CN116543873B - DOC evaluation and progress type consciousness rehabilitation guidance scheme decision-making system and platform based on AI - Google Patents
DOC evaluation and progress type consciousness rehabilitation guidance scheme decision-making system and platform based on AI Download PDFInfo
- Publication number
- CN116543873B CN116543873B CN202310515745.0A CN202310515745A CN116543873B CN 116543873 B CN116543873 B CN 116543873B CN 202310515745 A CN202310515745 A CN 202310515745A CN 116543873 B CN116543873 B CN 116543873B
- Authority
- CN
- China
- Prior art keywords
- consciousness
- rehabilitation
- module
- case
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 61
- 238000004458 analytical method Methods 0.000 claims abstract description 51
- 230000003542 behavioural effect Effects 0.000 claims abstract description 20
- 210000004556 brain Anatomy 0.000 claims description 49
- 208000027418 Wounds and injury Diseases 0.000 claims description 29
- 208000014674 injury Diseases 0.000 claims description 28
- 230000006378 damage Effects 0.000 claims description 26
- 238000000034 method Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 15
- 238000007405 data analysis Methods 0.000 claims description 14
- 238000013075 data extraction Methods 0.000 claims description 14
- VLARLSIGSPVYHX-UHFFFAOYSA-N (2,5-dioxopyrrolidin-1-yl) 6-(2,5-dioxopyrrol-1-yl)hexanoate Chemical compound O=C1CCC(=O)N1OC(=O)CCCCCN1C(=O)C=CC1=O VLARLSIGSPVYHX-UHFFFAOYSA-N 0.000 claims description 12
- 201000010099 disease Diseases 0.000 claims description 11
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 11
- 206010010071 Coma Diseases 0.000 claims description 6
- 210000000133 brain stem Anatomy 0.000 claims description 6
- 230000008030 elimination Effects 0.000 claims description 6
- 238000003379 elimination reaction Methods 0.000 claims description 6
- 206010008118 cerebral infarction Diseases 0.000 claims description 5
- 206010008111 Cerebral haemorrhage Diseases 0.000 claims description 4
- 208000030886 Traumatic Brain injury Diseases 0.000 claims description 4
- 230000003376 axonal effect Effects 0.000 claims description 3
- 208000026106 cerebrovascular disease Diseases 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 208000011580 syndromic disease Diseases 0.000 claims description 3
- 241000976983 Anoxia Species 0.000 claims description 2
- 208000002381 Brain Hypoxia Diseases 0.000 claims description 2
- 201000006474 Brain Ischemia Diseases 0.000 claims description 2
- 206010008120 Cerebral ischaemia Diseases 0.000 claims description 2
- 230000007953 anoxia Effects 0.000 claims description 2
- 238000003745 diagnosis Methods 0.000 abstract description 12
- 238000003384 imaging method Methods 0.000 abstract description 5
- 238000002001 electrophysiology Methods 0.000 abstract description 4
- 230000006399 behavior Effects 0.000 abstract 1
- 230000000638 stimulation Effects 0.000 description 45
- 230000004913 activation Effects 0.000 description 26
- 239000003814 drug Substances 0.000 description 13
- 238000011084 recovery Methods 0.000 description 13
- 230000002093 peripheral effect Effects 0.000 description 12
- 229940079593 drug Drugs 0.000 description 11
- 230000001953 sensory effect Effects 0.000 description 11
- 230000001054 cortical effect Effects 0.000 description 8
- 230000001515 vagal effect Effects 0.000 description 8
- 238000011282 treatment Methods 0.000 description 7
- 238000010801 machine learning Methods 0.000 description 6
- 210000000278 spinal cord Anatomy 0.000 description 6
- 230000008733 trauma Effects 0.000 description 6
- 230000001149 cognitive effect Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000009472 formulation Methods 0.000 description 5
- 239000000203 mixture Substances 0.000 description 5
- 230000000763 evoking effect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 210000005036 nerve Anatomy 0.000 description 4
- 210000002569 neuron Anatomy 0.000 description 4
- 206010072170 Skin wound Diseases 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000005764 inhibitory process Effects 0.000 description 3
- 230000005389 magnetism Effects 0.000 description 3
- 210000004126 nerve fiber Anatomy 0.000 description 3
- 210000003625 skull Anatomy 0.000 description 3
- 230000001154 acute effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 208000009973 brain hypoxia - ischemia Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000000747 cardiac effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000007831 electrophysiology Effects 0.000 description 2
- 230000001037 epileptic effect Effects 0.000 description 2
- 210000001652 frontal lobe Anatomy 0.000 description 2
- 239000007943 implant Substances 0.000 description 2
- 230000004060 metabolic process Effects 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 239000013643 reference control Substances 0.000 description 2
- 238000011491 transcranial magnetic stimulation Methods 0.000 description 2
- 238000002604 ultrasonography Methods 0.000 description 2
- 208000019901 Anxiety disease Diseases 0.000 description 1
- 201000004569 Blindness Diseases 0.000 description 1
- 208000028399 Critical Illness Diseases 0.000 description 1
- 208000032843 Hemorrhage Diseases 0.000 description 1
- 206010023201 Joint contracture Diseases 0.000 description 1
- 206010026749 Mania Diseases 0.000 description 1
- 208000001089 Multiple system atrophy Diseases 0.000 description 1
- 206010031127 Orthostatic hypotension Diseases 0.000 description 1
- 208000001132 Osteoporosis Diseases 0.000 description 1
- 208000007502 anemia Diseases 0.000 description 1
- 230000010100 anticoagulation Effects 0.000 description 1
- 230000036506 anxiety Effects 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 210000003050 axon Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000740 bleeding effect Effects 0.000 description 1
