Electronic Devices and Systems

Abstract

To provide an electronic apparatus that can estimate a health status of a subject noninvasively and readily, and to provide a system.SOLUTION: An electronic apparatus includes: a sensor part including at least one sensor and acquiring a pulse wave in one of the parts to be examined; and a control unit for estimating saccharometabolism from an index based on a pulse wave acquired by the at least one sensor of the sensor part. The control unit estimates saccharometabolism on the basis of an index relating to a pulse wave acquired at first timing and an index relating to a pulse wave acquired at second timing after the first timing.SELECTED DRAWING: Figure 10

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to Japanese Patent Application No. 2015-091577 (filed April 28, 2015), the entire disclosure of which is incorporated herein by reference. [Technical field]

[0002] The present invention relates to an electronic device and a system for estimating a health condition of a subject from measured biological information. [Background technology]

[0003] Conventionally, blood components and blood fluidity have been measured as a means for estimating the health condition of a subject (user). These measurements are performed using blood drawn from the subject. Electronic devices that measure biological information from a test site such as the subject's wrist are also known. For example, Patent Document 1 describes an electronic device that measures the subject's pulse by being worn on the subject's wrist. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2002-360530 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, drawing blood is painful, making it difficult to estimate one's own health condition on a daily basis. In addition, the electronic device described in Patent Document 1 only measures the pulse rate, and cannot estimate the subject's health condition other than the pulse rate.

[0006] In view of the above circumstances, an object of the present invention is to provide an electronic device and a system that can estimate the health condition of a subject in a non-invasive and simple manner. [Means for solving the problem]

[0007] In order to solve the above problems, one embodiment of the present invention system teeth, multiple a sensor unit having a sensor for acquiring a pulse wave at a test site; The sensor portion multiple and a control unit that estimates glucose metabolism from an index based on the pulse wave acquired by the sensor. The control unit estimates the glucose metabolism based on an index related to a pulse wave acquired at a first timing and an index related to a pulse wave acquired at a second timing that is after the first timing.

[0008] A system according to one embodiment includes: The device includes a first electronic device and a second electronic device. The first electronic device is multiple a sensor unit having a sensor for acquiring a pulse wave at a test site; A communication unit that communicates with the second electronic device; The sensor portion multiple A control unit that transmits information about the pulse wave acquired by the sensor from the communication unit to the second electronic device so that the second electronic device can estimate glucose metabolism; Equipped with. The second electronic device receives the multiple The information acquired by the sensor is received, and the glucose metabolism is estimated based on an index related to the pulse wave acquired at a first timing and an index related to the pulse wave acquired at a second timing that is after the first timing.

[0009] According to one embodiment system teeth, multiple a sensor unit having a sensor for acquiring a pulse wave at a test site; The sensor portion multiple and a control unit that estimates lipid metabolism from an index based on the pulse wave acquired by the sensor. The control unit estimates the lipid metabolism based on an index related to a pulse wave acquired at a first timing and an index related to a pulse wave acquired at a second timing that is after the first timing.

[0010] A system according to one embodiment includes: The device includes a first electronic device and a second electronic device. The first electronic device is multiple a sensor unit having a sensor for acquiring a pulse wave at a test site; A communication unit that communicates with the second electronic device; The sensor portion multiple A control unit that transmits information about the pulse wave acquired by the sensor from the communication unit to the second electronic device so that the lipid metabolism can be estimated in the second electronic device; Equipped with. The second electronic device receives the multiple The lipid metabolism is estimated based on information acquired by the sensor and an index related to the pulse wave acquired at a first timing and an index related to the pulse wave acquired at a second timing after the first timing. Effect of the Invention

[0011] According to the present invention, it is possible to provide an electronic device and a system capable of estimating the health condition of a subject in a non-invasive and simple manner. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a schematic diagram showing a schematic configuration of an electronic device according to a first embodiment of the present invention. [Diagram 2] 2 is a cross-sectional view showing a schematic configuration of a main body part of FIG. 1. [Diagram 3] FIG. 2 is a diagram showing an example of a state in which the electronic device in FIG. 1 is used. [Figure 4] 2 is a functional block diagram showing a schematic configuration of the electronic device of FIG. 1. [Diagram 5] FIG. 4 is a diagram showing an example of a pulse wave acquired by a sensor unit. [Figure 6] FIG. 13 is a diagram showing the time variation of the calculated AI. [Figure 7] FIG. 13 is a diagram showing the calculated AI and blood glucose measurement results. [Figure 8] FIG. 13 is a diagram showing the relationship between the calculated AI and blood glucose level. [Figure 9] FIG. 13 is a diagram showing the calculated AI and the measurement results of lipid levels. [Figure 10] FIG. 1 is a flow chart showing a procedure for estimating blood fluidity and the state of glucose metabolism or lipid metabolism based on AI. [Figure 11] 13A to 13C are diagrams illustrating an example of an estimation method based on a change in a pulse wave in an electronic device according to a second embodiment of the present invention. [Figure 12] FIG. 4 is a diagram showing an example of an acceleration pulse wave. [Figure 13A] 13A to 13C are diagrams illustrating pulse waves for explaining another example of an estimation method based on changes in pulse waves in an electronic device according to a second embodiment of the present invention. [Figure 13B] FIG. 13B is a diagram showing the result of FFT of the pulse wave in FIG. 13A. [Figure 14] FIG. 11 is a flow chart for creating an estimation equation used by an electronic device according to a second embodiment of the present invention. [Figure 15] FIG. 15 is a flow diagram for estimating the blood glucose level of a subject using the estimation equation created by the flow of FIG. 14. [Figure 16] FIG. 15 is a diagram showing a comparison between blood glucose levels estimated using the estimation equation created by the flow of FIG. 14 and actually measured blood glucose levels. [Figure 17] FIG. 11 is a flow chart for creating an estimation equation used by an electronic device according to a third embodiment of the present invention. [Figure 18] FIG. 18 is a flow diagram for estimating a blood glucose level of a subject using the estimation equation created by the flow of FIG. 17. [Figure 19] FIG. 18 is a diagram showing a comparison between blood glucose levels estimated using the estimation equation created by the flow of FIG. 17 and actually measured blood glucose levels. [Figure 20] FIG. 13 is a flow chart for creating an estimation equation used by an electronic device according to a fourth embodiment of the present invention. [Figure 21]FIG. 21 is a flow diagram for estimating lipid levels of a subject using the estimation equation created by the flow of FIG. 20. [Figure 22] FIG. 21 is a diagram showing a comparison between lipid values ​​estimated using the estimation equation created by the flow of FIG. 20 and actually measured lipid values. [Diagram 23] 1 is a schematic diagram showing a schematic configuration of a system according to an embodiment of the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0014] (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 unit 110 and a measurement unit 120. Fig. 1 is a view of electronic device 100 observed from a back surface 120a that comes into contact with a test portion.

[0015] 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 pulse wave of the subject that can be measured by the measurement unit 120. In the present embodiment, the electronic device 100 will be described below as being worn on the wrist of the subject and acquiring the pulse wave, as an example.

[0016] In this embodiment, the wearing unit 110 is a straight, elongated band. The pulse wave is measured, for example, with the subject wearing the wearing unit 110 of the electronic device 100 around his / her wrist. Specifically, the subject wraps the wearing unit 110 around his / her wrist so that the back surface 120a of the measuring unit 120 comes into contact with the part to be measured, and measures the pulse wave. The electronic device 100 measures the pulse wave of blood flowing through the ulnar artery or radial artery at the subject's wrist.

