (2) and blood oxygen saturation correspond one-to-one, and blood oxygen saturation can be obtained by obtaining k_a.

Further, the equation (3) is obtained by multiplying the equation (1) by k_v, and the following equation (4) is obtained by subtracting the equation (2) from the equation (3).

Similarly, equation (5) is obtained by multiplying the above equation (1) by k_a, and the following equation (6) is obtained by subtracting equation (2) from equation (5).

Here, the relationship that the signal component s and the noise component n are independent, that is, the following relational expression (7) is used, and under the condition that k_a and k_v are constant within a short time ( Equation (8) is obtained by correlating equations 4) and (6).

Here, Σ is the sum for a short time such that k_a and k_v are constant. i is the data number of the time-series data IR_signal and R_signal corresponding to the change in the intensity of light, and the measurement time interval of the data is Δt, the measurement start time is t0, and t = Δt * i + t0 Tied. Since equation (8) contains two unknowns k_v and k_a, k_a and k_v cannot be obtained from equation (8) alone.

Here, in order to obtain k_v, paying attention to the fact that the right side of equation (4) is almost periodic, k_v is obtained such that the left side of equation (4) has periodicity.

* Substituting k_v obtained in this way into equation (8) to obtain k_a when equation (8) is satisfied. Based on this k_a, the blood oxygen saturation with reduced noise components can be determined. In the above-described calculation method, for example, it is not necessary to use a Fourier transform or the like having a relatively large calculation processing amount, so that the calculation processing amount can be further reduced.

(Embodiment)
DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, an embodiment of the invention will be described with reference to the drawings. In addition, the component which attached | subjected the same code | symbol in each figure shows having the same structure and function, The description is abbreviate | omitted suitably.

First, the configuration of the embodiment of the present invention will be described. FIG. 2 is a block diagram illustrating a configuration of the biological information measurement device according to the embodiment. In addition, the solid line in the figure represents the flow between each block of the electric signal component corresponding to the time-series data of the pulse group described later.

This biological information measuring device 30 uses time-series data of the intensity of light transmitted or reflected through the living body of the measurement target (subject) in order to measure biological information from which the influence of noise components due to body movement or the like is removed. Thus, it is a measuring device that measures biological information such as blood oxygen saturation, for example, based on time-series data from which the influence on the biological information measurement value due to body movement or the like to be measured is removed and the noise component is removed.

More specifically, the biological information measuring device main body of the biological information measuring device 30 is obtained by irradiating the living body with a plurality of lights having different wavelengths and receiving each light transmitted or reflected through the living body. The biological information of the living body is measured based on the measured data. The biological information measuring device main body extracts a noise component that is a component excluding the signal component by using periodicity from data including a signal component having periodicity generated based on the measurement data, and the noise component is extracted from the data. A measurement unit 10 that measures biological information of a living body based on data that has been removed and from which noise components have been removed is provided.

That is, the biological information measuring device 30 includes a data acquisition unit 1 that acquires each measurement data by irradiating a living body with a plurality of lights having different wavelengths and receiving or transmitting each light that is transmitted or reflected through the living body. The living body information measuring apparatus main body for measuring living body information of the living body based on each measurement data obtained by the acquisition unit 1.

In the biological information measuring apparatus main body and the biological information measuring apparatus 30 configured as described above, the noise component is extracted by using the periodicity, so that the amount of calculation processing necessary for removing the noise component can be reduced as compared with the prior art. it can. For this reason, it becomes possible to reduce the power consumption accompanying the calculation of the noise component by reducing the amount of calculation processing.

Moreover, in the above-described biological information measuring apparatus 30, the measurement unit 10 extracts the noise component from the data by using a ratio of the direct current component to the alternating current component of the measurement data for two predetermined wavelengths among the plurality of wavelengths. The variable is determined so that the relational expression obtained by subtracting the DC / AC ratio for the remaining one wavelength from the DC / AC ratio for the one wavelength obtained by calculating a certain DC / AC ratio and multiplying the variable having information on the noise component has periodicity. To do.

According to such a configuration, since the variable is obtained from the relational expression and the noise component is extracted, the amount of calculation processing accompanying the extraction of the noise component can be effectively reduced as compared with the prior art. For this reason, it is possible to effectively suppress the power consumption accompanying the calculation of the noise component by reducing the calculation processing amount.

Hereinafter, the configuration of the biological information measuring device 30 will be described in more detail. The biological information measurement device 30 includes, for example, a data acquisition unit 1, a measurement unit 10, and an output unit 20, as shown in FIG.

The data acquisition unit 1 is a device for acquiring time series data related to pulsation of a living body, which is measured at a predetermined time interval necessary for measurement of biological information in the measuring unit 10. Here, as a method for detecting pulsation, various methods can be adopted. For example, a method using the light absorption characteristic of hemoglobin in a living tissue can be preferably employed. As is well known, oxygen is carried by hemoglobin to each cell in the living body, but hemoglobin combines with oxygen in the lung to become oxygenated hemoglobin, and returns to hemoglobin when oxygen is consumed by cells in the living body. Oxygen saturation refers to the proportion of oxygenated hemoglobin in the blood. The absorbance of these hemoglobins and the absorbance of oxyhemoglobin are wavelength-dependent.For example, hemoglobin absorbs more light than oxyhemoglobin for red light with a wavelength R in the red region, but the wavelength in the infrared region. It absorbs less light than oxyhemoglobin for IR infrared light. This method obtains biological information such as blood oxygen saturation and pulse rate by utilizing the difference in absorption characteristics of hemoglobin and oxyhemoglobin with respect to red light and infrared light. For example, as shown in FIG. 2, the data acquisition unit 1 includes a light emitting element (R) that irradiates a predetermined biological tissue with red light (hereinafter, R), and a light emitting element (R) that is irradiated with the light emitting element (R). A sensor (R) unit 2 including a light receiving element (R) that receives light transmitted or reflected through a biological tissue, and a light emitting element (IR) that irradiates the predetermined biological tissue with infrared light (hereinafter, IR); And a reflective or transmissive sensor including a sensor (IR) unit 3 having a light receiving element (IR) that receives each light that has been irradiated with the light emitting element (IR) and transmitted or reflected through the biological tissue to be measured. is there. The data acquisition unit 1 having such a configuration is set in a predetermined living tissue, monitors each received light amount by the light receiving element (R, IR), and converts each received light into an electrical signal according to the light intensity. Each time series data related to the pulse wave is acquired by performing photoelectric conversion. In addition, the data acquisition unit 1 may be a device that includes a pressure sensor or the like and acquires pulse wave data as the time-series data by directly detecting the pulse pressure due to the blood vessel pulsation. The data acquisition unit 1 is connected to the measurement unit 10 and outputs these time series data to an AC / DC (R) unit 11 described later.

The output unit 20 is connected to the measurement unit 10 and is a device for outputting measurement values of biological information related to the living body measured by the measurement unit 10. The output unit 20 includes, for example, a display device such as a liquid crystal display device (LCD), a 7-segment LED, an organic photoluminescence display device, a CRT (Cathode Ray Tube) display device and a plasma display device, a microphone and a speaker. A sound output device such as a printer or a printing device such as a printer. For example, various pieces of measurement information such as data analysis results of pulse wave data are output in an arbitrary form such as light lighting (including turning on / off and blinking), characters, images, sounds, and printing.

