CN114343573A - Closed-loop digital medicine system, equipment and medium for medicine addicts - Google Patents
Closed-loop digital medicine system, equipment and medium for medicine addicts Download PDFInfo
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Abstract
The invention provides a closed-loop digital drug system, equipment and a medium for drug addicts.A personalized game training system presents a psychophysical test game and receives data acquired after a patient carries out neurocognitive function training; the non-invasive brain stimulation modulation system outputs a signal for stimulating intervention to the brain according to a neural regulation method; the brain function dynamic monitoring system collects psychophysical data and/or brain function state data of a patient during personalized game training and/or stimulation intervention and outputs the psychophysical data and/or the brain function state data to the self-adaptive multitask training and distributing system; the self-adaptive multitask training distribution system receives psychophysical data and/or brain functional state data and adjusts training parameters of the personalized game training system and/or stimulation parameters of the non-invasive brain stimulation modulation system.
Description
Technical Field
The invention belongs to the field of rehabilitation for drug addiction treatment, and particularly relates to a closed-loop digital drug system, equipment and a medium for drug addicts.
Background
Drug addiction, also known as drug abuse or substance dependence, refers to a brain disease that presents with tolerance and withdrawal symptoms through the continuous use of certain classes of addictive drugs, compels to seek drugs and continuously increase the amount of drugs, and is very susceptible to relapse. In addition, drug addiction caused by using various drugs is a serious social problem for all countries in the world including China, not only causes serious harm to the health of addicts and families, but also causes a series of social harm behaviors and various illegal criminal events, and forms great risk for public safety and social stability. Aiming at the situation, the centre of gravity of the drug rehabilitation correction work in China gradually changes from striking punishment to correction rehabilitation combining prevention and correction treatment, but the equipment for diagnosis evaluation and correction treatment is limited and single.
The basic system of the drug-breaking measures in China at present comprises three modes of forced isolation drug-breaking, community drug-breaking and voluntary drug-breaking. The forced isolation for drug rehabilitation mainly helps drug rehabilitation through strict work and rest arrangement, cultivation of labor habits, restriction of living and activity spaces, supervision and control and other modes, but the mode hardly gives consideration to the physical and mental requirements of addicts, so that the situation of relapse after returning to the society is easy to occur. Community drug rehabilitation combines drug rehabilitation with family and social environment, and under the organization and supervision of the community, resources and forces of community, family, sanitation, public security, civil affairs and the like are integrated to help addiction patients to take drugs, but from the practical process, the situations of non-professional guidance and treatment means exist. Voluntary drug rehabilitation is a process of actively giving up drug addiction for addicts through self-provinces or by means of mechanisms such as hospitals and voluntary drug rehabilitation centers, but does not have good support and guidance, and even though physiological detoxification can be achieved by the addicts, the problems of psychological detoxification, social function recovery and the like are difficult to solve, so that the relapse situation is endless.
The specific methods for treating drug addiction mainly include two major types, namely, drug maintenance therapy and non-drug addiction treatment. Methadone maintenance therapy is commonly used in the drug maintenance therapy at present, but the problems of high treatment threshold, high shedding rate and the like exist, and the treatment regulation management also has great difficulty. At present, transcranial magnetic stimulation instruments or transcranial direct current stimulation instruments are purchased separately in places such as a part of mandatory isolation drug rehabilitation centers, but the devices are idle due to higher requirements on the speciality of users and certain operational error risks.
Disclosure of Invention
A first object of the present invention is to solve the above problems in the prior art by providing a closed-loop digital pharmaceutical system for drug addicts; a second object of the invention is to provide an apparatus; it is a third object of the present invention to provide a computer-readable storage medium.
