CN108693353A - A kind of long-range diabetes intelligent diagnosis system detecting breathing gas based on electronic nose - Google Patents
A kind of long-range diabetes intelligent diagnosis system detecting breathing gas based on electronic nose Download PDFInfo
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- CN108693353A CN108693353A CN201810432360.7A CN201810432360A CN108693353A CN 108693353 A CN108693353 A CN 108693353A CN 201810432360 A CN201810432360 A CN 201810432360A CN 108693353 A CN108693353 A CN 108693353A
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- diabetes
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- mobile terminal
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/64—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving ketones
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/04—Endocrine or metabolic disorders
- G01N2800/042—Disorders of carbohydrate metabolism, e.g. diabetes, glucose metabolism
Abstract
The present invention relates to a kind of long-range diabetes intelligent diagnosis systems detecting breathing gas based on electronic nose, including:Electronic nose detection module, wireless transport module, intelligent mobile terminal module and the machine learning application module being deployed in cloud platform.Machine learning application of the system by being deployed in cloud platform obtains diagnostic result and sends it in intelligent mobile terminal and show come the collected human exhaled breath's signal of remote analysis electronic nose.Electronic nose detection module can obtain the concentration information of sensitive composition in user's exhaled gas and response signal is sent in intelligent mobile terminal by wireless transport module, data are uploaded to Cloud Server processing by subsequent user, and the machine learning application being deployed in cloud platform can quickly obtain diagnostic result and the results are shown in intelligent mobile terminal.The present invention realizes the quick non-invasive diagnosis of diabetes, is provided with the potential of Telemedicine, while reducing the use threshold of user, is expected to be used for extensive screening and the routine testing of diabetes.
Description
Technical field
The present invention relates to medical diagnosis on disease fields, specifically propose a kind of long-range glycosuria detecting breathing gas based on electronic nose
Sick intelligent diagnosis system is expected to be used for long-range, non-invasive diagnosis to diabetes.
Background technology
In recent years, the quantity of diabetic is continuously increased in global range, especially in the developing countries, research
It was found that the early diagnosis of diabetes is of great significance.Routine diagnostic method is mainly based upon blood glucose at present, and this intrusive mood is examined
Disconnected method can bring a large amount of pains and heavy psychological burden to subject.Recent studies indicate that by detecting characteristics of contaminated respiratory droplets
The content of certain gas componants in gas, can effectively distinguish normal person and diabetic.In current expiration detection device,
Electronic nose can respond the various ingredients in exhaled gas, formed unique " expiration collection of illustrative plates ", be useful for glycosuria
The potential of sick clinical diagnosis.
But the field also has many problems to need to solve at present, although existing electric nasus system can be to diabetes
Non-invasive diagnosis is carried out, but these systems need to rely on local host computer progress pattern-recognition.Local host computer it is expensive,
This is not only the huge waste to hardware resource, and user cost has been significantly greatly increased, and improves user and uses threshold so that is
System is difficult to popularize in vast community and situation of all-level hospitals, is unfavorable for screening and routine testing of the system to large-scale crowd.
Invention content
The present invention provides a kind of long-range diabetes intelligent diagnosis systems detecting breathing gas based on electronic nose, with tradition
Electronic nose is compared, and this system tries hard to overcome the deficiencies in the prior art and uses limitation, it is intended that realizes that the quick of diabetes non-is invaded
Enter the long-distance intelligent diagnosis of formula.To achieve the above object, the technical solution adopted by the present invention is as follows:
A kind of long-range diabetes intelligent diagnosis system detecting breathing gas based on electronic nose, including electronic nose detect mould
Block, wireless transport module, intelligent mobile terminal module and the machine learning application module being deployed in Cloud Server.
The electronic nose detection module includes breathing gas acquisition module, expiration component enrichment module, the detection of expiration component
Module and data processing module.The breathing gas acquisition module is made of blow gun, expiration dehumidifier and airbag, the use being collected into
Family gas is stored in airbag.
The expiration component enrichment module, by the ventilation pipe of installation high efficiency particle air filter, exhale enrichment pipeline, filling
Enrichment material, gas samping pump and the multi-function electromagnetic valve composition of expiration enrichment pipeline.The installation high efficiency particle air filter
Ventilation pipe is to exclude the interference of impurity in air;Expiration enrichment pipeline be for purify in exhaled gas with glycosuria
Disease is metabolized the concentration of closely related volatile organic compounds (VOCs);The enrichment material is silica gel, aluminium oxide, activity
Charcoal, zeolite or combinations thereof material;The multi-function electromagnetic valve is the flow direction for controlling gas circuit;The gas samping pump is used
To provide inflation power;Solenoid valve and gas samping pump are connected to data processing module by signal wire.