The output unit 20 includes, for example, a SpO2 output unit 22, a pulse rate output unit 23, and a reliability output unit 24 as shown in FIG. The SpO2 output unit 22 is a device for outputting the blood oxygen saturation calculated by the SvO2 estimation SpO2 determination unit 19 described later. The pulse rate output unit 23 is a device for outputting the pulse rate calculated by the pulse rate calculation unit 20 described later. The reliability output unit 24 is a device for outputting the reliability calculated by the reliability calculation unit 21 described later.

For example, as shown in FIG. 2, the measuring unit 10 is functionally an AC / DC (R) conversion unit 11, an AC / DC (IR) conversion unit 12, a BPF (R) unit 13, and a BPF (IR). Unit 14, ΣR_signal * IR_signal calculation unit 15, ΣR_signal ² calculation unit 16, ΣIR_signal ² calculation unit 17, initial value calculation unit 18, SvO2 estimation SpO2 determination unit 19, pulse rate calculation unit 20, and reliability calculation unit 21, and In accordance with the control program stored in advance, the data acquisition unit 1 and the output unit 20 are controlled according to the function.

The measurement unit 10 calculates initial values required for noise component removal and biological information calculation after performing predetermined preprocessing on the time-series data of the pulse wave acquired by the data acquisition unit 1, Using the initial value, noise component removal processing is performed to remove the noise component included in the time series data of the pulse wave, and the biological information is measured based on the time series data of the pulse wave after the noise component is removed. It is a device that outputs a measurement value of biological information to the output unit 20. The measurement unit 10 includes, for example, a ROM (Read Only Memory) that stores arithmetic processing programs for performing arithmetic processing to be described later, a RAM (Random Access Memory) that functions as a so-called working memory, and temporarily stores data. A central processing unit (CPU) that reads a processing program from the ROM and executes it, and its peripheral circuits.

The AC / DC (R) conversion unit 11 converts the red light (R) time-series data input from the sensor (R) unit 2 of the data acquisition unit 1 into data that is a ratio of a direct current component and an alternating current component. It is a circuit to convert. In the present embodiment, the AC / DC (R) conversion unit 11 performs dark processing for removing components due to dark current as the preprocessing.

The BPF (R) unit 13 receives time-series data of red light (R) from the AC / DC (R) conversion unit 11 and removes noise components to obtain frequency components for red light (R). It is a band pass filter (BPF). The BPF (R) unit 13 outputs R_signal, which is time-series data after filtering, to the ΣR_signal * IR_signal calculation unit 15, the ΣR_signal ² calculation unit 16, and the SvO2 estimation SpO2 determination unit 19, respectively.

The AC / DC (IR) conversion unit 12 is data that is a ratio of a DC component to an AC component with respect to time series data of infrared light (IR) input from the sensor (IR) unit 3 of the data acquisition unit 1. It is a circuit to convert to. In the present embodiment, the AC / DC (IR) conversion unit 12 performs dark processing for removing components due to dark current as the preprocessing.

The BPF (IR) unit 14 receives time series data of infrared light (IR) from the AC / DC (IR) conversion unit 12 and removes noise components to obtain frequency components for infrared light (IR). This is a bandpass filter for the purpose. The BPF (IR) unit 14 outputs IR_signal, which is time-series data after filtering, to the ΣR_signal * IR_signal calculation unit 15, the ΣIR_signal ² calculation unit 17, and the SvO2 estimation SpO2 determination unit 19, respectively.

The ΣR_signal * IR_signal calculation unit 15 includes time-series data of red light (R) input from the BPF (R) unit 13 and time-series data of infrared light (IR) input from the BPF (IR) unit 14. Is used to calculate the cross-correlation ΣR_signal * IR_signal and output the calculated cross-correlation ΣR_signal * IR_signal to the initial value calculation unit 18. The ΣR_signal ² calculation unit 16 calculates autocorrelation ΣR_signal ² using the time series data of red light (R) input from the BPF (R) unit 13, and calculates the calculated autocorrelation ΣR_signal ² as an initial value calculation unit 18. The circuit that outputs to The ΣR_signal ² calculation unit 16 calculates autocorrelation ΣR_signal ² using the time series data of red light (R) input from the BPF (R) unit 13, and calculates the calculated autocorrelation ΣR_signal ² as an initial value calculation unit 18. The circuit that outputs to The ΣIR_signal ² calculation unit 17 calculates autocorrelation ΣIR_signal ² using time series data of infrared light (IR) input from the BPF (IR) unit 14, and calculates the calculated autocorrelation ΣIR_signal ² as an initial value calculation unit. 18 is a circuit that outputs the data. The initial value calculation unit 18 is a circuit that calculates an initial value, and more specifically, the cross-correlation ΣR_signal * IR_signal input from the ΣR_signal * IR_signal calculation unit 15 and the autocorrelation ΣR_signal input from the ΣR_signal ² calculation unit 16. ² and the autocorrelation ΣIR_signal ² input from the ΣIR_signal ² calculation unit 17, the initial value is calculated, and the calculated initial value is output to the SvO ² estimation SpO 2 determination unit 19.

The SvO2 estimation SpO2 determination unit 19 removes the noise component superimposed on the signal component using the periodicity from the time series data including the signal component having periodicity, and based on the time series data from which the noise component is removed, This is a circuit for calculating the blood oxygen saturation level. The SvO2 estimation SpO2 determination unit 19 corresponds to the signal generation unit and the estimation unit disclosed in the means for solving the problem. More specifically, the SvO2 estimation SpO2 determination unit 19 determines the initial value from the time series data R_signal input from the BPF (R) unit 13 and the time series data IR_signal input from the BPF (IR) unit 14. This is a circuit that calculates the blood oxygen saturation level of the artery based on the initial value calculated by the value calculation unit 18, calculates the blood oxygen saturation level of the vein based on the noise component as necessary, and calculates the calculated blood The medium oxygen saturation is output to the SpO2 output unit 22. The pulse rate calculation unit 20 is a circuit that calculates the pulse rate based on the time-series data from which the noise component is removed by the SvO2 estimation SpO2 determination unit 19 and outputs the calculated pulse rate to the pulse rate output unit 23. The reliability calculation unit 21 is a circuit that calculates a reliability indicating the degree of error for the measured biological information, and outputs the calculated reliability to the reliability output unit 24.

Note that the biological information measuring device 30 may further include an external storage unit (not shown) as necessary. The external storage unit is, for example, a memory card, flexible disk, CD-ROM (Compact Disc Read Only Memory), CD-R (Compact Disc Recordable), DVD-R (Digital Versatile Disc Recordable), and Blu-ray Disc (Blue-ray Disc). For example, a memory card interface, a flexible disk drive, a CD-ROM drive, a CD-R drive, a DVD-R drive, and a Blu-ray disc drive. Etc.

Here, when an arithmetic processing program or the like is not stored in the measurement unit 10, the biological information measurement device 30 is installed in the measurement unit 10 via the external storage unit from a recording medium that records the program or the like. You may be comprised so that. Alternatively, the biological information measuring device 30 may be configured such that data such as detected biological information is recorded on a recording medium via the external storage unit.

