CN115768348A - Device and equipment capable of realizing noninvasive blood glucose detection - Google Patents
Device and equipment capable of realizing noninvasive blood glucose detection Download PDFInfo
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- CN115768348A CN115768348A CN202180024619.4A CN202180024619A CN115768348A CN 115768348 A CN115768348 A CN 115768348A CN 202180024619 A CN202180024619 A CN 202180024619A CN 115768348 A CN115768348 A CN 115768348A
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- 239000008280 blood Substances 0.000 title claims abstract 46
- 210000004369 blood Anatomy 0.000 title claims abstract 46
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 title claims abstract 21
- 239000008103 glucose Substances 0.000 title claims abstract 21
- 238000001514 detection method Methods 0.000 title claims abstract 7
- 230000007613 environmental effect Effects 0.000 claims abstract 7
- 238000005259 measurement Methods 0.000 claims abstract 7
- 238000012360 testing method Methods 0.000 claims abstract 7
- 238000002329 infrared spectrum Methods 0.000 claims abstract 4
- 230000010354 integration Effects 0.000 claims abstract 4
- 230000002503 metabolic effect Effects 0.000 claims abstract 4
- 238000012549 training Methods 0.000 claims 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims 4
- 238000010276 construction Methods 0.000 claims 4
- 229910052760 oxygen Inorganic materials 0.000 claims 4
- 239000001301 oxygen Substances 0.000 claims 4
- 238000000034 method Methods 0.000 claims 3
- 238000013507 mapping Methods 0.000 claims 1
- 230000036284 oxygen consumption Effects 0.000 claims 1
- 208000015181 infectious disease Diseases 0.000 abstract 1
- 238000000691 measurement method Methods 0.000 abstract 1
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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 or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
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- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
一种可实现无创血糖检测的装置包括参数获取模块(101),特征确定模块(102)和血糖确定模块(103)。参数获取模块(101)用于获取血糖待测对象的生理参数和血糖待测对象当前所在区域的环境参数，生理参数通过无创方式获取。特征确定模块(102)用于根据生理参数和环境参数得到血糖待测对象的第一输入特征，第一输入特征包括血糖待测对象的红外光谱特征和代谢热整合特征。血糖确定模块(103)用于将第一输入特征输入至血糖测量模型，以通过血糖测量模型得到血糖待测对象的血糖值。采用上述装置可以解决相关技术中有创血糖测量方式不能连续进行血糖监测且易给患者带来感染风险的技术问题。
A device capable of realizing non-invasive blood sugar detection includes a parameter acquisition module (101), a feature determination module (102) and a blood sugar determination module (103). The parameter acquiring module (101) is used to acquire the physiological parameters of the blood glucose test subject and the environmental parameters of the area where the blood glucose test subject is currently located, and the physiological parameters are acquired in a non-invasive manner. The feature determination module (102) is used to obtain the first input feature of the blood sugar test object according to the physiological parameters and the environmental parameters, and the first input feature includes the infrared spectrum feature and the metabolic heat integration feature of the blood sugar test object. The blood glucose determination module (103) is used for inputting the first input feature into the blood glucose measurement model, so as to obtain the blood glucose value of the blood glucose test subject through the blood glucose measurement model. The above-mentioned device can solve the technical problem in the related art that the invasive blood sugar measurement method cannot continuously monitor the blood sugar and easily brings infection risks to patients.
Description
PCT国内申请，说明书已公开。PCT domestic application, specification has been published.
Claims (10)
- An apparatus for enabling non-invasive blood glucose testing, comprising: the device comprises a parameter acquisition module, a characteristic determination module and a blood sugar determination module;the parameter acquisition module is used for acquiring physiological parameters of a blood sugar object to be detected and environmental parameters of an area where the blood sugar object to be detected is located currently, and the physiological parameters are acquired in a non-invasive mode;the characteristic determination module is used for obtaining a first input characteristic of the blood sugar object to be tested according to the physiological parameter and the environmental parameter, wherein the first input characteristic comprises an infrared spectrum characteristic and a metabolic heat integration characteristic of the blood sugar object to be tested;the blood sugar determining module is used for inputting the first input characteristics into a blood sugar measuring model so as to obtain the blood sugar value of the object to be measured for blood sugar through the blood sugar measuring model.