The expiration component detection module include differentiation breath analysis sensor array, microsensor preheating device and
Gas reaction chamber.The differentiation breath analysis sensor array is by electrochemistry type sensor, electrolytic sensor, semiconductor
The sensor of the plurality of classes such as type sensor, catalytic combustion-type sensor and Temperature Humidity Sensor forms, and is embedded in gas
In reative cell.The microsensor preheating device, which is mounted on, exhales on enrichment pipeline and connects electromagnetic switch, institute by conducting wire
It states electromagnetic switch and data processing module is connected to by signal wire.
The signal that the data processing module can export sensor is handled, and the data processing module includes master control
Chip, multi-channel signal conditioning circuit, liquid crystal display and touch panel.The main control chip can signal passes through institute by treated
Wireless transport module is stated to be sent in intelligent mobile terminal.
The data received can be uploaded in Cloud Server by the intelligent mobile terminal mould vertical application in the block
Machine learning application processing.The machine learning application being deployed in Cloud Server can carry out intelligence to user data and examine
Disconnected, last diagnostic result will be shown in intelligent mobile terminal.
The present invention has carried out many improvement under the premise of non-invasive diagnosis diabetes, to current breath analysis technology.First,
Most of ingredient in human exhalation is vapor, is in addition to this volatility and half volatile object including VOCs
Matter and the nonvolatile matter for being dissolved in water, the present invention acquisition when using expiration dehumidifier to the vapor in exhaled gas into
Row absorbs, and is conducive to the detection of target VOCs.
Diabetes are predicted as gas marker and monitored to current many research single use acetone, and effect is simultaneously paid no attention to
Think.The present invention is multiple close with diabetic supersession by acetone, ethyl alcohol, isoprene, methyl nitrate, second benzene,toluene,xylene etc.
Relevant gas marker is expected to greatly improve diagnosis accuracy as Combining diagnosis model.
The present invention is not necessarily to user's operation, you can enrichment and detection are carried out at the same time to the diabetes gas marker in airbag,
Presently relevant Electronic Nose Technology is difficult to realize.This makes the testing requirements of the present invention low, is suitable for the big of different physiological status
Scale crowd.
Traditional electronic nose often uses multiple sensors under same kind or response theory of the same race, these sensors are in spirit
There is prodigious similitude so that accuracy rate of diagnosis is not high in terms of sensitivity and response model.The present invention is using highly sensitive, high-precision
The differentiation breath analysis sensor array of degree, the gas sensor in array is not pure gas specific sensor, and
Cross response sensor, i.e. gas with various sensor is different to the response of same gas (or mixed gas), meanwhile, same biography
Sensor is also different to the response of gas with various (or mixed gas).By this response modes, exhaled gas can be generated special
Sign property is more obviously exhaled collection of illustrative plates, so as to greatly improve diagnosis accuracy.
The present invention by wireless transport module and can only mobile terminal, detection data is uploaded in Cloud Server, by portion
The machine learning application intelligent diagnostics in Cloud Server are affixed one's name to, machine learning is by constantly training, and then Optimal Parameters obtain more
Good algorithm model so that diagnostic result is accurate.
User data and diagnostic result are stored in Cloud Server by the present invention, the memory capacity of Cloud Server database
Greatly, the storage demand of large-scale consumer can be met.Meanwhile the present invention is greatly reduced without relying on local host computer pattern-recognition
System development costs, have saved the use of hardware resource, reduce user and use threshold, are conducive to the extensive general of system
And.
Description of the drawings
The diabetes intelligent diagnosis system functional block diagram of Fig. 1 embodiment of the present invention
The functional block diagram of the electronic nose detection module of Fig. 2 embodiment of the present invention
The schematic diagram of the electronic nose detection module of Fig. 3 embodiment of the present invention
Have in figure:Blow gun 1, expiration dehumidifier 2, airbag 3, exhale enrichment pipeline 4, solenoid valve and gas samping pump 5, installation
The ventilation pipe 6 of high efficiency particle air filter, expiration detection module 7, data processing module 8, gas outlet 9
Specific implementation mode
The specific implementation mode of the present invention is described in detail below, this explanation is an example, without limitation on hair
Bright use scope and its application.
