Patent Description:
As devices for measuring a blood glucose level inside a living body in a non-invasive manner, for example, there are devices as disclosed in Patent Literature <NUM> and Patent Literature <NUM>. The device disclosed in Patent Literature <NUM> obtains a glucose concentration by irradiating the inside of a living body with near-infrared light within a range of <NUM>,<NUM> to <NUM>,<NUM>, and detecting the near-infrared light propagated through the living body. In order to reduce the influence of light absorption due to components other than glucose, this device quantitatively obtains a glucose concentration by using an absorption signal having a wavelength region of <NUM>,<NUM> to <NUM>,<NUM> for measuring absorption derived from the OH groups of glucose molecules, an absorption signal having a wavelength region of <NUM>,<NUM> to <NUM>,<NUM> for measuring absorption derived from the NH groups of components of a living body, and an absorption signal having a wavelength region of <NUM>,<NUM> to <NUM>,<NUM> for measuring absorption derived from the CH groups of components of the living body, and by performing multi-variable analysis of these three absorption signals.

The device disclosed in Patent Literature <NUM> utilizes heat dissipation in glycometabolism. That is, the device disclosed in Patent Literature <NUM> obtains a blood glucose level based on a plurality of body-surface temperatures measured with a temperature sensor, and a parameter corresponding to the oxygen content of blood measured with a near-infrared spectroscopy (NIRS) sensor.

Document <CIT> relates to a non-invasive blood component analyzer using spectrophotometry, with systole/diastole corrections for tissue absorbance, and with built-in monitoring of light path length to allow its accurate use in subjects with widely varying finger size and/or varying pulse amplitude. Document <CIT> relates to a non-invasive blood sugar measuring device that has a finger opening at a housing for inserting a finger. A spectro-photometry measurement is carried out for partial oxygen saturation. A keypad is arranged at the housing for inputting a relationship between the oxygen saturation and related blood sugar value as a calibration in a microcontroller. The microcontroller evaluates the oxygen saturation and exchange in the corresponding blood sugar value by reading the calibration. An existing blood sugar value is displayed in a display that is arranged in the housing. Document <CIT> relates to a concentration measurement device that includes: a probe having a light incident part making measurement light incident on a head, and a photodetection part detecting the measurement light having propagated inside the head; and a CPU obtaining the temporal relative change amounts of an oxygenated hemoglobin concentration and a deoxidized hemoglobin concentration, and obtaining a numerical value representing a phase shift between the relative change amounts. Summary of Invention.

Generally, diabetes is a disease with which there is unlikely to be awareness thereof. Therefore, it is necessary to regularly take a medical examination, such as a blood examination, for early detection. Diabetes is an illness with which the blood glucose level rises excessively due to the deterioration of insulin action. As a method of measuring a blood glucose level inside a living body, for example, there is a method of collecting blood by pricking the skin of an examinee with a puncture needle, but such a method causes an examinee to suffer pain. Therefore, the inventor has conceived establishment of a non-invasive measuring technique in which the blood glucose level is measured without collecting blood. As a non-invasive measuring technique of measuring a blood glucose level, a method of measuring a blood glucose level by using light can be conceived. For example, a device disclosed in Patent Literature <NUM> utilizes near-infrared light absorbed by glucose. However, a part of an absorption wavelength region of glucose overlaps the absorption wavelength regions of components such as water, lipids, and proteins. For example, the concentrations of these components change due to a meal or the like at any time. Therefore, in a method of utilizing light absorption of glucose, these components may adversely affect the measurement accuracy as noise. Consequently, there is concern that the measurement accuracy of a blood glucose level will be degraded. In a device disclosed in Patent Literature <NUM>, the error increases when the heat of glycometabolism is small, such that it is difficult to obtain an accurate blood glucose level.

An object of an embodiment is to provide a blood glucose measurement device, a blood glucose calculation method, and a blood glucose calculation program capable of accurately measuring a blood glucose level by using light.

According to an embodiment of the present invention, there is provided a blood glucose measurement device. The blood glucose measurement device includes a light outputting unit configured to output measurement light to be input to a living body; a light detecting unit configured to detect the measurement light propagated inside the living body and generate a detection signal in accordance with an intensity of the measurement light; and a computation unit configured to obtain a time lag between a temporal change in a first parameter related to an oxygenated hemoglobin concentration and a temporal change in a second parameter related to a deoxygenated hemoglobin concentration based on the detection signal, and obtain the data related to the blood glucose level based on the time lag.

In addition, according to another embodiment of the present invention, there is provided a blood glucose calculation method. The blood glucose calculation method includes a first computation step of obtaining a time lag between a temporal change in a first parameter related to an oxygenated hemoglobin concentration and a temporal change in a second parameter related to a deoxygenated hemoglobin concentration in the living body, and a second computation step of obtaining the data related to the blood glucose level based on the time lag.

In addition, according to another embodiment of the present invention, there is provided a blood glucose calculation program. The blood glucose calculation program causes a computer to execute a first computation step of obtaining a time lag between a temporal change in a first parameter related to an oxygenated hemoglobin concentration and a temporal change in a second parameter related to a deoxygenated hemoglobin concentration in the living body, and a second computation step of obtaining the blood glucose level based on the time lag.

According to the blood glucose measurement device, the blood glucose calculation method, and the blood glucose calculation program of the embodiments, a blood glucose level can be accurately measured by using light.

Hereinafter, embodiments of a blood glucose measurement device (blood glucose level calculation device), a blood glucose calculation method (blood glucose level measurement method), and a blood glucose calculation program (blood glucose level measurement program) will be described in detail with reference to the accompanying drawings. The same reference signs are applied to the same elements in description of the drawings, and duplicated description will be omitted. <FIG> is a conceptual diagram of a blood glucose measurement device <NUM> according to the present embodiment. The blood glucose measurement device <NUM> includes a light measurement instrument (probe) <NUM> and a main body unit <NUM>. The main body unit <NUM> obtains a temporal change in a parameter (first parameter) related to an oxygenated hemoglobin (O<NUM>Hb) concentration and a temporal change in a parameter (second parameter) related to a deoxygenated hemoglobin (HHb) concentration based on the intensity of light detected from a living body <NUM> by the light measurement instrument <NUM>. For example, the parameter related to the O<NUM>Hb concentration is a temporal fluctuation from an initial amount (relative amount of temporal change (ΔO<NUM>Hb)) in the O<NUM>Hb concentration, an absolute value (O<NUM>Hb) of the O<NUM>Hb concentration at a certain time, a time differential value of these O<NUM>Hb concentrations, or the like. In addition, the parameter related to the HHb concentration is a temporal fluctuation from an initial amount (relative amount of temporal change (ΔHHb)) in the HHb concentration, an absolute value (HHb) of the HHb concentration at a certain time, a time differential value of these HHb concentrations, or the like. In addition, the temporal change in parameters related to the O<NUM>Hb concentration and the temporal change in parameters related to the HHb concentration are time-series data of these parameters, for example. The main body unit <NUM> calculates the blood glucose level based on a time lag therebetween and informs an examinee of the blood glucose level. For example, the main body unit <NUM> is constituted of a computer such as a personal computer, a microcomputer, a cloud server, or a smart device (a smartphone, a tablet terminal, or the like).

