JP7367242B2 - Electronic equipment, estimation system, estimation method and estimation program - Google Patents

Description

The present invention relates to an electronic device, an estimation system, an estimation method, and an estimation program for estimating the health condition of a subject from measured biological information.

BACKGROUND ART Conventionally, blood components and blood fluidity have been measured as means for estimating the health condition of a subject (user). These measurements are performed using blood collected from a subject. Further, electronic devices are known that measure biological information from a test site such as the wrist of a test subject. For example, Patent Document 1 describes an electronic device that measures the pulse of a subject by wearing it on the wrist of the subject.

Japanese Patent Application Publication No. 2002-360530

However, since blood sampling is painful, it is difficult to estimate one's own health status on a daily basis. Further, the electronic device described in Patent Document 1 only measures the pulse, and cannot estimate the health condition of the subject other than the pulse.

An object of the present invention, made in view of the above circumstances, is to provide an electronic device, an estimation system, an estimation method, and an estimation program that can easily estimate the health condition of a subject.

In order to solve the above problems, an electronic device according to an embodiment of the present invention uses an estimation formula created based on a blood sugar level and a pulse wave of a subject associated with the blood sugar level, and a control unit that estimates the blood sugar level of the subject based on the age of the subject, the forward wave of the pulse wave, and the forward wave of the pulse wave as the estimation formula. An index created based on the results of a regression analysis using an index AI expressed as a ratio to a reflected wave that appears later is used.

Further, the electronic device according to an embodiment of the present invention uses an estimation formula created based on a blood sugar level and a pulse wave of the subject associated with the blood sugar level, and the age of the subject. a control unit for estimating the glucose metabolism of the subject based on the estimation formula, the control unit estimating the age of the subject, a forward wave of the pulse wave, and a reflection that appears later than the forward wave. An index created based on the results of regression analysis using the index AI expressed as the ratio to the wave is used.

Further, the electronic device according to an embodiment of the present invention uses an estimation formula created based on a lipid value and a pulse wave of the subject associated with the lipid value, and the age of the subject. a control unit that estimates a lipid value of the subject based on the estimation equation, the control unit estimates the age of the subject, a forward wave of the pulse wave, and a reflection that appears later than the forward wave. An index created based on the results of regression analysis using the index AI expressed as the ratio to the wave is used.

Further, the electronic device according to an embodiment of the present invention uses an estimation formula created based on a lipid value and a pulse wave of the subject associated with the lipid value, and the age of the subject. a controller for estimating the lipid metabolism of the subject based on the estimation equation, the controller includes the age of the subject, a forward wave of the pulse wave, and a reflection that appears later than the forward wave. An index created based on the results of regression analysis using the index AI expressed as the ratio to the wave is used.

Furthermore, the estimation method according to an embodiment of the present invention uses an estimation formula created based on a blood sugar level and a pulse wave of a subject associated with the blood sugar level, and the age of the subject. an estimating step of estimating the blood sugar level or sugar metabolism of the subject based on the estimation equation, the estimating step includes the age of the subject, a forward wave of the pulse wave, and a lag behind the forward wave of the pulse wave. An index created based on the results of regression analysis using the index AI, which is expressed as the ratio of the reflected wave to the reflected wave that appears, is used.

Furthermore, the estimation method according to an embodiment of the present invention uses an estimation formula created based on a lipid value and a pulse wave of the subject associated with the lipid value, and the age of the subject. an estimating step of estimating the lipid value or lipid metabolism of the subject based on the estimation equation, the estimation step includes the age of the subject, a forward wave of the pulse wave, and a lag behind the forward wave of the pulse wave. An index created based on the results of regression analysis using the index AI, which is expressed as the ratio of the reflected wave to the reflected wave that appears, is used.

Further, the estimation program according to an embodiment of the present invention uses an estimation formula created based on a blood glucose level and a pulse wave of a subject associated with the blood sugar level, and an estimation formula of the subject. The estimation program functions as a control unit that estimates the blood sugar level or glucose metabolism of the subject based on the age, the control unit including the age of the subject and the pulse rate as the estimation formula. An index created based on the results of a regression analysis using an index AI expressed as a ratio between a forward wave and a reflected wave that appears later than the forward wave is used.

Furthermore, the estimation program according to an embodiment of the present invention uses an estimation formula created based on a lipid value and a pulse wave of a subject associated with the lipid value, and The estimation program functions as a control unit that estimates the lipid value or lipid metabolism of the subject based on the age of the subject, the control unit including the age of the subject and the pulse rate as the estimation formula. An index created based on the results of a regression analysis using an index AI expressed as a ratio between a forward wave and a reflected wave that appears later than the forward wave is used.

According to the present invention, it is possible to provide an electronic device, an estimation system, an estimation method, and an estimation program that can easily estimate the health condition of a subject.

1 is a schematic diagram showing a schematic configuration of an electronic device according to a first embodiment of the present invention. FIG. 2 is a sectional view showing a schematic configuration of the main body of FIG. 1. FIG. 2 is a diagram illustrating an example of a usage state of the electronic device in FIG. 1. FIG. 2 is a functional block diagram showing a schematic configuration of the electronic device in FIG. 1. FIG. 2 is a diagram illustrating an example of an estimation method based on changes in pulse waves in the electronic device of FIG. 1. FIG. It is a figure showing an example of an accelerated pulse wave. It is a figure which shows an example of the pulse wave acquired by the sensor part. FIG. 2 is a diagram illustrating another example of an estimation method based on changes in pulse waves in the electronic device of FIG. 1. FIG. FIG. 2 is a flowchart for creating an estimation formula used by the electronic device of FIG. 1. FIG. 10 is a flowchart for estimating the postprandial blood sugar level of a subject using the estimation formula created according to the flow of FIG. 9. FIG. 10 is a diagram showing a comparison between a postprandial blood sugar level estimated using the estimation formula created by the flow of FIG. 9 and an actually measured postprandial blood sugar level. FIG. FIG. 7 is a flowchart for creating an estimation formula used by an electronic device according to a second embodiment of the present invention. FIG. 13 is a flowchart for estimating a subject's postprandial blood sugar level using the estimation formula created according to the flow of FIG. 12. FIG. 7 is a flowchart for creating an estimation formula used by an electronic device according to a third embodiment of the present invention. FIG. 15 is a flowchart for estimating a subject's postprandial lipid value using the estimation formula created according to the flow of FIG. 14. FIG. 15 is a diagram showing a comparison between postprandial lipid values estimated using the estimation formula created by the flow of FIG. 14 and actually measured postprandial lipid values. FIG. 2 is a diagram schematically showing communication between an electronic device and a blood glucose meter. 1 is a schematic diagram showing a schematic configuration of a system according to an embodiment of the present invention.