Next, the operation of this embodiment will be described. FIG. 3 is a flowchart for calculating an initial value in the biological information measuring apparatus according to the embodiment. FIG. 4 is a flowchart for calculating arterial oxygen saturation in the biological information measuring apparatus according to the embodiment. FIG. 5 is a flowchart for calculating the pulse rate in the biological information measuring apparatus according to the embodiment.

The biological information measuring device 30 executes the arithmetic processing program by, for example, its activation. By executing this arithmetic processing program, the units 11 to 21 of the measuring unit 10 are functionally configured. The biological information measuring apparatus 30 measures biological information such as blood oxygen saturation with reduced noise components based on the time-series data acquired by the data acquisition unit 1 by the following operation.

The flowcharts (steps S1 to S15) shown in FIGS. 3 and 4 are roughly composed of an initial value calculation flow in steps S1 to S7 and a blood oxygen saturation detection flow in steps S8 to S15. The flowchart (steps S16 to S20) shown in FIG. 5 is a pulse rate detection flow.

<Initial value calculation flow (S1 to S7)>
In the initial value calculation flow, after data preprocessing (steps S1 to S4) is performed, initial values are calculated (steps S5 to S7).

First, in step S <b> 1, the component R_dark due to the dark current of the sensor (R) unit 2 and the component IR_dark due to the dark current of the sensor (IR) unit 3 are input from the data acquisition unit 1 to the measurement unit 10. . Then, R_signal_and_dark (i) and IR_signal_and_dark (i), which are data time series related to the intensity change of the light with the wavelength IR and the wavelength R transmitted or reflected through the living tissue, are input from the data acquisition unit 1 to the measurement unit 10. The Here, i represents the i-th time series data from 1 to N, and the correspondence between i and time is as described in the principle of the invention. As N, the number of data necessary for calculating the oxygen saturation is selected, and a value such as 200 is used. For the sake of simplicity, hereinafter, i in the time series data is omitted. The data R_signal_and_dark related to the wavelength R includes the component R_dark due to the dark current of the sensor (R) unit 2, and the data IR_signal_and_dark related to the wavelength IR includes the component IR_dark due to the dark current of the sensor (IR) unit 3. .

Next, in step S2, the AC / DC (R) unit 11 performs a dark process of subtracting R_dark from R_signal_and_dark. Similarly, the AC / DC (IR) unit 12 performs dark processing by subtracting IR_dark from IR_signal_and_dark. Next, in step S3, the dark-processed signal of each wavelength is converted into a ratio between the DC component and the AC component (AC component / DC component). More specifically, the IR_dark component is removed from IR_signal_and_dark, and IR_signal, which is the ratio of the direct current component consisting only of the intensity of light derived from the living body to the alternating current component, is calculated. Similarly, the R_dark component is removed from R_signal_and_dark, and R_signal, which is the ratio of the direct current component consisting only of the intensity of light derived from the living body to the alternating current component, is calculated.

Next, in step S4, the BPF (R) unit 13 performs processing for filtering R_signal and removing unnecessary frequency components to obtain only desired frequency components. Similarly, the BPF (IR) unit 14 performs processing for filtering IR_signal and removing unnecessary frequency components to obtain a desired frequency component signal component.

Next, in step S5, with respect to the data R_signal and IR_signal extracted in step S4, the ΣR_signal * IR_signal calculation unit 15, the ΣR_signal ² calculation unit 16, and the ΣIR_signal ² calculation unit 17 perform cross correlation ΣR_signal * IR_signal and wavelength R calculating autocorrelation Shigumaaru_signal ² and autocorrelation ShigumaIR_signal ² for the wavelength IR respectively. As described in the principle of the invention described above, Σ is the sum taken from 1 to N with respect to i, and it is assumed that k_v and k_a are constant over a short time interval.

Next, in step S6, the initial value calculation unit 18 substitutes an appropriate value such as k_v obtained last time for k_v_0 which is the initial value of k_v.

Next, in step S7, ΣR_signal * IR_signal determined in step S5 and step S6, using ΣR_signal ^{^2,} ΣIR_signal ² and K_v_0, the initial value calculation section 18 is required in blood oxygen saturation detection flow, k_a Initial values k_a_0 and k_ns_0 which are initial values of k_ns are calculated.

k_ns is an index for determining whether or not to extract a noise component, and is represented by a ratio between the intensity of the noise component and the intensity of the signal component, for example, as shown in Expression (9).

Here, n_square, which is a value related to the intensity of the noise component, and s_square, which is a value related to the intensity of the signal component, are given by different expressions depending on whether or not an equal sign holds by substituting k_v_0 into Expression (8).

When k_v_0 is substituted into equation (8) and the value of k_a such that the equal sign holds (that is, the left side becomes equal to zero) is obtained, the value of k_a is set to k_a_0, and the following equation ( 10) and equations (11) and (9) are used to calculate k_ns_0.

On the other hand, if k_v_0 is substituted into equation (8) and k_a is not obtained where the equal sign does not hold (that is, the left side is not zero), an appropriate k_a such as the previous k_a is set to k_a_0, and K_ns_0 is calculated using Expression (12), Expression (13), and Expression (9). Further, assuming that the difference in oxygen saturation between arterial blood and venous blood is 10%, for example, a value obtained by subtracting a corresponding value from k_v_0 may be k_a_0.

The initial value calculation unit 18 stores k_a_0 in advance in a memory or the like (not shown) (step S7). The initial value calculation unit 18 stores ΣR_signal * IR_signal, ΣR_signal ² , ΣIR_signal ² , k_v_0, k_a_0, and k_ns_0 in advance in the memory or the like (not shown).

<Blood oxygen saturation detection flow (S8 to S15)>
After the initial value calculation is completed by the above steps, the blood oxygen saturation detection flow is executed.

In step S8, the SvO2 estimation SpO2 determination unit 19 first determines the noise component state by comparing k_ns_0, which is an index of the noise component, with a predetermined value. Processing is divided into the following two types according to the determination result. The predetermined value is appropriately selected according to the level of the noise component to be removed, and is, for example, 0.05 in this embodiment.

If the SvO2 estimation SpO2 determination unit 19 determines that the noise component index is zero or small (for example, | k_ns_0 | <0.05) (No), for example, k_a is known as the prior art, and the wavelength IR is known. K_a is calculated using Equation (14), which is a method for obtaining the average value of the ratio of the signal component corresponding to the change in absorbance of R and the signal component corresponding to the change in absorbance at wavelength R (step S9), and then to step S14. Is executed.

On the other hand, if the SvO2 estimation SpO2 determination unit 19 determines that the noise component index is large (for example, | k_ns_0 | ≧ 0.05) in Step S8 (Yes), Step S10 is executed to calculate the periodicity.

As described in the principle of the present invention, k_v selected so that q corresponding to the left side of Expression (4) (refer to Expression (15) below) has the strongest periodicity is the optimal k_v.

Here, q represents a value for the left side of Equation (4), which is the i-th data series, for a certain k_v.

More specifically, how the equation (15) changes according to the value of k_v will be described below with reference to FIG.

FIG. 6 is a diagram showing an R-kv * IR waveform based on the measurement data used in FIG. FIG. 6A shows a waveform according to the data sequence given by equation (15) for k_v when k_v is given by a value smaller than the optimum value, and FIG. 6B shows that k_v is approximated to the optimum value. FIG. 6C shows an equation for k_v when k_v is given by a value larger than the optimum value (FIG. 6C). The waveform by the data series given by 15) is shown. Note that the waveform is interpolated between the data of the time series data. 6A to 6C, these horizontal axes are predetermined short times (corresponding to data series from 1 to N in the data series number i), and the vertical axis is q in Expression 15. .