- 230000003925 brain function Effects 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 210000003414 extremity Anatomy 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000001771 impaired effect Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000007917 intracranial administration Methods 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 210000003141 lower extremity Anatomy 0.000 description 1
- 238000002582 magnetoencephalography Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 238000003333 near-infrared imaging Methods 0.000 description 1
- 230000000508 neurotrophic effect Effects 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 208000020016 psychiatric disease Diseases 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 201000000980 schizophrenia Diseases 0.000 description 1
- 239000000932 sedative agent Substances 0.000 description 1
- 230000001624 sedative effect Effects 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000003238 somatosensory effect Effects 0.000 description 1
- 230000005236 sound signal Effects 0.000 description 1
- 230000009885 systemic effect Effects 0.000 description 1
- 210000001103 thalamus Anatomy 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
- 238000011269 treatment regimen Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- 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
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- 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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Psychiatry (AREA)
- Developmental Disabilities (AREA)
- Biomedical Technology (AREA)
- Child & Adolescent Psychology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Psychology (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Hospice & Palliative Care (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Social Psychology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The application relates to an AI-based DOC evaluation and progress type consciousness rehabilitation guidance scheme decision system and a platform, wherein the consciousness evaluation system comprises a data acquisition module, a consciousness evaluation logic module and an evaluation report module; the data acquisition module is used for acquiring case basic data, behavioural scale data, neuro-electrophysiology data, CT/MRI data and PET/FMRI data; the consciousness level evaluation module acquires the preliminary consciousness level grade of the patient according to the behavioural scale data, and further judges according to the preliminary consciousness level grade and the PET/FMRI data to acquire the final consciousness level grade of the patient; the case feature analysis module outputs case analysis and evaluation results according to the case basic data, the neuroelectrophysiology data, the CT/MRI data and the PET/FMRI data; the assessment reporting module outputs a consciousness assessment report and a case analysis report, respectively, based on the final consciousness level and the case analysis assessment result. The application combines behaviours with imaging comprehensive analysis to evaluate the consciousness level of the patient, thereby realizing accurate diagnosis of consciousness.
Description
Technical Field
The application relates to the technical field of consciousness rehabilitation, in particular to an AI-based DOC evaluation and progress type consciousness rehabilitation guidance scheme decision system and platform.
Background
Consciousness assessment for patients with consciousness disturbance has been the greatest clinical challenge, and consciousness rehabilitation is a completely new clinical concept. Although in recent years, the assessment of patient consciousness level has evolved from simple physician clinical experience judgment to the introduction of partial image and electrophysiological technique assisted assessment, there are still limitations. The reading of the "diagnosis of chronic disturbance of consciousness and the consensus of therapeutic Chinese experts" of 2020 edition clearly indicates that when the imaging technique is applied to evaluate the brain activation state of a patient, there may be false negative results caused by sleep, sedative and motion artifacts, and meanwhile, since the disturbance of consciousness patient has a great part of cases caused by trauma, the skull often has defects or deformations, etc., which makes direct diagnosis according to individuals difficult. On the other hand, there are also factors that interfere with the diagnosis result due to subjectivity, time consumption, and electroencephalogram noise when electrophysiology is applied to consciousness assessment.
More importantly, the physiological level of a patient with impaired consciousness fluctuates at various stages of the disease, meaning that the patient's consciousness level is not a constant but a dynamic process of development. These above all illustrate that it is also a radical and unscientific practice if the patient's consciousness level is assessed by only one or two imaging and electrophysiological techniques alone, or by only a single outcome. These limiting factors keep the clinical misdiagnosis rate of consciousness assessment high, and misdiagnosis is difficult to estimate for injuries of consciousness disturbance patients, because misdiagnosis must bring about subsequent wrong prognosis prediction and rehabilitation treatment strategies, and even directly influences decisions of family members on subsequent treatments of patients.
Disclosure of Invention
In order to reduce the clinical misdiagnosis rate of consciousness assessment, the application provides an AI-based DOC assessment and progressive consciousness rehabilitation guidance scheme decision system and platform.