<FIG> is a conceptual diagram of the light measurement instrument <NUM> according to the present embodiment. The light measurement instrument <NUM> has a light source (light outputting unit) <NUM> and a light detector (light detecting unit) <NUM>. The light source <NUM> outputs rays of measurement light L1 which are input to a predetermined light inputting position on the surface of skin <NUM> of the living body <NUM> and have predetermined wavelength components (λ<NUM>, λ<NUM>, and λ<NUM>), respectively. This measurement light L1 is propagated inside the living body <NUM> and is output from the surface of the skin <NUM> of the living body <NUM>. The light detector <NUM> detects the measurement light L1 output from a predetermined light detecting position on the surface of the skin <NUM> of the living body <NUM> and generates a detection signal in accordance with the intensity of the detected measurement light L1. The blood glucose measurement device <NUM> calculates the O<NUM>Hb concentration and the HHb concentration based on the detection signal output from the light detector <NUM>, in consideration of the influence of absorption, scattering, or the like of the measurement light L1 due to O<NUM>Hb and HHb. For example, the predetermined wavelength components are included within a range of a red wavelength region of visible light to a near-infrared region (<NUM> to <NUM>,<NUM>). As an example, λ<NUM>, λ<NUM>, and λ<NUM> are <NUM>, <NUM>, and <NUM>, respectively. Here, the measurement light L1 need only include these wavelength components, and the measurement light L1 itself may be white light.

The part (a) of <FIG> is a plan view illustrating a configuration of the light measurement instrument <NUM>. In addition, the part (b) of <FIG> is a sectional side view cut along line III-III in the part (a) of <FIG>. The light source <NUM> and the light detector <NUM> are disposed with an interval of <NUM> therebetween, for example, and is integrated with a holder <NUM> made of soft black silicone rubber. This interval may be within a range of <NUM> to <NUM>.

For example, the light source <NUM> is a light source, such as a light emitting diode (LED), a laser diode (LD), or a super-luminescent diode (SLD). The measurement light L1 output from the light source <NUM> is input to the surface of the skin <NUM> of the living body <NUM> in a substantially perpendicular manner. The light detector <NUM> has N (N is an integer equal to or larger than <NUM>) light detecting elements <NUM> and a preamplifier <NUM>. The light detector <NUM> detects measurement light propagated inside the living body <NUM> and generates a detection signal in accordance with the intensity of the measurement light. For example, each of the light detecting elements <NUM> is a point sensor such as a photodiode or an avalanche photodiode, or an image sensor such as a CCD image sensor or a CMOS image sensor, having light sensitivity with respect to a wavelength region including the center wavelength of the measurement light output from the light source. For example, the light detector <NUM> has N light detecting elements <NUM> which are arranged in an array shape in a distance direction from the light source <NUM>. The preamplifier <NUM> integrates photocurrents output from the light detecting elements <NUM> and amplifies the integrated photocurrents. The light detector <NUM> sensitively detects a faint signal via the preamplifier <NUM>, generates a detection signal, and transmits this signal to the main body unit <NUM> through a cable <NUM>. For example, the light measurement instrument <NUM> may pinch the living body <NUM> such as a finger or an ear or may be fixed to the living body <NUM> such as the head with a stretchable band.

<FIG> is a block diagram illustrating an example of a configuration of the blood glucose measurement device <NUM>. The main body unit <NUM> is a computer having a CPU <NUM>, a display (display unit) <NUM>, a ROM <NUM>, a RAM <NUM>, a data bus <NUM>, a controller <NUM>, and an input device (input unit) <NUM>. The controller <NUM> includes a light source control unit <NUM>, a sample and hold circuit <NUM>, and an A/D converter circuit <NUM>. The controller <NUM> controls an optical output of the light measurement instrument <NUM>. For example, the controller <NUM> controls the output interval of measurement light and the intensity of measurement light.

The light source control unit <NUM> is electrically connected to the light source <NUM> of the light measurement instrument <NUM>. The light source control unit <NUM> is electrically connected to the data bus <NUM> and receives an instruction signal for instructing driving of the light source <NUM> from the CPU <NUM> which is also electrically connected to the data bus <NUM> in the same manner. An instruction signal includes information such as an optical intensity and a wavelength (for example, a wavelength of any of the wavelengths λ<NUM>, λ<NUM>, and λ<NUM>) of measurement light output from the light source <NUM>. The light source control unit <NUM> drives the light source <NUM> based on an instruction signal received from the CPU <NUM>. The light source control unit <NUM> outputs a drive signal to the light measurement instrument <NUM> through the cable <NUM>.

A detection signal transmitted from the light measurement instrument <NUM> through the cable <NUM> is input and retained in the sample and hold circuit <NUM> and the A/D converter circuit <NUM> and is converted into a digital signal, thereby being output to the CPU <NUM>. The sample and hold circuit <NUM> is electrically connected to the data bus <NUM> and receives a sample signal indicating a timing of retaining the detection signal from the CPU <NUM> via the data bus <NUM>. When a sample signal is received, the sample and hold circuit <NUM> retains N detection signals input from the light measurement instrument <NUM>. The sample and hold circuit <NUM> is electrically connected to the A/D converter circuit <NUM> and outputs each of N retained detection signals to the A/D converter circuit <NUM>.

The CPU <NUM> is a computation unit in the present embodiment. The CPU <NUM> computes the O<NUM>Hb concentration and the HHb concentration inside the living body <NUM>, based on a detection signal received from the A/D converter circuit <NUM>. The CPU <NUM> computes the time lag between a time change in the O<NUM>Hb concentration and a time change in the HHb concentration which have been calculated, and computes the blood glucose level based on the time lag, thereby sending the blood glucose level to the display <NUM> via the data bus <NUM>. A method of computing an O<NUM>Hb concentration and an HHb concentration based on a detection signal, a method of computing a time lag, and a method of computing a blood glucose level will be described below. The display <NUM> is electrically connected to the data bus <NUM> and displays results sent from the CPU <NUM> via the data bus <NUM>. For example, the display <NUM> and the input device <NUM> may be constituted of a touch panel display.