When k_v smaller than the optimum value is given to equation (15), when the value k_v approximated to the optimum value is given to equation (15) with the waveform shown in FIG. The waveform is as shown in b). As can be seen from a comparison of these two figures, when the value k_v approximated to the optimum value is substituted (in FIG. 6B), the waveform has strong periodicity even when the noise component is superimposed on the signal component. However, when k_v smaller than the optimum value is given in Equation (15), if the noise component is superimposed on the signal component, the waveform is disturbed and the periodicity is weak, and is larger than the optimum value. When k_v is given in equation (15), the waveform is as shown in Fig. 6 (c), and as can be seen by comparing Fig. 6 (c) with Fig. 6 (b), k_v larger than the optimum value is obtained. Is given to the equation (15) (FIG. 6C), if a noise component is superimposed on a signal component, the waveform is disturbed and the periodicity is weak.

In order to use the change of the equation (15) with respect to the above k_v, the periodicity of the equation (15) is detected. More specifically, in the present embodiment, the period of q given by Expression (15) is calculated. That is, in step S10, the initial value of k_v is set to k_v_0, and k given by equation (15) obtained by sequentially changing k_v within a predetermined range (for example, k_v_0−0.5 <k_v <k_v_0 + 0.5) is 2 By converting into a value, the width of the waveform is calculated. Note that the threshold value for binarization may be stored in the measurement unit 10 in advance, or the measurement unit 10 may appropriately determine from the amplitude characteristics of the data such as 40 percent of the maximum amplitude of the data, for example. Good.

A simple procedure for calculating the binarized waveform width will be briefly described. Binarization is a process of converting a value equal to or greater than a predetermined threshold into a constant (for example, 1) and converting a value less than the threshold to another constant (for example, 0). Note that an appropriate value (for example, 0) is selected as the binarization threshold, but the threshold is not limited to this, and can be changed as appropriate.

For example, if the constant is used, after q in Expression (15) is binarized, it takes a value of 1 or zero, but paying attention to adjacent 1 and zero, adjacent 1 and zero The period in which the set is repeated is the period of the waveform to be calculated. For adjacent 1s and zeros, calculate the period of all waveforms. In addition, for example, in order to remove fine sub-peaks, the time when the level of the predetermined amplitude or more is plotted while q in Equation (15) increases (or decreases), and the average value of the time differences in the adjacent plots It is good also as a period. This predetermined amplitude may be stored in the measurement unit 10 in advance, or the measurement unit 10 may determine from the amplitude characteristics of the data.

In step S11, the SvO2 estimation SpO2 determination unit 19 sequentially changes each k_v obtained in step S10 from the initial value k_v_0 within a predetermined range (for example, k_v_0−0.5 <k_v <k_v_0 + 0.5). A variation in the width of the obtained waveform is calculated, and k_v that minimizes the variation is determined.

In step S10 and step S11, the peak and valley periods of the waveform after binarization processing are used as an index for obtaining k_v such that Equation (15) has periodicity. However, the present invention is not limited to this. Is not to be done. As another index in the waveform after the binarization processing, only the period in which the peaks are repeated or only the period in which the valleys are repeated may be used. Also, the maximum value of peak period-minimum value of peak period, maximum value of peak period-minimum value of peak period, standard deviation of peak period, standard deviation of peak period, maximum value of peak width -Minimum peak width, maximum valley width-Minimum valley width, standard deviation of peak width, standard deviation of valley width may be used. Further, the maximum-minimum amplitude or the standard deviation obtained from the maximum and minimum values of the R_signal-k_v * IR_signal waveform may be used as an index.

In the above description, one threshold is provided. However, a plurality of thresholds may be set. For example, threshold value 1 and threshold value 2 are provided, and threshold value 1> 0 and threshold value 2 <0. If equation (15)> threshold 1, +1, equation (15) <− 1 if threshold 2, and threshold 1 ≦ equation (15) ≦ 0 if threshold 2, and +1 pulse width and pulse cycle variation ( The maximum value k_v that minimizes the variation of the pulse width or pulse period (maximum value-minimum value or standard deviation) of -1 and the maximum value-minimum value or standard deviation may be obtained as the optimum value. Further, the above-mentioned various periodicity indexes are obtained for a pulse binarized depending on whether the signal corresponding to the equation (15) exceeds the threshold value 1 and a pulse binarized depending on whether the signal exceeds the threshold value 2. The optimal value may be k_v that is calculated and minimizes the average value (including simple average, harmonic average, and geometric average). Further, three or more threshold values are provided, and the above-mentioned various periodicity indexes are obtained for each of three or more binarized pulses binarized depending on whether or not the signal corresponding to the equation (15) exceeds each threshold value. And k_v that minimizes the average value (including simple average, harmonic average, and geometric average) may be set as the optimal value. Increasing the number of thresholds increases the amount of calculation, but has the advantage of improving the accuracy of periodicity evaluation.

In any index, k_v that minimizes the variation of the index within a predetermined time may be obtained from the standard deviation of the index or the difference between the maximum value and the minimum value of the index. When a plurality of indices are used, k_v that minimizes each index may be different. In that case, the harmonic average value of k_v that minimizes each index is set as the optimum value. The average value may be a simple average value or a geometric average value. Alternatively, the closest k_v among the plurality of k_v that minimize the plurality of indices may be employed. Further, an average value of a plurality of indexes (simple average value, harmonic average value, geometric average value, etc.) may be evaluated as an index. Since k_v does not change abruptly, when evaluating the above index by changing k_v, the value obtained by multiplying the previous k_v optimum value and the absolute value of the difference between k_v or the corresponding coefficient is used as the index. You may evaluate.

In step S10 and step S11, as an example of using periodicity, an example in which k_v is obtained so that the left side of equation (15) satisfies the periodicity is given, but the present invention is not limited to equation (15). , K_v may be used in place of equation (15) as long as it is an equation represented by signal components.

Next, in step S12, the SvO2 estimation SpO2 determination unit 19 calculates k_a by substituting k_v obtained in step S11 into equation (8) and solving this equation. Expression (8) may be obtained by replacing R_signal and IR_signal with respective time differences.

In step S10 and step S11, when a plurality of k_v is obtained according to a plurality of indices, k_a is calculated for each k_v so that the equal sign of equation (8) is established. Also good. The method of determining the final k_a from a plurality of k_a may be an average value (simple average value, harmonic average value, geometric average value, etc.) of the plurality of k_a as the final k_a, The one closest to the final k_a may be the final k_a this time, or the average value of the plurality of k_a excluding the maximum and minimum may be the final k_a. Furthermore, the load average may be applied by applying a weight corresponding to the absolute value of the difference from the previous k_a. In addition, k_v is determined by the above method for about 200 sets of R_signal and IR_signal, and when obtaining k_a, 200 sets of data are divided into four, for example, and four sets of equations (8) are solved to calculate four k_a. Then, those average values (simple average, harmonic average, geometric average value, load average value weighted by the absolute value of the difference from the previous k_a, etc.) may be obtained. You may further average the average value of k_a calculated | required with the whole 200 sets of data, and said 4 k_a.

