WO2014087768A1 - 血糖値予測装置、測定装置、血糖値予測方法、及びコンピュータ読み取り可能な記録媒体 - Google Patents
血糖値予測装置、測定装置、血糖値予測方法、及びコンピュータ読み取り可能な記録媒体 Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4866—Evaluating metabolism
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7278—Artificial waveform generation or derivation, e.g. synthesising signals from measured signals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
- A61B2560/02—Operational features
- A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
- A61B2560/0238—Means for recording calibration data
Definitions
- the present invention relates to a blood sugar level predicting apparatus and a blood sugar level predicting method for predicting a blood sugar level of a user, and further, a measuring device using them, and a computer readable recording program for realizing them.
- the present invention relates to a recording medium.
- the concentration of glucose in a person's blood that is, the blood glucose level
- the blood glucose level changes during the day. Specifically, when the person is hungry, the blood sugar level decreases as the time during which no meal is taken, but the blood sugar level rises when the meal is taken. Therefore, in order to grasp an accurate blood glucose level, it is necessary to measure the blood glucose level many times during the day. Therefore, many diabetic patients manage their blood sugar levels by measuring their own blood sugar levels with a portable blood glucose meter.
- Patent Document 1 discloses a method for predicting a change in blood glucose level of a diabetic patient in order to reduce the burden on the diabetic patient and facilitate the determination of the dose of insulin. Specifically, in the method disclosed in Patent Document 1, focusing on the fact that the behavior of blood glucose level over time is a chaos phenomenon, the current blood glucose level is determined from the measurement time series data of the blood glucose level by the local fuzzy reconstruction method. The blood glucose level after the next day is predicted.
- the subsequent blood glucose level can be predicted only by first obtaining the blood glucose level measurement time series data. Therefore, the burden on the diabetic patient can be reduced and the dosage of insulin can be determined. It is thought that simplification is achieved.
- Patent Document 1 it is necessary to prepare a large number of actually measured values in the first stage. Further, in order to increase the accuracy, it is necessary to shorten the measurement value acquisition interval as much as possible. From these points, it is considered that the method disclosed in Patent Document 1 has a small effect of reducing the burden on diabetic patients.
- An example of an object of the present invention is to solve the above-described problem and to accurately predict the blood glucose level of the user while suppressing the burden on the user at the time of measuring the blood glucose level, and to predict the blood glucose level of the user. And providing a program.
- a blood sugar level predicting device is a device for predicting a blood sugar level of a user, A pattern selection unit that selects one pattern from a plurality of preset time-series blood glucose levels based on the fasting blood glucose level of the user; A correction processing unit that corrects the selected pattern using a plurality of measurement values obtained by performing optical measurement of blood glucose level by changing the measurement date for the user; and It is characterized by having.
- a measuring device is a measuring device that measures a user's blood glucose level by optical measurement, A measurement unit for performing the optical measurement; A calibration curve database for storing a calibration curve for converting a measurement value obtained by execution of optical measurement into a blood glucose level for each corresponding blood glucose level; A control processing unit that selects any one of the calibration curves and calculates a blood glucose level by applying a measurement value obtained by executing an optical measurement to the selected calibration curve; Based on the fasting blood glucose level of the user, one pattern is selected from a plurality of preset time-series blood glucose level patterns, and the measurement date is changed for the user and the optical is changed.
- a blood sugar level predicting unit that predicts the blood sugar level of the user by correcting the selected pattern using a plurality of measured values obtained by performing a physical measurement; With When the selection and correction of the pattern is executed by the blood sugar level prediction unit, the control processing unit predicts a blood sugar level using the corrected pattern, and the calibration is performed based on the predicted blood sugar level. Select a line, It is characterized by that.
- the blood glucose level prediction method of the present invention is a method for predicting the blood glucose level of a user, (A) selecting one pattern from a plurality of preset blood glucose level time-series changes based on a user's fasting blood glucose level; and (B) correcting the selected pattern using a plurality of measurement values obtained by changing the measurement date and performing optical measurement of blood glucose level for the user; and It is characterized by having.
- a computer-readable recording medium is a computer-readable recording medium that records a program for predicting a user's blood glucose level by a computer, In the computer, (A) selecting one pattern from a plurality of preset blood glucose level time-series changes based on a user's fasting blood glucose level; and (B) correcting the selected time-series change pattern using a plurality of measurement values obtained by changing the measurement date and executing optical measurement of blood glucose level for the user; When, A program including an instruction for executing is recorded.
- FIG. 1 is a block diagram showing a configuration of a blood sugar level predicting apparatus and a measuring apparatus in the present embodiment.
- FIG. 2 is a diagram illustrating an example of a time-series change pattern of a general blood glucose level.
- FIG. 3 is a flowchart showing a time-series pattern selection process and a correction process by the blood sugar level predicting apparatus according to the embodiment of the present invention.
- FIG. 4 is a diagram illustrating an example in which the initial pattern is corrected in the present embodiment.
- FIG. 5 is a flowchart showing time-series pattern reselection processing by the blood sugar level predicting apparatus according to the embodiment of the present invention.
- FIG. 6 is a flowchart showing the operation of the measuring apparatus according to the embodiment of the present invention.
- FIG. 1 is a block diagram showing a configuration of a blood sugar level predicting apparatus and a measuring apparatus in the present embodiment.
- the blood sugar level predicting device 10 includes a pattern selecting unit 11 and a correction processing unit 12, and predicts the blood sugar level of the user 30 using these.
- the pattern selection unit 11 has a plurality of preset time-series blood glucose level patterns (based on the fasting blood glucose level of the user 30 (hereinafter referred to as “fasting blood glucose level”)).