[0017] Fig. 2 is a cross-sectional view showing a schematic configuration of the measurement unit 120 in Fig. 1. In Fig. 2, the attachment unit 110 around the measurement unit 120 is also shown in addition to the measurement unit 120.

[0018] The measuring unit 120 has a back surface 120a that contacts the subject's wrist when worn, and a front surface 120b opposite to the back surface 120a. The measuring unit 120 has an opening 111 on the back surface 120a side. The sensor unit 130 is supported by the measuring unit 120 with one end protruding from the opening 111 to the back surface 120a side when the elastic body 140 is not pressed. One end of the sensor unit 130 is provided with a pulse contact part 132. One end of the sensor unit 130 is displaceable in a direction approximately perpendicular to the plane of the back surface 120a. The other end of the sensor unit 130 is supported by the measuring unit 120 by a support part 133 so that one end of the sensor unit 130 is displaceable.

[0019] One end of the sensor unit 130 is in contact with the measurement unit 120 via the elastic body 140 and is displaceable. The elastic body 140 is, for example, a spring. However, the elastic body 140 is not limited to a spring and can be any other elastic body, for example, a resin, a sponge, or the like.

[0020] Although not shown, the measurement unit 120 may also include a control unit, a storage unit, a communication unit, a power supply unit, a notification unit, circuits for operating these units, connecting cables, and the like.

[0021] The sensor unit 130 includes an angular velocity sensor 131 that detects the displacement of the sensor unit 130. The angular velocity sensor 131 may be capable of detecting the angular displacement of the sensor unit 130. The sensor included in the sensor unit 130 is not limited to the angular velocity sensor 131, and may be, for example, an acceleration sensor, an angle sensor, or another motion sensor, or may include a plurality of these sensors.

[0022] 3 is a diagram showing an example of a state in which the electronic device 100 is used by a subject. The subject uses the electronic device 100 by wrapping it around the wrist. The electronic device 100 is worn with the back surface 120a of the measuring unit 120 in contact with the part to be measured. With the wearing unit 110 wrapped around the wrist, the position of the measuring unit 120 can be adjusted so that the pulse contact unit 132 comes into contact with the position where the ulnar artery or radial artery is present.

[0023] In FIG. 3, when the electronic device 100 is worn, one end of the sensor unit 130 is in contact with the skin on the radial artery, which is an artery on the thumb side of the subject's left hand. One end of the sensor unit 130 is in contact with the skin on the radial artery of the subject due to the elastic force of the elastic body 140 arranged between the measurement unit 120 and the sensor unit 130. The sensor unit 130 is displaced according to the movement of the radial artery of the subject, i.e., pulsation. The angular velocity sensor 131 detects the displacement of the sensor unit 130 to obtain a pulse wave. The pulse wave is a waveform obtained by capturing the volume change over time of a blood vessel caused by the inflow of blood from the body surface.

[0024] Referring again to FIG. 2, the sensor unit 130 has one end protruding 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 unit 130 is in contact with the skin on the subject's radial artery, and the elastic body 140 expands and contracts in response to pulsation, displacing the one end of the sensor unit 130. The elastic body 140 has an appropriate elastic modulus so as not to impede pulsation and to expand and contract in response to pulsation. The opening width W of the opening 111 is sufficiently larger than the blood vessel diameter, which is the radial artery diameter in this embodiment. By providing the opening 111 in the measurement unit 120, the rear surface 120a of the measurement unit 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, improving the accuracy of the measurement.

[0025] 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, but the present invention is not limited to this. For example, the electronic device 100 may acquire a pulse wave of blood flowing through the carotid artery in the subject's neck. Specifically, the subject may measure the pulse wave by lightly pressing the pulse application unit 132 against the position of the carotid artery. The subject may also wear the attachment unit 110 by wrapping it around the neck so that the pulse application unit 132 is located at the position of the carotid artery.

[0026] 4 is a functional block diagram showing a schematic configuration of electronic device 100. Electronic device 100 includes sensor unit 130, control unit 143, power supply unit 144, storage unit 145, communication unit 146, and notification unit 147. In this embodiment, control unit 143, power supply unit 144, storage unit 145, communication unit 146, and notification unit 147 are included inside measurement unit 120 or wearing unit 110.

[0027] The sensor unit 130 includes an angular velocity sensor 131, and detects pulsation from the subject site to obtain a pulse wave.

[0028] 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. The control unit 143 is also a processor that calculates an index based on the propagation phenomenon of the pulse wave 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 calculates an index based on the propagation phenomenon of the pulse wave, and such a program is stored in a storage medium such as the storage unit 145. The control unit 143 also estimates a state related to the glucose metabolism or lipid metabolism of the subject based on the calculated index. The control unit 143 notifies the notification unit 147 of data.

[0029] 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 .

[0030] 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. The storage unit 145 may include a plurality of 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 reader. 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 and programs for operating the electronic device 100, and also functions as a work memory. The storage unit 145 may store, for example, a measurement result of a pulse wave acquired by the sensor unit 130.

[0031] 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 biological information of a subject to manage the health condition, and transmits the measurement results of the pulse wave measured by the electronic device 100 and the health condition estimated by the electronic device 100 to the external device.

[0032] The notification unit 147 notifies information by sound, vibration, image, etc. The notification unit 147 may include a speaker, a vibrator, and a display device such as a liquid crystal display (LCD), an organic electro-luminescence display (OELD), or an inorganic electro-luminescence display (IELD). In the present embodiment, the notification unit 147 notifies, for example, the state of glucose metabolism or lipid metabolism of the subject.

[0033] Fig. 5 is a diagram showing an example of a pulse wave acquired at the wrist using electronic device 100. Fig. 5 shows a case where angular velocity sensor 131 is used as a pulsation detection means. Fig. 5 shows the time integration of the angular velocity acquired by angular velocity sensor 131, with the horizontal axis representing time and the vertical axis representing angle. Since the acquired pulse wave may contain noise caused by, for example, the subject's body movement, correction may be performed using a filter that removes DC (Direct Current) components, and only the pulsation components may be extracted.

[0034] A method for calculating indices based on the pulse wave from the acquired pulse wave will be described with reference to FIG. 5. Pulse wave propagation is a phenomenon in which the pulsation of blood pushed out from the heart travels through the walls of arteries and blood. The pulsation of blood pushed out from the heart reaches the extremities of the limbs as a forward wave, and part of it is reflected at branching points of blood vessels, points of change in blood vessel diameter, etc., and returns as a reflected wave. Indices based on the pulse wave include, for example, the pulse wave velocity PWV (Pulse Wave Velocity) of the forward wave, the magnitude of the reflected pulse wave P R , the time difference Δt between the forward and reflected pulse waves, and AI (Augmentation Index), which is expressed as the ratio of the magnitude of the forward and reflected pulse waves.

[0035] The pulse wave shown in FIG. 5 represents n pulses of the user, where n is an integer equal to or greater than 1. The pulse wave is a composite wave in which a forward wave generated by the ejection of blood from the heart overlaps with a reflected wave generated from a branching of blood vessels or a change in the diameter of the blood vessel. In FIG. 5, the peak magnitude of the pulse wave due to the forward wave for each pulse is represented as P Fn The peak magnitude of the pulse wave due to the reflected wave for each pulse is P Rn , the minimum value of the pulse wave per pulse is P Sn In addition, in FIG. 5, the interval between the peaks of the pulse is represented as T PR As shown in Fig.