The DOC evaluation and process type consciousness rehabilitation guidance scheme decision system based on AI provided by the application adopts the following technical scheme:
the DOC evaluation and progress type consciousness rehabilitation guidance scheme decision system based on the AI comprises a consciousness evaluation system, wherein the consciousness evaluation system comprises a data acquisition module, a consciousness evaluation logic module and an evaluation report module;
the data acquisition module is used for acquiring case basic data, behavioural scale data, neuroelectrophysiology data, CT/MRI data and PET/FMRI data;
the awareness assessment logic module comprises an awareness level assessment module and a case feature analysis module; the consciousness level evaluation module acquires the preliminary consciousness level grade of the patient according to the data of the behavioural scale, and further judges according to the preliminary consciousness level grade and the PET/FMRI data so as to acquire the final consciousness level grade of the patient; the case feature analysis module outputs case analysis and evaluation results according to the case basic data, the neuroelectrophysiology data, the CT/MRI data and the PET/FMRI data; the assessment reporting module outputs a consciousness assessment report and a case analysis report, respectively, based on the final consciousness level and the case analysis assessment result.
Optionally, the behavioural scale data comprises a CRS-R behavioural scale for assessment of awareness level levels, wherein awareness level levels comprise coma, UWS, MCS-, mcs+, EMCS and LIS.
Optionally, the consciousness level assessment module is used for further judging in combination with PET/FMRI data to obtain a final consciousness level of the patient when the initial consciousness level of the patient is obtained as UWS;
the consciousness level assessment module is used for defining the preliminary consciousness level as a final consciousness level when the preliminary consciousness level of the patient is not obtained as the UWS.
Optionally, the consciousness level assessment module is used for further judging in the process of acquiring the preliminary consciousness level grade of the patient as UWS and combining PET/FMRI data;
the brain activation region and level in the PET/FMRI data will be output as the final consciousness level of the LIS when the similarity of the brain activation region and level with the normal brain activation region and level exceeds the preset value;
when the similarity between the brain activation area and the level in the PET/FMRI data and the normal brain activation area and the level is not more than a preset value, the brain activation area and the level after the instruction are compared by further instructions of two specific task norms, and the final consciousness level grade which is MCS when the brain activation area is high is output.
Optionally, the system further comprises a consciousness rehabilitation scheme decision system, wherein the consciousness rehabilitation decision system comprises a data extraction and analysis module, a consciousness rehabilitation scheme decision module and a rehabilitation scheme execution module;
the data extraction and analysis module is used for extracting the final consciousness level grade in the consciousness evaluation report and the case analysis evaluation result in the case analysis report;
the consciousness rehabilitation scheme decision module is pre-stored with a plurality of consciousness rehabilitation schemes, and comprises a case analysis and scheme elimination module and a case feature and rehabilitation measure matching module, wherein the case analysis and scheme elimination module disables the corresponding consciousness rehabilitation scheme based on the case feature in the case analysis and evaluation result, and the case feature and rehabilitation measure matching module selects one or at least one unconstrained consciousness rehabilitation scheme based on the final consciousness level grade;
the rehabilitation regimen execution module combines the real-time electroencephalogram monitor to form a rehabilitation result based on the selected one or at least one consciousness rehabilitation regimen.
Optionally, the consciousness rehabilitation scheme decision module further includes a case feature weight optimization module;
the case feature weight optimization module is provided with a plurality of case rehabilitation factors, at least one case rehabilitation set is matched in each case rehabilitation factor, each case rehabilitation set is matched with a corresponding one or at least one consciousness rehabilitation scheme, and each case rehabilitation set is matched with a corresponding weight value;
the case feature weight optimization module sequentially matches corresponding case rehabilitation sets in priority based on the weight value based on the final consciousness level grade.
Optionally, the data extraction and analysis module is configured to extract one or at least one consciousness rehabilitation scheme that is not disabled when the final consciousness level in the consciousness assessment report is UWS, LIS or EMCS, through the case feature and rehabilitation measure matching module;
and the data extraction and analysis module is used for extracting a corresponding case rehabilitation set through the case characteristic weight optimization module when the final consciousness level in the consciousness assessment report is MCS, MCS-or MCS+.
The DOC evaluation and process type consciousness rehabilitation guidance scheme decision platform based on the AI provided by the application adopts the following technical scheme:
the DOC evaluation and process type consciousness rehabilitation guidance scheme decision-making platform based on the AI comprises a cloud server and at least one clinical base, wherein at least one handheld hardware device is matched on the clinical base, the cloud server is provided with the DOC evaluation and process type consciousness rehabilitation guidance scheme decision-making system based on the AI according to the technical scheme, and the cloud server is used for carrying out data interaction with the clinical base.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the application solves the problems of high misdiagnosis rate, blindness in rehabilitation strategy formulation and the like caused by subjectivity and technical limitation in the existing consciousness assessment of patients with clinical consciousness disturbance, realizes the standardized and dynamic accurate diagnosis of consciousness level of patients with consciousness disturbance through the multi-modal comprehensive assessment of the patients with multiple times, dynamic behavioural and imaging and neurophysiologic results, and reduces the clinical misdiagnosis rate of consciousness assessment;
2. a reasonable consciousness rehabilitation strategy is provided for the consciousness level of the patient at the stage, so that the cognitive rehabilitation and the functional rehabilitation of the consciousness disturbance patient are parallel, and the consciousness accurate objective diagnosis and the personalized dynamic rehabilitation scheme formulation of the clinical consciousness disturbance patient are truly realized;
3. through AI artificial intelligence learning, the academic results published in the latest authoritative academic journal are combined and accessed according to clinical experience to be applied to clinic, and the rehabilitation strategy is timely adjusted according to the rehabilitation result, so that accurate objective diagnosis of consciousness of patients with clinical consciousness disturbance and personalized dynamic rehabilitation scheme formulation are truly realized
Drawings
FIG. 1 is a system block diagram of a consciousness assessment system.