Next, an operation of the blood glucose measurement device <NUM> will be described. Furthermore, the blood glucose calculation method according to the present embodiment will be described. For example, this blood glucose calculation method is suitably performed by the CPU <NUM> which reads and executes a program stored in a non-transitory storage medium such as the ROM <NUM>. <FIG> is a flowchart illustrating the blood glucose calculation method according to the present embodiment. First, the light source control unit <NUM> sequentially outputs rays of measurement light having the wavelengths λ<NUM> to λ<NUM>, based on an instruction signal from the CPU <NUM>. The rays of measurement light are input to the inside of the living body <NUM> from the light inputting position (light inputting step, S11). The rays of measurement light input to the inside scatter inside the living body <NUM> and are propagated while being absorbed into measurement subject components, and a part of the rays of light reaches the light detecting position of the living body <NUM>. The rays of measurement light which have reached the light detecting position are detected by N light detecting elements <NUM> (light detecting step, S12). Each of the light detecting elements <NUM> generates a photocurrent in accordance with the intensity of detected measurement light. The photocurrent is converted into a detection signal by the preamplifier <NUM>. The detection signal is sent to the sample and hold circuit <NUM> of the main body unit <NUM> and is retained therein, thereby being converted into a digital signal by the A/D converter circuit <NUM>.

Here, the part (a) of <FIG> is a view illustrating an input timing of the rays of measurement light having the wavelengths λ<NUM> to λ<NUM>, and the part (b) of <FIG> is a view illustrating an output timing of a digital signal from the A/D converter circuit <NUM>. As illustrated in the part (a) of <FIG> and the part (b) of <FIG>, when a laser beam having the wavelength λ<NUM> is input, N digital signals D<NUM>(<NUM>) to D<NUM>(N) corresponding to N light detecting elements <NUM> are sequentially obtained. Subsequently, when measurement light having the wavelength λ<NUM> is input, N digital signals D<NUM>(<NUM>) to D<NUM>(N) corresponding to N light detecting elements <NUM> are sequentially obtained. In this manner, (<NUM>×N) digital signals D<NUM>(<NUM>) to D<NUM>(N) are output from the A/D converter circuit <NUM>.

Subsequently, the CPU <NUM> computes the O<NUM>Hb concentration and the HHb concentration by using at least one digital signal of the digital signals D<NUM>(<NUM>) to D<NUM>(N) (first computation step, S13).

Here, the computation of the CPU <NUM> in Step S13 will be described in detail based on an example of the relative amount of temporal change in the O<NUM>Hb concentration (ΔO<NUM>Hb) and the relative amount of temporal change in the HHb concentration (ΔHHb). When values of detection signals corresponding to the measurement light wavelengths λ<NUM> to λ<NUM> at a time T<NUM> at a certain light detecting position are Dλ1(T<NUM>) to Dλ3(T<NUM>), respectively, and when values thereof at a time T<NUM> are Dλ1(T<NUM>) to Dλ3(T<NUM>) similarly, the amounts of change in the intensity of detected light at the times T<NUM> to T<NUM> are expressed as in the following Expressions (<NUM>) to (<NUM>), respectively. <NUM>] <MAT> [Math. <NUM>] <MAT> [Math. <NUM>] <MAT>.

Here, in Expressions (<NUM>) to (<NUM>), ΔOD<NUM>(T<NUM>) indicates the amount of temporal change in the intensity of detected light having the wavelength λ<NUM>, ΔOD<NUM>(T<NUM>) indicates the amount of change in the intensity of detected light having the wavelength λ<NUM>, and ΔOD<NUM>(T<NUM>) indicates the amount of temporal change in the intensity of detected light having the wavelength λ<NUM>. In addition, when the relative amounts of temporal change in the concentrations of O<NUM>Hb and HHb during a period of the time T<NUM> to the time T<NUM> are ΔO<NUM>Hb(T<NUM>) and ΔHHb(T<NUM>), respectively, these can be obtained by the following Expression (<NUM>). <NUM>] <MAT>.

Here, in Expression (<NUM>), coefficients a<NUM> to a<NUM> are constants obtained from light absorption coefficients of O<NUM>Hb and HHb with respect to rays of light having the wavelengths λ<NUM>, λ<NUM>, and λ<NUM>. The CPU <NUM> performs the foregoing computation of one detection signal of those at N light detecting positions and calculates ΔO<NUM>Hb and ΔHHb. For example, the calculation cycle thereof is <NUM> milliseconds.

Subsequently, the CPU <NUM> performs time differentiation of the time-series data of each of ΔO<NUM>Hb and ΔHHb once or more to obtain a differential value (first differential value) of ΔO<NUM>Hb and a differential value (second differential value) of ΔHHb (first computation step, S14). The relative amounts of temporal change ΔO<NUM>Hb and ΔHHb include undulation components due to breathing or physiological action inside the body. The undulation components are frequency components smaller than that in a frequency caused by spontaneous heartbeats. Accordingly, there is concern that the computation accuracy will be degraded. Therefore, in the present embodiment, correction of ΔO<NUM>Hb and ΔHHb is performed to reduce (or eliminate) frequency components smaller than that in a frequency caused by spontaneous heartbeats. That is, the first differential value and the second differential value, in which small frequency components are reduced, are obtained by performing differentiation of ΔO<NUM>Hb and ΔHHb once or performing differentiation thereof twice. Instead of such a method, frequency components smaller than that in a frequency of spontaneous heartbeats (for example, components of <NUM> or lower) may be eliminated by performing filtering.

The part (a) of <FIG> is a graph showing the time-series data of actual measurement values of ΔO<NUM>Hb and ΔHHb. A graph G10 shows the time-series data of ΔO<NUM>Hb (time change in the O<NUM>Hb concentration). A graph G11 shows the time-series data of ΔHHb (time change in the HHb concentration). The part (b) of <FIG> is a graph showing the data obtained by performing differentiation of the time-series data of the actual measurement values of ΔO<NUM>Hb once and the data obtained by performing differentiation of the time-series data of the actual measurement values of ΔHHb once. A graph G12 shows the time-series data of the value obtained by performing differentiation of ΔO<NUM>Hb once (time change in the value obtained by performing differentiation of the O<NUM>Hb concentration once). A graph G13 shows the time-series data of the value obtained by performing differentiation of ΔHHb once (time change in the value obtained by performing differentiation of the HHb concentration once). The part (c) of <FIG> is a graph showing the data obtained by performing differentiation of the time-series data of the actual measurement values of ΔO<NUM>Hb twice and the data obtained by performing differentiation of the time-series data of the actual measurement values of ΔHHb twice. A graph G14 shows the time-series data of the values obtained by performing differentiation of ΔO<NUM>Hb twice (time change in the values obtained by performing differentiation of the O<NUM>Hb concentration twice). A graph G15 shows the time-series data of the values obtained by performing differentiation of ΔHHb twice (time change in the values obtained by performing differentiation of the HHb concentration twice).