The first object of the present invention can be achieved by the following technical solutions: a closed-loop digital pharmaceutical system for drug addicts, comprising: the system comprises an individualized game training system, a non-invasive brain stimulation modulation system, a brain function dynamic monitoring system and a self-adaptive multi-task training distribution system;
the personalized game training system is used for presenting a stored psychophysical test game and receiving data acquired after a drug addicted patient is subjected to neurocognitive function training when receiving an instruction sent by the adaptive multi-task training and distributing system;
the non-invasive brain stimulation modulation system is used for storing the nerve regulation and control method, and when receiving the stimulation instruction, the non-invasive brain stimulation modulation system is used for outputting a signal for stimulating and intervening the brain of the drug addict according to the nerve regulation and control method;
the brain function dynamic monitoring system is used for collecting psychophysical data and/or brain function state data of a drug addiction patient during personalized game training and/or stimulation intervention, displaying the psychophysical data and/or the brain function state data in real time, and outputting the psychophysical data and/or the brain function state data to the self-adaptive multitask training and distributing system in real time;
the self-adaptive multitask training distribution system is used for receiving psychophysical data and/or brain function state data output by the brain function dynamic monitoring system in real time, and adjusting training parameters of the personalized game training system and/or stimulation parameters of the non-invasive brain stimulation modulation system by using a preset artificial intelligence algorithm.
The working principle of the invention is as follows: the invention utilizes a personalized game training system and a non-invasive brain stimulation modulation system to carry out rehabilitation training on drug addicts, utilizes a brain function dynamic monitoring system to carry out real-time monitoring on psychophysical data and brain function data of the drug addicts during personalized game training and whole brain stimulation intervention, and utilizes a self-adaptive multi-task training distribution system to optimize and adjust a training scheme until the drug addicts are rehabilitated.
The closed-loop digital drug system for drug addicts further comprises a drug addict comprehensive risk evaluation system, wherein the drug addict comprehensive risk evaluation system is used for collecting data of drug addicts, forming evaluation results after analyzing and processing the data, and respectively sending the evaluation results to the personalized game training system, the non-invasive brain stimulation modulation system and the display terminal.
In the closed-loop digital drug system for drug addicts, the drug addiction comprehensive risk assessment system comprises a psychological game breakthrough module, a data acquisition system, a feature extraction system, an algorithm analysis system and an assessment report system;
the psychology game breakthrough module is used for presenting a stored psychology and physics testing game, and when receiving a testing instruction, the psychology game breakthrough module is used for presenting the psychology and physics testing game to perform psychology and physics testing on a drug addicted patient, acquiring psychology and physics data and sending the psychology and physics data to the data acquisition system;
the data acquisition system is used for acquiring psychophysical data, clinical diagnosis and social information data, brain functional state data and physiological data of a drug addiction patient and sending the psychophysical data, the clinical diagnosis and social information data, the brain functional state data and the physiological data to the feature extraction system;
the characteristic extraction system is used for receiving the psychophysical data, the clinical diagnosis and social information data, the brain functional state data and the physiological data sent by the data acquisition system, processing the data to form a data index, and sending the data index to the algorithm analysis system;
the algorithm analysis system is used for receiving the data indexes sent by the characteristic extraction system, analyzing the data indexes to form risk indexes and risk probabilities, and sending the risk indexes and the risk probabilities to the evaluation report system;
the evaluation report system is used for receiving the risk indexes and the risk probabilities sent by the algorithm analysis system, forming evaluation results according to the risk indexes and the risk probabilities, and sending the evaluation results to the personalized game training system, the non-invasive brain stimulation modulation system and the display terminal.
In the above closed-loop digital medicine system for the patient with drug addiction, the data acquisition system comprises an information entry module, a monitoring module and a detection module;
the information input module is used for acquiring clinical diagnosis and social information data of the drug addict and sending the clinical diagnosis and the social information data to the feature extraction system;
the monitoring module is used for collecting psychophysical data and/or brain functional state data of a drug addiction patient during personalized game training and/or psychogame clearance testing and/or stimulation intervention, and sending the psychophysical data and/or the brain functional state data to the feature extraction system and/or the brain function dynamic monitoring system;
the detection module is used for collecting physiological data of a drug addiction patient and sending the physiological data to the feature extraction system.
In the above closed-loop digital medicine system for the drug addict, the brain functional state data includes change data of electroencephalogram signals, change data of oxyhemoglobin in the brain, change data of deoxyhemoglobin, and eye movement trajectory data.