Embodiments of the present invention will be described in detail below with reference to the drawings.

(First embodiment)
FIG. 1 is a schematic diagram showing a schematic configuration of an electronic device according to a first embodiment of the present invention. Electronic device 100 includes a mounting section 110 and a measuring section 120. FIG. 1 is a diagram of the electronic device 100 observed from the back surface 120a that contacts the test part.

The electronic device 100 measures biological information of the subject while the subject is wearing the electronic device 100 . The biological information measured by the electronic device 100 is the subject's pulse wave that can be measured by the measurement unit 120. In the present embodiment, the electronic device 100 will be described below, as an example, assuming that the electronic device 100 is attached to the wrist of a subject to acquire a pulse wave.

In this embodiment, the mounting portion 110 is a linear, elongated band. The measurement of the pulse wave is performed, for example, with the subject wearing the mounting portion 110 of the electronic device 100 around his or her wrist. Specifically, the subject measures the pulse wave by wrapping the mounting section 110 around the wrist so that the back surface 120a of the measurement section 120 contacts the test site. The electronic device 100 measures the pulse wave of blood flowing through the ulnar artery or the radial artery at the subject's wrist.

FIG. 2 is a sectional view showing a schematic configuration of the measuring section 120 in FIG. 1. As shown in FIG. In FIG. 2, the mounting section 110 around the measuring section 120 is also illustrated along with the measuring section 120.

The measurement unit 120 has a back surface 120a that contacts the wrist of the subject when worn, and a surface 120b opposite to the back surface 120a. The measuring section 120 has an opening 111 on the back surface 120a side. The sensor section 130 is supported by the measurement section 120 with one end protruding from the opening 111 toward the back surface 120a when the elastic body 140 is not pressed. A pulse abutment part 132 is provided at one end of the sensor part 130. One end of the sensor section 130 is movable in a direction substantially perpendicular to the plane of the back surface 120a. The other end of the sensor section 130 is supported by the measuring section 120 by a support section 133 so that one end of the sensor section 130 can be displaced.

One end of the sensor section 130 is in contact with the measurement section 120 via the elastic body 140 and is movable. The elastic body 140 is, for example, a spring. However, the elastic body 140 is not limited to a spring, and may be any other elastic body, such as resin or sponge.

Although not shown, the measurement unit 120 may include a control unit, a storage unit, a communication unit, a power supply unit, a notification unit, a circuit for operating these units, a connecting cable, and the like.

The sensor section 130 includes an angular velocity sensor 131 that detects displacement of the sensor section 130. The angular velocity sensor 131 only needs to be able to detect the angular displacement of the sensor section 130. The sensor included in the sensor unit 130 is not limited to the angular velocity sensor 131, but may be an acceleration sensor, an angle sensor, or another motion sensor, or may include a plurality of these sensors.

The electronic device 100 includes an input section 141 on the surface 120b side of the measurement section 120. The input unit 141 accepts operation input from the subject, and includes, for example, operation buttons (operation keys). The input unit 141 may be configured by, for example, a touch screen.

FIG. 3 is a diagram showing an example of how the electronic device 100 is used by the subject. The subject uses the electronic device 100 by wrapping it around his or her wrist. The electronic device 100 is mounted with the back surface 120a of the measurement section 120 in contact with the test section. With the attachment section 110 wrapped around the wrist, the measurement section 120 can adjust its position so that the pulse abutment section 132 contacts the position where the ulnar artery or radial artery exists.

In FIG. 3, when the electronic device 100 is attached, one end of the sensor unit 130 is in contact with the skin on the radial artery, which is the artery on the thumb side of the subject's left hand. Due to the elastic force of the elastic body 140 disposed between the measurement section 120 and the sensor section 130, one end of the sensor section 130 is in contact with the skin above the radial artery of the subject. The sensor unit 130 is displaced according to the movement of the radial artery of the subject, that is, the pulsation. The angular velocity sensor 131 acquires a pulse wave by detecting the displacement of the sensor section 130. A pulse wave is a waveform obtained from the body surface that represents a time change in the volume of a blood vessel caused by the inflow of blood.

Referring to FIG. 2 again, the sensor section 130 is in a state in which one end protrudes from the opening 111 when the elastic body 140 is not pressed. When the electronic device 100 is attached to the subject, one end of the sensor section 130 is in contact with the skin above the radial artery of the subject, and the elastic body 140 expands and contracts in response to the pulsation. is displaced. The elastic body 140 has an appropriate elastic modulus so that it does not interfere with pulsations and expands and contracts in response to pulsations. The opening width W of the opening 111 is sufficiently larger than the diameter of the blood vessel, which in this embodiment is the diameter of the radial artery. By providing the opening 111 in the measuring section 120, the back surface 120a of the measuring section 120 does not press the radial artery when the electronic device 100 is attached. Therefore, the electronic device 100 can acquire a pulse wave with less noise, and the accuracy of measurement is improved.