- fasting blood glucose level the fasting blood glucose level of the user 30
- one time series change pattern is selected from “time series change pattern”.
- the selected time-series change pattern is referred to as an “initial pattern”.
- the correction processing unit 12 acquires a plurality of measurement values obtained by performing optical measurement of blood glucose levels by changing the measurement date for the user 30. Then, the correction processing unit 12 corrects the initial pattern using the plurality of acquired measurement values.
- the time series change pattern that is considered to be closest to the time series change of the blood sugar level of the user 30 is selected from the blood sugar level of the user 30 when fasting. Since the selected time-series change pattern (initial pattern) is corrected using the actual measurement value, it follows the actual state of the user 30. In addition, the user 30 only has to input the fasting blood glucose level and change the measurement date to perform optical measurement of the blood glucose level several times. That is, according to the blood sugar level prediction device 10, it is possible to accurately predict the blood sugar level of the user 30 while suppressing the burden on the user 30.
- the blood glucose level prediction apparatus 10 is a part of the measurement apparatus 20, and functions as a blood glucose level prediction part.
- the measuring device 20 is a device that optically measures a user's blood glucose level, as will be described later.
- the blood glucose level prediction apparatus 10 includes a pattern database 13 that stores a plurality of time-series patterns and a setting condition determination unit 14 in addition to the pattern selection unit 11 and the correction processing unit 12 described above. Yes. Furthermore, the blood sugar level prediction apparatus 10 can receive an input operation from the user via an operation reception unit 23 described later.
- the pattern selection unit 11 receives a fasting blood glucose level input by the user via an operation receiving unit 23 described later, and is stored in the pattern database 13 based on the received fasting blood glucose level.
- One time series pattern suitable for the user is selected from the existing time series patterns.
- the “fasting blood glucose level” is preferably the blood glucose level of blood collected from the user after two hours or more have passed since the most recent eating.
- the “fasting blood glucose level” includes a blood glucose level of blood collected at the time of a medical examination.
- the time series pattern includes a set of functions set for each time zone with the user's fasting as a reference time, and each function includes an elapsed time and a blood glucose level from the reference time. Identify the relationship. Specific examples of time series patterns are shown below. The following equations (1) to (16) are functions constituting one time series pattern.
- the time-series pattern is composed of a plurality of linear functions set every hour.
- ⁇ represents a blood glucose level
- a 1 to a 16 and b 1 to b 16 represent arbitrary variables.
- the pattern selection unit 11 selects a time-series pattern using the blood sugar level after eating in addition to the blood sugar level at the time of the user's fasting. Is preferred.
- the selection criteria of the time series pattern will be described with reference to FIG.
- FIG. 2 is a diagram illustrating an example of a time-series change pattern of a general blood glucose level.
- graphs A to E each show an example of a time-series change pattern.
- FIG. 2 only the time from the reference time to 6 hours later is shown.
- graph A shows a time-series change pattern in which the fasting blood glucose level is 120 mg / dl or more.
- the time-series change pattern is the same as or similar to the graph A.
- Graphs B and C show time-series change patterns in which the fasting blood glucose level is less than 120 mg / dl, but the blood glucose level rapidly rises 1 hour after eating and becomes 140 mg / dl or more after 2 hours. ing.
- the time-series change pattern is the same as or similar to one of graphs B and C.
- Graph D shows that the fasting blood glucose level is less than 120 mg / dl and there is no rapid increase in blood glucose level in 1 hour after eating, but the blood glucose level becomes 140 mg / dl or more after 2 hours A series change pattern is shown. When the user is a diabetic patient and is already taking medication, the time-series change pattern is the same as or similar to the graph D.
- Graph E shows a time series change pattern in which the fasting blood glucose level is less than 120 mg / dl, and the blood glucose level rises from 1 hour to 2 hours after eating, but is less than 140 mg / dl.
- the time-series change pattern is the same as or similar to the graph E.
- the pattern selection unit 11 has five types of graphs A to E. A time series change pattern more suitable for the user can be reliably selected from the time series patterns.
- the measuring system of the blood glucose level after eating is not specifically limited, The blood glucose level after eating may be measured by the measurement part 22.
- the correction processing unit 12 first accesses the measurement value database 25 and extracts the measurement value obtained by each optical measurement by the measurement unit 22 and its measurement date and time. Further, the correction processing unit 12 converts the extracted measurement time of each measurement date into an elapsed time t from the reference time.
- the correction processing unit 12 corrects the initial pattern by applying the least square method to each measurement value. Specifically, the correction processing unit 12 calculates a value calculated by applying each measured value and the converted elapsed time t to each linear function of the initial pattern for each measured value having the same elapsed time t. The square root of the residual with (blood glucose level ⁇ ) is obtained, and each linear function of the initial pattern is corrected so that the total value becomes minimum when the square roots are summed.
- each linear function of the initial pattern represents the actual state of the user 30 more accurately.
- the correction processing unit 12 stores the corrected time series pattern in the measurement value database 25.
- the setting condition determination unit 14 determines whether or not the measurement value satisfies the setting condition after the setting period has elapsed from the selection of the time series pattern by the pattern selection unit 11.
- the set period includes a period in which a change in the blood glucose level appears when diabetes occurs, for example, about two months.
- the number of data accumulated in the measured value database 25 within a set period is sufficient (for example, 20 or more), and an error between the time-series change pattern before the correction and the measured value. Is within a certain range (for example, within ⁇ 10%).