- The apparatus capable of performing non-invasive blood glucose detection according to claim 1, further comprising: a characteristic obtaining module and a model constructing module,the characteristic acquisition module is used for acquiring training characteristics and target characteristics, the training characteristics comprise second input characteristics of at least one object with known blood sugar, the second input characteristics comprise infrared spectrum characteristics and metabolic heat integration characteristics of the object with known blood sugar, and the target characteristics comprise blood sugar values of the at least one object with known blood sugar;the model building module is used for building the blood sugar measurement model through the training features and the target features.
- The apparatus capable of noninvasive blood glucose detection of claim 2, wherein the model building module comprises:a current feature determining unit, configured to use the training feature as a current first feature, and use the target feature as a current second feature;a first principal component axis vector determining unit, configured to obtain a first principal component axis vector of the current first feature according to the current first feature and the current second feature;a first residual determining unit configured to determine, according to the first principal component axis vector, a first principal component of the current first feature and a first residual required when the first principal component and the first principal component axis vector represent the current first feature;a second residual determining unit, configured to determine, according to the first principal component and the current second feature, a target coefficient and a second residual that are required when the current second feature is represented by the first principal component;the updating unit is used for updating the first residual error into a current first characteristic, updating the second residual error into a current second characteristic, and returning to execute the operation of obtaining a first principal component axis vector of the current first characteristic according to the current first characteristic and the current second characteristic until an iteration stop condition is met;the linear expression construction unit is used for constructing a linear expression of the target feature relative to the training feature by using the first principal component axis vector, the first principal component and the target coefficient obtained by each iteration;and the model construction unit is used for taking the linear expression as a regression equation used by the blood glucose measurement model so as to finish construction of the blood glucose measurement model.
- The apparatus enabling noninvasive blood glucose detection of claim 3, wherein the first principal component axis vector determination unit comprises:a mapping subunit, configured to map the current first feature to a first candidate principal component axis vector to obtain a first candidate principal component of the current first feature, and map the current second feature to a second candidate principal component axis vector to obtain a second candidate principal component of the current second feature;and an axis vector determining subunit, configured to determine, by using a lagrange multiplier method, a first candidate principal component axis vector and a second candidate principal component axis vector when the first candidate principal component and the second candidate principal component are optimal, and use the first candidate principal component axis vector when the first candidate principal component and the second candidate principal component are optimal as the first principal component axis vector of the current first feature.
- The apparatus capable of implementing a non-invasive blood glucose detecting method according to claim 3, wherein the first residual determining unit comprises:a first principal component determining subunit, configured to map the current first feature to the first principal component axis vector, so as to obtain a first principal component of the current first feature;a first residual calculating subunit, configured to determine, according to the first principal component and the first principal component axis vector, a first residual required when the first principal component and the first principal component axis vector represent the current first feature.
- The apparatus enabling non-invasive blood glucose testing according to claim 3, wherein the second residual error determining unit includes:a regression expression equation constructing subunit, configured to construct a regression expression equation of the current second feature using the first principal component, the target coefficient, and the second residual error;a target coefficient determination subunit configured to determine the target coefficient in the regression expression equation by a least square method;and the second residual error calculation subunit is configured to substitute the first principal component and the target coefficient into the regression expression equation to obtain the second residual error.
- The apparatus for enabling noninvasive glucose sensing of claim 3, wherein the iteration stop condition comprises a current number of iterations reaching a target number of iterations.
- The apparatus capable of achieving noninvasive blood glucose detection of claim 3, wherein the linear expression construction unit comprises:the first expression constructing subunit is used for constructing a first expression of the target feature by using the first principal component and the target coefficient obtained by each iteration;and the linear expression determining subunit is used for replacing the first principal component in the first expression by using the training feature and the first principal component axis vector obtained by each iteration to obtain a linear expression of the target feature relative to the training feature.