Although FIG. 3 shows an example in which the electronic device 100 is worn on the wrist and a pulse wave in the radial artery is acquired, the present invention is not limited to this. For example, the electronic device 100 may acquire a pulse wave of blood flowing through a carotid artery in the subject's neck. Specifically, the subject may measure the pulse wave by lightly pressing the pulse abutting section 132 against the carotid artery. Further, the subject may wear the attachment part 110 around the neck so that the pulse abutting part 132 is located at the carotid artery.

FIG. 4 is a functional block diagram showing a schematic configuration of the electronic device 100. The electronic device 100 includes a sensor section 130, an input section 141, a control section 143, a power supply section 144, a storage section 145, a communication section 146, and a notification section 147. In this embodiment, the control section 143, the power supply section 144, the storage section 145, the communication section 146, and the notification section 147 are included inside the measurement section 120 or the mounting section 110.

The sensor section 130 includes an angular velocity sensor 131, and detects pulsation from the test site to obtain a pulse wave.

The control unit 143 is a processor that controls and manages the entire electronic device 100, including each functional block of the electronic device 100. Further, the control unit 143 is a processor that estimates the blood sugar level of the subject from the acquired pulse wave. The control unit 143 is composed of a processor such as a CPU (Central Processing Unit) that executes a program that defines a control procedure and a program that estimates the blood sugar level of the subject, and the program is stored in a storage medium such as the storage unit 145. is stored in Furthermore, the control unit 143 estimates the state of the subject regarding sugar metabolism, lipid metabolism, etc., based on the calculated index. The control unit 143 notifies the notification unit 147 of data.

The power supply unit 144 includes, for example, a lithium ion battery and a control circuit for charging and discharging the battery, and supplies power to the entire electronic device 100.

The storage unit 145 stores programs and data. The storage unit 145 may include any non-transitory storage medium such as a semiconductor storage medium and a magnetic storage medium. Storage unit 145 may include multiple types of storage media. The storage unit 145 may include a combination of a portable storage medium such as a memory card, an optical disk, or a magneto-optical disk, and a storage medium reading device. The storage unit 145 may include a storage device used as a temporary storage area, such as a RAM (Random Access Memory). The storage unit 145 stores various information, programs for operating the electronic device 100, and the like, and also functions as a work memory. The storage unit 145 may store, for example, the measurement results of the pulse wave acquired by the sensor unit 130.

The communication unit 146 transmits and receives various data by performing wired or wireless communication with an external device. For example, the communication unit 146 communicates with an external device that stores the biological information of the subject in order to manage the health condition, and transmits the pulse wave measurement results measured by the electronic device 100 and the health estimated by the electronic device 100. Send the status to the external device.

The notifying unit 147 notifies information using sounds, vibrations, images, and the like. The notification unit 147 includes a speaker, a vibrator, and a display such as a liquid crystal display (LCD), an organic EL display (OELD), or an inorganic electro-luminescence display (IELD). It may also include a device. In this embodiment, the notification unit 147 reports, for example, the state of sugar metabolism or lipid metabolism of the subject.

Electronic device 100 estimates the subject's blood sugar level based on the estimation formula created by regression analysis. The electronic device 100 stores in advance, for example, an estimation formula for estimating the blood sugar level based on the pulse wave in the storage unit 145. Electronic device 100 estimates the blood sugar level using these estimation formulas.

Here, an estimation theory regarding estimating blood sugar level based on pulse waves will be explained. After a meal, the blood sugar level rises, resulting in a decrease in blood fluidity (increase in viscosity), dilation of blood vessels, and increase in circulating blood volume, and vascular dynamics and blood flow are adjusted to balance these conditions. The dynamics are determined. A decrease in blood fluidity is caused by, for example, an increase in the viscosity of plasma or a decrease in the deformability of red blood cells. Further, dilation of blood vessels is caused by secretion of insulin, secretion of digestive hormones, increase in body temperature, and the like. When blood vessels dilate, the pulse rate increases to suppress the drop in blood pressure. The increase in circulating blood volume also compensates for blood consumption for digestion and absorption. Changes in blood vessel dynamics and hemodynamics between before and after a meal due to these factors are also reflected in the pulse wave. Therefore, the electronic device 100 can acquire a pulse wave and estimate the blood sugar level based on a change in the waveform of the acquired pulse wave.

Based on the above estimation theory, an estimation formula for estimating blood sugar levels can be created by performing regression analysis based on sample data of pre-meal and post-prandial blood sugar levels and pulse waves obtained from multiple subjects. can. At the time of estimation, the blood sugar level of the subject can be estimated by applying the created estimation formula to the index based on the subject's pulse wave. In creating the estimation formula, in particular, by performing regression analysis and creating the estimation formula using sample data whose blood sugar level variation is close to a normal distribution, it is possible to calculate the Blood sugar level can be estimated.

FIG. 5 is a diagram illustrating an example of an estimation method based on changes in pulse waves, and shows an example of pulse waves. An estimation formula for estimating the blood sugar level is created, for example, by regression analysis regarding an index (rise index) Sl indicating the rise of a pulse wave, an AI (Augmentation Index), and a pulse rate PR.

The rising index Sl is derived based on the waveform shown in area D1 in FIG. Specifically, the rise index Sl is the ratio of the first minimum value to the first maximum value in the accelerated pulse wave derived by differentiating the pulse wave twice. The rising index Sl is represented by -b/a in the accelerated pulse wave shown as an example in FIG. 6, for example. The rise index Sl decreases due to a decrease in blood fluidity after a meal, dilation (relaxation) of blood vessels due to insulin secretion, and a rise in body temperature.