Refering to fig. 1 to Fig. 3, one time embodiment mainly includes the following steps that:The first step acquires mould by the breathing gas
Block collects user's exhaled gas, it is proposed that user elder generation clear water gargles 3-5 times and deeply breathes 2-3 times, and subsequent user exhales to sampling
Bag, sampler bag is tightened after being full of.The airbag can be Tedlar airbags, and capacity fixes, standing time preferably no more than
12h, this step user complete the quantitative collection to exhaled gas.Second step opens the more of the expiration component enrichment module
Surrounding air is passed into gas reaction by function solenoid valve and gas samping pump through installing the ventilation pipe of high efficiency particle air filter
(it is passed through the time 30-80 seconds) in room, it is miscellaneous that the high efficiency particle air filter can remove solid that may be present in surrounding air
Object avoids damage sensor and influences baseline stability;The then direction of adjustment multi-function electromagnetic valve, is continuing with gas samping pump
Through exhaling, the gas in airbag is passed into reaction (10-20 seconds reaction time) in gas reaction chamber by enrichment pipeline.Third walks,
The differentiation breath analysis sensor array responds the diabetes gas marker in exhaled gas, each in array
The concentration information of sensitive gas can be converted to corresponding voltage signal output by a sensor.4th step, the data processing mould
The processing such as the signal conditioning circuit of block can be amplified the voltage signal of output, filter, baseline correction, can then send out signal
It is sent to the main control chip to be further processed, including multichannel data reading, AD conversion, temperature and humidity compensation and data format are beaten
Packet, the liquid crystal display of the data processing module can show humiture in gas reaction chamber, solenoid valve direction and
The working time of gas samping pump, the touch panel can change the gas circuit direction of solenoid valve and opening for control gas samping pump
It closes.5th step, the main control chip can connect the wireless transport module by relevant interface, subsequent wireless transport module can and
The intelligent mobile terminal establishes channel by associated network services, to which data can be passed through wireless transport module by main control chip
It sends it in intelligent mobile terminal.6th step, user are communicated with Cloud Server by intelligent mobile terminal and will be counted
It is preserved according to uploading, the machine learning application being deployed in Cloud Server can carry out intelligent diagnostics, cloud service to user data
Machine learning application in device can directly invoke correlation machine study cloud service, by constantly training come Optimized model parameter,
Its accuracy rate of diagnosis is far above general mode recognition methods, and last diagnostic result can be shown in intelligent mobile terminal.
The diabetes gas marker includes mainly acetone, alcohols, isoprene, methyl nitrate, aromatic compounds
Etc. the multiple and closely related VOCs components of diabetic supersession.Acetone is the body since the insulin metabolism of diabetes patient is abnormal
The glucose of interior generation can not normally enter cell by the carrying of insulin, and body can divide in the case of sugar insufficient supply
Solution fat energy supply, at this time ketoboidies constantly increased at the intermediate product concentration in blood of aliphatic acid as lipolysis, when dense
The strong volatility of acetone can make its menses tube wall penetrate into alveolar to be excreted by respiratory system again in ketoboidies when spending high.Alcohols
Substance such as methanol and ethyl alcohol are that glucide is generated through enteron aisle microflora fermentation, and methyl nitrate is due to cell under hyperglycemia state
The metabolism of mitochondrial enhances the synthesis for causing the appearance of super oxidation reaction to exacerbate methyl nitrate;Diabetes also with internal fat
Matter peroxidating process is related, and hydrocarbons are the final stable products of peroxidatic reaction of lipid, and wherein isoprene is expiratory air
The middle highest hydrocarbons of content, its solubility in blood is very low quickly to be evaporate into exhaled gas.Ethylbenzene, toluene,
The aromatic compounds such as dimethylbenzene may be from liver, and the hyperglycemic state of diabetic can inhibit liver enzyme (cell color
Plain P450 systems) these gases are metabolized, make it transfer to be recycled in hematological system.
The machine learning application module being deployed in Cloud Server can carry out intelligent diagnostics to user data, at present city
There are many publicly-owned cloud platforms to provide machine learning service on, the machine learning service is asked comprising the most of bases of machine learning
Topic such as data prediction, model training, model evaluation, classification predict that the overall of full-automatic or semi-automatic cloud platform defines.
Compared with tradition is based on the mode identification method of local host computer, the present invention can directly invoke publicly-owned cloud platform and provide
Machine learning service, so as to the artificial intelligence approach of a set of diabetes diagnosis of rapid build.The machine learning service
It is a machine learning environment, it provides rapid modeling and deployment tool to simplify the work of developer, can not only skip data
The old process such as cleaning, modeling, moreover it is possible to which machine processing, while engineering are given in the work of complicated arameter optimization during tradition is developed
Acclimatization business built-in algorithm is also provided, in distributed system large data collection and calculating be optimized.
It includes three data prediction, feature extraction and classification prediction parts, the data that the machine learning, which is applied,
Preprocessing part using the machine learning service provide Integrated Development Environment, execute data check, data cleansing, pretreatment
Etc. tasks.Related algorithm such as Principal Component Analysis (PCA) and Fisher face can be used in the characteristic extraction part
(LDA) feature extraction is carried out to user data.