With reference to the part (a) of <FIG>, in the graphs G10 and G11, the peak value and the bottom value repeatedly appearing in a cycle significantly fluctuate in each cycle. The peak value indicates the maximum value in a heartbeat cycle, and the bottom value indicates a starting point of heartbeats in a heartbeat cycle. This indicates that undulation components due to breathing or physiological action inside the body are included in ΔO<NUM>Hb and ΔHHb. In contrast, with reference to the part (b) of <FIG> and the part (c) of <FIG>, in the graphs G12 to G15, fluctuations in each cycle of the peak value and the bottom value repeatedly appearing in a cycle are reduced. That is, undulation components (low-frequency components) of ΔO<NUM>Hb and ΔHHb are relatively restrained. The method of correction (suppression of undulation components) performed with respect to ΔO<NUM>Hb and ΔHHb is not limited to such a method. For example, processing of eliminating smaller frequency components than a predetermined frequency from ΔO<NUM>Hb and ΔHHb may be performed.

With reference to <FIG> again, the blood glucose measurement device <NUM> according to the present embodiment performs operations as follows. That is, the CPU <NUM> calculates the time lag between the O<NUM>Hb concentration and the HHb concentration based on the O<NUM>Hb concentration and the HHb concentration calculated by the method described above (first computation step, S15). Next, based on this time lag, the CPU <NUM> calculates the blood glucose level (second computation step, S16). In the blood glucose calculation method and the blood glucose calculation program according to the present embodiment, the foregoing Steps S11 to S16 are repeatedly performed. Hereinafter, a method of calculating a time lag and a method of calculating a blood glucose level will be described in detail based on an example of the relative amount of temporal change in the O<NUM>Hb concentration (ΔO<NUM>Hb) and the relative amount of temporal change in the HHb concentration (ΔHHb).

For example, the time lag between ΔO<NUM>Hb and ΔHHb is suitably calculated by a first method, a second method, or a third method. First, the first method is a calculation method in which a feature point is extracted. In the first method, the CPU <NUM> obtains a first feature point repeatedly appearing in a cycle in ΔO<NUM>Hb and a second feature point, corresponding to the first feature point, repeatedly appearing in a cycle in ΔHHb. Then, the CPU <NUM> obtains the time lag based on the time difference between the first feature point and the second feature point. Alternatively, the CPU <NUM> obtains the first feature point repeatedly appearing in a cycle in the values obtained by performing differentiation of ΔO<NUM>Hb M times (M is an integer equal to or larger than <NUM>) and the second feature point, corresponding to the first feature point, repeatedly appearing in a cycle in the values obtained by performing differentiation of ΔHHb M times. Then, the CPU <NUM> obtains the time lag based on the time difference between the first feature point and the second feature point.

As an example, the part (a) of <FIG> is a graph for describing the method of calculating a time lag performed by extracting a feature point in the value obtained by performing differentiation of ΔO<NUM>Hb and ΔHHb once. A graph G20 shows the value obtained by performing differentiation of ΔO<NUM>Hb once. A graph G21 shows the value obtained by performing differentiation of ΔHHb once. For example, these graphs include several feature points, such as a peak point, a bottom point, and a notch point. The notch point is a point indicating a local depression in the time-series data. The CPU <NUM> extracts the feature point at a plurality of spots. For example, with reference to the part (a) of <FIG>, peak points P1 to P3 and bottom points B1 to B3 repeatedly appearing in a cycle in ΔO<NUM>Hb, and peak points P4 to P6 and the bottom points B4 to B6 repeatedly appearing in a cycle in ΔHHb are extracted. The CPU <NUM> obtains time lags Δt1 to Δt3 between peak points and time lags Δt4 to Δt6 between bottom points, corresponding to each other. For example, the average value of these time lags Δt1 to Δt6 may be adopted as the time lag between ΔO<NUM>Hb and ΔHHb.

The second method is a calculation method performed with a value of an inner product. In the second method, the CPU <NUM> obtains the time lag based on the value of the inner product of a function of ΔO<NUM>Hb and a function of ΔHHb during a predetermined period. Alternatively, the CPU <NUM> may obtain a time lag based on the value of the inner product of a function of the values obtained by performing differentiation of ΔO<NUM>Hb M times and a function of the values obtained by performing differentiation of ΔHHb M times during a predetermined period. As an example, the part (b) of <FIG> is a graph for describing the method of calculating a time lag performed based on an inner product of functions of the value obtained by performing differentiation of ΔO<NUM>Hb once and the value obtained by performing differentiation of ΔHHb once. Similar to vectors, the inner product can also be applied to the functions. When the function of ΔO<NUM>Hb and the function of ΔHHb are standardized and the inner product is computed, the value of the inner product thereof becomes equivalent to the value of cos(Δθ). The factor Δθ is a phase shift between ΔO<NUM>Hb and ΔHHb. The phase shift (Δθ) can be obtained based on this relationship, and the time lag is calculated by the following Expression (<NUM>). Here, T is a heartbeat cycle. <NUM>]
<MAT>.

The third method is a calculation method performed by comparing centroid positions to each other. In the third method, the CPU <NUM> obtains a centroid position in ΔO<NUM>Hb (first centroid position) and obtains a centroid position in ΔHHb (second centroid position), thereby obtaining a time lag based on the time difference between these centroid positions, during a predetermined period. Alternatively, the CPU <NUM> may obtain a centroid position in the values obtained by performing differentiation of ΔO<NUM>Hb M times (first centroid position) and may obtain a centroid position in the values obtained by performing differentiation of ΔHHb M times (second centroid position), thereby obtaining a time lag based on the time difference between these centroid positions, during a predetermined period. In the calculation method of comparing centroid positions, the centroid position is unlikely to fluctuate due to an influence of noise. Therefore, a time lag can be accurately obtained.