[0036] An index based on a pulse wave is a quantification of information obtained from a pulse wave. For example, PWV, which is one of the indices based on a pulse wave, is calculated based on the propagation time difference of a pulse wave measured at two test points, such as the upper arm and ankle, and the distance between the two points. Specifically, PWV is calculated by acquiring pulse waves at two points on an artery (e.g., the upper arm and ankle) in synchronization with each other, and dividing the difference in distance between the two points (L) by the pulse wave time difference (PTT) between the two points. For example, the magnitude P of the reflected wave, which is one of the indices based on a pulse wave, is calculated based on the propagation time difference of a pulse wave measured at two test points, such as the upper arm and ankle, and the distance between the two points (L) by the pulse wave time difference (PTT) between the two points. R is the peak magnitude of the reflected pulse wave P Rn You can also calculate the average of n times, Rave For example, the time difference Δt between the forward wave and the reflected wave of the pulse wave, which is one of the indices based on the pulse wave, may be calculated as follows: n You can also calculate the time difference by averaging the time difference of n times. ave For example, the AI, which is one of the indices based on the pulse wave, is calculated by dividing the magnitude of the reflected wave by the magnitude of the forward wave, and is expressed as AI n =(P Rn -P Sn ) / (P Fn -P Sn ) where AIn is the AI ​​for each pulse. For example, AI is calculated by measuring the pulse wave for several seconds and calculating the AI ​​for each pulse. n (n=integer from 1 to n) average value AI ave may be calculated and used as an index based on the pulse wave.

[0037] Pulse wave velocity PWV, reflected wave magnitude P R The time difference Δt between the forward wave and the reflected wave, and AI, change depending on the hardness of the vascular wall, and can therefore be used to estimate the state of arteriosclerosis. For example, if the vascular wall is hard, the pulse wave velocity PWV increases. For example, if the vascular wall is hard, the magnitude of the reflected wave P Rwill be larger. For example, if the blood vessel wall is stiff, the time difference Δt between the forward wave and the reflected wave will be smaller. For example, if the blood vessel wall is stiff, AI will be larger. Furthermore, the electronic device 100 can estimate the state of arteriosclerosis and can estimate the fluidity (viscosity) of blood using these indices based on the pulse wave. In particular, the electronic device 100 can estimate changes in blood fluidity from changes in indices based on the pulse wave acquired at the same test site of the same test subject and during a period in which the state of arteriosclerosis remains almost unchanged (for example, within a few days). Here, blood fluidity refers to the ease with which blood flows, and for example, if blood fluidity is low, the pulse wave velocity PWV will be small. For example, if blood fluidity is low, the magnitude of the reflected wave P R For example, if blood fluidity is low, the time difference Δt between the forward wave and the reflected wave will be large. For example, if blood fluidity is low, AI will be small.

[0038] In the present embodiment, the electronic device 100 uses, as an example of an index based on a pulse wave, the pulse wave velocity PWV, the reflected wave magnitude P R Although an example has been shown in which the time difference Δt between the forward wave and the reflected wave, and AI are calculated, the index based on the pulse wave is not limited to this. For example, the electronic device 100 may use the posterior systolic blood pressure as the index based on the pulse wave.

[0039] 6 is a diagram showing the time variation of the calculated AI. In this embodiment, the pulse wave is acquired for about 5 seconds using electronic device 100 equipped with angular velocity sensor 131. Control unit 143 calculates the AI ​​for each pulse from the acquired pulse wave, and further calculates the average value AI ave In this embodiment, the electronic device 100 acquires pulse waves at multiple timings before and after a meal, and calculates an average AI (hereinafter referred to as AI) as an example of an index based on the acquired pulse waves. The horizontal axis of FIG. 6 indicates the passage of time, with the first measurement time after a meal set as 0. The vertical axis of FIG. 6 indicates the AI ​​calculated from the pulse waves acquired at that time. The subject was at rest, and the pulse waves were acquired on the radial artery.

[0040] The electronic device 100 acquired pulse waves before a meal, immediately after a meal, and every 30 minutes after a meal, and calculated multiple AIs based on each pulse wave. The AI ​​calculated from the pulse waves acquired before a meal was about 0.8. Compared to before a meal, the AI ​​immediately after a meal was smaller, and the AI ​​reached its minimum extreme value about one hour after a meal. The AI ​​gradually increased until the measurement was terminated three hours after the meal.

[0041] The electronic device 100 can estimate the change in blood fluidity from the change in the calculated AI. For example, when red blood cells, white blood cells, and platelets in the blood clump together or become more adhesive, the blood fluidity decreases. For example, when the water content of the plasma in the blood decreases, the blood fluidity decreases. These changes in blood fluidity vary depending on the subject's health condition, such as the glycolipid state described below, heat stroke, dehydration, and hypothermia. Before the subject's health condition becomes serious, the subject can know the change in his or her blood fluidity using the electronic device 100 of this embodiment. From the change in AI before and after the meal shown in FIG. 6, it can be estimated that the blood fluidity becomes low after the meal, is lowest about one hour after the meal, and then gradually increases. The electronic device 100 may notify the state of low blood fluidity by expressing it as "thick" and the state of high blood fluidity as "smooth." For example, the electronic device 100 may determine whether the blood is "thick" or "smooth" based on the average value of the AI ​​for the actual age of the subject. The electronic device 100 may determine whether the blood is "thick" or "smooth" if the calculated AI is greater than the average value, and may determine whether the calculated AI is less than the average value. The electronic device 100 may determine whether the blood is "thick" or "smooth" based on the AI ​​before a meal. The electronic device 100 may estimate the degree of "thickness" by comparing the AI ​​after a meal with the AI ​​before a meal. The electronic device 100 may use, for example, the AI ​​before a meal, i.e., the AI ​​when fasting, as an index of the subject's vascular age (hardness of blood vessels). For example, if the electronic device 100 calculates the amount of change in the calculated AI based on the AI ​​before a meal, i.e., the AI ​​when fasting, it is possible to reduce estimation errors due to the subject's vascular age (hardness of blood vessels), and therefore to more accurately estimate changes in blood fluidity.

[0042] FIG. 7 is a diagram showing the measurement results of the calculated AI and blood glucose level. The method of acquiring the pulse wave and the method of calculating the AI ​​are the same as those in the embodiment shown in FIG. 6. The right vertical axis of FIG. 7 indicates the blood glucose level in the blood, and the left vertical axis indicates the calculated AI. The solid line in FIG. 7 indicates the AI ​​calculated from the acquired pulse wave, and the dotted line indicates the measured blood glucose level. The blood glucose level was measured immediately after acquiring the pulse wave. The blood glucose level was measured using a blood glucose meter "MediSafeFit" manufactured by Terumo Corporation. Compared with the blood glucose level before the meal, the blood glucose level immediately after the meal increased by about 20 mg / dl. The blood glucose level reached its maximum extreme value about one hour after the meal. After that, the blood glucose level gradually decreased until the end of the measurement, and about three hours after the meal, it was almost the same as the blood glucose level before the meal.

[0043] As shown in Figure 7, blood glucose levels before and after a meal are negatively correlated with the AI ​​calculated from the pulse wave. When blood glucose levels are high, the sugar in the blood causes red blood cells and platelets to clump together or become more adhesive, which can result in reduced blood fluidity. When blood fluidity is reduced, pulse wave velocity PWV can decrease. When pulse wave velocity PWV decreases, the time difference Δt between the forward wave and the reflected wave can increase. When the time difference Δt between the forward wave and the reflected wave increases, the magnitude of the forward wave P F The magnitude of the reflected wave P R can be small. The magnitude of the forward wave P F The magnitude of the reflected wave P R When becomes small, AI may become small. Since AI within several hours after a meal (3 hours in this embodiment) is correlated with blood glucose level, it is possible to estimate the fluctuation of the blood glucose level of the subject from the fluctuation of AI. Furthermore, if the blood glucose level of the subject is measured in advance and the correlation with AI is obtained, the electronic device 100 can estimate the blood glucose level of the subject from the calculated AI.