- the setting conditions are not limited to the above-described conditions, and may be any conditions that can specify that the user's state has changed to some extent that the time-series change pattern needs to be selected again.
- the pattern selection unit 11 performs selection of the time series pattern again, and the correction processing unit 12 newly selects the time series pattern.
- the time series pattern is corrected.
- the measurement device 20 includes a control processing unit 21, a measurement unit 22, an operation reception unit 23 that receives an input operation from a user 30, and a calibration curve database 24.
- the measurement value database 25 and a display device 26 such as a liquid crystal panel are provided.
- the measurement unit 22 performs optical measurement.
- the measurement unit 22 includes a light source that emits light of a set wavelength, a light receiving element that receives light reflected by a part of the user 30 (such as a finger), an optical filter, and a lens. It is equipped with the optical parts.
- the structure of the measurement part 22 is not specifically limited, The measurement part 22 can be comprised by the existing components.
- the measurement unit 22 performs optical measurement in accordance with an instruction from the control processing unit 21, and data obtained by execution of the optical measurement, specifically, light received by the light receiving element.
- the data for specifying the intensity is output to the control processing unit 21.
- the calibration curve database 24 stores a calibration curve for converting a measurement value obtained by execution of optical measurement into a blood glucose level.
- the calibration curve is created by performing optical measurement and blood glucose level measurement on a sample prepared in advance, and determining the relationship between the obtained absorbance and blood glucose level. Further, since the optimal calibration curve varies depending on the blood glucose level, a plurality of calibration curves are created according to the blood glucose level. That is, a plurality of calibration curves having different target blood glucose levels are created and stored in the calibration curve database 24. Note that blood glucose level measurement for creating a calibration curve is performed by a measurement method other than optical measurement.
- the control processing unit 21 instructs the measurement unit 22 to execute optical measurement when the operation start unit 23 receives a measurement start operation by the user 30.
- the control processing unit 21 calculates the absorbance based on the data. Further, the control processing unit 21 acquires a calibration curve from the calibration curve database 24, and applies the calculated absorbance to the acquired calibration curve to calculate a blood glucose level (measurement value). Further, the control processing unit 21 stores the obtained blood glucose level (measured value) in the measured value database 25 in association with the date and time at the time of measurement.
- control processing unit 21 can acquire the measurement value and the measurement date / time from the measurement value database 25 and display them on the display device 26. Furthermore, in the present embodiment, the measurement value and the measurement date and time stored in the measurement value database 25 are used in the correction process by the correction processing unit 12.
- the control process part 21 of the measuring apparatus 20 estimates a blood glucose level using a time series change pattern. Based on the predicted blood glucose level, an optimal calibration curve is selected.
- control processing unit 21 first obtains a corrected time series pattern stored in the measurement value database 25, selects a linear function corresponding to the time of measurement from among them, The blood glucose level is calculated using the selected linear function. Next, the control processing unit 21 specifies a calibration curve corresponding to the calculated blood glucose level from the calibration curves stored in the calibration curve database 24, and acquires this. As described above, according to the present embodiment, the optimal calibration curve can be easily specified, so that the measurement accuracy of the measuring device 20 can be improved.
- FIGS. 1 and 2 are referred to as appropriate. Furthermore, in the present embodiment, the blood sugar level prediction method is implemented by operating the blood sugar level prediction apparatus 10. Therefore, the description of the blood sugar level prediction method in the present embodiment is replaced with the following description of the operation of the blood sugar level prediction apparatus.
- FIG. 3 is a flowchart showing a time-series pattern selection process and a correction process by the blood sugar level predicting apparatus according to the embodiment of the present invention.
- the pattern selection unit 11 receives necessary data, specifically, fasting blood glucose level and blood glucose level after eating via the operation reception unit 23. It is determined whether or not an input has been made (step A1).
- step A2 If the result of determination in step A1 shows that the necessary data has not been input, the pattern selection unit 11 enters a standby state. On the other hand, if the necessary data has been input as a result of the determination in step A1, the pattern selection unit 11 accesses the pattern database 13, and prompts the user 30 based on the fasting blood glucose level and the blood glucose level after eating. One suitable time series pattern is selected (step A2).
- step A2 when step A2 is executed, the correction processing unit 12 accesses the measurement value database 25, and extracts the measurement value obtained by each optical measurement by the measurement unit 22 and its measurement date and time (step A3). ). In step A3, conditions may be set for the extraction of the measurement value and the measurement date and time by the correction processing unit 12.
- the condition is that the period from the oldest measurement date and time to the latest measurement date and time is a period in which a change in blood glucose level appears when diabetes occurs, for example, about 2 months, extraction
- the number of data that can be generated is sufficient (for example, 20 or more).
- the correction processing unit 12 converts the measurement time into the elapsed time t from the reference time for each extracted measurement date and time (step A4). Subsequently, the correction processing unit 12 corrects the initial pattern by applying the least square method to each measurement value (step A5). Specifically, for each measurement value having the same elapsed time t, the correction processing unit 12 calculates a residual between each measurement value and a value calculated by applying the elapsed time t to each linear function of the initial pattern. And the slope and intercept of each linear function are corrected so that the total value is minimized when the square roots are summed.
- the correction processing unit 12 stores each corrected linear function in the measured value database 25 (step A6). If step A6 is performed, the process in the blood glucose level prediction apparatus 10 will once complete
- steps A1 to A6 are executed, the time series change pattern closest to the time series change of the blood glucose level of the user 30 is selected, and this initial pattern is corrected by the actual measurement value. . Therefore, since a time-series change pattern according to the actual state of the user 30 can be easily obtained, the blood sugar level of the user 30 can be accurately predicted while suppressing the burden on the user 30.