The part (c) of <FIG> is a graph for describing a method of calculating a time lag performed by extracting a centroid position based on the time-series data of the value obtained by performing differentiation of ΔO<NUM>Hb and ΔHHb once. As illustrated in the part (c) of <FIG>, the CPU <NUM> obtains a first centroid position G<NUM> and a second centroid position G<NUM> of ΔO<NUM>Hb and ΔHHb during a predetermined period and calculates the time lag Δt based on these centroid positions G<NUM> and G<NUM>. For example, the time lag Δt is obtained as a time difference between the centroid positions G<NUM> and G<NUM>. For example, the predetermined period may be set based on the time lag between ΔO<NUM>Hb and ΔHHb calculated by either the first method or second method described above and cycles thereof.

Subsequently, the CPU <NUM> calculates the blood glucose level based on the time lag calculated by the first method, the second method, or the third method described above. The part (a) of <FIG> is a graph showing an example of the time-series data of the value obtained by performing differentiation of ΔO<NUM>Hb and ΔHHb once when an examinee is in a hyperglycemia state. The part (b) of <FIG> is a graph showing an example of the time-series data of the value obtained by performing differentiation of ΔO<NUM>Hb and ΔHHb once when an examinee is in a hypoglycemia state. In the part (a) of <FIG> and the part (b) of <FIG>, graphs G30 and G32 show the time-series data of the value obtained by performing differentiation of ΔO<NUM>Hb once. Graphs G31 and G33 show the time-series data of the value obtained by performing differentiation of ΔHHb once.

With reference to the part (a) of <FIG> and the part (b) of <FIG>, the time lag of ΔHHb with respect to ΔO<NUM>Hb is small when an examinee is in a hyperglycemia state and is large when an examinee is in a hypoglycemia state. That is, it is understood that there is a meaningful correlationship between a blood glucose level and a time lag. Moreover, from Examples (which will be described below), the inventor has found that a relationship between a blood glucose level and a time lag is expressed as the following mathematical expression. The factor G is a blood glucose level, and the factor Δt is a time lag. A first coefficient α and a second coefficient β are coefficients set in accordance with the maximum of glycometabolism ability and a measurement site. <NUM>] <MAT>.

The first coefficient α and the second coefficient β are coefficients depending on the measurement site such as an ear lobe, a finger, or the forehead. In addition, the first coefficient α and the second coefficient β also depends on glycometabolism ability. Therefore, the blood glucose level can be accurately obtained by setting the first coefficient α and the second coefficient β for each measurement site in consideration of the maximum of glycometabolism ability of an examinee and using Expression (<NUM>).

The blood glucose measurement device, the blood glucose calculation method, and the blood glucose calculation program according to the present embodiment may further include the input device <NUM> that receives inputs of the first coefficient α and the second coefficient β from outside (refer to <FIG>). For example, when the values of the first coefficient α and the second coefficient β are set through comparison between the blood glucose level obtained by a technique in the related art in a regular examination and a blood glucose level calculated by Expression (<NUM>), the values of the first coefficient α and the second coefficient β may be input by using the input device <NUM> and may be saved in the ROM <NUM> serving as a storing unit (refer to <FIG>). In addition, for example, when age and gender of an examinee, and the parameters of the blood glucose level at the time of a regular examination are statistically saved in the ROM <NUM>, and when personal information of an examinee is input to the input device <NUM>, an adequate first coefficient α and an adequate second coefficient β may be selected.

The effects of the blood glucose measurement device <NUM>, the blood glucose calculation method, and the blood glucose calculation program according to the present embodiment having the foregoing configuration will be described below. In the present embodiment, the blood glucose level is calculated based on the time lag between the parameter related to the O<NUM>Hb concentration (for example, the O<NUM>Hb concentration or the values obtained by performing differentiation of the O<NUM>Hb concentration M times) and the parameter related to the HHb concentration (for example, the HHb concentration or the values obtained by performing differentiation of the HHb concentration M times). In the related art, it has been assumed that the temporal change in the O<NUM>Hb concentration and the temporal change in the HHb concentration in accordance with spontaneous heartbeats are synchronized with each other. However, the inventor has found that a time lag sometimes occurs between the temporal change in the O<NUM>Hb concentration and the temporal change in the HHb concentration, and the magnitude of the time lag depends on the glucose concentration in blood (blood glucose level). The magnitude of the time lag is within a range of <NUM> seconds to <NUM> seconds in a case of a healthy person, for example.

An absorption wavelength region of hemoglobin scarcely overlaps the absorption wavelength regions of components such as water, lipids, and proteins. Furthermore, the weight ratio of hemoglobin in blood is remarkably higher than the weight ratio of glucose. Therefore, the blood glucose level can be accurately measured by measuring the blood glucose level based on the time lag between the temporal change in the O<NUM>Hb concentration and the temporal change in the HHb concentration. In addition, the amount of the time lag between the temporal change in the O<NUM>Hb concentration and the temporal change in the HHb concentration is equivalent to the amount of the time lag between the temporal changes in the values obtained by performing differentiation of these M times. Therefore, similarly, the blood glucose level can also be accurately measured by measuring the blood glucose level based on the time lag between the temporal change in the values obtained by performing differentiation of the O<NUM>Hb concentration M times and the temporal change in the values obtained by performing differentiation of the HHb concentration M times.

In addition, as in the present embodiment, each of the differential values of the O<NUM>Hb concentration and the HHb concentration used in calculation of a time lag may be a value in which frequency components smaller than that in a frequency caused by spontaneous heartbeats are eliminated by performing time differentiation at least once or more with respect to the time-series data of the O<NUM>Hb concentration and the HHb concentration. Accordingly, the influence of frequency components smaller than that in a frequency caused by spontaneous heartbeats applied to computation results can be restrained, so that the time lag can be more accurately obtained.

In addition, as in the present embodiment, the CPU <NUM> may obtain a time lag based on the time difference between the feature point repeatedly appearing in a cycle in the temporal change in the parameter related to the O<NUM>Hb concentration and the feature point repeatedly appearing in a cycle in the temporal change in the parameter related to the HHb concentration. Accordingly, the time lag in parameters can be easily obtained.

In addition, as in the present embodiment, the CPU <NUM> may obtain a time lag between the temporal changes in the parameters thereof based on the value of the inner product of the function of the parameter related to the O<NUM>Hb concentration and the function of the parameter related to the HHb concentration obtained during a predetermined period. Accordingly, the time lag in parameters can be easily obtained.

In addition, as in the present embodiment, the CPU <NUM> may obtain a time lag between the temporal changes in the parameters thereof based on the time difference between the centroid position in the temporal change in the parameter related to the O<NUM>Hb concentration and the centroid position in the temporal change in the parameter related to the HHb concentration obtained during a predetermined period. Accordingly, the time lag in parameters can be easily obtained.