AI is an index expressed by the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. The method for deriving AI will be explained with reference to FIG. FIG. 7 is a diagram showing an example of a pulse wave acquired at the wrist using the electronic device 100. FIG. 7 shows a case where the angular velocity sensor 131 is used as a pulsation detection means. FIG. 7 shows the time-integrated angular velocity acquired by the angular velocity sensor 131, with the horizontal axis representing time and the vertical axis representing angle. Since the acquired pulse wave may include noise caused by, for example, the subject's body movement, it may be corrected using a filter that removes the DC (Direct Current) component to extract only the pulsation component.

Pulse wave propagation is a phenomenon in which pulsations caused by blood pumped from the heart are transmitted through the arterial walls and blood. The pulsations caused by the blood pumped from the heart reach the extremities of the limbs as forward waves, and a portion of the waves are reflected at branch points of blood vessels, changes in blood vessel diameter, etc., and return as reflected waves. AI is the magnitude of this reflected wave divided by the magnitude of the forward wave, and is expressed as AI _n =(P _Rn - P _Sn )/(P _Fn - P _Sn ). Here, AI _n is the AI per pulse. AI may be obtained by, for example, measuring pulse waves for several seconds and calculating the average value AI _ave of AI _n (n=an integer from 1 to n) for each pulse. AI is derived based on the waveform shown in area D2 in FIG. AI decreases due to decreased blood fluidity after meals and dilation of blood vessels due to increased body temperature.

The pulse rate PR is derived based on the pulse wave period TPR shown in FIG. Pulse rate PR increases after eating.

The electronic device 100 can estimate the blood sugar level using an estimation formula created based on the rising index Sl, AI, and pulse rate PR.

FIG. 8 is a diagram illustrating another example of the estimation method based on changes in pulse waves. FIG. 8(a) shows a pulse wave, and FIG. 8(b) shows the result of performing FFT (Fast Fourier Transform) on the pulse wave of FIG. 8(a). An estimation formula for estimating the blood sugar level is created, for example, by regression analysis regarding the fundamental wave and harmonic components (Fourier coefficients) derived by FFT. The peak value in the FFT result shown in FIG. 8(b) changes based on the change in the waveform of the pulse wave. Therefore, the blood sugar level can be estimated using an estimation formula created based on Fourier coefficients.

The electronic device 100 estimates the blood sugar level of the subject using an estimation formula based on the above-mentioned rise index Sl, AI, pulse rate PR, Fourier coefficient, and the like.

Here, a method for creating an estimation formula used when the electronic device 100 estimates the blood sugar level of the subject will be described. Creation of the estimation formula does not need to be executed by the electronic device 100, and may be created in advance using another computer or the like. In this specification, a device that creates an estimation equation will be referred to as an estimation equation creation device. The created estimation formula is stored in the storage unit 145 in advance, for example, before the subject estimates the blood sugar level using the electronic device 100.

FIG. 9 is a flowchart for creating an estimation formula used by the electronic device 100 of FIG. The estimation formula is based on sample data obtained by measuring the subject's pre-prandial and post-prandial pulse waves using a pulse wave meter, and measuring the subject's pre-prandial and post-prandial blood sugar levels using a blood glucose meter. It is created by performing regression analysis. Note that "before a meal" refers to when the subject is on an empty stomach, and "after a meal" refers to the time when the blood sugar level increases after a predetermined time after the meal (for example, about 1 hour after starting the meal). The sample data to be acquired is not limited to before and after meals, but may be data from time periods in which blood sugar levels fluctuate significantly.

In creating the estimation formula, first, information regarding the subject's blood glucose level before meals and the pulse wave associated with the blood sugar level, which are measured by a blood glucose meter and a pulse wave meter, respectively, are input into the estimation formula creation device (step S101). ).

Further, information regarding the subject's postprandial blood sugar level and the pulse wave associated with the blood sugar level, which are measured by a blood glucose meter and a pulse wave meter, respectively, are input to the estimation formula creation device (step S102). The blood sugar level input in step S101 and step S102 is measured by a blood glucose meter, for example, by collecting blood. Furthermore, in step S101 or step S102, the age of the subject of each sample data is also input.

The estimation formula creation device determines whether the number of samples of the sample data input in step S101 and step S102 is equal to or greater than N, which is sufficient for performing regression analysis (step S103). The number of samples N can be determined as appropriate, and can be set to 100, for example. If the estimation formula creation device determines that the number of samples is less than N (in the case of No), it repeats step S101 and step S102 until the number of samples becomes N or more. On the other hand, when the estimation formula creation device determines that the number of samples is equal to or greater than N (in the case of Yes), the process proceeds to step S104 and calculates the estimation formula.

In calculating the estimation equation, the estimation equation creation device analyzes the input pre-prandial and post-prandial pulse waves (step S104). In this embodiment, the estimation formula creation device analyzes the pulse wave rise index Sl, AI, and pulse rate PR before and after meals. Note that the estimation formula creation device may perform FFT analysis as the analysis of the pulse wave.

The estimation formula creation device then performs regression analysis (step S105). The objective variable in regression analysis is postprandial blood glucose level. Furthermore, the explanatory variables in the regression analysis are the age input in step S101 or step S102, and the pre-prandial and post-prandial pulse wave rise indicators Sl, AI, and pulse rate PR analyzed in step S104. Note that when the estimation formula creation device performs FFT analysis in step S104, the explanatory variables may be, for example, Fourier coefficients calculated as a result of the FFT analysis.

The estimation formula creation device creates an estimation formula for estimating the postprandial blood sugar level based on the results of the regression analysis (step S106). An example of an estimation equation for estimating the postprandial blood sugar level is shown in equation (1) below.