- FIG. 4 is a diagram illustrating an example in which the initial pattern is corrected in the present embodiment. As shown in FIG. 4, when the initial pattern indicated by the solid line is selected and the measurement result indicated by the circle is obtained, the corrected initial pattern is the pattern indicated by the broken line.
- FIG. 5 is a flowchart showing time-series pattern reselection processing by the blood sugar level predicting apparatus according to the embodiment of the present invention.
- the setting condition determination unit 14 determines whether or not the setting period has elapsed since the selection of the time series pattern by the pattern selection unit 11 (step B1). .
- step B1 determines whether the setting period has not elapsed. If the result of determination in step B1 is that the setting period has not elapsed, the setting condition determination unit 14 enters a standby state. On the other hand, if the setting period has passed as a result of the determination in step B1, the setting condition determination unit 14 determines that the number of data accumulated in the measurement value database 25 within the setting period is a set value (for example, 20). It is determined whether or not this is the case (step B2).
- step B4 is executed. On the other hand, if the number of data is greater than or equal to the set value as a result of the determination in step B2, the setting condition determining unit 14 indicates that the error between the initial pattern before correction and the measured value is within a set range (eg, ⁇ 10% ) Is determined (step B3).
- a set range eg, ⁇ 10%
- step B4 is executed. On the other hand, as a result of the determination in step B3, if the error between the initial pattern before correction and the measured value is within the setting range, the setting condition determination unit 14 ends the process.
- step B4 the pattern selection unit 11 determines whether or not a fasting blood glucose level and a blood glucose level after eating are newly input via the operation reception unit 23. As a result of step B4, when the fasting blood glucose level and the blood glucose level after eating are not newly input, step B6 is executed.
- step B4 when the fasting blood glucose level and the blood glucose level after eating are newly input as a result of step B4, the pattern selection unit 11 is suitable for the user 30 based on the newly input blood glucose level. A series pattern is selected again (step B5).
- Step B6 When the blood glucose level is not newly input in Step B4 and when Step B5 is executed, the correction processing unit 12 executes Steps B6 to B9. As a result, the initial pattern or the reselected initial pattern is more suitable for the user 30. Steps B6 to B9 correspond to steps A3 to A6 shown in FIG. 3, respectively.
- steps B1 to B9 are executed, it is possible to flexibly cope with a change in the health state of the user 30. For this reason, even when the user 30 becomes a diabetic patient from a healthy person, the blood sugar level of the user 30 can be predicted.
- FIG. 6 is a flowchart showing the operation of the measuring apparatus according to the embodiment of the present invention.
- control processing unit 21 first determines whether or not an operation reception unit 23 has accepted a measurement start operation by the user 30 (step C ⁇ b> 1).
- step C1 If the result of determination in step C1 is that a measurement start operation has not been accepted, the control processing unit 21 enters a standby state. On the other hand, as a result of the determination in step C1, if the measurement start operation is accepted, the control processing unit 21 converts the current time into the elapsed time t from the reference time (step C2).
- control processing unit 21 acquires a corrected time-series change pattern from the measurement value database 25, and calculates the blood glucose level by adding the calculated elapsed time t to this (step C3). Subsequently, the control processing unit 21 accesses the calibration curve database 24, selects an optimal calibration curve using the blood glucose level calculated in step C3, and acquires it (step C4).
- control processing unit 21 instructs the measurement unit 22 to perform optical measurement (step C5).
- the measurement unit 22 irradiates a part of the user 30 with light, receives the reflected light reflected by the user 30, and sends data specifying the intensity of the received reflected light to the control processing unit 21. Output.
- control processing unit 21 calculates the absorbance from the result of the optical measurement in Step C5, applies this to the calibration curve selected in Step C4, and calculates the blood glucose level (Step C6). Thereafter, the control processing unit 21 associates the blood glucose level calculated in Step C6 with the date and time at the time of measurement, stores the blood glucose level in the measurement value database 25, and causes the display device 26 to display the measurement value and the measurement date and time (Step Step). C7).
- the optimal calibration curve can be easily selected, so that the measurement accuracy of the measuring device 20 can be improved.
- the program in the present embodiment may be a program that causes a computer to execute steps A1 to A6 shown in FIG. 3 and steps B1 to B9 shown in FIG.
- examples of the computer include a computer mounted on a device such as a measuring device, and a general-purpose personal computer.
- the blood sugar level predicting apparatus and the blood sugar level predicting method in the present embodiment can be realized.
- the CPU (Central Processing Unit) of the computer functions as the pattern selection unit 11, the correction processing unit 12, and the setting condition determination unit 14, and performs processing.
- the program in the present embodiment may be a program that causes a computer to execute steps C1 to C7 shown in FIG.
- the measuring apparatus according to the present embodiment can be realized, and the CPU (Central Processing Unit) of the computer functions as the control processing unit 21 and performs processing.
- the CPU Central Processing Unit
- the program in the present embodiment may be distributed on the Internet or provided in a state stored in a computer-readable recording medium.
- the recording medium include general-purpose semiconductor storage devices such as CF (Compact Flash (registered trademark)) and SD (Secure Digital), magnetic storage media such as a flexible disk, or CD-ROM (Compact Optical storage media such as Disk (Read Only Memory).