[0044] The minimum extreme value of AI first detected after a meal, AI PBased on the occurrence time of the above, the electronic device 100 can estimate the state of glucose metabolism of the subject. The electronic device 100 estimates, for example, the blood glucose level as the state of glucose metabolism. As an example of estimating the state of glucose metabolism, for example, the minimum extreme value AI of AI first detected after a meal P is detected after a predetermined time or more (for example, about 1.5 hours or more after a meal), the electronic device 100 can infer that the subject has a glucose metabolism disorder (a diabetic patient).

[0045] AI before meals B and the minimum extreme value of AI first detected after a meal, AI P Difference with AI B -AI P ) based on which the electronic device 100 can estimate the state of glucose metabolism of the subject. B -AI P ) is a predetermined value or more (for example, 0.5 or more), the subject can be estimated to have a glucose metabolism disorder (a patient with postprandial hyperglycemia).

[0046] FIG. 8 is a diagram showing the relationship between the calculated AI and the blood glucose level. The calculated AI and blood glucose level were obtained within 1 hour after a meal, when blood glucose level fluctuates greatly. The data in FIG. 8 includes data from the same subject after a number of different meals. As shown in FIG. 8, the calculated AI and blood glucose level showed a negative correlation. The correlation coefficient between the calculated AI and blood glucose level was 0.9 or more, indicating a very high correlation. For example, if the correlation between the calculated AI and blood glucose level as shown in FIG. 8 is obtained in advance for each subject, the electronic device 100 can also estimate the subject's blood glucose level from the calculated AI.

[0047] FIG. 9 is a diagram showing the calculated AI and the measurement results of triglyceride. The method of acquiring the pulse wave and the method of calculating the AI ​​are the same as those in the embodiment shown in FIG. 6. The right vertical axis of FIG. 9 indicates the triglyceride level in blood, and the left vertical axis indicates the AI. The solid line in FIG. 9 indicates the AI ​​calculated from the acquired pulse wave, and the dotted line indicates the measured triglyceride level. The triglyceride level was measured immediately after acquiring the pulse wave. The triglyceride level was measured using a lipid measuring device "Pocket Lipid" manufactured by Technomedica Co., Ltd. Compared with the triglyceride level before the meal, the maximum extreme value of the triglyceride level after the meal increased by about 30 mg / dl. About 2 hours after the meal, the triglyceride level reached the maximum extreme value. After that, the triglyceride level gradually decreased until the end of the measurement, and about 3.5 hours after the meal, it was almost the same as the triglyceride level before the meal.

[0048] In contrast, the minimum extreme value of the calculated AI was the first minimum extreme value AI, which occurred about 30 minutes after the meal. P1 was detected, and the second minimum extreme AI was detected approximately 2 hours after the meal. P2 The first minimum extreme AI was detected about 30 minutes after the meal. P1 It can be assumed that this is due to the influence of postprandial blood glucose levels mentioned above. The second minimum extreme AI was detected approximately 2 hours after the meal. P2 The time of occurrence of the maximum triglyceride peak detected about 2 hours after a meal is almost the same as that of the second minimum peak AI detected after a certain time from a meal. P2 It can be assumed that this is due to the influence of triglycerides. It was found that the triglyceride levels before and after a meal, like the blood glucose level, have a negative correlation with the AI ​​calculated from the pulse wave. In particular, the minimum extreme value AI of the AI ​​detected after a certain time (about 1.5 hours in this embodiment) after a meal P2 Since AI is correlated with the triglyceride level, the variation in the subject's triglyceride level can be estimated from the variation in AI. In addition, if the subject's triglyceride level is measured in advance and the correlation with AI is obtained, the electronic device 100 can estimate the subject's triglyceride level from the calculated AI.

[0049] The second minimum extreme value AI detected after a certain time after a meal P2Based on the occurrence time of the above, the electronic device 100 can estimate the state of lipid metabolism of the subject. The electronic device 100 estimates, for example, a lipid value as the state of lipid metabolism. As an example of the estimation of the state of lipid metabolism, for example, the second minimum extreme value AI P2 When the blood pressure is detected a predetermined time or more (for example, four hours or more) after a meal, the electronic device 100 can infer that the subject has a lipid metabolism disorder (a hyperlipidemic patient).

[0050] AI before meals B and the second minimum extreme value AI detected after a certain time after the meal. P2 Difference with AI B -AI P2 Based on this, the electronic device 100 can estimate the lipid metabolism state of the subject. B -AI P2 ) is 0.5 or more, the electronic device 100 can infer that the subject suffers from abnormal lipid metabolism (a patient with postprandial hyperlipidemia).

[0051] Moreover, from the measurement results shown in FIG. 7 to FIG. 9, the electronic device 100 of the present embodiment detects the first minimum extreme value AI 1 , which is detected earliest after a meal. P1 Based on the first minimum extreme value AI P1 The second minimum extreme value AI detected after a certain time P2 Based on the time of occurrence of the abnormality, the state of lipid metabolism of the subject can be estimated.

[0052] In this embodiment, the case of neutral fat has been described as an example of lipid metabolism estimation, but the estimation of lipid metabolism is not limited to neutral fat. The lipid values ​​estimated by the electronic device 100 include, for example, total cholesterol, good (HDL: High Density Lipoprotein) cholesterol, and bad (LDL: Low Density Lipoprotein) cholesterol. These lipid values ​​also show the same tendency as the above-mentioned case of neutral fat.

[0053] Fig. 10 is a flow diagram showing a procedure for estimating the state of blood fluidity and glucose metabolism and lipid metabolism based on AI. The flow of estimating the state of blood fluidity and glucose metabolism and lipid metabolism by electronic device 100 according to the embodiment will be described with reference to Fig. 10.

[0054] As shown in Fig. 10, the electronic device 100 acquires an AI reference value of the subject as an initial setting (step S101). The AI ​​reference value may be an average AI estimated from the age of the subject, or an AI of the subject when fasting acquired in advance. The electronic device 100 may set the AI ​​determined to be pre-meal in steps S102 to S108 as the AI ​​reference value, or may set the AI ​​calculated immediately before measuring the pulse wave as the AI ​​reference value. In this case, the electronic device 100 executes step S101 after steps S102 to S108.

[0055] Next, the electronic device 100 acquires a pulse wave (step S102). For example, the electronic device 100 determines whether or not a pulse wave acquired during a predetermined measurement time (e.g., 5 seconds) has an amplitude equal to or greater than a predetermined value. If the acquired pulse wave has an amplitude equal to or greater than the predetermined value, the process proceeds to step S103. If the acquired pulse wave does not have an amplitude equal to or greater than the predetermined value, step S102 is repeated (these steps are not shown).

[0056] The electronic device 100 calculates an AI as an index based on the pulse wave from the pulse wave acquired in step S102, and stores the AI ​​in the storage unit 145 (step S103). n (n = an integer between 1 and n) to find the average value AI ave Alternatively, the electronic device 100 may calculate the AI ​​at a specific pulse rate.

[0057] AI is, for example, the pulse rate PR, pulse pressure (P F -P S), body temperature, the temperature of the detected part, etc. It is known that there is a negative correlation between the pulse and AI, and a positive correlation between the temperature and AI. When making the correction, for example, in step S103, the electronic device 100 calculates the pulse and pulse pressure in addition to the AI. For example, the electronic device 100 may be equipped with a temperature sensor in the sensor unit 130, and may acquire the temperature of the detected part when acquiring the pulse wave in step S102. The AI ​​is corrected by substituting the acquired pulse, pulse pressure, temperature, etc. into a correction formula created in advance.