In addition, as in the present embodiment, the CPU <NUM> may obtain a blood glucose level based on the fact that the blood glucose level is in inverse proportion to the time lag between the temporal change in the O<NUM>Hb concentration and the temporal change in the O<NUM>Hb concentration. Accordingly, the blood glucose level can be accurately obtained. Particularly, the blood glucose level can be more accurately obtained when the CPU <NUM> obtains the blood glucose level by using Expression (<NUM>) described above.

<FIG> is a scatter diagram showing a correlationship between a blood glucose level of an examinee A obtained by using an invasive blood glucose meter and the time lag Δt between ΔO<NUM>Hb and ΔHHb measured in an ear lobe of the examinee A by using the blood glucose measurement device <NUM> according to the embodiment after the examinee A in a seated position has ingested a carbonated drink (Coca-Cola (registered trademark)). In <FIG>, the vertical axis indicates the time lag Δt (unit: second), and the horizontal axis indicates the blood glucose level (unit: mg/dl). The blood glucose level and the time lag Δt were measured at a predetermined time interval during <NUM> days after the examinee A ingested a carbonated drink. Since the carbonated drink includes a large amount of glucose, when the carbonated drink was ingested, the blood glucose level of the examinee A increased. Accordingly, the correlationship between the blood glucose level and the time lag Δt could be ascertained.

With reference to <FIG>, it was possible to confirm that when the blood glucose level decreased, the time lag Δt increased, and when the blood glucose level increased, the time lag Δt decreased and the correlationship therebetween had approximately a linear shape. In other words, it was possible to confirm that there was a correlationship between the time lag Δt measured by using the blood glucose measurement device <NUM> of the embodiment and the blood glucose level obtained by using an invasive blood glucose meter in substantially inverse proportion to each other. In all of the measurement points shown in <FIG>, measurement was performed while the examinee A was under the same condition (ingesta, the measurement device, the measurement site, and the posture). Therefore, it was ascertained that measurement using the blood glucose measurement device <NUM> according to the embodiment had reproducibility.

The part (a) of <FIG> is a scatter diagram showing a correlationship between the blood glucose level of the examinee A obtained by using an invasive blood glucose meter and the time lag Δt measured in an ear lobe of the examinee A by using the blood glucose measurement device <NUM> according to the embodiment after the examinee A in the supine position has ingested a carbonated drink. The part (b) of <FIG> is a scatter diagram showing a correlationship between the blood glucose level of an examinee B obtained by using an invasive blood glucose meter and the time lag Δt measured in an ear lobe of the examinee B by using the blood glucose measurement device <NUM> of the embodiment after the examinee B in the supine position has ingested a pastry. The part (a) of <FIG> is a scatter diagram showing a correlationship between the blood glucose level of an examinee C obtained by using an invasive blood glucose meter and the time lag Δt measured in an ear lobe of the examinee C by using the blood glucose measurement device <NUM> of the embodiment after the examinee C in the supine position has ingested a pastry. The part (b) of <FIG> is a view in which the scatter diagrams of the part (a) of <FIG>, the part (b) of <FIG>, and the part (a) of <FIG> are combined in one. In these diagrams, the horizontal axis indicates the time lag Δt (unit: second), and the vertical axis indicates the blood glucose level (unit: mg/dl). Graphs G40 to G43 are power approximation curves of these scatter diagrams. Since a carbonated drink and a pastry are foods including a large amount of glucose, the blood glucose levels of the examinees A to C significantly rose after these were ingested.

With reference to the part (a) of <FIG>, the part (b) of <FIG>, the part (a) of <FIG>, and the part (b) of <FIG>, it was possible to confirm that there is a correlationship between the time lag Δt measured by using the blood glucose measurement device <NUM> and the blood glucose level obtained by using an invasive blood glucose meter in substantially inverse proportion to each other, in the examinees A to C. Therefore, there was a reversely proportional correlationship between the time lag Δt measured by using the blood glucose measurement device <NUM> and the blood glucose level obtained by using an invasive blood glucose meter, and it was indicated that the influence due to the difference between the examinees (individual difference) was small in this correlationship.

The part (a) of <FIG>, the part (b) of <FIG>, the part (a) of <FIG>, and the part (b) of <FIG> are scatter diagrams when the horizontal axes of the part (a) of <FIG>, the part (b) of <FIG>, the part (a) of <FIG>, and the part (b) of <FIG> are set to the reciprocal of the time lag (<NUM>/Δt, unit: Hz). Graphs G44 to G47 are power approximation curves of these scatter diagrams. In these diagrams, the vertical axis indicates the blood glucose level (unit: mg/dl). With reference to the part (a) of <FIG>, the part (b) of <FIG>, the part (a) of <FIG>, and the part (b) of <FIG>, it was possible to confirm that the blood glucose level measured by using an invasive blood glucose meter was substantially in proportional to the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM>.

From the measurement results of the present Example described above, the relationship between a blood glucose level (G) and the reciprocal of the time lag (<NUM>/Δt) is expressed as the following mathematical expression. <NUM>] <MAT>.

In the present Example, the foregoing Expression (<NUM>) was introduced as a common relation expression with respect to the plurality of examinees A to C. As the reason for this, it was assumed that the examinees A to C was comparatively healthy and measurement was performed in the same measurement site (ear lobe). When the glycometabolism ability and the measurement site are different from each other, it is desirable that each of the coefficients in Expression (<NUM>), that is, the coefficients α and β in Expression (<NUM>) be adjusted. Accordingly, the blood glucose level can be more accurately obtained. According to the experiments of the inventor, the coefficients α and β were constant over a long period of time (half year) in measurement of the same person at the same site.

The part (a) to the part (c) of <FIG> are graphs showing time changes in the blood glucose levels of the examinees A to C measured by using an invasive blood glucose meter (graphs G53 to <NUM>) and time changes in the reciprocal of the time lag (<NUM>/Δt) measured in an ear lobe of the examinee A by using the blood glucose measurement device <NUM> (graphs G50 to <NUM>), in an overlapping manner and correspond to the part (a) of <FIG>, the part (b) of <FIG>, and the part (a) of <FIG>, respectively. In these diagrams, the horizontal axis indicates the time (unit: minute), the vertical axis on the left indicates the blood glucose level (unit: mg/dl) obtained by using an invasive blood glucose meter, and the vertical axis on the right indicates the reciprocal of the time lag (<NUM>/Δt, unit: Hz) measured by using the blood glucose measurement device <NUM>. With reference to the part (a) to the part (c) of <FIG>, it was possible to confirm that the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> followed the blood glucose level obtained by using an invasive blood glucose meter without significantly depending on the individual difference.