In the above closed-loop digital pharmaceutical system for drug addicts, the neuromodulation method comprises transcranial magnetic stimulation and transcranial direct current stimulation.
In the above closed-loop digital medicine system for the drug addict, the training parameters include game type, level difficulty and number of rounds, and the stimulation parameters include stimulation frequency, stimulation intensity and pulse number of transcranial magnetic stimulation, stimulation type, current intensity and duration of transcranial direct current stimulation.
In the closed-loop digital drug system for drug addicts, the initial training parameters and the initial stimulation parameters are determined by the initial evaluation results of the drug addiction risk evaluation system.
The second object of the present invention can be achieved by the following technical solutions: an electronic device, comprising:
a display;
one or more processors;
a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for executing a closed loop digital medicine system for a drug addicted patient as described above.
The third object of the present invention can be achieved by the following technical solutions: a computer readable storage medium storing a computer program for use in conjunction with an electronic device having a display, the computer program being executable by a processor to perform a closed loop digital drug system for a drug addicted patient as described above.
Compared with the prior art, the invention has the following advantages:
1. the invention forms a full-flow digital medicine closed-loop system from evaluation to real-time monitoring and personalized rehabilitation training for a medicine addict through a drug addiction comprehensive risk evaluation system, a brain function dynamic monitoring system, a self-adaptive multi-task training distribution system, a personalized game training system, a non-invasive brain stimulation modulation system and the like, and can realize self-adaptive monitoring, optimization and adjustment of a training scheme in the closed-loop system until the medicine addict recovers.
2. The drug addiction comprehensive risk assessment system integrates multi-dimensional data such as psychophysical tests, clinical and social information, brain, gene, biochemistry (namely physiology) and the like, carries out comprehensive assessment based on the neurocognitive function of a drug addiction patient, and can reflect the brain function damage of the drug addiction patient caused by long-term drug intake.
3. The invention can provide decision of related parameters in the transcranial magnetic stimulation or transcranial direct current stimulation process through a matched drug addiction comprehensive risk evaluation system, a brain function dynamic monitoring system and a self-adaptive multitask training distribution system, greatly reduces the professional threshold of a user, implements non-invasive, mild and non-refined whole brain stimulation on the whole design, and is combined with a game training system to realize two-way intervention of cognition and brain function, thereby having no side effect, higher efficiency and higher safety.
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FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
As shown in fig. 1, a first embodiment of the present invention provides a closed-loop digital pharmaceutical system for drug addicts, comprising: the system comprises a drug addiction comprehensive risk evaluation system, a personalized game training system, a non-invasive brain stimulation modulation system, a brain function dynamic monitoring system and a self-adaptive multitask training distribution system.
(1) Drug addiction comprehensive risk assessment system
The drug addiction comprehensive risk evaluation system is used for collecting data of drug addicts, analyzing and processing the data to form an evaluation result, and sending the evaluation result to the personalized game training system, the non-invasive brain stimulation modulation system and the display terminal.
The drug addiction comprehensive risk assessment system comprises a psychological game breakthrough module, a data acquisition system, a feature extraction system, an algorithm analysis system and an assessment report system;
the psychology game breakthrough module is used for presenting a stored psychology and physics test game, and when receiving a test instruction, the psychology game breakthrough module is used for presenting the psychology and physics test game to perform psychology test on the drug addict, acquiring psychology and physics data and sending the psychology and physics data to the data acquisition system;
the data acquisition system is used for acquiring psychophysical data, clinical diagnosis and social information data, brain functional state data and physiological data of a drug addiction patient and sending the psychophysical data, the clinical diagnosis and social information data, the brain functional state data and the physiological data to the feature extraction system;
the data acquisition system comprises an information input module, a monitoring module and a detection module;
the information input module is used for collecting clinical diagnosis and social information data of the drug addict and sending the clinical diagnosis and the social information data to the feature extraction system.