[0058] Next, the electronic device 100 compares the AI ​​reference value acquired in step S101 with the AI ​​calculated in step S103 to estimate the fluidity of the subject's blood (step S104). If the calculated AI is greater than the AI ​​reference value (YES), the blood fluidity is estimated to be high, and the electronic device 100 notifies, for example, "The blood is smooth" (step S105). If the calculated AI is not greater than the AI ​​reference value (NO), the blood fluidity is estimated to be low, and the electronic device 100 notifies, for example, "The blood is thick" (step S106).

[0059] Next, the electronic device 100 asks the subject whether or not to estimate the state of glucose metabolism and lipid metabolism (step S107). If the glucose metabolism and lipid metabolism are not estimated in step S107 (if NO), the electronic device 100 ends the process. If the glucose metabolism and lipid metabolism are estimated in step S107 (if YES), the electronic device 100 checks whether the calculated AI was acquired before or after a meal (step S108). If it is not after a meal (if it is before a meal) (if NO), the electronic device 100 returns to step S102 and acquires the next pulse wave. If it is after a meal (if YES), the electronic device 100 stores the acquisition time of the pulse wave corresponding to the calculated AI (step S109). If the pulse wave is to be acquired next (if NO in step S110), the electronic device 100 returns to step S102 and acquires the next pulse wave. If the pulse wave measurement is to be terminated (if YES in step S110), the process proceeds to step S111 and thereafter, where the electronic device 100 estimates the state of glucose metabolism and lipid metabolism of the subject.

[0060] Next, the electronic device 100 extracts the minimum extreme value and its time from the multiple AIs calculated in step S104 (step S111). For example, when the AIs shown by the solid line in FIG. 9 are calculated, the electronic device 100 extracts the first minimum extreme value AI about 30 minutes after the meal. P1 , and the second minimum extreme AI about 2 hours after the meal P2 Extract.

[0061] Next, the electronic device 100 detects the first minimum extreme value AI P1 From the time, the state of glucose metabolism of the subject is estimated (step S112). P2 From the time, the state of lipid metabolism of the subject is estimated (step S113). An example of estimating the state of glucose metabolism and lipid metabolism of the subject is the same as that shown in FIG.

[0062] Next, the electronic device 100 notifies the estimation results of steps S112 and S113 (step S114), and ends the process shown in Fig. 10. The notification unit 147 issues notifications such as "your glucose metabolism is normal", "your glucose metabolism is suspected to be abnormal", "your lipid metabolism is normal", "your lipid metabolism is suspected to be abnormal", etc. The notification unit 147 may also notify advice such as "visit a hospital", "review your diet", etc. Then, the electronic device 100 ends the process shown in Fig. 10.

[0063] In the above embodiment, the electronic device 100 can estimate the fluidity of the blood of the subject and the state of glucose metabolism and lipid metabolism from the indices based on the pulse wave. Therefore, the electronic device 100 can estimate the fluidity of the blood of the subject and the state of glucose metabolism and lipid metabolism in a non-invasive manner and in a short time.

[0064] In the above embodiment, the electronic device 100 can estimate the state of glucose metabolism and the state of lipid metabolism from the extreme values ​​of the index based on the pulse wave and the time of the extreme values. Therefore, the electronic device 100 can estimate the state of glucose metabolism and lipid metabolism of the subject non-invasively and in a short time.

[0065] In the above embodiment, the electronic device 100 can estimate the state of glucose metabolism and lipid metabolism of the subject based on, for example, an index based on a pulse wave before a meal (fasting state) as a reference. Therefore, the fluidity of blood and the state of glucose metabolism and lipid metabolism of the subject can be accurately estimated without considering blood vessel diameter, blood vessel hardness, etc., which do not change in the short term.

[0066] In the above embodiment, electronic device 100 can estimate the blood glucose level and lipid level of a subject non-invasively and in a short time by calibrating the index based on a pulse wave with the blood glucose level and lipid level.

[0067] (Second embodiment) In the first embodiment, an example is described in which the electronic device 100 estimates the glucose metabolism and lipid metabolism of a subject based on AI calculated from a pulse wave as an index based on the pulse wave. In the second embodiment, an example is described in which the electronic device 100 estimates the glucose metabolism of a subject based on an estimation equation created by regression analysis. In this embodiment, the electronic device 100 estimates a blood glucose level as the glucose metabolism of the subject. Note that the configuration of the electronic device 100 according to this embodiment is the same as that of the first embodiment, and therefore a description thereof will be omitted.

[0068] The electronic device 100 stores in advance, for example, in the storage unit 145, estimation equations for estimating a blood glucose level based on a pulse wave. The electronic device 100 estimates a blood glucose level using these estimation equations.

[0069] Here, first, an estimation theory for estimating blood glucose level based on a pulse wave will be described. After a meal, the blood glucose level in the blood increases, causing a decrease in blood fluidity (increase in viscosity), dilation of blood vessels, and an increase in circulating blood volume, and vascular dynamics and hemodynamics are determined so that these states are in equilibrium. The decrease in blood fluidity occurs, for example, due to an increase in the viscosity of plasma or a decrease in the deformability of red blood cells. In addition, vascular dilation occurs due to the secretion of insulin, the secretion of digestive hormones, an increase in body temperature, and the like. When blood vessels dilate, the pulse rate increases to suppress a decrease in blood pressure. In addition, an increase in circulating blood volume compensates for blood consumption for digestion and absorption. Changes in vascular dynamics and hemodynamics 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 a blood glucose level based on the changes in the acquired pulse wave.

[0070] Based on the above estimation theory, an estimation formula for estimating blood glucose level can be created by performing regression analysis based on sample data of pre- and post-prandial blood glucose levels and pulse waves obtained from multiple subjects. During estimation, the blood glucose level of the subject can be estimated by applying the created estimation formula to an index based on the subject's pulse wave. In particular, by performing regression analysis using sample data whose blood glucose level variation is close to a normal distribution, the blood glucose level of the subject to be tested can be estimated regardless of whether it is before or after a meal.

[0071] FIG. 11 is a diagram for explaining an example of an estimation method based on a change in a pulse wave, and shows an example of a pulse wave. An estimation formula for estimating a blood glucose level is created, for example, by a regression analysis of an index (rising index) Sl indicating the rising of a pulse wave, AI, and a pulse rate PR. The rising index Sl is derived based on a waveform shown in an area D1 of FIG. 11. Specifically, the rising index Sl is a ratio of a first minimum value to a first maximum value in an accelerated pulse wave derived by differentiating a pulse wave twice. For example, the rising index Sl is expressed by -b / a in an accelerated pulse wave shown as an example in FIG. 12. The rising index Sl becomes smaller due to a decrease in blood fluidity after a meal, the secretion of insulin, and the expansion (relaxation) of blood vessels due to a rise in body temperature. The AI ​​is derived based on a waveform shown in an area D2 of FIG. 11. The AI ​​becomes lower due to a decrease in blood fluidity after a meal, the expansion of blood vessels due to a rise in body temperature, and the like. The pulse rate PR is derived based on a period T PR The pulse rate PR increases after a meal. The blood glucose level can be estimated by an estimation formula created based on these rising indexes Sl, AI and the pulse rate PR.