In formula (1), GLa indicates the postprandial blood sugar level. In addition, age is age, PRb is pre-meal pulse rate PR, AIb is pre-meal AI, Slb is pre-meal rising index Sl, PRa is post-prandial pulse rate PR, AIa is post-prandial AI, Sla is post-prandial rising index Sl, BLG each indicates the blood sugar level input by the subject (measured by drawing blood). The blood sugar level BLG input by the subject is a blood sugar level measured at a different timing from the estimated blood sugar level GLa. In this embodiment, the blood sugar level BLG input by the subject is the blood sugar level measured by collecting blood before meals. By using the blood sugar level BLG measured by blood sampling in the estimation formula, the accuracy of estimating the blood sugar level is improved.

Next, a flow of estimating a subject's blood sugar level using an estimation formula will be described. FIG. 10 is a flowchart for estimating a subject's postprandial blood sugar level using the estimation formula created by the flow shown in FIG. Here, a case will be described in which the subject inputs a pre-meal blood sugar level measured using a blood glucose meter from the input unit 141 of the electronic device 100.

First, the electronic device 100 inputs the age of the subject based on the operation of the input unit 141 by the subject (step S201).

Furthermore, the electronic device 100 inputs the blood glucose level before meals measured by the subject using the blood glucose meter, based on the subject's operation of the input unit 141 (step S202).

Furthermore, the electronic device 100 measures the subject's pre-meal pulse wave based on the operation by the subject (step S203).

Then, after the subject eats a meal, the electronic device 100 measures the subject's postprandial pulse wave based on the operation by the subject (step S204).

Next, the electronic device 100 analyzes the measured pulse wave (step S205). Specifically, the electronic device 100 analyzes the rise index Sl, AI, and pulse rate PR regarding the measured pulse wave, for example.

The electronic device 100 applies the pre-meal blood sugar level input in step S202, the rise index Sl, AI, and pulse rate PR analyzed in step S205, and the age of the subject to, for example, the above equation (1). Then, the subject's postprandial blood sugar level is estimated (step S206). The estimated postprandial blood sugar level is notified to the subject from, for example, the notification unit 147 of the electronic device 100.

FIG. 11 is a diagram showing a comparison between the postprandial blood sugar level estimated using the estimation formula created according to the flow of FIG. 9 and the actually measured postprandial blood sugar level. In the graph shown in FIG. 11, the horizontal axis shows the measured value (actual value) of the postprandial blood sugar level, and the vertical axis shows the estimated value of the postprandial blood sugar level. The blood glucose level was measured using a blood glucose meter Medisafefit manufactured by Terumo Corporation. As shown in FIG. 11, the measured value and the estimated value are generally within a range of ±20%. That is, it can be said that the estimation accuracy using the estimation formula is within 20%.

In this way, the electronic device 100 can noninvasively and quickly estimate the postprandial blood glucose level based on the preprandial blood glucose level measured by collecting blood from the subject. In this embodiment, the estimation formula was created using the blood sugar level and pulse wave before and after a meal, but the creation of the estimation formula is not limited to this. You can also create an expression. Further, the electronic device 100 may estimate the blood sugar level of the subject at any timing, not only the postprandial blood sugar level. The electronic device 100 can non-invasively and quickly estimate the blood sugar level at any timing.

Electronic device 100 according to the present embodiment updates the estimation formula stored in storage unit 145 based on the subject's pre-meal blood sugar level and pulse wave obtained in step S202 and step S203 in estimating blood sugar level. You may. That is, the electronic device 100 can use the pre-meal blood sugar level and pulse wave acquired when estimating the blood sugar level as sample data for updating the estimation formula. Thereby, the estimation formula is updated every time the subject estimates the blood sugar level, and the accuracy of estimating the postprandial blood sugar level using the estimation formula increases.

(Second embodiment)
In the first embodiment, a case has been described in which an estimation formula is created based on the subject's pre-prandial and post-prandial blood sugar levels and pulse waves. In the second embodiment, an example will be described in which the estimation formula is created based on the subject's own pre-meal and post-meal blood sugar levels and pulse waves.

FIG. 12 is a flowchart for creating an estimation formula used by electronic device 100 according to the present embodiment. In this embodiment, the estimation formula will be described as being created by electronic device 100. Note that the estimation equation may be created by an estimation equation creation device different from the electronic device 100, as described in the first embodiment.

First, the electronic device 100 inputs a pre-meal blood sugar level measured by the subject using a blood glucose meter, based on the subject's operation of the input unit 141 (step S301).

Further, the electronic device 100 measures the subject's pre-meal pulse wave based on the operation by the subject (step S302).

After the subject eats a meal, the electronic device 100 inputs the postprandial blood sugar level measured by the subject using the blood glucose meter based on the subject's operation of the input unit 141 (step S303). ). The blood glucose level input in step S301 and step S303 is measured by a blood glucose meter, for example, when the subject draws blood.

Further, the electronic device 100 measures the subject's postprandial pulse wave based on the operation by the subject (step S304).

The electronic device 100 determines whether the number of samples of the sample data input in steps S301 to S304 is equal to or greater than N, which is sufficient for performing regression analysis (step S305). The number of samples N can be determined as appropriate, and can be set to 5, for example. If the estimation formula creation device determines that the number of samples is less than N (in the case of No), it repeats steps S301 to S304 until the number of samples becomes N or more. On the other hand, when the estimation formula creation device determines that the number of samples is equal to or greater than N (in the case of Yes), the process proceeds to step S306 and calculates the estimation formula.

The method of calculating the estimation formula in steps S306 to S308 is the same as that in steps S104 to S106 in FIG. 9, so detailed explanation thereof will be omitted here. The estimation equation created by electronic device 100 according to the flow shown in FIG. 12 is, for example, equation (1) with different coefficients.

Next, a flow of estimating a subject's blood sugar level using an estimation formula will be described. FIG. 13 is a flowchart for estimating the postprandial blood sugar level of the subject using the estimation formula created according to the flow of FIG. 12. Here, a case will be described in which the subject inputs a blood sugar level measured using a blood glucose meter from the input unit 141 of the electronic device 100.