The monitoring module is used for collecting psychophysical data and/or brain functional state data of a drug addiction patient during personalized game training and/or psychogame clearance testing and/or stimulation intervention, and sending the psychophysical data and/or the brain functional state data to the feature extraction system and/or the brain function dynamic monitoring system;
the brain functional state data comprises brain electrical signal change condition data, brain oxygenated hemoglobin change condition data, deoxygenated hemoglobin change condition data and eye movement track data. The monitoring module comprises monitoring equipment such as a portable electroencephalograph monitor, a portable near-infrared imaging system, an eye tracker and the like.
When a drug addict carries out a psychological game breakthrough test, the psychological game breakthrough module acquires the psychophysical data of the drug addict and sends the psychophysical data to the monitoring module, the monitoring module acquires the psychophysical data and sends the psychophysical data to the feature extraction system, and meanwhile, monitoring equipment of the monitoring module acquires the brain functional state data of the drug addict when the drug addict carries out the psychological game breakthrough test. The monitoring module sends the psychophysical data and the brain functional state data to the feature extraction system; the psychological game breakthrough test is used for indicating that the drug addicted patient uses the psychological game breakthrough module to perform the test.
When a drug addict carries out personalized game training, the personalized game training system presents a psychophysical test game, carries out nerve cognitive function training on the drug addict, acquires psychophysical data, sends the psychophysical data to the monitoring module, and the monitoring module acquires the psychophysical data and sends the psychophysical data to the brain function dynamic monitoring system. Meanwhile, the monitoring equipment of the monitoring module collects brain functional state data of a drug addiction patient during personalized game training. And the monitoring module sends the psychophysical data and the brain function state data to the brain function dynamic monitoring system. Personalized game training is used to indicate that a drug addict is training using a personalized game training system.
When a drug addiction patient carries out stimulation intervention, monitoring equipment of the monitoring module collects brain functional state data and sends the brain functional state data to the brain functional dynamic monitoring system; the stimulation intervention is used for expressing the stimulation intervention of the non-invasive brain stimulation modulation system on the brain of a patient suffering from drug addiction.
The detection module is used for collecting physiological data of a drug addiction patient and sending the physiological data to the feature extraction system. The physiological data includes gene test results, hair test results, urine test results, BMI and body fat rates, pain tolerance, and the like. The detection module comprises detection equipment, such as a gene detector, a hair detector, a urine detector, a body fat scale, a cold pressing pain induction instrument and the like.
The characteristic extraction system is used for receiving the psychophysical data, the clinical diagnosis and social information data, the brain functional state data and the physiological data sent by the data acquisition system, processing the data to form a data index, and sending the data index to the algorithm analysis system; the data indexes include psychophysical test result indexes, clinical and social dimension data indexes, brain functional state data indexes and gene biochemical data indexes.
The feature extraction system is mainly used for preprocessing data obtained by the data acquisition system, extracting features and reducing dimensions of the data, and finally forming a psychophysical test result index, a clinical and social dimension data index, a brain functional state data index and a gene biochemical data index respectively.
The algorithm analysis system is used for receiving the data indexes sent by the characteristic extraction system, analyzing the data indexes to form risk indexes and risk probabilities, and sending the risk indexes and the risk probabilities to the evaluation report system;
the algorithm analysis system mainly uses an artificial intelligence technology to establish an algorithm model for psychophysical test result indexes, clinical and social dimension data indexes, brain functional state data indexes and gene biochemical data indexes output by the feature extraction system, uses a machine learning algorithm (such as random forest, support vector machine, k-means clustering algorithm and the like) to establish an integrated learning model consisting of a plurality of models at the initial stage of the application algorithm part, and further adopts a deep learning algorithm (such as convolutional neural network, cyclic neural network, hierarchical clustering and the like) to establish the integrated learning model consisting of a plurality of models at the later stage on the basis of the machine learning algorithm. The algorithm analysis system outputs 7 risk indicators and risk probabilities corresponding to the 7 risk indicators, as shown in table 1.
TABLE 1 Risk indices and Risk probabilities
The evaluation report system is used for receiving the risk indexes and the risk probabilities sent by the algorithm analysis system, forming evaluation results according to the risk indexes and the risk probabilities, and sending the evaluation results to the personalized game training system, the non-invasive brain stimulation modulation system and the display terminal.