In addition, in step S10 and step S11, when a plurality of k_vs are obtained from a plurality of indices according to each, the median value of the continuous k_v range or the immediately preceding arterial oxygen saturation calculation is obtained. A value closest to k_v may be set as the optimum value. In the above description, when the noise component index is zero or small, k_a is calculated by a conventional method such as Expression (14). However, the previous k_v (= k_v_0) is substituted into Expression (8). K_a may be obtained. When the noise is small, the left side of equation (8) is Σ {IR_signal * k_v−R_signal} * {IR_signal * k_a−R_signal} ≈ (k_v−k_a) * ΣIR_signal * {IR_signal * k_a−R_signal} ≈ (k_v−k_a) * {ΣIR_signal ² * k_a−ΣIR_signal * R_signal}, so if a value different from k_a is substituted for k_v, the equation is equivalent to ΣIR_signal ² * k_a−ΣIR_signal * R_signal = 0. This solution gives k_a = ΣIR_signal * R_signal / ΣIR_signal ² . When noise is small, R_signal≈true k_a * IR_signal, so the right side of the above equation is true k_a. Therefore, a value sufficiently close to true k_a can be obtained by substituting a value that seems to be different from k_a, such as the previous k_v, into Equation (8). Also, when noise is small, R_signal-k_v * IR_signal has a strong periodicity over a wide range of k_v, but even in this case, the value obtained by multiplying the above-mentioned various indicators by the absolute value of the difference between the previous k_v and k_v Since the optimal value of k_v is almost the same as the previous k_v, a value sufficiently close to true k_a can be obtained even if the same processing as when the noise index is large is performed. When importance is attached to the simplification of the program rather than the calculation speed, the processing may not be divided depending on the size of the noise index.

Next, in step S <b> 13, the SvO2 estimation SpO2 determination unit 19 calculates k_ns that serves as an index of the noise component using Expression (9). For calculating k_ns, ΣR_signal * IR_signal, ΣR_signal ² and ΣIR_signal ² stored in the memory in step S7 are used.

Next, in step S14, the SvO2 estimation SpO2 determination unit 19 calculates the reliability of k_a or the reliability of k_v, and calculates the blood oxygen saturation based on k_a. Here, the reliability of k_a is a value representing the degree of error included in k_a. Similarly, the reliability of k_v is a value indicating the degree of error included in k_v.

Next, in step S15, the SvO2 estimation SpO2 determination unit 19 stores k_v as k_v_0 in preparation for the next measurement. The SpO2 output unit 22 and the reliability output unit 24 output the blood oxygen saturation level and the k_a reliability level detected in step S14, respectively. The reliability output unit 24 may output the reliability of k_v instead of the reliability of k_a. In addition, the measurement unit 10 compares k_ns calculated in step S13 with the first predetermined value, and when k_ns exceeds the first predetermined value, the fact that k_ns has exceeded the first predetermined value. Is output to the SpO2 output unit 22. Further, the measurement unit 10 compares the reliability of k_a with a second predetermined value, and if k_a exceeds the second predetermined value, a warning indicating that k_a has exceeded the second predetermined value. May be further output to the reliability output unit 24. Further, the measurement unit 10 compares the reliability of k_v with the third predetermined value using the reliability of k_v instead of the reliability of k_a, and when k_v exceeds the third predetermined value May further output a warning indicating that k_v exceeds the third predetermined value to the reliability output unit 24.

In general, it is known that the error contained therein varies depending on the value of the calculated arterial oxygen saturation, for example, the arterial oxygen saturation is approximately 95% and 70%. The magnitude of the degree error is different. Therefore, the predetermined value of k_ns is changed as follows. The predetermined value of k_ns is set to a small value when the arterial oxygen saturation is smaller than the predetermined arterial oxygen saturation (when k_a and k_v are large and the venous blood oxygen saturation is small) On the other hand, if the arterial oxygen saturation is greater than the predetermined arterial oxygen saturation (k_a and k_v are small, corresponding to the case where the venous blood oxygen saturation is large), the predetermined value of k_ns is set to be large. Good. By changing the predetermined value of k_ns according to the value of arterial oxygen saturation calculated in this way, for example, when arterial oxygen saturation is large, the threshold value is increased, and when arterial oxygen saturation is small, Warning and output prohibition can be performed with the same value. Here, for the predetermined arterial blood oxygen saturation and the predetermined value of k_ns corresponding to the predetermined arterial oxygen saturation, values determined based on past measurement data may be stored in the measurement unit 10 in advance. Alternatively, it may be appropriately changed according to the living body or the state of the living body. In addition to k_ns, for example, a value z given by any one of Equations (16) to (21) may be used as an index of the reliability of the obtained k_a or k_v. When the absolute value of z is large, the reliability of k_a is low. Further, when the absolute value of z is large, the reliability of k_v is low.

Further, as a substitute for the reliability index of k_a, the variation of the value w given by the following equation (22) (standard deviation, maximum value−minimum value, etc.) may be used. A small variation in the value w indicates that the reliability of the obtained k_a is high.

In addition, the reliability of k_a may be evaluated from the variation of y with respect to the regression line (x, y) where R_signal is y and IR_signal is x, the difference between the maximum value and the minimum value, and the like.

<Pulse rate detection flow (S16 to S20)>
In FIG. 5, from step S16, the pulse rate calculation unit 20 starts a pulse rate detection flow. In step S17, the pulse rate calculation unit 20 obtains a pulse waveform from which the noise component has been removed from k_v calculated in step S11 of the blood oxygen saturation flow. The pulse rate calculation unit 20 performs binarization processing on the pulse wave waveform from which the noise component has been removed, similarly to the binarization processing performed in step S10. The threshold value in this case may be 0, or may be the threshold value used when determining the k_v.

Next, in step S18, the pulse rate calculator 20 calculates an average value T_ave (T_ave = 1 / N (ΣT (j)) of the period T (j) within a predetermined time of R_signal (i) −k_v * IR_signal (i). )) Is calculated.

Next, in step S19, the pulse rate is calculated. The pulse rate is obtained as the reciprocal of the period. That is, it is expressed as Pulse = 60 / T_ave, where the pulse rate is Pulse (times / min). The pulse rate may be calculated from the Fourier transform of R_signal (i) −k_v * IR_signal (i) or the frequency that gives the peak of its absolute value.

Next, in step S20, the pulse rate calculation unit 20 outputs the calculated pulse rate to the pulse rate output unit 23, and the pulse rate detection flow ends.

By operating in this way, the biological information measuring device 30 measures the oxygen saturation and the pulse rate by the oxygen saturation detection flow and the pulse rate detection flow.

According to the embodiment as described above, the noise component is removed by utilizing the periodicity of the signal component, so that the arithmetic processing can be simplified and the amount of arithmetic processing can be reduced compared to the prior art. . Therefore, it is possible to suppress power consumption associated with arithmetic processing. As a result, a battery can be used for the biological information measuring device 30, and a small and lightweight biological information measuring device convenient for carrying can be provided. For this reason, for example, even if a mountain climber or a home oxygen therapy patient always carries, a physical burden can be reduced.

Moreover, since the biological information measuring apparatus 30 according to the present embodiment determines whether to extract a noise component based on an index having information on the ratio between the intensity of the signal component and the intensity of the noise component, When the noise component is negligibly small, the calculation for removing the noise component can be omitted, so that the calculation time for obtaining biological information is shortened, and the battery operating time is lengthened by suppressing power consumption. In addition, the biological information can be obtained more quickly.