The classification predicted portions include model construction, model training and model verification, these services are by related public cloud
Platform provides.The machine learning service carries common supervised learning and unsupervised learning algorithm and frame.The present invention's
Machine learning application belongs to Supervised classification problem, and specific disaggregated model can be created by container instrument and passes through training set number
According to training, training process distributed on Cloud Server can be run, and to reach faster training speed, while model parameter can not
Disconnected to carry out Automatic Optimal, the model after training verification can be used to the prediction of the classification to user.
The final machine learning application being deployed in Cloud Server can be by the diagnostic result of user at intelligent mobile end
It is shown in end, to complete primary complete diagnosis.The above content is combine specific implementation mode made for the present invention into one
Step is described in detail, and it cannot be said that the specific implementation of the present invention is confined to these explanations.For the technical field of the invention
For technical staff, without departing from the inventive concept of the premise, if can also be made to the embodiment that these have been described
Dry replacement or modification, and these are substituted or variant all shall be regarded as belonging to protection scope of the present invention.
Claims (10)
1. a kind of long-range diabetes intelligent diagnosis system detecting breathing gas based on electronic nose, which is characterized in that including:Electronics
Nose detection module, wireless transport module, intelligent mobile terminal module and the machine learning application mould being deployed in Cloud Server
Block.
2. diabetes intelligent diagnosis system as described in claim 1, which is characterized in that the electronic nose detection module includes exhaling
Air-breathing body acquisition module, expiration component enrichment module, expiration component detection module and data processing module.
3. diabetes intelligent diagnosis system as claimed in claim 2, which is characterized in that the expiration component enrichment module includes
The ventilation pipe of high efficiency particle air filter, enrichment pipeline of exhaling, the enrichment material of filling expiration enrichment pipeline, gas sampling are installed
Pump and multi-function electromagnetic valve.
4. diabetes intelligent diagnosis system as claimed in claim 2, which is characterized in that the expiration component detection module includes
Differentiation breath analysis sensor array, microsensor preheating device and gas reaction chamber.
5. diabetes intelligent diagnosis system as claimed in claim 4, which is characterized in that the differentiation breath analysis sensor
Array includes electrochemistry type sensor, electrolytic sensor, semiconductor type sensor, catalytic combustion-type sensor and warm and humid
Spend the sensor of the plurality of classes such as sensor.
6. diabetes intelligent diagnosis system as claimed in claim 2, which is characterized in that the data processing module includes master control
Chip, multi-channel signal conditioning circuit, liquid crystal display and touch panel, data that treated can be sent by wireless transport module
To intelligent mobile terminal.
7. diabetes intelligent diagnosis system as claimed in claim 6, which is characterized in that the wireless transport module is using blue
Tooth, WIFI or dedicated transport channels, intelligent mobile terminal receive data using vertical application.
8. diabetes intelligent diagnosis system as claimed in claim 7, which is characterized in that the intelligent mobile terminal can be by user
Data are uploaded to Cloud Server, are then handled by the machine learning application being deployed in Cloud Server.
9. diabetes intelligent diagnosis system as claimed in claim 8, which is characterized in that the machine learning application is to pass through tune
The machine learning service provided with publicly-owned cloud platform, a set of artificial intelligence approach for diagnosing diabetes of rapid build, packet
Include three data prediction, feature extraction and classification prediction parts.
10. diabetes intelligent diagnosis system as claimed in claim 9, the machine learning services package that the publicly-owned cloud platform provides
Include most of underlying issues of machine learning such as data prediction, model training, model evaluation, classification prediction etc. it is full-automatic or half from
The overall definition of dynamic cloud platform.
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| CN111297403A (en) * | 2020-02-25 | 2020-06-19 | 中国矿业大学 | Rapid and accurate screening and early warning method for pneumoconiosis group |
| CN112666344A (en) * | 2021-01-22 | 2021-04-16 | 重庆医事通科技发展有限公司 | Medical and defense fusion-based diabetes high risk group case data remote screening and extracting working method |
| CN114755271A (en) * | 2022-04-15 | 2022-07-15 | 四川大学 | Electronic nose system for detecting multiple volatile organic compounds |
| CN115097064A (en) * | 2021-09-24 | 2022-09-23 | 深圳大学 | Gas detection method and device, computer equipment and storage medium |
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| CN110333343A (en) * | 2019-07-30 | 2019-10-15 | 中国科学院合肥物质科学研究院 | A kind of sugared tolerance quantization evaluating apparatus based on detection of exhaling |
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| CN114755271A (en) * | 2022-04-15 | 2022-07-15 | 四川大学 | Electronic nose system for detecting multiple volatile organic compounds |
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Application publication date: 20181023 |