- An apparatus for predicting a user's blood sugar level An apparatus for predicting a user's blood sugar level, A pattern selection unit that selects one pattern from a plurality of preset time-series blood glucose levels based on the fasting blood glucose level of the user; A correction processing unit that corrects the selected pattern using a plurality of measurement values obtained by performing optical measurement of blood glucose level by changing the measurement date for the user; and A blood sugar level predicting apparatus comprising:
- the pattern selection unit In addition to the fasting blood glucose level of the user, the pattern selection unit also uses the blood glucose level after eating to select the pattern, The blood glucose level prediction apparatus according to appendix 1.
- Each of the plurality of patterns includes a set of functions that are set for each time zone with the user's fasting time as a reference time, and that specify a relationship between an elapsed time from the reference time and a blood glucose level
- the correction processing unit converts the measurement time of the optical measurement into an elapsed time from the reference time, and converts the measurement value and the function of the selected pattern for each of the plurality of measurement values.
- the time series selected so that the sum of the square roots of the residual with the value calculated by applying the elapsed time is calculated to be the smallest when the square roots are summed. Correct each of the functions of the change pattern,
- the blood glucose level prediction apparatus according to appendix 1.
- a setting condition determination unit that determines whether a measurement value obtained by optical measurement of a blood glucose level for the blood of the user satisfies a setting condition after a setting period has elapsed from the selection of the pattern; Prepared, When it is determined by the setting condition determination unit that the obtained measurement value does not satisfy the setting condition, the pattern selection unit performs the pattern selection again, and the correction processing unit newly Correct the pattern selected in The blood glucose level prediction apparatus according to appendix 1.
- a measuring device for measuring a user's blood glucose level by optical measurement A measurement unit for performing the optical measurement;
- a calibration curve database for storing a calibration curve for converting a measurement value obtained by execution of optical measurement into a blood glucose level for each corresponding blood glucose level;
- a control processing unit that selects any one of the calibration curves and calculates a blood glucose level by applying a measurement value obtained by executing an optical measurement to the selected calibration curve; Based on the fasting blood glucose level of the user, one pattern is selected from a plurality of preset time-series blood glucose level patterns, and the measurement date is changed for the user and the optical is changed.
- a blood sugar level predicting unit that predicts the blood sugar level of the user by correcting the selected pattern using a plurality of measured values obtained by performing a physical measurement; With When the selection and correction of the pattern is executed by the blood sugar level prediction unit, the control processing unit predicts a blood sugar level using the corrected pattern, and the calibration is performed based on the predicted blood sugar level. Select a line, A measuring device.
- a method for predicting a user's blood glucose level comprising: (A) selecting one pattern from a plurality of preset blood glucose level time-series changes based on a user's fasting blood glucose level; and (B) correcting the selected pattern using a plurality of measurement values obtained by changing the measurement date and performing optical measurement of blood glucose level for the user; and A method for predicting blood glucose level, comprising:
- step (a) in addition to the fasting blood glucose level of the user, the blood glucose level after eating is also used to select the pattern.
- Each of the plurality of patterns includes a set of functions that are set for each time zone with the user's fasting time as a reference time, and that specify a relationship between an elapsed time from the reference time and a blood glucose level,
- the measurement time of the optical measurement is converted into an elapsed time from the reference time, and for each of the plurality of measurement values, the measurement value and the function of the selected pattern are used.
- the square root of the residual with the value calculated by applying the elapsed time after conversion is obtained, and when the square roots are further summed, the selected value is selected so that the total value is minimized. Correct each of the functions of the time series change pattern, The blood glucose level prediction method according to appendix 6.
- (Appendix 9) (C) a step of determining whether a measurement value obtained by optical measurement of a blood glucose level for the user's blood satisfies a setting condition after a set period has elapsed since the selection of the pattern; Have In the step (c), when it is determined that the obtained measurement value does not satisfy the setting condition, the step (a) and the step (b) are executed again.
- the blood glucose level prediction method according to appendix 6.
- a computer-readable recording medium recording a program for predicting a user's blood glucose level by a computer, In the computer, (A) selecting one pattern from a plurality of preset blood glucose level time-series changes based on a user's fasting blood glucose level; and (B) correcting the selected time-series change pattern using a plurality of measurement values obtained by changing the measurement date and executing optical measurement of blood glucose level for the user;
- the computer-readable recording medium which has recorded the program containing the instruction
- Each of the plurality of patterns includes a set of functions that are set for each time zone with the user's fasting time as a reference time, and that specify a relationship between an elapsed time from the reference time and a blood glucose level,
- the measurement time of the optical measurement is converted into an elapsed time from the reference time, and for each of the plurality of measurement values, the measurement value and the function of the selected pattern are used.
- the square root of the residual with the value calculated by applying the elapsed time after conversion is obtained, and when the square roots are further summed, the selected value is selected so that the total value is minimized. Correct each of the functions of the time series change pattern,
- the program is stored in the computer. (C) executing a step of determining whether or not a measurement value obtained by optical measurement of blood glucose level for the blood of the user satisfies a setting condition after a set period has elapsed since the selection of the pattern Further including an instruction to In the step (c), when it is determined that the obtained measurement value does not satisfy the setting condition, the step (a) and the step (b) are executed again.
- the computer-readable recording medium according to appendix 10.
- the present invention it is possible to accurately predict a user's blood glucose level while suppressing a burden on the user during blood glucose level measurement.
- the present invention is useful for blood glucose level measuring devices and medical systems.