In addition, since the biological information measuring device 30 according to the present embodiment outputs biological information, signal reliability, and noise reliability by voice, characters, light, and the like, it is possible to easily check these. Furthermore, the biological information measuring device 30 according to the present embodiment outputs a warning indicating that the index exceeds a predetermined value, or a warning indicating a decrease in signal reliability or noise reliability. It is also possible to alert a person or the like regarding the handling of biological information. The handling of the calculated biological information can be changed according to the index, the noise reliability, and the signal reliability. For example, remeasurement or the like can be performed based on the index.

In addition, the biological information measuring apparatus 30 according to the present embodiment can reduce erroneous measurement due to, for example, a double peak by using a binarized waveform when calculating the pulse rate.

In addition, the biological information measuring method according to the present embodiment can calculate biological information in an earlier calculation time with the same calculation resource (hardware resource).

In the above-described embodiment, as an example, the biological information measuring apparatus 30 that detects a change in the intensity of light having two wavelengths with periodicity and obtains blood oxygen saturation is illustrated. In addition, based on changes in the intensity of light of one wavelength with periodicity, pulse, arrhythmia (atrial fibrillation / extrasystole) detection, defibrillation monitoring, autonomic nerve damage, blood vessel age, etc. The present invention can also be applied to a biological information measuring device to be performed. Furthermore, in addition to the pulse wave waveform, the present invention can also be applied to a device that measures biological information such as electrocardiogram.

In the above embodiment, the periodicity of the signal component is used when calculating k_v. However, instead of Step S11 and Step S12, Equation (23) obtained by Fourier transforming q given by Equation (15) is used. It may be used.

Here, F is a Fourier transform, and ω is a frequency. The peak height / peak width of | F (ω) | may be used as an index, and k_v may be obtained from ω that maximizes the peak height / peak width. An index indicating whether k_v is optimal may be obtained by multiplying this index by the absolute value of the difference between k_v and the previous k_v. The pulse rate may be obtained by multiplying ω giving the peak of F (ω) when the optimum k_v is obtained by multiplying 60, or the pulse rate is obtained from the period of R_signal−k_v * IR_signal using the optimum k_v. May be.

In addition, in the above embodiment, the periodicity of the signal component is used to calculate k_v. However, instead of step S11 and step S12, q given by equation (15) is changed from the square of q to q You may use Formula (24) which is the Fourier transform of the signal which subtracted the time average of a square.

Where P is the Fourier transform and ω is the frequency. The peak height / peak width of P (ω) may be used as an index, and k_v may be obtained from ω that maximizes the peak height / peak width. An index indicating whether k_v is optimal may be obtained by multiplying this index by the absolute value of the difference between k_v and the previous k_v. A value obtained by multiplying ω giving the peak of P (ω) when the optimum k_v is obtained by 30 times can be used as the pulse rate.

In step S9 of the above embodiment, k_a may be obtained using any one of the following equations (25) to (34) instead of equation (14).

Here, Expression (25) and Expression (26) are expressions for obtaining k_a from the ratio of time differences between R_signal and IR_signal.

Expressions (27) to (29) are expressions for obtaining k_a from the ratio between the autocorrelation and cross-correlation of R_signal and IR_signal.

Expression (30) is an expression for obtaining k_a from the slope of the regression line of R_signal and IR_signal. Note that Δ in the following formulas (30) to (34) represents a time difference of each data series.

As described above, the novel biological information signal processing apparatus includes the first time-series signal including the first signal component having the periodicity and the first noise component, and the first signal component having a predetermined relationship with the first signal component. Based on a second time-series signal including two signal components and a second noise component having a predetermined relationship with the first noise component, and the predetermined relationship between the first noise component and the second noise component. A signal generator that generates a signal including the first signal component from the first time-series signal and the second time-series signal, and a periodicity of the generated signal in a first predetermined time range And an estimation unit for estimating the predetermined relationship between the first noise component and the second noise component in the first predetermined time range.

Moreover, in the above-described biological information signal processing device, the signal generation unit further generates a binarized signal by performing binarization processing on the generated signal, and the estimation unit generates a cycle of the binarized signal. It is preferable to estimate the period of the generated signal using the property.

Further, in the above-described biological information signal processing device, the estimation unit calculates at least any one of a pulse width variation and a pulse cycle variation of the binarized signal in order to calculate the periodicity of the binarized signal. It is preferable to use these.

Further, in the above-described biological information signal processing device, the estimation unit calculates a plurality of maximum values and / or a plurality of minimum values of the generated signal in a first predetermined time range, and fluctuations in the plurality of maximum values. It is preferable to calculate the period of the generated signal using fluctuations in the plurality of minimum values.

Moreover, in the above-described biological information signal processing device, it is preferable that the estimation unit calculates the period of the generated signal based on a plurality of indexes having information on the period of the generated signal.

Moreover, in the above-described biological information signal processing device, the signal generation unit further generates a Fourier signal by performing Fourier transform on the generation signal in the first predetermined time range, and the estimation unit It is preferable to estimate the predetermined relationship between the first noise component and the second noise component in the first predetermined time range based on a peak width of a Fourier signal.

In the above-described biological information signal processing device, it is preferable that the estimation unit further calculates the period of the first signal component or the period of the second signal component using the periodicity of the generated signal.

Further, in the above-described biological information signal processing device, the estimation unit includes the predetermined relationship between the first noise component and the second noise component in the first predetermined time range, and the first predetermined time. Preferably, the predetermined relationship between the first signal component and the second signal component is further estimated using the first signal component and the first noise component being independent in range.

Further, in the above-described biological information signal processing device, the estimation unit includes the predetermined relationship between the first noise component and the second noise component in the first predetermined time range, and a second predetermined time range. And further estimating the predetermined relationship between the first signal component and the second signal component in the second predetermined time range using the first signal component and the first noise component being independent of each other. It is preferable to do this.

In the above-described biological information signal processing device, the second predetermined time range is plural, and the estimation unit includes the first signal component and the second signal component for each second predetermined time range. It is preferable to estimate each of the predetermined relationships.

Further, in the above-described biological information signal processing device, the estimation unit uses the predetermined relationship between the first signal component and the second signal component for each second predetermined time range, and uses the first signal. It is preferable to calculate an average value of the predetermined relationship between a component and the second signal component.

Further, in the above-described biological information signal processing device, the estimation unit includes the predetermined relationship between the first signal component and the second signal component in the first predetermined time range, and the second predetermined time. Preferably, an average value of the predetermined relationship between the first signal component and the second signal component is calculated using the predetermined relationship between the first signal component and the second signal component for each range. .

In the above-described biological information signal processing apparatus, the average value is preferably a value obtained by weighted average.

Further, in the above-described biological information signal processing device, the estimation unit relates to a ratio between the first noise component and the first signal component as an index for determining whether or not to remove the first noise component. If it is determined that the first noise component is to be removed by further calculating the index having information and comparing the index with a first predetermined value, the first time-series signal and the first Preferably, the predetermined relationship between the first signal component and the second signal component is estimated based on two time series signals.