Fig. 2 is a system block diagram of a conscious rehabilitation regimen decision system.
Fig. 3 is a system block diagram of a conscious rehabilitation instruction solution decision platform.
Detailed Description
The application is described in further detail below with reference to fig. 1-3.
Example 1
An AI-based DOC assessment and procedural consciousness rehabilitation instruction scheme decision system is shown with reference to FIGS. 1 and 2, and comprises a consciousness assessment system and a consciousness rehabilitation scheme decision system. According to the application, the situation that the clinical consciousness disturbance patient consciousness evaluation has high misdiagnosis rate due to subjectivity and technical limitation and the recovery strategy is formulated blindly is solved through the consciousness evaluation system and the consciousness recovery scheme decision system, the patient data is automatically read for analysis and comparison, the consciousness level standardization and dynamic accurate diagnosis of the consciousness disturbance patient is realized, and a reasonable recovery strategy is given for the consciousness level of the patient at the stage, so that the cognitive recovery and functional recovery of the consciousness disturbance patient are parallel.
Referring to fig. 1, the awareness assessment system includes a data acquisition module, an awareness assessment logic module, and an assessment reporting module.
The data acquisition module is used for acquiring case basic data, behavioural scale data, neuro-electrophysiology data, CT/MRI data and PET/FMRI data.
The case basic data comprises basic data and basic sign information of a patient, and literal record data such as etiology, course of disease, focus position, region size and the like of the case are correspondingly recorded in the basic data.
The behavioural scale data includes CRS-R behavioural scale for assessment of consciousness level classes including coma, UWS, MCS-, mcs+, EMCS and LIS and NCSR pain scale. The NCSR pain scale is used to detect the presence of pain in patients and is used for the formulation of rehabilitation regimens.
Illustratively, coma, UWS, MCS-, mcs+, EMCS, and LIS represent the patient's consciousness level levels, respectively. UWS is unresponsive wake syndrome, a plant human; MCS is the minimum conscious state (behavioural cognitive separation) of the MCS for which the behavioural assessment is UWS and the image file assessment in PET/FMRI data; MCS-is the minimum conscious state without language understanding support; mcs+ is the minimum conscious state with language understanding capability; EMCS is a state of minimum consciousness of departure; LIS is a locking syndrome.
Neurophysiologic data includes somatosensory evoked potentials, visual evoked potentials, auditory evoked potentials, olfactory evoked potentials, event related potentials, and the like.
The CT/MRI data may be raw data or image files. The data acquisition module is internally provided with an image generation and identification module which is used for carrying out image generation or image identification on CT/MRI data. When the CT/MRI data are original data, the image generation and identification module processes the CT/MRI data into an image file; when the CT/MRI data is an image file, the image generation and identification module identifies the image file of the CT/MRI data. Wherein, the image file pre-stored with CT/MRI data of normal human cranium is used as reference control in the database in the consciousness evaluation system. The image generation and identification module obtains the size of the damaged part and the area of the cranium structure in the image file of the cranium of the patient by comparing the image file of the CT/MRI data of the patient with the image file of the CT/MRI data of the cranium of the normal person, and marks the function of the damaged brain area. I.e. CT/MRI data can analyze the type and extent of damage to the cortex, axons, brainstem, location and extent of brain trauma, cerebral hemorrhage, peduncles and cerebral ischemia hypoxia.
The PET/FMRI data may be raw data or image files. The data acquisition module is internally provided with an image generation and identification module which is used for carrying out image generation or image identification on PET/FMRI data. When the PET/FMRI data are original data, the image generation and identification module processes the PET/FMRI data into an image file; when the PET/FMRI data is an image file, the image generation and identification module identifies the image file of the PET/FMRI data. Wherein, the image file pre-stored with PET/FMRI data of normal human cranium is used as reference control in the database in the consciousness evaluation system. The image generation and identification module obtains the size of the position and the area of the inhibition or activation of the brain metabolism of the patient by comparing the image file of the PET/FMRI data of the patient with the image file of the PET/FMRI data of the normal brain, calculates the inhibition or activation degree of the corresponding position, and can also draw a brain metabolism three-dimensional image. Namely, PET/FMRI data can analyze the damage position and degree of cortex function, the functional condition of brain executing task and the damage condition of nerve fiber bundle.
It should be noted that, the data acquisition module may also acquire other data, including but not limited to nutrition data, near infrared imaging scan data, magnetoencephalography data, etc. of the patient, where the data of the data acquisition module may be expanded according to the need, and the embodiment does not describe the later-stage function expansion part in detail.