The evaluation report system generates a visual evaluation report (namely an evaluation result) according to the risk index and risk probability production chart and the word explanation output by the algorithm analysis system, and a manager can view or print the evaluation report through a display terminal. In addition, the evaluation report can also record the historical evaluation record of the drug addict, draw a risk change trend chart of the drug addict and help a manager to know the drug-dropping change of the drug addict in the whole drug-dropping rehabilitation period.
The evaluation reporting system also sends the evaluation result to the personalized game training system and the non-invasive brain stimulation modulation system. The initial training parameters of the personalized game training system and the initial stimulation parameters of the non-invasive brain stimulation modulation system are determined by the initial evaluation result of the drug addiction comprehensive risk evaluation system on the drug addiction patient, and then are fed back to the self-adaptive multi-task training distribution system by the brain function dynamic monitoring system and are automatically adjusted by the self-adaptive multi-task training distribution system.
The drug addiction comprehensive risk assessment system integrates multi-dimensional data such as psychophysical tests, clinical and social information, brain functional state, genes, biochemistry (namely physiology) and the like, carries out comprehensive assessment based on the neurocognitive function of drug addicts, and can reflect the brain function damage of the drug addicts caused by long-term drug intake.
(2) Personalized game training system
The personalized game training system is used for presenting a stored psychophysical test game, and when receiving an instruction sent by the self-adaptive multi-task training and distributing system, the personalized game training system is used for presenting the psychophysical test game to perform neurocognitive function training on a drug addicted patient; acquiring psychophysical data and sending the psychophysical data to a monitoring module;
the essence of the personalized game training system is a neurocognitive function training system, and the neurocognitive function of a drug addicted patient is trained through a stored psychophysical test game, wherein the neurocognitive function comprises but is not limited to an attention control level, a reaction inhibition capability, a working memory capability, a plan processing capability, a prediction decision making capability and the like, the neurocognitive function level of the brain is exercised, the craving for drugs is reduced, and relapse prevention is achieved.
The training parameters include game type, level difficulty, number of rounds, etc.
The type of the psychophysical test game in the personalized game training system is basically consistent with the type of the psychophysical test game in the psychophysical game breakthrough module, but the psychophysical test game is more consistent with the process of game and entertainment in the aspects of interestingness, element diversity, color richness and the like, so that a patient can be willing to participate in the psychophysical test game, the neurocognitive function level of the brain can be exercised, the craving for drugs is reduced, and relapse prevention is prevented.
The personalized game training system is mainly used for training the neurocognitive function of a drug addicted patient, and the patient is trained in a targeted manner by adaptively adjusting the game type, the level difficulty, the number of rounds and the like through the adaptive multi-task training distribution system.
(3) Non-invasive brain stimulation modulation system
The non-invasive brain stimulation modulation system is used for storing the neural regulation and control method, and when receiving the stimulation instruction, the non-invasive brain stimulation modulation system outputs a signal for stimulating and intervening the brain of the drug addict according to the neural regulation and control method; the nerve regulation method comprises non-invasive nerve regulation technologies such as transcranial magnetic stimulation, transcranial direct current stimulation and the like, and plays a role in exciting, inhibiting or regulating signal transmission of neurons or neural networks at adjacent or distant parts of a central nervous system, a peripheral nervous system and an autonomic nervous system, so that the aim of improving the life quality of a patient or improving the neurocognitive function is fulfilled.
The Transcranial Magnetic Stimulation (TMS) is based on the electromagnetic induction and electromagnetic conversion principle, and after being electrified, a coil of the transcranial magnetic stimulation generates an induction electric field and a magnetic field, and the induction electric field and the magnetic field act on a cerebral cortex to generate action potential, so that a corresponding cerebral area is activated, and The Magnetic Stimulation (TMS) has good effects of improving cognitive function and promoting nerve regeneration;
transcranial Direct Current Stimulation (TDCS) utilizes low-intensity direct current to modulate cortical neuronal activity, specifically, to modulate the activity of the autonomic neural network and the balance of bilateral cerebral hemisphere excitability by changing the polarity of resting potential of neurons.