In the biological information signal processing apparatus having such a configuration, since the noise component is removed by using the periodicity, the amount of signal processing necessary for removing the noise component can be reduced as compared with the conventional technique. For this reason, by reducing the signal processing amount, it is possible to suppress the power consumption accompanying the calculation of noise component removal. Further, by performing signal processing according to the index, unnecessary signal processing is not required, so that signal processing can be speeded up.

Further, in the above-described biological information signal processing device, the estimation unit further calculates a signal reliability indicating a degree of error included in the predetermined relationship between the first signal component and the second signal component, It is preferable to further include an output unit that outputs the signal reliability.

Further, in the above-described biological information signal processing device, the estimation unit further calculates a noise reliability indicating a degree of error included in the predetermined relationship between the first noise component and the second noise component, It is preferable to further include an output unit that outputs noise reliability.

Further, in the above-described biological information signal processing device, the estimation unit compares the signal reliability with a second predetermined value, and when the signal reliability exceeds the second predetermined value, Preferably, the output unit further outputs a warning indicating that the signal reliability has exceeded the second predetermined value.

Further, in the above-described biological information signal processing device, the estimation unit compares the noise reliability with a third predetermined value, and when the noise reliability exceeds the third predetermined value, Preferably, the output unit further outputs a warning indicating that the noise reliability has exceeded the third predetermined value.

According to the biological information signal processing device having such a configuration, it is possible to recognize the degree of error included in the predetermined relationship estimated by the estimation unit by referring to the noise reliability or the signal reliability. It becomes.

The novel biological information signal processing method includes a first time-series signal including a first signal component having a periodicity and a first noise component in a first predetermined time range, and the first signal. A second time-series signal including a second signal component having a predetermined relationship with a component and a second noise component having a predetermined relationship with the first noise component; and the first noise component and the second noise component Based on the predetermined relationship, a signal generation step of generating a signal including the first signal component from the first time-series signal and the second time-series signal, and using the periodicity of the generated signal And an estimating step for estimating the predetermined relationship between the first noise component and the second noise component.

In the biological information signal processing method described above, in the signal generation step, a binarization signal is further generated by performing binarization processing on the generation signal, and in the estimation step, a cycle of the binarization signal is generated. It is preferable to estimate the period of the generated signal using the property.

Further, in the above-described biological information signal processing method, in the signal generation step, the generated signal is compared with at least two threshold values, and at least two types are determined based on whether the generated signal exceeds the threshold value. Preferably, the above binarized signal is further generated, and in the estimation step, the period of the generated signal is estimated using the periodicity of the at least two types of binarized signals.

Further, in the above-described biological information signal processing method, the binarization processing compares the generated signal with a positive threshold value and a negative threshold value that serve as a reference for binarization, and the generated signal exceeds the positive threshold value. The generated signal is converted to a positive first constant, and if the generated signal is less than a negative threshold, the generated signal is converted to a negative second constant, and the generated signal is negative It is preferable that the generated signal is converted to zero when the threshold value is greater than or equal to the threshold value and less than or equal to the positive threshold value.

In the biological information signal processing method described above, in the estimation step, the period of the binarized signal is calculated based on at least one of a pulse width variation and a pulse cycle variation of the binarized signal. It is preferable to do this.

In the above-described biological information signal processing method, in the estimation step, a plurality of maximum values and / or a plurality of minimum values of the generated signal are further calculated, and fluctuations in the plurality of maximum values and / or a plurality of minimum values are calculated. It is preferable to calculate the period of the generated signal using fluctuation.

Further, in the above-described biological information signal processing method, in the estimation step, the first noise component and the second noise in the first predetermined time range using a plurality of indexes representing the periodicity of the generated signal. It is preferable to estimate the predetermined relationship with components.

In the above-described biological information signal processing method, it is preferable that the estimation step further estimates an amount corresponding to the period of the first signal component or the second signal component using the periodicity of the generated signal. .

In the biological information signal processing method having such a configuration, since the noise component is removed by using the periodicity, the amount of signal processing necessary for removing the noise component can be reduced as compared with the prior art. For this reason, by reducing the amount of signal processing, it is possible to suppress power consumption associated with signal processing for noise component removal.

Furthermore, the novel biological information measuring device receives at least the first measurement data or the second data obtained by irradiating the living body with a plurality of lights having different wavelengths and receiving the lights transmitted or reflected by the living body. A biological information measuring apparatus for measuring biological information of the living body based on measurement data, wherein the first measuring unit measures first measurement data including a first signal component having a periodicity and a first noise component; A second measurement unit for measuring second measurement data including a second signal component having a predetermined relationship with the first signal component and a second noise component having a predetermined relationship with the first noise component; and the first measurement data And the first measurement data and the second measurement data based on the second measurement data and the predetermined relationship between the first noise component and the second noise component. A signal generation unit that generates a signal including a signal component, and an estimation unit that estimates the predetermined relationship between the first noise component and the second noise component using the periodicity of the generated signal. .

Moreover, in the above-described biological information measurement device, it is preferable that the estimation unit estimates the predetermined relationship between the first noise component and the second noise component using the periodicity of the generated signal.

Further, in the above-described biological information measuring device, the estimation unit is independent of the predetermined relationship between the first noise component and the second noise component, and the first signal component and the first noise component. It is preferable to further estimate the predetermined relationship between the first signal component and the second signal component.

In the above-described biological information measuring device, the period of the first signal component or the period of the second signal component is due to pulsation of arterial blood, and the first noise component or the second noise component is the biological body. It is preferable that the period includes information on the pulse rate.

Further, in the above-described biological information measuring device, it is preferable that the predetermined relationship between the first signal component and the second signal component includes information on arterial oxygen saturation.

According to the living body information measuring apparatus having such a configuration, it is possible to provide a living body information measuring apparatus that measures the blood oxygen saturation of a living body with reduced calculation processing amount and reduced power consumption.

In order to express the present invention, the present invention has been properly and fully described through the embodiments with reference to the drawings. However, those skilled in the art can easily change and / or improve the above-described embodiments. It should be recognized that this is possible. Accordingly, unless the modifications or improvements implemented by those skilled in the art are at a level that departs from the scope of the claims recited in the claims, the modifications or improvements are not covered by the claims. It is interpreted that it is included in