Referring to fig. 1, the awareness assessment logic module includes an awareness level assessment module and a case feature analysis module; the consciousness level evaluation module acquires the preliminary consciousness level grade of the patient according to the behavioural scale data, and further judges according to the preliminary consciousness level grade and the PET/FMRI data to acquire the final consciousness level grade of the patient; the case feature analysis module outputs case analysis evaluation results according to the case basic data, the neuroelectrophysiology data, the CT/MRI data and the PET/FMRI data.
Specifically, the consciousness level assessment module is used for further judging by combining PET/FMRI data to obtain the final consciousness level of the patient when the initial consciousness level of the patient is obtained as UWS; the consciousness level assessment module is used for defining the preliminary consciousness level as the final consciousness level when the preliminary consciousness level of the patient is not obtained as the UWS.
The consciousness level evaluation module is used for further judging in the combination of PET/FMRI data when the obtained primary consciousness level grade of the patient is UWS.
Brain activation regions and levels in PET/FMRI data will be output as the final level of consciousness of the LIS when the similarity to normal brain activation regions and levels exceeds a preset value.
When the similarity between the brain activation area and the level in the PET/FMRI data and the normal brain activation area and the level is not more than a preset value, the brain activation area and the level after the instruction are compared by further instructions of two specific task norms, and the final consciousness level grade which is MCS when the brain activation area is high is output. If one task pattern instruction is the audio of a designated task, the other task pattern instruction is the audio signal of the same-frequency average amplitude, and the two specific task pattern instructions are used as a reference task pattern, after the instructions are compared, the brain activation area and the brain activation level in the PET/FMRI data are higher than those of the reference task Fan Shida, if the activation occurs in the frontal lobe area and the frontal lobe area, the patient is determined to be misdiagnosed, and the patient is actually a patient with the MCS awareness level.
The following describes 5 cases in detail.
First, when the consciousness level assessment module obtains that the primary consciousness level of the patient is coma, the patient does not respond at all, and the consciousness level assessment module defines the primary consciousness level as a final consciousness level, and the final consciousness level is coma.
Second, when the consciousness level evaluation module obtains that the primary consciousness level of the patient is UWS, the patient is further judged by combining with PET/FMRI data, and when the brain activation area and the level in the PET/FMRI data are similar to the normal brain activation area and the level, the final consciousness level of the LIS is output. When the similarity between the brain activation area and the level in the PET/FMRI data and the normal brain activation area and the level is not more than a preset value, comparing the brain activation area and the level after the command through the commands of two specific task norms, and outputting the final consciousness level grade as MCS when the brain activation area degree is higher than that of the reference task Fan Shida.
Third, when the consciousness level assessment module obtains that the primary consciousness level of the patient is MCS-, the consciousness level assessment module defines the primary consciousness level as a final consciousness level, and the final consciousness level is MCS-.
Fourth, when the consciousness level assessment module obtains that the primary consciousness level of the patient is mcs+, the consciousness level assessment module defines the primary consciousness level as a final consciousness level, and the final consciousness level is mcs+.
Fifth, when the consciousness level assessment module obtains that the primary consciousness level of the patient is EMCS, the consciousness level assessment module defines the primary consciousness level as a final consciousness level, and the final consciousness level is EMCS.
The consciousness evaluation system is also provided with a consciousness evaluation machine learning module, the consciousness level evaluation module can acquire the final consciousness level grade of the patient according to the behavioural scale data and the PET/FMRI data, when the final consciousness level grade is UWS, the expert group carries out manual calculation, the result of the manual calculation is input into the consciousness evaluation machine learning module and then is imported into the consciousness level evaluation module, the consciousness evaluation machine learning module AI carries out self-contrast learning, an algorithm is adjusted, and an algorithm model of the consciousness level evaluation module for the PET/FMRI data is perfected, so that the accuracy of the output of an evaluation result is further improved.
The assessment reporting module outputs a consciousness assessment report and a case analysis report, respectively, based on the final consciousness level and the case analysis assessment result.
The case analysis report objectively expresses the etiology, focus position and area size, disease course, nerve electrophysiology condition, brain region cortex inhibition or activation condition reflected by PET/FMRI data and brain region structure damage and function correspondence reflected by CT/MRI data of the case, and the case analysis report can also draw a three-dimensional brain graph for the patient, wherein the three-dimensional brain graph comprises brain function graphs corresponding to dead neuron distribution, damaged neuron distribution, normal but inhibited neuron distribution, normal neuron distribution, damaged nerve fiber distribution, normal but inhibited fiber distribution, normal nerve fiber distribution.
In summary, the application solves the problem of high misdiagnosis rate of consciousness assessment of patients with clinical consciousness disturbance caused by subjectivity and technical limitation through the consciousness assessment system, so that the system automatically reads patient data for analysis and comparison by introducing AI technology machine learning through combining multiple and dynamic behavioural and imaging and electrophysiological result multi-mode comprehensive assessment of patients, and realizes the standardized and dynamic accurate diagnosis of consciousness level of patients with consciousness disturbance.
Referring to fig. 2, the consciousness rehabilitation decision system includes a data extraction and analysis module, a consciousness rehabilitation scheme decision module, and a rehabilitation scheme execution module.