- The apparatus capable of performing non-invasive blood glucose detection according to claim 1, wherein the environmental parameters comprise an environmental temperature and an environmental humidity, and the physiological parameters comprise a body surface temperature, a blood oxygen saturation, an electrocardiograph signal and a multichannel pulse signal;the feature determination module includes:the metabolic heat integration characteristic determining unit is used for obtaining a temperature difference characteristic according to the body surface temperature and the environment temperature; and/or acquiring a first water vapor pressure corresponding to the body surface temperature and a second water vapor pressure corresponding to the environment temperature, and acquiring a water vapor pressure difference characteristic according to the first water vapor pressure, the second water vapor pressure and the environment humidity; and/or, using the blood oxygen saturation as a blood oxygen saturation characteristic; and/or obtaining pulse rate characteristics according to the multi-channel pulse signals; and/or obtaining blood oxygen consumption characteristics in unit time according to the blood oxygen saturation, the multichannel pulse signals and the electrocardiosignals;the infrared spectrum characteristic determining unit is used for taking the main frequency energy of the multi-channel pulse signal as a main frequency energy characteristic; and/or, taking the main frequency of the multi-channel pulse signal as a main frequency characteristic.
- An apparatus for performing non-invasive blood glucose measurement, comprising the apparatus for performing non-invasive blood glucose measurement according to any one of claims 1-9.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2021/100746 WO2022261917A1 (en) | 2021-06-17 | 2021-06-17 | Apparatus that can implement non-invasive blood glucose testing, and device |
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| Publication Number | Publication Date |
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| CN115768348A true CN115768348A (en) | 2023-03-07 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN202180024619.4A Pending CN115768348A (en) | 2021-06-17 | 2021-06-17 | Device and equipment capable of realizing noninvasive blood glucose detection |
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| CN (1) | CN115768348A (en) |
| WO (1) | WO2022261917A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118749995A (en) * | 2024-07-05 | 2024-10-11 | 深圳技术大学 | A blood glucose monitoring method based on improved residual network and multi-expert model |
| CN119700102A (en) * | 2025-02-27 | 2025-03-28 | 大连理工大学 | A non-invasive blood sugar dynamic detection system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103349553A (en) * | 2013-08-01 | 2013-10-16 | 赵巍 | Double-wavelength differential near-infrared non-invasive glucose meter |
| CN106361305A (en) * | 2016-09-19 | 2017-02-01 | 爱国者电子科技有限公司 | Glucose metabolic rate measurement method and device |
| CN106980746A (en) * | 2016-12-16 | 2017-07-25 | 清华大学 | A kind of general Woundless blood sugar Forecasting Methodology based on Time-Series analysis |
| CN107174258A (en) * | 2017-06-02 | 2017-09-19 | 北京信息科技大学 | Blood sugar concentration Forecasting Methodology |
| CN107174259A (en) * | 2017-06-26 | 2017-09-19 | 上海理工大学 | Woundless blood sugar value harvester and computational methods based on multi-wavelength conservation of energy |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2152895A2 (en) * | 2007-05-11 | 2010-02-17 | Sigmed, Inc. | Non-invasive characterization of a physiological parameter |
| CN105962949A (en) * | 2016-06-14 | 2016-09-28 | 上海理工大学 | Noninvasive blood glucose calculating method based on near-infrared light energy conservation law and signal collecting device |
| CN110575181A (en) * | 2019-09-10 | 2019-12-17 | 重庆大学 | Near-infrared spectroscopy non-invasive blood glucose detection network model training method |
-
2021
- 2021-06-17 CN CN202180024619.4A patent/CN115768348A/en active Pending
- 2021-06-17 WO PCT/CN2021/100746 patent/WO2022261917A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103349553A (en) * | 2013-08-01 | 2013-10-16 | 赵巍 | Double-wavelength differential near-infrared non-invasive glucose meter |
| CN106361305A (en) * | 2016-09-19 | 2017-02-01 | 爱国者电子科技有限公司 | Glucose metabolic rate measurement method and device |
| CN106980746A (en) * | 2016-12-16 | 2017-07-25 | 清华大学 | A kind of general Woundless blood sugar Forecasting Methodology based on Time-Series analysis |
| CN107174258A (en) * | 2017-06-02 | 2017-09-19 | 北京信息科技大学 | Blood sugar concentration Forecasting Methodology |
| CN107174259A (en) * | 2017-06-26 | 2017-09-19 | 上海理工大学 | Woundless blood sugar value harvester and computational methods based on multi-wavelength conservation of energy |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118749995A (en) * | 2024-07-05 | 2024-10-11 | 深圳技术大学 | A blood glucose monitoring method based on improved residual network and multi-expert model |
| CN119700102A (en) * | 2025-02-27 | 2025-03-28 | 大连理工大学 | A non-invasive blood sugar dynamic detection system |
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| Publication number | Publication date |
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| WO2022261917A1 (en) | 2022-12-22 |
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