Claims

A first time-series signal related to biological information including a first signal component having a periodicity and a first noise component, a second signal component having a predetermined relationship with the first signal component, and a predetermined relationship with the first noise component The first time-series signal based on the second time-series signal related to the biological information including the second noise component having the relationship and the predetermined relationship between the first noise component and the second noise component And a signal generator that generates a signal including the first signal component from the second time-series signal,
An estimation unit that estimates the predetermined relationship between the first noise component and the second noise component in the first predetermined time range by using the periodicity of the generated signal in the first predetermined time range; A biological information signal processing apparatus comprising:
The biological information signal processing apparatus according to claim 1, wherein the signal generation unit generates a binarized signal by performing binarization processing on the generated signal, and the estimation unit is configured to output the binarized signal. The period of the generated signal is estimated using periodicity.
The biological information signal processing apparatus according to claim 2, wherein the estimation unit calculates a periodicity of the binarized signal among a pulse width variation and a pulse cycle variation of the binarized signal. At least one of them is used.
The biological information signal processing device according to claim 1, wherein the estimation unit calculates a plurality of maximum values and / or a plurality of minimum values of the generated signal in a first predetermined time range, and a plurality of maximum values. The period of the generated signal is calculated using fluctuations in and / or fluctuations in a plurality of minimum values.
2. The biological information signal processing apparatus according to claim 1, wherein the estimation unit calculates a cycle of the generated signal based on a plurality of indexes having information on the cycle of the generated signal.
The biological information signal processing device according to claim 1, wherein the signal generation unit generates a Fourier signal by performing Fourier transform on the generation signal in the first predetermined time range, and the estimation unit includes: The predetermined relationship between the first noise component and the second noise component in the first predetermined time range is estimated based on the peak width of the Fourier signal.
2. The biological information signal processing apparatus according to claim 1, wherein the estimation unit calculates a period of the first signal component or a period of the second signal component using the periodicity of the generated signal.
2. The biological information signal processing apparatus according to claim 1, wherein the estimation unit includes the predetermined relationship between the first noise component and the second noise component in the first predetermined time range; The predetermined relationship between the first signal component and the second signal component is estimated using the fact that the first signal component and the first noise component are independent in a predetermined time range.
The biological information signal processing device according to claim 7, wherein the estimation unit includes a second predetermined predetermined relationship between the first noise component and the second noise component in the first predetermined time range. The predetermined relationship between the first signal component and the second signal component in the second predetermined time range is obtained by using the first signal component and the first noise component independent of each other in the time range. presume.
The biological information signal processing device according to claim 9, wherein the second predetermined time range is plural, and the estimation unit performs the first signal component and the second signal for each second predetermined time range. Each of the predetermined relationships with components is estimated.
The biological information signal processing apparatus according to claim 10, wherein the estimation unit uses the predetermined relationship between the first signal component and the second signal component for each second predetermined time range. An average value of the predetermined relationship between one signal component and the second signal component is calculated.
The biological information signal processing device according to claim 10, wherein the estimation unit includes the predetermined relationship between the first signal component and the second signal component in the first predetermined time range, and the second signal component. An average value of the predetermined relationship between the first signal component and the second signal component is calculated using the predetermined relationship between the first signal component and the second signal component for each predetermined time range.
The biological information signal processing apparatus according to claim 12, wherein the average value is a value obtained by weighted average.
The biological information signal processing device according to claim 9, wherein the estimation unit uses the first noise component and the first signal component as an index for determining whether or not to remove the first noise component. When it is determined that the first noise component is to be removed by calculating the index having information relating to the ratio and comparing the index with a first predetermined value, the first time-series signal and the Based on the second time series signal, the predetermined relationship between the first signal component and the second signal component is estimated.
The biological information signal processing device according to claim 14, wherein the estimation unit calculates a signal reliability indicating a degree of error included in the predetermined relationship between the first signal component and the second signal component, An output unit for outputting the signal reliability is further provided.
The biological information signal processing device according to claim 14, wherein the estimation unit calculates a noise reliability indicating a degree of error included in the predetermined relationship between the first noise component and the second noise component, An output unit for outputting the noise reliability is further provided.
16. The biological information signal processing device according to claim 15, wherein the estimation unit compares the signal reliability with a second predetermined value, and the signal reliability exceeds the second predetermined value. In addition, the output unit outputs a warning indicating that the signal reliability exceeds the second predetermined value.
17. The biological information signal processing device according to claim 16, wherein the estimation unit compares the noise reliability with a third predetermined value, and the noise reliability exceeds the third predetermined value. In addition, the output unit outputs a warning indicating that the noise reliability has exceeded the third predetermined value.
A first time-series signal relating to biological information including a first signal component having periodicity and a first noise component in a first predetermined time range, and a first time-series signal having a predetermined relationship with the first signal component. A second time-series signal related to biological information including two signal components and a second noise component having a predetermined relationship with the first noise component; and the predetermined relationship between the first noise component and the second noise component. A signal generation step of generating a signal including the first signal component from the first time-series signal and the second time-series signal,
A biological information signal processing method comprising: an estimation step of estimating the predetermined relationship between the first noise component and the second noise component using the periodicity of the generated signal.
The biological information signal processing method according to claim 19, wherein in the signal generation step, a binarization signal is generated by performing binarization processing on the generation signal, and in the estimation step, the binarization signal The period of the generated signal is estimated using periodicity.
The biological information signal processing method according to claim 19, wherein in the signal generation step, the generated signal is compared with at least two threshold values, and at least based on whether the generated signal exceeds the threshold value or not. Two or more types of binarized signals are generated, and in the estimation step, the period of the generated signal is estimated using the periodicity of the at least two types of binarized signals.
21. The biological information signal processing method according to claim 20, wherein the binarization processing compares the generated signal with a positive threshold and a negative threshold serving as a binarization reference, and the generated signal is a positive threshold. The generated signal is converted to a positive first constant, and if the generated signal is less than a negative threshold, the generated signal is converted to a negative second constant, and the generated signal is This is a process of converting the generated signal to zero when it is not less than the negative threshold and not more than the positive threshold.
21. The biological information signal processing method according to claim 20, wherein in the estimation step, a cycle of the binarized signal is based on at least one of a pulse width variation and a pulse cycle variation of the binarized signal. Is calculated.
The biological information signal processing method according to claim 19, wherein in the estimation step, a plurality of maximum values and / or a plurality of minimum values of the generated signal are calculated, and fluctuations in the plurality of maximum values and / or a plurality of minimum values are calculated. The period of the generated signal is calculated using the fluctuation in.
20. The biological information signal processing method according to claim 19, wherein in the estimation step, the first noise component and the first noise in the first predetermined time range using a plurality of indices representing the periodicity of the generated signal. The predetermined relationship with two noise components is estimated.
20. The biological information signal processing method according to claim 19, wherein in the estimation step, an amount corresponding to a period of the first signal component or the second signal component is estimated using a periodicity of the generated signal.
Based on at least the first measurement data or the second measurement data obtained by irradiating the living body with a plurality of lights having different wavelengths and receiving the light transmitted or reflected through the living body, respectively, the living body of the living body A biological information measuring device for measuring information,
A first measurement unit that measures first measurement data including a first signal component having a periodicity and a first noise component;
A second measurement unit for measuring second measurement data including a second signal component having a predetermined relationship with the first signal component and a second noise component having a predetermined relationship with the first noise component;
Based on the first measurement data, the second measurement data, and the predetermined relationship between the first noise component and the second noise component, from the first measurement data and the second measurement data, the A signal generator for generating a signal including the first signal component;
A biological information measuring apparatus comprising: an estimation unit that estimates the predetermined relationship between the first noise component and the second noise component using the periodicity of the generated signal.
28. The biological information measuring apparatus according to claim 27, wherein the estimation unit estimates the predetermined relationship between the first noise component and the second noise component using periodicity of the generated signal.
28. The biological information measuring apparatus according to claim 27, wherein the estimation unit is independent of the predetermined relationship between the first noise component and the second noise component, and the first signal component and the first noise component. The predetermined relationship between the first signal component and the second signal component is estimated.
30. The biological information measuring apparatus according to claim 29, wherein the period of the first signal component or the period of the second signal component is due to pulsation of arterial blood, and the first noise component or the second noise component is The cycle includes information related to the pulse rate.
31. The biological information measuring apparatus according to claim 30, wherein the predetermined relationship between the first signal component and the second signal component includes information related to arterial oxygen saturation.