First, the electronic device 100 inputs the age of the subject based on the operation of the input unit 141 by the subject (step S401).

Furthermore, the electronic device 100 inputs the blood glucose level before meals measured by the subject using the blood glucose meter, based on the subject's operation of the input unit 141 (step S402).

Furthermore, the electronic device 100 measures the subject's pre-meal pulse wave based on the operation by the subject (step S403).

Then, after the subject eats a meal, the electronic device 100 measures the subject's postprandial pulse wave based on the operation by the subject (step S404).

Next, the electronic device 100 analyzes the measured pulse wave (step S405). Specifically, the electronic device 100 analyzes the rise index Sl, AI, and pulse rate PR regarding the measured pulse wave, for example.

The electronic device 100 applies the rise index Sl, AI, and pulse rate PR analyzed in step S405 and the age of the subject to the estimation formula created in the flow diagram of FIG. The blood sugar level is estimated (step S406). The estimated postprandial blood sugar level is notified to the subject from, for example, the notification unit 147 of the electronic device 100.

In this way, the electronic device 100 can noninvasively and quickly estimate the postprandial blood glucose level based on the preprandial blood glucose level measured by collecting blood from the subject. In this embodiment, the estimation formula for estimating the postprandial blood sugar level is created based on sample data obtained from the subject, so the accuracy of estimating the postprandial blood sugar level of the subject is improved. do.

As described in the first embodiment, the electronic device 100 according to the present embodiment also uses the premeal blood sugar level and pulse wave of the subject acquired in step S402 and step S403 in estimating the blood sugar level. Based on this, the estimation formula stored in the storage unit 145 may be updated. Thereby, the estimation formula is updated every time the subject estimates the blood sugar level, and the accuracy of estimating the postprandial blood sugar level using the estimation formula increases.

Furthermore, if a sufficient number of sample data can be collected from the subject, the electronic device 100 calculates the blood glucose level based on the subject's pulse wave, without using the blood glucose level measured by blood sampling. may be estimated. For example, the electronic device 100 estimates the subject's pre-meal blood sugar level based on the subject's pre-meal pulse wave. In this way, when the subject measures the pre-meal pulse wave using the electronic device 100, the electronic device 100 uses the estimation formula based on the pre-meal pulse wave to estimate the subject's pre-meal blood sugar level. value can be estimated. In this case, the electronic device 100 can non-invasively and quickly estimate the blood sugar level before a meal. Note that sufficient sample data refers to an amount of data that is sufficient to create an estimation formula that can estimate the premeal blood glucose level of the subject with a predetermined accuracy or higher based on the premeal pulse wave. Furthermore, the blood sugar level to be estimated is not limited to the one before a meal, but the blood sugar level after a meal may be estimated based on the pulse wave after a meal. Furthermore, the blood sugar level to be estimated is not limited to before and after a meal, and the blood sugar level at any timing may be estimated based on a pulse wave measured at any timing.

(Third embodiment)
In the first embodiment, a case has been described in which the electronic device 100 estimates the postprandial blood sugar level of the subject. In the third embodiment, an example will be described in which the electronic device 100 estimates a postprandial lipid value of a subject. Here, the lipid value includes neutral fat, total cholesterol, HDL cholesterol, LDL cholesterol, and the like. In the description of this embodiment, the description of the same points as in the first embodiment will be omitted as appropriate.

The electronic device 100 stores in advance, for example, an estimation formula for estimating a lipid value based on a pulse wave in the storage unit 145. Electronic device 100 estimates lipid values using these estimation formulas.

The estimation theory for estimating lipid values based on pulse waves is the same as the estimation theory for blood sugar levels described in the first embodiment. That is, changes in blood lipid levels are also reflected in changes in the waveform of the pulse wave. Therefore, the electronic device 100 can acquire a pulse wave and estimate a lipid value based on a change in the acquired pulse wave. By inputting the blood sugar level together with the pulse wave at the time of lipid estimation, the electronic device 100 improves the estimation accuracy of the lipid value.

FIG. 14 is a flowchart for creating an estimation formula used by electronic device 100 according to the present embodiment. Also in this embodiment, the estimation formula is created by performing regression analysis based on sample data. In this embodiment, an estimation formula is created based on a pre-meal pulse wave, lipid value, and blood sugar level as sample data. In this embodiment, "before a meal" refers to when the subject is on an empty stomach. In addition, "postprandial" refers to the time when the lipid value becomes high after a predetermined time after the meal (for example, about 3 hours after starting the meal). In creating the estimation formula, in particular, by performing regression analysis and creating the estimation formula using sample data in which the variation in lipid values is close to a normal distribution, it is possible to calculate the Lipid values can be estimated at any time.

In creating the estimation formula, first, information about the subject's blood sugar level before meals, as well as pulse waves and lipid values associated with the blood sugar level, measured by a blood glucose meter, pulse wave meter, and lipid measuring device, respectively, is used to create the estimation formula. The information is input to the device (step S501).

In addition, information regarding the subject's postprandial blood sugar level, as well as pulse waves and lipid values associated with the blood sugar level, which are measured by a blood glucose meter, a pulse wave meter, and a lipid measuring device, respectively, are input into the estimation formula creation device ( Step S502). The blood sugar level input in step S501 and step S502 is measured by a blood glucose meter, for example, by collecting blood. Furthermore, in step S501 or step S502, the age of the subject of each sample data is also input.

The estimation formula creation device determines whether the number of samples of the sample data input in step S501 and step S502 is equal to or greater than N, which is sufficient for performing regression analysis (step S503). The number of samples N can be determined as appropriate, and can be set to 100, for example. If the estimation formula creation device determines that the number of samples is less than N (in the case of No), it repeats step S501 and step S502 until the number of samples becomes N or more. On the other hand, when the estimation formula creation device determines that the number of samples is equal to or greater than N (in the case of Yes), the process proceeds to step S504 and calculates the estimation formula.