The data extraction and analysis module is used for extracting the final consciousness level in the consciousness assessment report and the case analysis assessment result in the case analysis report.
The consciousness rehabilitation scheme decision module is pre-stored with a plurality of consciousness rehabilitation schemes, and comprises a case analysis and scheme elimination module and a case feature and rehabilitation measure matching module, wherein the case analysis and scheme elimination module disables the corresponding consciousness rehabilitation scheme based on the case feature in the case analysis and evaluation result, and the case feature and rehabilitation measure matching module selects one or at least one unconstrained consciousness rehabilitation scheme based on the final consciousness level grade. The rehabilitation regimen execution module combines the real-time electroencephalogram monitor to form a rehabilitation result based on the selected one or at least one consciousness rehabilitation regimen.
The consciousness rehabilitation scheme decision module further comprises a case feature weight optimization module; the case feature weight optimization module is provided with a plurality of case rehabilitation factors, at least one case rehabilitation set is matched in each case rehabilitation factor, each case rehabilitation set is matched with a corresponding one or at least one consciousness rehabilitation scheme, and each case rehabilitation set is matched with a corresponding weight value; the case feature weight optimization module sequentially matches corresponding case rehabilitation sets in priority based on the weight value based on the final consciousness level.
The data extraction and analysis module is used for extracting one or at least one consciousness rehabilitation scheme which is not disabled through the case feature and rehabilitation measure matching module when the final consciousness level grade in the consciousness assessment report is UWS, LIS or EMCS. The data extraction and analysis module is used for extracting a corresponding case rehabilitation set through the case feature weight optimization module when the final consciousness level in the consciousness assessment report is MCS, MCS-or MCS+.
That is, the patients with final consciousness level of UWS, LIS or EMCS directly select the unconstrained consciousness rehabilitation regimen for treatment, and the patients with final consciousness level of MCS x, MCS-or mcs+ will select the consciousness rehabilitation regimen in the corresponding matched case rehabilitation set for treatment by the case feature weight optimization module.
The consciousness rehabilitation regimen decision module is described in detail below.
A number of consciousness rehabilitation schemes are pre-stored in a consciousness rehabilitation scheme decision module, and the consciousness rehabilitation schemes include but are not limited to: upright bed, medicine, peripheral sensory system stimulation, transcranial direct current stimulation, transcranial magnetic stimulation, ear vagal electrical stimulation, focused low frequency ultrasound stimulation, spinal cord electrical stimulation, cervical vagal nerve electrical stimulation, deep brain electrical stimulation, hyperbaric oxygen, neurotrophic, etc.
The case analysis and scheme exclusion module disables the corresponding consciousness rehabilitation scheme based on the case characteristics in the case analysis evaluation result.
The upright bed in the happiness rehabilitation scheme has the following case characteristics that need to be disabled: 1. the vital signs of the cerebrovascular patient in the acute phase are not stably forbidden; 2. patients with orthostatic hypotension should be cautious; 3. patients with severe anemia are forbidden; 4. patients with mental disorder (mania, anxiety, pseudovision, pseudohearing, schizophrenia, etc.) are disabled; 5. severe osteoporosis, joint contracture, body deformity, and severe low muscle strength of the lower limbs. Transcranial magnetic stimulation is required to be disabled as follows: 1. patients with metallic foreign bodies in the skull or with cardiac pacemakers, patients with cardiac stent implants, patients with cochlear implants, and patients with significant increases in intracranial pressure; 2. foreign bodies implanted in the body, such as hearing aids, medical pumps, etc., are at risk of being damaged by metal or electronic instruments near the stimulation coil site; 3. epileptic patients or epileptic family history patients. Spinal cord electrical stimulation has the following case characteristics that need to be disabled: uncontrolled bleeding and anticoagulation are forbidden for systemic or local infected persons. Deep brain electrical stimulation has the following characteristics that the brain electrical stimulation needs to be disabled: the disease course is more than 3 months, the thalamus, the frontal top and the language area are not damaged, and the skull defect is avoided or the repairing can be used.
The case signature and rehabilitation measure matching module then selects one or more consciousness rehabilitation regimens that are not disabled based on the final consciousness level.
For example, the final level of consciousness level of the patient with UWS will be subjected to consciousness rehabilitation regimens of drugs, peripheral sensory stimulation, vertical bed and transcranial direct current, wherein the dosage and duration of each consciousness rehabilitation regimen in drugs, peripheral sensory stimulation, vertical bed and transcranial direct current are preset, and the embodiment is not limited and described.
Patients with a final level of consciousness, e.g., EMCS, will perform cognitive and limb rehabilitation as usual.
If the final level of consciousness is at LIS, the patient will use the brain-computer interface to establish communication, and since LIS patients typically have lesions in the brainstem area, brainstem consciousness rehabilitation programs can be administered.
A patient with a final consciousness level of MCS will select a corresponding case rehabilitation set by the case feature weight optimization module. Wherein the final awareness level being MCS includes the final awareness level being MCS, the final awareness level being MCS-or the final awareness level being mcs+.