[0072] Fig. 13A is a diagram showing a pulse wave illustrating another example of an estimation method based on changes in the pulse wave. Fig. 13B shows the result of FFT (Fast Fourier Transform) of the pulse wave in Fig. 13A. An estimation equation for estimating blood glucose level is created, for example, by regression analysis of fundamental wave and harmonic components (Fourier coefficients) derived by FFT. The peak value in the FFT result shown in Fig. 13B changes based on changes in the pulse wave. Therefore, the blood glucose level can be estimated by the estimation equation created based on the Fourier coefficients.

[0073] Coefficients related to the characteristics of the pulse wave used to create the estimation equation by regression analysis, such as the above-mentioned rising index Sl, AI, and pulse rate PR, as well as Fourier coefficients, are referred to as characteristic coefficients in this specification. The electronic device 100 estimates the glucose metabolism of the subject using the estimation equation based on the characteristic coefficients of the pulse wave as an index based on the pulse wave.

[0074] Here, a method for creating an estimation equation used when the electronic device 100 estimates the glucose metabolism of a subject based on the characteristic coefficients of a pulse wave will be described. Here, an example will be described in which Fourier coefficients are used as the characteristic coefficients. Note that the creation of the estimation equation 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, the device that creates the estimation equation will be referred to as an estimation equation creation device. The created estimation equation is stored in advance, for example, in the storage unit 145, before the subject estimates the blood glucose level using the electronic device 100.

[0075] Fig. 14 is a flow diagram for creating an estimation formula used by electronic device 100 according to this embodiment. The estimation formula is created by measuring the subject's pre- and post-prandial pulse waves using a pulse wave meter, and measuring the subject's pre- and post-prandial blood glucose levels using a blood glucose meter, and performing regression analysis based on sample data acquired by the measurements. Note that pre-meal here refers to the subject's fasting state, and post-meal here refers to the time when blood glucose levels rise a predetermined time after a meal (for example, about one hour after starting a meal). The definitions of pre- and post-meal are the same hereinafter in this specification. The sample data to be acquired is not limited to pre- and post-meal, and may be data from a time period when blood glucose levels fluctuate greatly.

[0076] In creating an estimation equation, first, information on the subject's pre-prandial pulse wave and blood glucose level measured by a pulse wave meter and blood glucose meter, respectively, is input to the estimation equation creation device (step S201).

[0077] Furthermore, information regarding the subject's postprandial pulse wave and blood glucose level measured by a pulse wave meter and blood glucose meter, respectively, is input to the estimation equation generating device (step S202).

[0078] The estimation formula creation device determines whether the number of samples of the sample data input in steps S201 and S202 is equal to or greater than N, which is sufficient for performing regression analysis (step S203). If the estimation formula creation device determines that the number of samples is less than N (if NO), it repeats steps S201 and S202 until the number of samples becomes equal to or greater than N. On the other hand, if the estimation formula creation device determines that the number of samples is equal to or greater than N (if YES), it proceeds to step S204 and executes calculation of the estimation formula.

[0079] In calculating the estimation equation, the estimation equation creating device performs FFT analysis on the input pre- and post-prandial pulse waves (step S204). The estimation equation creating device extracts fundamental and harmonic components, which are Fourier coefficients, through the FFT analysis.

[0080] The estimation equation generating device also calculates the pulse rate of each subject based on the input pulse wave (step S205).

[0081] Then, the estimation equation creation device executes regression analysis (step S206). The regression analysis may be any method, for example, the PLS (Partial Least Squares) method. Here, the objective variable in the regression analysis is the blood glucose level, including the pre- and post-prandial blood glucose levels. The explanatory variables in the regression analysis are calculated based on the pre- and post-prandial Fourier coefficients (fundamental and harmonic components) and pulse rate. Specifically, the estimation equation creation device calculates the explanatory variables by normalizing the fundamental and harmonic components and multiplying them by the pulse rate.

[0082] The estimation formula creating device creates an estimation formula for estimating blood glucose levels based on the results of the regression analysis (step S207). An example of an estimation formula for estimating blood glucose levels is shown in the following formulas (1) and (2).

[0083]

number

[0084] In formulas (1) and (2), GLa is the postprandial blood glucose level, and GLB is the preprandial blood glucose level. PRa is the postprandial pulse rate, and PRb is the preprandial pulse rate. Sb 1 From Sb 5 are the first to fifth order Fourier coefficients when the pulse wave before a meal is analyzed by FFT. 1 From Sa 5 are the first to fifth order Fourier coefficients obtained when the postprandial pulse rate is analyzed using FFT.

[0085] In addition, in Fig. 14, an example of calculating explanatory variables based on Fourier coefficients and pulse rate has been described, but explanatory variables are not limited to this. For example, explanatory variables may be calculated based on the subject's age, rising index Sl, AI, pulse rate PR, etc. In this case, in steps S201 and S202 of Fig. 14, the subject's age, rising index Sl, AI, pulse rate PR, etc. are input.

[0086] Next, a flow of estimating the blood glucose level of a subject using the estimation equation will be described. Fig. 15 is a flow diagram of estimating the blood glucose level of a subject using the estimation equation created by the flow of Fig. 14.

[0087] First, the electronic device 100 measures the pre-prandial pulse wave of the subject based on an operation by the subject (step S301).

[0088] After the subject has eaten a meal, the electronic device 100 measures the subject's postprandial pulse wave based on an operation by the subject (step S302).

[0089] Then, the electronic device 100 calculates the subject's pulse rates before and after the meal based on the measured pulse wave (step S303).

[0090] Furthermore, the electronic device 100 performs an FFT analysis on the subject's pulse waves before and after the meal based on the measured pulse waves (step S304). Through the FFT analysis, the electronic device 100 calculates a feature coefficient.

[0091] The electronic device 100 estimates the subject's pre- and post-prandial blood glucose levels by applying the feature coefficient calculated in step S304 to, for example, the above-mentioned formulas (1) and (2) (step S305). The estimated blood glucose levels are notified to the subject by, for example, the notification unit 147 of the electronic device 100.

[0092] In addition, in deriving the estimation formula in Fig. 14, when the explanatory variables are calculated based on the age, rising index Sl, AI, pulse rate PR, etc. of the subject, the electronic device 100 calculates the rising index Sl, AI, pulse rate PR, etc. based on the measured pulse in step S204 of the flow diagram in Fig. 14. Then, in step S305, the electronic device 100 estimates the blood glucose level of the subject using the estimation formula based on the calculated rising index Sl, AI, pulse rate PR, etc.

[0093] Fig. 16 is a diagram showing a comparison between blood glucose levels estimated using the estimation equation created by the flow of Fig. 14 and actually measured blood glucose levels. In the graph shown in Fig. 16, the horizontal axis shows the measured blood glucose levels (actual measured values) and the vertical axis shows the estimated blood glucose levels. As shown in Fig. 16, the measured values ​​and the estimated values ​​are generally within a range of ±20%. In other words, it can be said that the estimation accuracy using the estimation equation is within 20%.

[0094] In this way, the electronic device 100 can estimate the glucose metabolism of the subject non-invasively and in a short period of time.

[0095] (Third embodiment) In the second embodiment, an example is described in which the estimation formula is created by performing regression analysis based on sample data of pre- and post-prandial blood glucose levels and pulse waves. In the third embodiment, an example is described in which the estimation formula is created based on sample data of pre- and post-prandial blood glucose levels and post-prandial pulse waves. Note that the description of the same points as in the second embodiment will be omitted as appropriate.

[0096] 17 is a flow diagram for creating an estimation formula used by electronic device 100 according to the present embodiment. The estimation formula is created by measuring the subject's postprandial pulse wave using a pulse wave meter and the subject's preprandial and postprandial blood glucose levels using a blood glucose meter, and performing regression analysis based on sample data acquired by the measurements.