In the part (a) to the part (c) of <FIG>, a correlation coefficient R<NUM> between the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> and the blood glucose level obtained by using an invasive blood glucose meter was -<NUM> in the part (a) of <FIG>, was -<NUM> in the part (b) of <FIG>, and was -<NUM> in the part (c) of <FIG>, manifesting a close correlationship between all the cases. Therefore, it was possible to confirm that there was a close correlationship between the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> according to the embodiment and the blood glucose level obtained by using an invasive blood glucose meter. A method of calculating the correlation coefficient R<NUM> will be described below.

The part (a) of <FIG> is a graph showing a time change in the blood glucose level of the examinee A obtained by using an invasive blood glucose meter (graph G60) and a time change in the reciprocal of the time lag (<NUM>/Δt) measured in the front forehead of the examinee A by using the blood glucose measurement device <NUM> (graph G63) after the examinee A in a seated position has ingested jelly-like nutritional supplementary food (Weider In Jelly (registered trademark)), in an overlapping manner. The part (b) of <FIG> is a graph showing a time change in the blood glucose level of the examinee A obtained by using an invasive blood glucose meter (graph G61) and a time change in the reciprocal of the time lag (<NUM>/Δt) measured in the front forehead of the examinee A by using the blood glucose measurement device <NUM> (graph G64) after the examinee A in a seated position has ingested chicken meat (white meat), in an overlapping manner. In these diagrams, the horizontal axis indicates the elapsed time (unit: minute), the vertical axis on the left indicates the blood glucose level (unit: mg/dl) obtained by using an invasive blood glucose meter, and the vertical axis on the right indicates the reciprocal of the time lag (<NUM>/Δt, unit: Hz) measured by using the blood glucose measurement device <NUM>. Since jelly-like nutritional supplementary food includes a large amount of glucose, the blood glucose level of the examinee A rose after the food was ingested and fell thereafter, as shown in the graph G60 in the part (a) of <FIG>. In contrast, since white meat has proteins as a main component and includes little glucose, there was little change in the blood glucose level of the examinee A after white meat was ingested, as shown in the graph G61 in the part (b) of <FIG>.

With reference to the part (a) of <FIG>, the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> rose with the lapse of time and fell thereafter after the examinee A ingested jelly-like nutritional supplementary food. Accordingly, it was possible to confirm that the reciprocal of the time lag (<NUM>/Δt) favorably followed the time change in the blood glucose level obtained by using an invasive blood glucose meter. In addition, with reference to the part (b) of <FIG>, it was possible to confirm that there is little change in the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> regardless of the lapse of time, after the examinee A ingested white meat. From these results, it was indicated that the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> according to the embodiment could suitably follow the time change in the blood glucose level.

In the part (a) of <FIG> and the part (b) of <FIG>, the correlation coefficient R<NUM> between the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> and the blood glucose level obtained by using an invasive blood glucose meter was <NUM> in the part (a) of <FIG> and was <NUM> in the part (b) of <FIG>. Generally, in the field of biological measurement, the correlation coefficient R<NUM> of <NUM> or larger is considered to have a close correlationship. Accordingly, these results can be considered to have a close correlationship. Therefore, it was possible to confirm that there was a close correlationship between the reciprocal of the time lag (<NUM>/Δt) measured by using the blood glucose measurement device <NUM> according to the embodiment and the blood glucose level obtained by using an invasive blood glucose meter.

Here, in a scatter diagram having x and y as variables, the correlation coefficient R<NUM> can be obtained by the following Expression (<NUM>). <NUM>] <MAT>.

Here, Sx is the variance of x, Sy is the variance of y, and Sxy is the covariance of x and y. The variances Sx and Sy and the covariance Sxy are obtained by the following mathematical expressions (<NUM>) to (<NUM>), respectively. Here, x<NUM> and y<NUM> are the average values of x and y, respectively. In addition, n is a sample number. <NUM>] <MAT> [Math. <NUM>] <MAT> [Math. <NUM>] <MAT>.

For the sake of fast computation processing, the variances Sx and Sy and the covariance Sxy may be obtained by the method described below. That is, the variances Sx and Sy and the covariance Sxy can also be suitably obtained by the following mathematical expressions (<NUM>) to (<NUM>), respectively. <NUM>] <MAT> [Math. <NUM>] <MAT> [Math. <NUM>] <MAT>.

Therefore, for example, the variances Sx and Sy, the covariance Sxy, and the average values x<NUM> and y<NUM> is favorably obtained while having the time-series data of ΔO<NUM>Hb set to x<NUM> to xn and the time-series data of ΔHHb set to y<NUM> to yn, obtained during a certain period of time (for example, for <NUM> seconds). The correlation coefficient R<NUM> can be obtained by substituting these in the foregoing mathematical expression (<NUM>).

The blood glucose measurement device, the blood glucose calculation method, and the blood glucose calculation program are not limited to the embodiment and Examples described above, and various other modifications can be made. For example, the blood glucose measurement device <NUM>, the blood glucose calculation method, and the blood glucose calculation program according to the embodiments described above presents an examinee with the blood glucose level calculated based on the time lag between ΔO<NUM>Hb and ΔHHb. However, the blood glucose measurement device, the blood glucose calculation method, and the blood glucose calculation program may be applied as a diabetes diagnostic device, a diabetes diagnostic method, and a diabetes diagnostic program presenting an examinee with a fact whether or not he/she is diabetic, using a calculated blood glucose level as a clue to determination.

The inventor presumes the reversely proportional correlationship between the time lag Δt and the blood glucose level described above as follows. There are oxygen metabolism and a glycolysis system in metabolism of a living body. In the process of converting glucose in the glycolysis system into energy, substances called <NUM>,<NUM>-BPG are generated. The substance <NUM>,<NUM>-BPG has characteristics of separating oxygen from hemoglobin. Consequently, oxygenated hemoglobin is converted into deoxygenated hemoglobin due to <NUM>,<NUM>-BPG. Since the substance <NUM>,<NUM>-BPG increases when the blood glucose level rises, <NUM>,<NUM>-BPG promotes deoxygenation of hemoglobin. Therefore, when the blood glucose level rises, a delay of the time change in the deoxygenated hemoglobin concentration with respect to the time change in the oxygenated hemoglobin concentration is decreased. That is, a reversely proportional correlationship is established between the time lag Δt and the blood glucose level.

In addition, in the example of the foregoing embodiment, the computation unit (CPU <NUM>) is built in the main body unit <NUM> such as a smart device. However, for example, the computation unit may be provided separately from the main body unit, such as a cloud server or a personal computer. In such a case, the computation unit may be connected to the main body unit via a network such as radio or the internet. In addition, in the foregoing embodiment, one computation unit performs calculation of ΔO<NUM>Hb and ΔHHb and calculation of the time lag therebetween. However, a part calculating ΔO<NUM>Hb and ΔHHb and a part calculating time lags therebetween may be provided separately from each other in the computation unit.