The case recovery factors include the following factors: 1. a brain region injury site; 2. type of brain region injury; 3. injury parts of the cortex; 4. the priorities of the rehabilitation factors of the cases are respectively arranged from small to large according to the numbers in different time periods when the illness happens, and the priorities are weighted more when the numbers are smaller.
The case rehabilitation factors comprise at least one case rehabilitation set, and the brain region injury position comprises cortex injury, axonal injury and brain stem injury. The brain region injury type comprises brain trauma, cerebral ischemia and anoxia, cerebral hemorrhage and cerebral infarction. The injury site of the cortex comprises a posterior cortex wound and an anterior cortex wound. Different time periods of disease occurrence include within 1 month after disease occurrence, and 3 months after disease occurrence.
Wherein each case recovery set comprises one or at least one consciousness recovery regimen, e.g., cortical trauma comprises the following consciousness recovery regimen: drug, peripheral sensory stimulation, vertical bed, transcranial direct current, transcranial magnetic, vagal electrical stimulation, median electrical stimulation. Axonal injury involves the following conscious rehabilitation regimen: drug, vagal nerve electrical stimulation, median electrical stimulation, spinal cord electrical stimulation, deep brain electrical stimulation, transcranial direct current. Brainstem injury involves the following conscious rehabilitation regimen: median electrical stimulation, focused low frequency ultrasound stimulation, spinal cord electrical stimulation, deep brain electrical stimulation, transcranial direct current.
Brain trauma contains the following conscious rehabilitation regimen: drugs, peripheral sensory stimulation, transcranial direct current, vertical bed, transcranial magnetism, vagal electrical stimulation, median electrical stimulation, spinal cord electrical stimulation, deep brain electrical stimulation. Cerebral ischemia hypoxia comprises the following conscious rehabilitation regimen: drugs, peripheral sensory stimulation, upright bed, transcranial direct current. Cerebral hemorrhage comprises the following conscious rehabilitation regimen: drugs, peripheral sensory stimulation, transcranial direct current, transcranial magnetism, vagal electrical stimulation, median electrical stimulation, spinal cord electrical stimulation, deep brain electrical stimulation. Cerebral infarction contains the following consciousness recovery regimen: drug, peripheral sensory stimulation, vertical bed, transcranial direct current, transcranial magnetic, vagal electrical stimulation, median electrical stimulation.
Posterior cortical trauma contains the following conscious rehabilitation regimen: drugs, peripheral sensory stimulation, upright bed, transcranial direct current. Anterior cortical trauma contains the following conscious rehabilitation regimen: medicine, transcranial direct current, transcranial magnetism, vagal nerve electrical stimulation, median electrical stimulation, peripheral sensory stimulation, and upright bed.
The disease condition is mainly stimulated by drugs and peripheral sensations within 1 month, and various electrostimulation techniques are used with cautions. The non-invasive stimulation is mainly used after the illness state occurs for 1 month, and the operation stimulation can be applied after the illness state occurs for 3 months.
Thus, the final patient with the consciousness level of MCS will select the corresponding case rehabilitation set to which the corresponding consciousness rehabilitation scheme is matched through the case feature weight optimization module.
If the final consciousness level is MCS patient, if cortical trauma is extracted from the case analysis evaluation results in the case analysis report, anterior cortical trauma and illness occur within 1 month, the drug and peripheral sensory stimulus will be selected.
If the cortical wound, the anterior cortical wound and the illness state occur for 1 month, the non-invasive stimulus applicable to the cortical wound is applied.
If the skin wound, the front skin wound and the illness state occur in the case analysis and evaluation result in the case analysis report are extracted, the operation stimulation operation applicable to the skin wound is applied after the illness state occurs for 3 months.
In the executing process of the consciousness rehabilitation scheme, the brain electricity is used for monitoring in real time, and a final rehabilitation result is obtained after the consciousness rehabilitation scheme is executed.
It is worth to say that the consciousness rehabilitation scheme decision system is provided with a rehabilitation scheme machine learning module. The on-site expert doctor can modify the consciousness rehabilitation scheme generated by the consciousness rehabilitation scheme decision module according to the rehabilitation result, can also directly modify the consciousness rehabilitation scheme in the execution process, inputs the modified consciousness rehabilitation scheme into the rehabilitation scheme machine learning module, and imports the result into the consciousness rehabilitation scheme decision module, so as to perfect the algorithm model of the consciousness rehabilitation scheme output consciousness rehabilitation scheme by the consciousness rehabilitation scheme decision module.
In the executing process of the consciousness rehabilitation scheme, the monitored brain data are transmitted back to the consciousness assessment system, the consciousness assessment system re-assesses and judges the consciousness level grade of the patient (re-assesses the curative effect), the consciousness assessment system re-generates a consciousness assessment report and a case analysis report, the final consciousness level grade in the consciousness assessment report is extracted through the data extraction and analysis module, the case analysis assessment result in the case analysis report is obtained, and the consciousness rehabilitation scheme is readjusted through the consciousness rehabilitation scheme decision system.