[0097] In creating an estimation equation according to this embodiment, information about the subject's preprandial blood glucose level measured by a blood glucose meter is input to the estimation equation creation device (step S401). Steps S402 and S403 are similar to steps S202 and S203 in FIG. 14, respectively.

[0098] When the estimation formula creation device determines that the number of samples is equal to or greater than N (if YES), it performs FFT analysis on the input postprandial pulse wave (step S404). The estimation formula creation device extracts fundamental and harmonic components, which are Fourier coefficients, through FFT analysis.

[0099] The estimation equation generating device also calculates the pulse rate of each subject based on the input pulse wave (step S405).

[0100] Then, the estimation equation creation device executes regression analysis (step S406). The objective variable of the regression analysis in this embodiment is the blood glucose level, including the pre-prandial and post-prandial blood glucose levels. The explanatory variable of the regression analysis in this embodiment is calculated based on the post-prandial Fourier coefficients (fundamental and harmonic components) and the pulse rate.

[0101] The estimation formula creation device creates an estimation formula for estimating blood glucose levels based on the results of the regression analysis (step S407). An example of an estimation formula for estimating blood glucose levels created according to the flow chart in FIG. 17 is shown in the following formulas (3) and (4).

[0102]

number

[0103] Next, a flow of estimating the blood glucose level of a subject using the estimation equation will be described. Fig. 18 is a flow diagram of estimating the blood glucose level of a subject using the estimation equation created by the flow of Fig. 17.

[0104] After the subject has eaten a meal, the electronic device 100 measures the subject's postprandial pulse wave based on an operation by the subject (step S501).

[0105] The electronic device 100 calculates the subject's pulse rates before and after the meal based on the measured pulse wave (step S502).

[0106] Furthermore, the electronic device 100 performs an FFT analysis on the postprandial pulse wave of the subject based on the measured pulse wave (step S503). Through the FFT analysis, the electronic device 100 calculates a feature coefficient.

[0107] The electronic device 100 estimates the subject's pre- and post-prandial blood glucose levels by applying the feature coefficients calculated in step S503 to, for example, the above-mentioned formulas (3) and (4) (step S504). The estimated blood glucose levels are notified to the subject by, for example, the notifying unit 147 of the electronic device 100.

[0108] Fig. 19 is a diagram showing a comparison between blood glucose levels estimated using the estimation equation created by the flow of Fig. 17 and actually measured blood glucose levels. In the graph shown in Fig. 19, the horizontal axis shows blood glucose measurement values ​​(actual measurements) and the vertical axis shows blood glucose estimation values. In this embodiment as well, as shown in Fig. 19, the measurement values ​​and estimation values ​​are generally within a range of ±20%. In other words, it can be said that the estimation accuracy using the estimation equation is within 20%.

[0109] In this way, even if the estimation equation is created based on a postprandial pulse wave, if sample data that allows regression analysis can be obtained, the electronic device 100 can use the estimation equation to estimate the subject's glucose metabolism non-invasively and in a short period of time.

[0110] (Fourth embodiment) In the second and third embodiments, an example in which the electronic device 100 estimates the blood glucose level (sugar metabolism) of a subject has been described. In the fourth embodiment, an example in which the electronic device 100 estimates the lipid metabolism of a subject will be described. In this embodiment, the electronic device 100 estimates a lipid value as the lipid metabolism of the subject. Here, the lipid value includes neutral fat, total cholesterol, HDL cholesterol, LDL cholesterol, etc. In the description of this embodiment, the description of the same points as in the second embodiment will be omitted as appropriate.

[0111] The electronic device 100 stores in advance, for example, in the storage unit 145, estimation equations for estimating lipid levels based on pulse waves. The electronic device 100 estimates lipid levels using these estimation equations.

[0112] The estimation theory for estimating lipid levels based on pulse waves is the same as the estimation theory for blood glucose levels described in the second embodiment. That is, the lipid levels in blood after a meal are different from the lipid levels in blood before a meal, and changes in the lipid levels are also reflected in changes in the pulse waves. Therefore, the electronic device 100 can acquire a pulse wave and estimate lipid levels based on the changes in the acquired pulse wave.

[0113] The estimation equation for estimating lipid levels can be created by performing regression analysis based on sample data of preprandial lipid levels and pulse waves obtained from multiple subjects. During estimation, the lipid level of the subject can be estimated by applying the estimation equation created to an index based on the subject's pulse wave.

[0114] 20 is a flow diagram for creating an estimation formula used by electronic device 100 according to a fourth embodiment of the present invention. The estimation formula is created by measuring a subject's pre-prandial pulse wave using a pulse wave meter and measuring the subject's pre-prandial lipid level using a lipid measuring device, and performing regression analysis based on sample data acquired by the measurements.

[0115] In creating the estimation equation, first, information on the subject's preprandial pulse wave and lipid value measured by a pulse wave meter and lipid measurement device, respectively, is input to the estimation equation creation device (step S601). At this time, the subject's age is also input.

[0116] The estimation formula creation device determines whether the number of samples of the sample data input in step S601 is equal to or greater than N, which is sufficient for performing regression analysis (step S602). If the estimation formula creation device determines that the number of samples is less than N (if NO), it repeats step S601 until the number of samples becomes equal to or greater than N. On the other hand, if the estimation formula creation device determines that the number of samples is equal to or greater than N (if YES), it proceeds to step S603 and executes calculation of the estimation formula.

[0117] In calculating the estimation equation, the estimation equation creating device analyzes the input pre-prandial pulse wave (step S603). Specifically, the measurement equation creating device analyzes, for example, the pulse wave rise index Sl, AI, and pulse rate PR.

[0118] Then, the estimation equation creation device executes regression analysis (step S604). The objective variable in the regression analysis is the preprandial lipid value. The explanatory variables in the regression analysis are, for example, the age input in step S601, and the preprandial pulse wave onset index Sl, AI, and pulse rate PR analyzed in step S603. The explanatory variables may be, for example, Fourier coefficients calculated as a result of FFT analysis.

[0119] The estimation formula creating device creates an estimation formula for estimating lipid values ​​based on the results of the regression analysis (step S605). An example of the estimation formula for estimating lipid values ​​is shown in the following formulas (5) to (8).

[0120]

number

[0121] In the formulas (5) to (8), HDL represents HDL cholesterol, LDL represents LDL cholesterol, Chol represents total cholesterol, and Tg represents neutral fat. Furthermore, age represents age.

[0122] Next, a flow of estimating the lipid level of a subject using the estimation equation will be described. Fig. 21 is a flow diagram of estimating the lipid level of a subject using the estimation equation created by the flow of Fig. 20.

[0123] First, the electronic device 100 inputs the age of the subject based on the operation of the subject (step S701).

[0124] Furthermore, the electronic device 100 measures the subject's postprandial pulse wave at a predetermined time after the subject has eaten a meal, based on the subject's operation (step S702). Here, the predetermined postprandial time is any time at which the change in lipid level due to the meal is reflected in the change in the pulse wave. Note that the predetermined postprandial time may be a time excluding the time immediately after the end of the meal when the blood glucose level rises.

[0125] Then, the electronic device 100 analyzes the measured pulse wave (step S703). Specifically, the electronic device 100 analyzes, for example, the rising index Sl, AI, and pulse rate PR related to the measured pulse wave.

[0126] The electronic device 100 estimates the lipid level of the subject by applying the rising index Sl, AI, and pulse rate PR analyzed in step S703 and the age of the subject to, for example, the above-mentioned formulas (5) to (8) (step S704). The estimated lipid level is notified to the subject by, for example, the notifying unit 147 of the electronic device 100.