In addition, in the foregoing embodiment, a modified Beer-Lambert method (MBL method) is adopted as a method of calculating ΔO<NUM>Hb and ΔHHb. However, other methods for near-infrared spectroscopy such as space-resolved spectroscopy (SRS method) may be used. In addition, the absolute value of the O<NUM>Hb concentration and the absolute value of the HHb concentration can be obtained by using near-infrared spectroscopy such as time-resolved spectroscopy (TRS method) or phase modulation spectroscopy (PMS method).

In the related art, it has been assumed that a temporal change in the oxygenated hemoglobin concentration and a temporal change in the deoxygenated hemoglobin concentration in accordance with spontaneous heartbeats are synchronized with each other. However, the inventor has found that a time lag sometimes occurs between these temporal changes. Moreover, the inventor has found that the magnitude of the time lag depends on the glucose concentration in blood (blood glucose level). The absorption wavelength region of hemoglobin scarcely overlaps the absorption wavelength regions of components such as water, lipids, and proteins. Furthermore, the weight ratio of hemoglobin in blood is remarkably higher than the weight ratio of glucose. Therefore, as in the blood glucose measurement device, the blood glucose calculation method, and the blood glucose calculation program, the blood glucose level can be accurately measured (or calculated) by measuring the blood glucose level based on the time lag between the temporal change in the first parameter related to the oxygenated hemoglobin concentration and the temporal change in the second parameter related to the deoxygenated hemoglobin concentration.

In the embodiment, the first parameter may be the relative amount of temporal change or the absolute value of the oxygenated hemoglobin concentration, and the second parameter may be the relative amount of temporal change or the absolute value of the deoxygenated hemoglobin concentration. The blood glucose level can be accurately measured (calculated) by using a relative hemoglobin concentration or an absolute hemoglobin concentration as a parameter for obtaining a time lag.

In addition, the first parameter may be the first differential value obtained by performing time differentiation of the oxygenated hemoglobin concentration at least once, and the second parameter may be the second differential value obtained by performing time differentiation of the deoxygenated hemoglobin concentration at least once. The amount of the time lag between the temporal changes in the oxygenated hemoglobin concentration and the deoxygenated hemoglobin concentration may be equivalent to the amount of the time lag between the differential values of these temporal changes. Therefore, when the time lag is obtained by using a time change in the first differential value and a time change in the second differential value, the influence of frequency components smaller than that in a frequency caused by spontaneous heartbeats applied to computation results can be restrained, so that the time lag can be more accurately obtained.

In addition, the computation unit or the first computation step may obtain a time lag based on the time difference between the first feature point and the second feature point by obtaining the first feature point repeatedly appearing in a cycle in the temporal change in the first parameter and the second feature point, corresponding to the first feature point, repeatedly appearing in a cycle in the temporal change in the second parameter. Accordingly, the time lag can be easily obtained.

In addition, the computation unit or the first computation step may obtain a time lag based on the value of the inner product by obtaining the value of the inner product of the function of the first parameter and the function of the second parameter during a predetermined period. For example, when the function of the first parameter and the function of the second parameter are standardized and the inner product is obtained, the value thereof becomes equivalent to the value of cos(Δθ). The time lag can be easily obtained based on the phase shift (Δθ) calculated from this relationship.

In addition, the computation unit or the first computation step may obtain a time lag based on the time difference between the first centroid position and the second centroid position by obtaining the first centroid position in the temporal change in the first parameter and obtaining the second centroid position in the temporal change in the second parameter during a predetermined period. Accordingly, the time lag can be easily obtained.

In addition, the computation unit or the second computation step may obtain data related to the blood glucose level based on the fact that the blood glucose level is in inverse proportion to the time lag. The inventor has found that a reversely proportional correlationship is established between the blood glucose level and the time lag. Therefore, the blood glucose level can be accurately obtained based on this relationship.

In addition, the blood glucose measurement device described above may be characterized in that the computation unit or the second computation step obtains data related to the blood glucose level by using the following expression. <NUM>] <MAT>.

Here, the factor G is a blood glucose level, the factor Δt is a time lag, the factor α is the first coefficient set in accordance with the maximum of glycometabolism ability and the measurement site, and the factor β is the second coefficient set in accordance with the maximum of glycometabolism ability and the measurement site. The inventor has found that there is a correlationship which is expressed by the foregoing Expression and is established between the blood glucose level and the time lag. Since the first coefficient α and the second coefficient β are set in accordance with the maximum of glycometabolism ability of an examinee, the blood glucose level is obtained in consideration of the maximum of glycometabolism ability of an examinee. In addition, since the first coefficient α and the second coefficient β depend on the measurement site, the blood glucose level is obtained in consideration of the difference in measurement sites of an examinee. Accordingly, the blood glucose level can be more accurately obtained.

In addition, in the computation unit or the second computation step, the first coefficient and the second coefficient may be input from outside, and the computation unit may obtain the data related to the blood glucose level by using the first coefficient and the second coefficient input from outside. For example, the first coefficient and the second coefficient are adequately set in accordance with the maximum of glycometabolism ability by comparing the blood glucose level obtained by a technique in the related art a regular examination and the blood glucose level calculated by the foregoing device. Therefore, it is possible to suitably obtain a more accurate blood glucose level in accordance with the glycometabolism ability of a testee based on the first coefficient and the second coefficient input from outside.

In addition, the blood glucose calculation method of the embodiment may further include a light inputting step of inputting measurement light to a living body, and a light detecting step of detecting measurement light propagated inside the living body and generating a detection signal in accordance with the intensity of the measurement light.

Claim 1:
A blood glucose measurement device for obtaining data related to a blood glucose level of a living body, the device comprising:
a light outputting unit configured to output measurement light to be input to the living body;
a light detecting unit configured to detect the measurement light propagated inside the living body and generate a detection signal in accordance with an intensity of the measurement light; and
a computation unit configured to obtain a time lag between a temporal change in a first parameter related to an oxygenated hemoglobin concentration and a temporal change in a second parameter related to a deoxygenated hemoglobin concentration based on the detection signal, and obtain the data related to the blood glucose level based on the time lag,
characterized in that the first parameter is a first differential value obtained by performing time differentiation of the oxygenated hemoglobin concentration at least once, and the second parameter is a second differential value obtained by performing time differentiation of the deoxygenated hemoglobin concentration at least once.