The stimulation parameters include the stimulation frequency, stimulation intensity and pulse number of transcranial magnetic stimulation, and the stimulation type, current intensity and duration of transcranial direct current stimulation. The mild and non-refined whole brain stimulation mode is selected on the whole, and the brain function is improved on the basis of ensuring the safety of the patient. Furthermore, the digital medicine closed-loop system is a fuzzy closed-loop digital medicine, and the 'fuzzy' means that the brain function is integrally regulated without depth and precision in the process of carrying out nerve regulation, and the drug addiction is prevented and treated in a safe range.
The invention can provide decision of related parameters in the transcranial magnetic stimulation or transcranial direct current stimulation process through a matched drug addiction comprehensive risk evaluation system, a brain function dynamic monitoring system and a self-adaptive multitask training distribution system, greatly reduces the professional threshold of a user, implements non-invasive, mild and non-refined whole brain stimulation on the whole design, and is combined with a game training system to realize two-way intervention of cognition and brain function, thereby having no side effect, higher efficiency and higher safety.
(4) Brain function dynamic monitoring system
The brain function dynamic monitoring system is used for acquiring psychophysical data and/or brain function state data of a drug addiction patient during personalized game training and/or stimulation intervention and displaying the psychophysical data and/or the brain function state data in real time, and outputting the psychophysical data and/or the brain function state data to the self-adaptive multitask training and distributing system in real time;
the brain function dynamic monitoring system displays psychophysical data and brain function state data of the drug addict who is acquired by a monitoring module in the data acquisition system and performs personalized game training, and brain function state data of the drug addict during stimulation intervention in real time, and outputs the psychophysical data and/or the brain function state data to the self-adaptive multi-task training distribution system in real time.
(5) Adaptive multitask training distribution system
The self-adaptive multitask training distribution system is used for receiving psychophysical data and/or brain function state data output by the brain function dynamic monitoring system in real time, and adjusting training parameters of the personalized game training system and/or stimulation parameters of the non-invasive brain stimulation modulation system by using a preset artificial intelligence algorithm. The adaptive multitask training distribution system is used for guiding and adjusting a training and intervention scheme in real time. The system is a control module in the whole digital medicine closed-loop system, and analyzes and judges whether current training parameters such as game types, checkpoint difficulty and turn number, stimulation parameters such as stimulation frequency, strength and pulse number of Transcranial Magnetic Stimulation (TMS), stimulation type, current strength and duration of Transcranial Direct Current Stimulation (TDCS) and the like are suitable for a patient or not through a preset artificial intelligent algorithm based on psychophysical data and/or brain function state data received by a brain function dynamic monitoring system of a medicine addict in the personalized game training and stimulation intervention process, and adjusts the training parameters and the stimulation parameters according to the psychophysical data and/or the brain function state data received by the brain function dynamic monitoring system if necessary, so that the rehabilitation training efficiency of the medicine addict is improved, and the training effect and value are maximized.
A second embodiment of the present invention provides an electronic apparatus, including:
a display;
one or more processors;
a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for executing a closed-loop digital medicine system for a drug addicted patient as described above.
A third embodiment of the present invention provides a computer readable storage medium storing a computer program for use in conjunction with an electronic device having a display, the computer program being executable by a processor to perform a closed loop digital drug system for a drug addicted patient as described above.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although a large number of terms are used here more, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Claims (10)
1. A closed-loop digital pharmaceutical system for a drug addict, comprising: the system comprises an individualized game training system, a non-invasive brain stimulation modulation system, a brain function dynamic monitoring system and a self-adaptive multi-task training distribution system;
the personalized game training system is used for presenting a stored psychophysical test game and receiving data acquired after a drug addicted patient is subjected to neurocognitive function training when receiving an instruction sent by the adaptive multi-task training and distributing system;
the non-invasive brain stimulation modulation system is used for storing the nerve regulation and control method, and when receiving the stimulation instruction, the non-invasive brain stimulation modulation system is used for outputting a signal for stimulating and intervening the brain of the drug addict according to the nerve regulation and control method;
the brain function dynamic monitoring system is used for collecting psychophysical data and/or brain function state data of a drug addiction patient during personalized game training and/or stimulation intervention, displaying the psychophysical data and/or the brain function state data in real time, and outputting the psychophysical data and/or the brain function state data to the self-adaptive multitask training and distributing system in real time;
the self-adaptive multitask training distribution system is used for receiving psychophysical data and/or brain function state data output by the brain function dynamic monitoring system in real time, and adjusting training parameters of the personalized game training system and/or stimulation parameters of the non-invasive brain stimulation modulation system by using a preset artificial intelligence algorithm.