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Abstract
Description
ユーザの空腹時の血糖値に基づいて、複数の、予め設定された血糖値の時系列変化のパターンの中から、1のパターンを選択する、パターン選択部と、
前記ユーザを対象として、測定日を変えて血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正する、補正処理部と、
を備えていることを特徴とする。
前記光学的測定を実行する測定部と、
光学的測定の実行によって得られる測定値を血糖値に換算するための検量線を、対応する血糖値の大きさ毎に格納する、検量線データベースと、
いずれかの前記検量線を選択し、選択した検量線に、光学的測定を実行することによって得られた測定値を適用することによって血糖値を算出する制御処理部と、
ユーザの空腹時の血糖値に基づいて、複数の、予め設定された血糖値の時系列変化のパターンの中から、1のパターンを選択し、前記ユーザを対象として、測定日を変えて前記光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正することによって、ユーザの血糖値を予測する血糖値予測部と、
を備え、
前記制御処理部は、前記血糖値予測部によって、前記パターンの選択と補正とが実行された場合に、補正後の前記パターンを用いて血糖値を予測し、予測した血糖値に基づいて前記検量線を選択する、
ことを特徴とする。
(a)ユーザの空腹時の血糖値に基づいて、予め設定された複数の血糖値の時系列変化のパターンの中から、1のパターンを選択する、ステップと、
(b)前記ユーザを対象として、測定日を変えて血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正する、ステップと、
を有することを特徴とする。
前記コンピュータに、
(a)ユーザの空腹時の血糖値に基づいて、予め設定された複数の血糖値の時系列変化のパターンの中から、1のパターンを選択する、ステップと、
(b)前記ユーザを対象として、測定日を変えて、血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記時系列変化パターンを補正する、ステップと、
を実行させる、命令を含むプログラムを記録していることを特徴とする。
以下、本発明の実施の形態における、血糖値予測装置、測定装置、血糖値予測方法、及びプログラムについて、図1~図6を参照しながら説明する。
最初に、本実施の形態における血糖値予測装置10及び測定装置20の構成について図1を用いて説明する。図1は、本実施の形態における血糖値予測装置及び測定装置の構成を示すブロック図である。
λ=a2t+b2 (1<t≦2) (2)
λ=a3t+b3 (2<t≦3) (3)
λ=a4t+b4 (3<t≦4) (4)
λ=a5t+b5 (4<t≦5) (5)
λ=a6t+b6 (5<t≦6) (6)
λ=a7t+b7 (6<t≦7) (7)
λ=a8t+b8 (7<t≦8) (8)
λ=a9t+b9 (8<t≦9) (9)
λ=a10t+b10 (9<t≦10) (10)
λ=a11t+b11 (10<t≦11) (11)
λ=a12t+b12 (11<t≦12) (12)
λ=a13t+b13 (12<t≦13) (13)
λ=a14t+b14 (13<t≦14) (14)
λ=a15t+b15 (14<t≦15) (15)
λ=a16t+b16 (15<t≦16) (16)
次に、本実施の形態における血糖値予測装置10の動作について図3~図6を用いて説明する。また、以下の説明においては、適宜図1及び図2を参酌する。更に、本実施の形態では、血糖値予測装置10を動作させることによって、血糖値予測方法が実施される。よって、本実施の形態における血糖値予測方法の説明は、以下の血糖値予測装置の動作説明に代える。
最初に、図3を用いて、血糖値予測装置10による時系列パターンの選択と補正とについて説明する。図3は、本発明の実施の形態における血糖値予測装置による時系列パターンの選択処理と補正処理とを示すフロー図である。
続いて、図5を用いて、血糖値予測装置10による時系列パターンの再選択について説明する。図5は、本発明の実施の形態における血糖値予測装置による時系列パターンの再選択処理を示すフロー図である。
また、ここで、本実施の形態における測定装置20の動作について図6を用いて説明する。図6は、本発明の実施の形態における測定装置の動作を示すフロー図である。
本実施の形態におけるプログラムは、コンピュータに、図3に示すステップA1~A6、図5に示すステップB1~B9を実行させるプログラムであれば良い。また、本実施の形態において、コンピュータとしては、測定装置等の機器に搭載されているコンピュータ、更には汎用のパーソナルコンピュータが挙げられる。
ユーザの血糖値を予測するための装置であって、
ユーザの空腹時の血糖値に基づいて、複数の、予め設定された血糖値の時系列変化のパターンの中から、1のパターンを選択する、パターン選択部と、
前記ユーザを対象として、測定日を変えて血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正する、補正処理部と、
を備えていることを特徴とする血糖値予測装置。
前記パターン選択部が、前記ユーザの空腹時の血糖値に加え、喫食後の血糖値も用いて、前記パターンを選択する、
付記1に記載の血糖値予測装置。
複数の前記パターンそれぞれが、前記ユーザの空腹時を基準時とした時間帯毎に設定され、且つ、前記基準時からの経過時間と血糖値との関係を特定する、関数の集合を含み、
前記補正処理部が、前記光学的測定の測定時間を前記基準時からの経過時間に換算し、前記複数の測定値それぞれ毎に、当該測定値と、選択された前記パターンの前記関数に換算後の前記経過時間を適用して算出される値との残差の2乗根を求め、更に、各2乗根を合計したときに、合計値が最小となるように、選択された前記時系列変化パターンの前記関数それぞれを補正する、
付記1に記載の血糖値予測装置。