In calculating the estimation equation, the estimation equation creation device analyzes the input pre-prandial and post-prandial pulse waves (step S504). In this embodiment, the estimation formula creation device analyzes the pulse wave rise index Sl, AI, and pulse rate PR before and after meals. Note that the estimation formula creation device may perform FFT analysis as the analysis of the pulse wave.

The estimation formula creation device then performs regression analysis (step S505). The objective variable in the regression analysis is the postprandial lipid value. Furthermore, explanatory variables in the regression analysis are the age input in step S501 or step S502, and the pre-prandial and post-prandial pulse wave rise indicators Sl, AI, and pulse rate PR analyzed in step S504. Note that when the estimation formula creation device performs FFT analysis in step S504, the explanatory variables may be, for example, Fourier coefficients calculated as a result of the FFT analysis.

The estimation formula creation device creates an estimation formula for estimating the postprandial lipid value based on the results of the regression analysis (step S506).

Next, a flow of estimating a subject's lipid value using an estimation formula will be described. FIG. 15 is a flowchart for estimating the subject's postprandial lipid value using the estimation formula created according to the flow of FIG. 14. Here, a case will be described in which the subject inputs a blood sugar level measured using a blood glucose meter from the input unit 141 of the electronic device 100.

First, the electronic device 100 inputs the age of the subject based on the operation of the input unit 141 by the subject (step S601).

Furthermore, the electronic device 100 inputs the blood glucose level before meals measured by the subject using the blood glucose meter, based on the subject's operation of the input unit 141 (step S602).

Furthermore, the electronic device 100 measures the subject's pre-meal pulse wave based on the operation by the subject (step S603).

Then, after the subject has eaten a meal, the electronic device 100 inputs the postprandial blood sugar level measured by the subject using the blood glucose meter based on the operation by the subject (step S604).

Furthermore, the electronic device 100 measures the subject's postprandial pulse wave based on the operation by the subject (step S605).

Next, the electronic device 100 analyzes the measured pulse wave (step S606). Specifically, the electronic device 100 analyzes the rise index Sl, AI, and pulse rate PR regarding the measured pulse wave, for example.

The electronic device 100 applies the rise index Sl, AI, and pulse rate PR analyzed in step S606 and the age of the subject to the estimation formula created in the flow diagram of FIG. A lipid value is estimated (step S607). The estimated postprandial lipid value is notified to the subject from the notification unit 147 of the electronic device 100, for example.

FIG. 16 is a diagram showing a comparison between the postprandial lipid value estimated using the estimation formula created by the flow of FIG. 14 and the actually measured postprandial lipid value. In the graph shown in FIG. 16, the horizontal axis shows the measured value (actual value) of the postprandial lipid value, and the vertical axis shows the estimated value of the postprandial lipid value. Note that the lipid values were measured using Cobas b101 manufactured by Roche Diagnostics. As shown in FIG. 16, the measured value and the estimated value are generally within a range of ±20%. That is, it can be said that the estimation accuracy using the estimation formula is within 20%.

In this way, the electronic device 100 can estimate the postprandial lipid level based on the preprandial and postprandial blood sugar levels measured by collecting blood from the subject.

Furthermore, the electronic device 100 estimates the lipid value using the blood sugar levels before and after meals. Therefore, the electronic device 100 can estimate the lipid value by correcting (removing) the influence of the blood sugar level on the pulse wave after a meal. Thereby, according to the electronic device 100, the accuracy of estimating lipid values is improved.

In this embodiment, the estimation formula was created using pre-meal and post-prandial blood sugar levels, pulse waves, and lipid values, but the creation of the estimation formula is not limited to this. , an estimation formula may be created using lipid values. Furthermore, the electronic device 100 may estimate the subject's lipid value at any timing, not only the postprandial lipid value. The electronic device 100 can noninvasively and quickly estimate lipid values at any timing.

As described in the first embodiment, the electronic device 100 according to the present embodiment also uses the premeal blood sugar level and pulse wave of the subject acquired in steps S602 to S605 in estimating the lipid value. The estimation formula stored in the storage unit 145 may be updated based on the postprandial blood sugar level and pulse wave. Thereby, the estimation formula is updated every time the subject estimates the blood sugar level, and the accuracy of estimating the postprandial lipid value using the estimation formula increases.

Note that in the first and second embodiments described above, when estimating a postprandial blood sugar level using the electronic device 100, the subject uses the electronic device 100 to estimate the blood sugar level before a meal measured using a blood glucose meter. An example of input using the input unit 141 has been described. However, the blood sugar level before a meal may be automatically input into the electronic device 100 from, for example, a blood glucose meter.

FIG. 17 is a diagram schematically showing communication between electronic device 100 and blood glucose meter 160. The blood glucose meter 160 includes a communication section and is capable of transmitting and receiving information via the communication section 146 of the electronic device 100. For example, when the blood glucose meter 160 measures the blood sugar level (blood sugar level before a meal) based on the operation of the subject, it transmits the blood sugar level as the measurement result to the electronic device 100. The electronic device 100 estimates the subject's postprandial blood glucose level using the blood glucose level acquired from the blood glucose meter 160, for example, according to the flow shown in FIG. 10 or 13.

Note that, similarly in the case of the third embodiment, the electronic device 100 may acquire the blood glucose level from the communicable blood glucose meter 160. In this case, the electronic device 100 can estimate the lipid value based on the blood sugar level obtained from the blood glucose meter 160.

Further, in the above embodiment, an example in which the electronic device 100 estimates the blood sugar level and the lipid value has been described, but the estimation of the blood sugar level and the lipid value does not necessarily have to be executed by the electronic device 100. . An example in which a device other than the electronic device 100 executes estimation of blood sugar levels and lipid values will be described.