2. The closed-loop digital drug system for drug addicts according to claim 1, further comprising a drug addict comprehensive risk assessment system for collecting data of drug addicts, analyzing and processing the data to form assessment results, and sending the assessment results to the personalized game training system, the noninvasive brain stimulation modulation system and the display terminal, respectively.
3. The closed-loop digital drug system for drug addicts according to claim 2, wherein the drug addiction comprehensive risk assessment system comprises a psycho-game breakthrough module, a data acquisition system, a feature extraction system, an algorithm analysis system, an assessment report system;
the psychology game breakthrough module is used for presenting a stored psychology and physics testing game, and when receiving a testing instruction, the psychology game breakthrough module is used for presenting the psychology and physics testing game to perform psychology and physics testing on a drug addicted patient, acquiring psychology and physics data and sending the psychology and physics data to the data acquisition system;
the data acquisition system is used for acquiring psychophysical data, clinical diagnosis and social information data, brain functional state data and physiological data of a drug addiction patient and sending the psychophysical data, the clinical diagnosis and social information data, the brain functional state data and the physiological data to the feature extraction system;
the characteristic extraction system is used for receiving the psychophysical data, the clinical diagnosis and social information data, the brain functional state data and the physiological data sent by the data acquisition system, processing the data to form a data index, and sending the data index to the algorithm analysis system;
the algorithm analysis system is used for receiving the data indexes sent by the characteristic extraction system, analyzing the data indexes to form risk indexes and risk probabilities, and sending the risk indexes and the risk probabilities to the evaluation report system;
the evaluation report system is used for receiving the risk indexes and the risk probabilities sent by the algorithm analysis system, forming evaluation results according to the risk indexes and the risk probabilities, and sending the evaluation results to the personalized game training system, the non-invasive brain stimulation modulation system and the display terminal.
4. The closed-loop digital drug system for drug addicts according to claim 1, wherein the data acquisition system comprises an information entry module, a monitoring module, a detection module;
the information input module is used for acquiring clinical diagnosis and social information data of the drug addict and sending the clinical diagnosis and the social information data to the feature extraction system;
the monitoring module is used for collecting psychophysical data and/or brain functional state data of a drug addiction patient during personalized game training and/or psychogame clearance testing and/or stimulation intervention, and sending the psychophysical data and/or the brain functional state data to the feature extraction system and/or the brain function dynamic monitoring system;
the detection module is used for collecting physiological data of a drug addiction patient and sending the physiological data to the feature extraction system.
5. The closed-loop digital drug system for drug addicts according to claim 1, wherein the brain functional state data includes brain electrical signal variation data, brain oxyhemoglobin variation data, deoxyhemoglobin variation data, eye movement trajectory data.
6. The closed-loop digital drug system for drug addicts according to claim 1, wherein the neuromodulation method comprises transcranial magnetic stimulation and transcranial direct current stimulation.
7. The closed-loop digital drug system for drug addicts oriented according to claim 1, wherein the training parameters include game type, level difficulty and number of rounds, and the stimulation parameters include stimulation frequency, stimulation intensity and pulse number of transcranial magnetic stimulation, stimulation type, current intensity and duration of transcranial direct current stimulation.
8. The closed-loop digital drug system for drug addicts according to claim 1, wherein the initial training parameters and the initial stimulation parameters are determined by the initial evaluation results of the drug addiction risk evaluation system.
9. An electronic device, comprising:
a display;
one or more processors;
a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing a closed-loop digital medicine system for a drug addicted patient according to any one of claims 1-8.