[0127] FIG. 22 is a diagram showing a comparison between lipid values ​​estimated using the estimation formula created by the flow of FIG. 20 and actually measured lipid values. In the graph shown in FIG. 22, the horizontal axis shows the measured lipid values ​​(actual measured values), and the vertical axis shows the estimated lipid values. The measured values ​​of various lipid values ​​were measured using Cobas b101 manufactured by Roche Diagnostics. As shown in FIG. 22, the measured values ​​and the estimated values ​​are generally within a range of ±20%. In other words, it can be said that the estimation accuracy using the estimation formula is within 20%.

[0128] In this manner, the electronic device 100 can estimate the lipid metabolism of the subject non-invasively and in a short period of time.

[0129] FIG. 23 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. 23 includes an electronic device 100, a server 151, a mobile terminal 150, and a communication network. As shown in FIG. 23, an index based on a pulse wave calculated by the electronic device 100 is transmitted to the server 151 through the communication network and stored in the server 151 as personal information of the subject. The server 151 estimates the fluidity of the blood of the subject and the state of glucose metabolism and lipid metabolism by comparing with the previously acquired information of the subject and various databases. The server 151 further creates optimal advice for the subject. The server 151 returns the estimation result and advice to the mobile terminal 150 owned by the subject. The mobile terminal 150 can report the received estimation result and advice from the display unit of the mobile terminal 150, thereby constructing a system. By utilizing the communication function of the electronic device 100, the server 151 can collect information from multiple users, thereby further improving the accuracy of the estimation. Moreover, since the mobile terminal 150 is used as a notification means, the electronic device 100 does not require the notification unit 147, and is further miniaturized. Furthermore, since the estimation of the subject's blood fluidity and the state of glucose metabolism and lipid metabolism is performed by the server 151, the computational burden on the control unit 143 of the electronic device 100 can be reduced. Furthermore, since the server 151 can store the previously obtained information of the subject, the burden on the storage unit 145 of the electronic device 100 can be reduced. Therefore, the electronic device 100 can be further miniaturized and simplified. Furthermore, the computational processing speed is improved.

[0130] Although the system according to the present embodiment has been shown to have 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. It may also be configured in such a way that electronic device 100 and mobile terminal 150 are directly connected via a communication network without using server 151.

[0131] Although the present invention has been described with respect to specific embodiments in order to fully and clearly disclose the present invention, the appended claims should not be construed as being limited to the above-described embodiments, but should be construed to embody all modifications and alternative arrangements that may be made by those skilled in the art within the scope of the basic subject matter presented herein.

[0132] For example, in the above embodiment, the sensor unit 130 is provided with the angular velocity sensor 131, but the electronic device 100 according to the present invention is not limited to this. The sensor unit 130 may be provided with an optical pulse wave sensor consisting of a light-emitting unit and a light-receiving unit, or may be provided with a pressure sensor. Furthermore, the electronic device 100 is not limited to being worn on the wrist. The sensor unit 130 may be placed on an artery on the neck, ankle, thigh, ear, or the like.

[0133] For example, in the first embodiment, the state of glucose metabolism and lipid metabolism of the subject is estimated based on the first and second extreme values ​​of the index based on the pulse wave and their times, but the electronic device according to the present invention is not limited to this. In some cases, only one extreme value appears, or no extreme value appears, and the state of glucose metabolism and lipid metabolism of the subject may be estimated based on the overall tendency of the time variation of the index based on the calculated pulse wave (e.g., integral value, Fourier transform, etc.). In addition, instead of extracting the extreme value of the index based on the pulse wave, the state of glucose metabolism and lipid metabolism of the subject may be estimated based on the time range in which the index based on the pulse wave becomes equal to or less than a predetermined value.

[0134] For example, in the above embodiment, the case of estimating blood fluidity before and after a meal has been described, but the electronic device according to the present invention is not limited to this. The electronic device according to the present invention may estimate blood fluidity before, after, and during exercise, or may estimate blood fluidity before, after, and during bathing. [Explanation of symbols]

[0135] 100 Electronic equipment 110 Mounting part 120 Measuring section 120a Back 120b surface 111 Opening 130 Sensor section 131 Angular rate sensor 132 Vein Placement 133 Support part 140 Elastic Body 143 Control Unit 144 Power supply section 145 Storage section 146 Communications Department 147 Information Department 150 Mobile Devices 151 Server

Claims

1. A sensor unit having a plurality of sensors and configured to acquire a pulse wave at a test site; A control unit that estimates glucose metabolism from an index based on a pulse wave acquired by the plurality of sensors of the sensor unit, The control unit estimates the glucose metabolism based on an index related to a pulse wave acquired at a first timing and an index related to a pulse wave acquired at a second timing that is after the first timing. system.

2. the control unit calculates an amount of change in an index related to the pulse wave acquired at the second timing based on an index related to the pulse wave acquired at the first timing. The system of claim 1 .

3. The system according to claim 1 or 2, wherein the first timing and the second timing are timings within a time period during which fluctuations in blood glucose level are greater than or equal to a predetermined value.

4. the first timing is before a meal; The second timing is after a meal.

3. A system according to claim 1 or 2.

5. The system according to claim 1 or 2, wherein the first timing and the second timing are included in pre-exercise and during-exercise timing, or during-exercise and post-exercise timing.

6. The system according to claim 1 or 2, wherein the first timing and the second timing are included in timings before and during bathing, or timings during and after bathing.

7. The second timing is a timing at which the index after a meal becomes an extreme value.

5. The system of claim 1, 2 or 4.

8. The control unit estimates the glucose metabolism based on the extreme value that appears earliest within a predetermined time after the meal and its appearance time. The system of claim 7.

9. The extreme value is the smallest extreme value.

9. A system according to claim 7 or 8.

10. The index is an index related to a reflected wave of a pulse wave acquired by the plurality of sensors of the sensor unit. A system according to any one of claims 1 to 9.

11. The system of claim 1 , wherein the multiple sensors are located in a single sensor portion.

12. A system including a first electronic device and a second electronic device, The first electronic device is a sensor unit having a plurality of sensors and configured to acquire a pulse wave at a test site; A communication unit that communicates with the second electronic device; a control unit that transmits information about the pulse wave acquired by the plurality of sensors of the sensor unit from the communication unit to the second electronic device so that the second electronic device can estimate glucose metabolism; Equipped with The second electronic device receives information acquired by the multiple sensors from the first electronic device, and estimates the glucose metabolism based on an index related to the pulse wave acquired at a first timing and an index related to the pulse wave acquired at a second timing after the first timing.

13. A sensor unit having a plurality of sensors and acquiring a pulse wave at a test site; A control unit that estimates lipid metabolism from an index based on a pulse wave acquired by the plurality of sensors of the sensor unit, The control unit estimates the lipid metabolism based on an index related to a pulse wave acquired at a first timing and an index related to a pulse wave acquired at a second timing after the first timing. system.

14. A system including a first electronic device and a second electronic device, The first electronic device is a sensor unit having a plurality of sensors and configured to acquire a pulse wave at a test site; A communication unit that communicates with the second electronic device; a control unit that transmits information about the pulse wave acquired by the plurality of sensors of the sensor unit from the communication unit to the second electronic device so that lipid metabolism can be estimated in the second electronic device; Equipped with The second electronic device receives information acquired by the multiple sensors from the first electronic device, and estimates the lipid metabolism based on an index related to the pulse wave acquired at a first timing and an index related to the pulse wave acquired at a second timing after the first timing.

Images