前記パターンの選択から設定期間が経過した後に、前記ユーザの血液を対象とした血糖値の光学測定によって得られた測定値が、設定条件を満たしているかどうかを判定する、設定条件判定部を更に備え、
前記設定条件判定部によって、得られた前記測定値が前記設定条件を満たしていないと判定された場合に、前記パターン選択部が、前記パターンの選択を再度実行し、前記補正処理部が、新たに選択された前記パターンを補正する、
付記1に記載の血糖値予測装置。
ユーザの血糖値を光学的測定によって測定する測定装置であって、
前記光学的測定を実行する測定部と、
光学的測定の実行によって得られる測定値を血糖値に換算するための検量線を、対応する血糖値の大きさ毎に格納する、検量線データベースと、
いずれかの前記検量線を選択し、選択した検量線に、光学的測定を実行することによって得られた測定値を適用することによって血糖値を算出する制御処理部と、
ユーザの空腹時の血糖値に基づいて、複数の、予め設定された血糖値の時系列変化のパターンの中から、1のパターンを選択し、前記ユーザを対象として、測定日を変えて前記光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正することによって、ユーザの血糖値を予測する血糖値予測部と、
を備え、
前記制御処理部は、前記血糖値予測部によって、前記パターンの選択と補正とが実行された場合に、補正後の前記パターンを用いて血糖値を予測し、予測した血糖値に基づいて前記検量線を選択する、
ことを特徴とする測定装置。
ユーザの血糖値を予測するための方法であって、
(a)ユーザの空腹時の血糖値に基づいて、予め設定された複数の血糖値の時系列変化のパターンの中から、1のパターンを選択する、ステップと、
(b)前記ユーザを対象として、測定日を変えて血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正する、ステップと、
を有することを特徴とする血糖値予測方法。
前記(a)のステップにおいて、前記ユーザの空腹時の血糖値に加え、喫食後の血糖値も用いて、前記パターンを選択する、
付記6に記載の血糖値予測方法。
複数の前記パターンそれぞれが、前記ユーザの空腹時を基準時とした時間帯毎に設定され、且つ、前記基準時からの経過時間と血糖値との関係を特定する、関数の集合を含み、
前記(b)のステップにおいて、前記光学的測定の測定時間を前記基準時からの経過時間に換算し、前記複数の測定値それぞれ毎に、当該測定値と、選択された前記パターンの前記関数に換算後の前記経過時間を適用して算出される値との残差の2乗根を求め、更に、各2乗根を合計したときに、合計値が最小となるように、選択された前記時系列変化パターンの前記関数それぞれを補正する、
付記6に記載の血糖値予測方法。
(c)前記パターンの選択から設定期間が経過した後に、前記ユーザの血液を対象とした血糖値の光学測定によって得られた測定値が、設定条件を満たしているかどうかを判定する、ステップを更に有し、
前記(c)のステップにおいて、得られた前記測定値が前記設定条件を満たしていないと判定された場合に、前記(a)のステップと前記(b)のステップとが再度実行される、
付記6に記載の血糖値予測方法。
コンピュータによってユーザの血糖値を予測するためのプログラムを記録したコンピュータ読み取り可能な記録媒体であって、
前記コンピュータに、
(a)ユーザの空腹時の血糖値に基づいて、予め設定された複数の血糖値の時系列変化のパターンの中から、1のパターンを選択する、ステップと、
(b)前記ユーザを対象として、測定日を変えて、血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記時系列変化パターンを補正する、ステップと、
を実行させる、命令を含むプログラムを記録しているコンピュータ読み取り可能な記録媒体。
前記(a)のステップにおいて、前記ユーザの空腹時の血糖値に加え、喫食後の血糖値も用いて、前記パターンを選択する、
付記10に記載のコンピュータ読み取り可能な記録媒体。
複数の前記パターンそれぞれが、前記ユーザの空腹時を基準時とした時間帯毎に設定され、且つ、前記基準時からの経過時間と血糖値との関係を特定する、関数の集合を含み、
前記(b)のステップにおいて、前記光学的測定の測定時間を前記基準時からの経過時間に換算し、前記複数の測定値それぞれ毎に、当該測定値と、選択された前記パターンの前記関数に換算後の前記経過時間を適用して算出される値との残差の2乗根を求め、更に、各2乗根を合計したときに、合計値が最小となるように、選択された前記時系列変化パターンの前記関数それぞれを補正する、
付記10に記載のコンピュータ読み取り可能な記録媒体。
前記プログラムが、前記コンピュータに、
(c)前記パターンの選択から設定期間が経過した後に、前記ユーザの血液を対象とした血糖値の光学測定によって得られた測定値が、設定条件を満たしているかどうかを判定する、ステップを実行させる命令を更に含み、
前記(c)のステップにおいて、得られた前記測定値が前記設定条件を満たしていないと判定された場合に、前記(a)のステップと前記(b)のステップとが再度実行される、
付記10に記載のコンピュータ読み取り可能な記録媒体。
11 パターン選択部
12 補正処理部
13 パターンデータベース
14 設定条件判定部
20 測定装置
21 制御処理部
22 測定部
23 操作受付部
24 検量線データベース
25 測定値データベース
26 表示装置
30 ユーザ
Claims (13)
- ユーザの血糖値を予測するための装置であって、
ユーザの空腹時の血糖値に基づいて、複数の、予め設定された血糖値の時系列変化のパターンの中から、1のパターンを選択する、パターン選択部と、
前記ユーザを対象として、測定日を変えて血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正する、補正処理部と、
を備えていることを特徴とする血糖値予測装置。 - 前記パターン選択部が、前記ユーザの空腹時の血糖値に加え、喫食後の血糖値も用いて、前記パターンを選択する、