10. A computer-readable storage medium, in which a computer program for use in conjunction with an electronic device having a display is stored, the computer program being executable by a processor to perform a closed-loop digital pharmaceutical system for a drug addicted patient according to any one of claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115445091A (en) * | 2022-09-02 | 2022-12-09 | 成都市第四人民医院 | Drug addiction treatment system based on transcranial magnetic stimulation |
| CN116616722A (en) * | 2023-07-24 | 2023-08-22 | 苏州国科康成医疗科技有限公司 | Brain function abnormality detection method and device based on dynamic cerebral cortex function connection |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101569778A (en) * | 2009-03-10 | 2009-11-04 | 深圳先进技术研究院 | Biofeedback simulation system and biofeedback simulation method |
| CN106933348A (en) * | 2017-01-24 | 2017-07-07 | 武汉黑金科技有限公司 | A kind of brain electric nerve feedback interventions system and method based on virtual reality |
| CN109671486A (en) * | 2018-12-14 | 2019-04-23 | 上海市精神卫生中心（上海市心理咨询培训中心） | For synthesizing the cognitive rehabilitative training system of class drug addiction |
| CN110522983A (en) * | 2018-05-23 | 2019-12-03 | 深圳先进技术研究院 | Brain stimulation system, method, equipment and storage medium based on artificial intelligence |
| CN111840747A (en) * | 2020-06-22 | 2020-10-30 | 上海大学 | Comprehensive intervention rehabilitation method for drug addiction |
| CN112089398A (en) * | 2020-08-17 | 2020-12-18 | 上海大学 | Method for detecting drug addiction degree |
| CN112545459A (en) * | 2020-12-04 | 2021-03-26 | 杭州赛翁思科技有限公司 | Drug addiction assessment correction system |
| US20210162217A1 (en) * | 2019-12-02 | 2021-06-03 | West Virginia University Board of Governors on behalf of West Virginia University | Methods and systems of improving and monitoring addiction using cue reactivity |
-
2021
- 2021-12-23 CN CN202111591169.5A patent/CN114343573B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101569778A (en) * | 2009-03-10 | 2009-11-04 | 深圳先进技术研究院 | Biofeedback simulation system and biofeedback simulation method |
| CN106933348A (en) * | 2017-01-24 | 2017-07-07 | 武汉黑金科技有限公司 | A kind of brain electric nerve feedback interventions system and method based on virtual reality |
| CN110522983A (en) * | 2018-05-23 | 2019-12-03 | 深圳先进技术研究院 | Brain stimulation system, method, equipment and storage medium based on artificial intelligence |
| CN109671486A (en) * | 2018-12-14 | 2019-04-23 | 上海市精神卫生中心（上海市心理咨询培训中心） | For synthesizing the cognitive rehabilitative training system of class drug addiction |
| US20210162217A1 (en) * | 2019-12-02 | 2021-06-03 | West Virginia University Board of Governors on behalf of West Virginia University | Methods and systems of improving and monitoring addiction using cue reactivity |
| CN111840747A (en) * | 2020-06-22 | 2020-10-30 | 上海大学 | Comprehensive intervention rehabilitation method for drug addiction |
| CN112089398A (en) * | 2020-08-17 | 2020-12-18 | 上海大学 | Method for detecting drug addiction degree |
| CN112545459A (en) * | 2020-12-04 | 2021-03-26 | 杭州赛翁思科技有限公司 | Drug addiction assessment correction system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115445091A (en) * | 2022-09-02 | 2022-12-09 | 成都市第四人民医院 | Drug addiction treatment system based on transcranial magnetic stimulation |
| CN115445091B (en) * | 2022-09-02 | 2023-10-27 | 成都市第四人民医院 | Drug addiction treatment system based on transcranial magnetic stimulation |
| CN116616722A (en) * | 2023-07-24 | 2023-08-22 | 苏州国科康成医疗科技有限公司 | Brain function abnormality detection method and device based on dynamic cerebral cortex function connection |
| CN116616722B (en) * | 2023-07-24 | 2023-10-03 | 苏州国科康成医疗科技有限公司 | Brain function abnormality detection method and device based on dynamic cerebral cortex function connection |
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