FIG. 18 is a schematic diagram showing a schematic configuration of a system according to an embodiment of the present invention. The system according to the embodiment shown in FIG. 18 includes an electronic device 100, a server 151, a mobile terminal 150, and a communication network. As shown in FIG. 18, the pulse wave measured by the electronic device 100 is transmitted to the server 151 through the communication network, and is stored in the server 151 as the subject's personal information. The server 151 estimates the blood sugar level or lipid level of the subject by comparing the information obtained in the past with the subject and various databases. The server 151 may further create optimal advice for the subject. The server 151 returns the estimation results and advice to the mobile terminal 150 owned by the subject. It is possible to construct a system in which the mobile terminal 150 notifies the received estimation results and advice from the display section of the mobile terminal 150. By using the communication function of the electronic device 100, the server 151 can collect information from multiple users, which further improves the accuracy of estimation. Furthermore, since the mobile terminal 150 is used as a notification means, the electronic device 100 does not require the notification section 147, and is further miniaturized. Furthermore, since the blood sugar level or lipid level of the subject is estimated by the server 151, the calculation load on the control unit 143 of the electronic device 100 can be reduced. Furthermore, since past acquired information of the subject can be stored in the server 151, the burden on the storage unit 145 of the electronic device 100 can be reduced. Therefore, the electronic device 100 can be further downsized and simplified. In addition, the processing speed of calculations is also improved.

Although the system according to the present embodiment has a configuration in which electronic device 100 and mobile terminal 150 are connected via a communication network via server 151, the system according to the present invention is not limited to this. The electronic device 100 and the mobile terminal 150 may be directly connected via a communication network without using the server 151.

Specific embodiments have been described in order to provide a thorough and clear disclosure of the invention. However, the appended claims should not be limited to the above-mentioned embodiments, but include all modifications and alternatives that can be created by a person skilled in the art within the scope of the basic matters presented herein. It should be configured to embody the possible configurations.

For example, in the above-described embodiment, a case has been described in which the sensor section 130 includes the angular velocity sensor 131, but the electronic device 100 according to the present invention is not limited to this. The sensor section 130 may include an optical pulse wave sensor consisting of a light emitting section and a light receiving section, or may include a pressure sensor. Furthermore, the electronic device 100 is not limited to being worn on the wrist. The sensor section 130 may be placed on an artery such as the neck, ankle, thigh, or ear.

100 Electronic device 110 Mounting part 120 Measuring part 120a Back surface 120b Surface 111 Opening part 130 Sensor part 131 Angular velocity sensor 132 Pulse contact part 133 Support part 140 Elastic body 141 Input part 143 Control part 144 Power supply part 145 Storage part 146 Communication part 147 Information department 150 Mobile terminal 151 Server 160 Blood sugar meter

Claims (9)

A control unit that estimates the blood sugar level or sugar metabolism of the subject based on the blood sugar level and the pulse wave of the subject and an estimation formula created based on the blood sugar level and the pulse wave associated with the blood sugar level. Prepare,
The control unit may include, as the estimation formula ,
an index AI expressed as a ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave;
Fourier coefficients derived by fast Fourier transform of the pulse wave;
An estimation device that uses something created based on the results of regression analysis using . The estimation formula further uses a rising index that is the ratio of the first minimum value to the first maximum value in the accelerated pulse wave derived by differentiating the pulse wave consisting of the forward wave and the reflected wave of the forward wave. , the estimation device according to claim 1. The estimation device according to claim 1 or 2, wherein the estimation formula further uses a cycle of the pulse wave or a pulse rate derived based on the cycle. A control unit that estimates the lipid value or lipid metabolism of the subject based on the estimation formula created based on the lipid value and the pulse wave associated with the lipid value and the pulse wave of the subject. Prepare,
The control unit may include, as the estimation formula ,
an index AI expressed as a ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave;
Fourier coefficients derived by fast Fourier transform of the pulse wave;
An estimation device that uses something created based on the results of regression analysis using . a blood glucose meter that measures the blood sugar level of the subject;
The estimation device according to any one of claims 1 to 4 ,
An estimation system comprising: An estimation step of estimating the blood sugar level or sugar metabolism of the subject based on the blood sugar level and the pulse wave of the subject and an estimation formula created based on the blood sugar level and the pulse wave associated with the blood sugar level. Prepare,
In the estimation step, as the estimation formula ,
an index AI expressed as a ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave;
Fourier coefficients derived by fast Fourier transform of the pulse wave;
An estimation method that uses a method created based on the results of regression analysis using An estimation step of estimating the lipid value or lipid metabolism of the subject based on the estimation formula created based on the lipid value and the pulse wave associated with the lipid value and the pulse wave of the subject. Prepare,
In the estimation step, as the estimation formula ,
an index AI expressed as a ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave;
Fourier coefficients derived by fast Fourier transform of the pulse wave;
An estimation method that uses a method created based on the results of regression analysis using . computer,
A control unit that estimates the blood sugar level or sugar metabolism of the subject based on the blood sugar level and the pulse wave of the subject and an estimation formula created based on the blood sugar level and the pulse wave associated with the blood sugar level. An estimation program that functions,
The control unit may include, as the estimation formula ,
an index AI expressed as a ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave;
Fourier coefficients derived by fast Fourier transform of the pulse wave;
An estimation program created based on the results of regression analysis using . computer,
A control unit that estimates the lipid value or lipid metabolism of the subject based on the pulse wave of the subject and an estimation formula created based on the lipid value and the pulse wave associated with the lipid value. An estimation program that functions,
The control unit may include, as the estimation formula ,
an index AI expressed as a ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave;
Fourier coefficients derived by fast Fourier transform of the pulse wave;
An estimation program created based on the results of regression analysis using .

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