請求項1に記載の血糖値予測装置。 - 複数の前記パターンそれぞれが、前記ユーザの空腹時を基準時とした時間帯毎に設定され、且つ、前記基準時からの経過時間と血糖値との関係を特定する、関数の集合を含み、
前記補正処理部が、前記光学的測定の測定時間を前記基準時からの経過時間に換算し、前記複数の測定値それぞれ毎に、当該測定値と、選択された前記パターンの前記関数に換算後の前記経過時間を適用して算出される値との残差の2乗根を求め、更に、各2乗根を合計したときに、合計値が最小となるように、選択された前記時系列変化パターンの前記関数それぞれを補正する、
請求項1または2に記載の血糖値予測装置。 - 前記パターンの選択から設定期間が経過した後に、前記ユーザの血液を対象とした血糖値の光学測定によって得られた測定値が、設定条件を満たしているかどうかを判定する、設定条件判定部を更に備え、
前記設定条件判定部によって、得られた前記測定値が前記設定条件を満たしていないと判定された場合に、前記パターン選択部が、前記パターンの選択を再度実行し、前記補正処理部が、新たに選択された前記パターンを補正する、
請求項1~3のいずれかに記載の血糖値予測装置。 - ユーザの血糖値を光学的測定によって測定する測定装置であって、
前記光学的測定を実行する測定部と、
光学的測定の実行によって得られる測定値を血糖値に換算するための検量線を、対応する血糖値の大きさ毎に格納する、検量線データベースと、
いずれかの前記検量線を選択し、選択した検量線に、光学的測定を実行することによって得られた測定値を適用することによって血糖値を算出する制御処理部と、
ユーザの空腹時の血糖値に基づいて、複数の、予め設定された血糖値の時系列変化のパターンの中から、1のパターンを選択し、前記ユーザを対象として、測定日を変えて前記光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正することによって、ユーザの血糖値を予測する血糖値予測部と、
を備え、
前記制御処理部は、前記血糖値予測部によって、前記パターンの選択と補正とが実行された場合に、補正後の前記パターンを用いて血糖値を予測し、予測した血糖値に基づいて前記検量線を選択する、
ことを特徴とする測定装置。 - ユーザの血糖値を予測するための方法であって、
(a)ユーザの空腹時の血糖値に基づいて、予め設定された複数の血糖値の時系列変化のパターンの中から、1のパターンを選択する、ステップと、
(b)前記ユーザを対象として、測定日を変えて血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記パターンを補正する、ステップと、
を有することを特徴とする血糖値予測方法。 - 前記(a)のステップにおいて、前記ユーザの空腹時の血糖値に加え、喫食後の血糖値も用いて、前記パターンを選択する、
請求項6に記載の血糖値予測方法。 - 複数の前記パターンそれぞれが、前記ユーザの空腹時を基準時とした時間帯毎に設定され、且つ、前記基準時からの経過時間と血糖値との関係を特定する、関数の集合を含み、
前記(b)のステップにおいて、前記光学的測定の測定時間を前記基準時からの経過時間に換算し、前記複数の測定値それぞれ毎に、当該測定値と、選択された前記パターンの前記関数に換算後の前記経過時間を適用して算出される値との残差の2乗根を求め、更に、各2乗根を合計したときに、合計値が最小となるように、選択された前記時系列変化パターンの前記関数それぞれを補正する、
請求項6または7に記載の血糖値予測方法。 - (c)前記パターンの選択から設定期間が経過した後に、前記ユーザの血液を対象とした血糖値の光学測定によって得られた測定値が、設定条件を満たしているかどうかを判定する、ステップを更に有し、
前記(c)のステップにおいて、得られた前記測定値が前記設定条件を満たしていないと判定された場合に、前記(a)のステップと前記(b)のステップとが再度実行される、
請求項6~8のいずれかに記載の血糖値予測方法。 - コンピュータによってユーザの血糖値を予測するためのプログラムを記録したコンピュータ読み取り可能な記録媒体であって、
前記コンピュータに、
(a)ユーザの空腹時の血糖値に基づいて、予め設定された複数の血糖値の時系列変化のパターンの中から、1のパターンを選択する、ステップと、
(b)前記ユーザを対象として、測定日を変えて、血糖値の光学的測定を実行することによって得られた複数の測定値を用いて、選択された前記時系列変化パターンを補正する、ステップと、
を実行させる、命令を含むプログラムを記録しているコンピュータ読み取り可能な記録媒体。 - 前記(a)のステップにおいて、前記ユーザの空腹時の血糖値に加え、喫食後の血糖値も用いて、前記パターンを選択する、
請求項10に記載のコンピュータ読み取り可能な記録媒体。 - 複数の前記パターンそれぞれが、前記ユーザの空腹時を基準時とした時間帯毎に設定され、且つ、前記基準時からの経過時間と血糖値との関係を特定する、関数の集合を含み、
前記(b)のステップにおいて、前記光学的測定の測定時間を前記基準時からの経過時間に換算し、前記複数の測定値それぞれ毎に、当該測定値と、選択された前記パターンの前記関数に換算後の前記経過時間を適用して算出される値との残差の2乗根を求め、更に、各2乗根を合計したときに、合計値が最小となるように、選択された前記時系列変化パターンの前記関数それぞれを補正する、
請求項10または11に記載のコンピュータ読み取り可能な記録媒体。 - 前記プログラムが、前記コンピュータに、
(c)前記パターンの選択から設定期間が経過した後に、前記ユーザの血液を対象とした血糖値の光学測定によって得られた測定値が、設定条件を満たしているかどうかを判定する、ステップを実行させる命令を更に含み、
前記(c)のステップにおいて、得られた前記測定値が前記設定条件を満たしていないと判定された場合に、前記(a)のステップと前記(b)のステップとが再度実行される、
請求項10~12のいずれかに記載のコンピュータ読み取り可能な記録媒体。
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