Therefore, by giving a reasonable recovery strategy aiming at the consciousness level of the patient at the stage, the cognitive recovery and functional recovery of the patient with consciousness disturbance are parallel. Meanwhile, through AI artificial intelligence learning, the academic results published in the latest authoritative academic journal are combined and accessed according to clinical experience to be applied to clinic, and the rehabilitation strategy is timely adjusted according to the rehabilitation result, so that the accurate objective diagnosis of consciousness and personalized dynamic rehabilitation scheme formulation of patients with clinical consciousness disturbance are truly realized.
Example two
The DOC evaluation and process type consciousness rehabilitation guidance scheme decision platform based on the AI provided by the application adopts the following technical scheme:
the DOC evaluation and process type consciousness rehabilitation guidance scheme decision platform based on the AI is shown by referring to FIG. 3, and comprises a cloud server and at least one clinical base, wherein the clinical base is matched with at least one handheld hardware device, the cloud server is provided with the DOC evaluation and process type consciousness rehabilitation guidance scheme decision system based on the AI, and the cloud server is used for carrying out data interaction with the clinical base.
The clinical base communicates with the cloud server in a wireless communication mode, the clinical base transmits the evaluation data and the treatment result data to the cloud server, and the evaluation result and the rehabilitation scheme obtained by the cloud server are transmitted back to the clinical base through a consciousness evaluation system and a consciousness rehabilitation decision system of the cloud server.
The cloud server is also provided with an online case system, and can transmit the case and clinical summary data to a clinical base for reference.
In summary, for clinical application, accurate assessment of consciousness of patients with consciousness disturbance is a key element and difficulty of patient treatment and rehabilitation, and a scientific and correct rehabilitation strategy cannot be formulated without accurate assessment. Since most patients with consciousness disturbance are critically ill patients, huge financial burden is usually borne in the acute phase, and after the physiological characteristics are stable, the biggest problem is how to recover the consciousness of the patients. Consciousness assessment is a precondition of consciousness rehabilitation, and the patent technology realizes accurate consciousness assessment and intelligent implementation of rehabilitation strategies through an AI technology of deep learning. For patients, the physiological and psychological injuries brought by misdiagnosis to the patients are reduced, and the treatment effect is improved. For the family of the patient, the accurate diagnosis and scientific rehabilitation strategy greatly lighten unnecessary financial and manpower burden of the family members. Meanwhile, accurate diagnosis results also lead families to have correct psychological expectation, and the most suitable treatment scheme can be selected for patients no matter actively treating or selecting peace guard. For doctors, objective evaluation is realized, misdiagnosis is reduced, and medical efficiency is improved. The dynamic evaluation result can enable doctors to conveniently know the awareness level and rehabilitation effect of the patient in the whole course, and the medical safety is improved. Therefore, the system has important practical application significance for improving the rehabilitation effect of patients, relieving the household and social burden and optimizing the medical resource allocation.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (3)
1. The system is characterized by comprising a consciousness assessment system and a consciousness rehabilitation scheme decision system, wherein the consciousness assessment system comprises a data acquisition module, a consciousness assessment logic module and an assessment report module;
the data acquisition module is used for acquiring case basic data, behavioural scale data, neuroelectrophysiology data, CT/MRI data and PET/FMRI data; the behavioural scale data comprises a CRS-R behavioural scale which is used for evaluating consciousness level grades, wherein the consciousness level grades comprise coma, UWS, MCS-, MCS+, EMCS and LIS, the MCS is the minimum consciousness state of the MCS in the behavioural evaluation, and the LIS is the locking syndrome in the image file evaluation in the PET/FMRI data;
the awareness assessment logic module comprises an awareness level assessment module and a case feature analysis module; the consciousness level evaluation module acquires the preliminary consciousness level grade of the patient according to the data of the behavioural scale, and further judges according to the preliminary consciousness level grade and the PET/FMRI data so as to acquire the final consciousness level grade of the patient; the case feature analysis module outputs case analysis and evaluation results according to the case basic data, the neuroelectrophysiology data, the CT/MRI data and the PET/FMRI data;
the evaluation report module outputs a consciousness evaluation report and a case analysis report based on the final consciousness level grade and the case analysis evaluation result respectively;
the consciousness rehabilitation scheme decision system comprises a data extraction and analysis module, a consciousness rehabilitation scheme decision module and a rehabilitation scheme execution module;
the data extraction and analysis module is used for extracting the final consciousness level grade in the consciousness evaluation report and the case analysis evaluation result in the case analysis report;
the consciousness rehabilitation scheme decision module is pre-stored with a plurality of consciousness rehabilitation schemes, and comprises a case analysis and scheme elimination module and a case feature and rehabilitation measure matching module, wherein the case analysis and scheme elimination module disables the corresponding consciousness rehabilitation scheme based on the case feature in the case analysis and evaluation result, and the case feature and rehabilitation measure matching module selects at least one unconstrained consciousness rehabilitation scheme based on the final consciousness level grade;
the consciousness rehabilitation scheme decision module further comprises a case feature weight optimization module;
the case feature weight optimization module is provided with a plurality of case rehabilitation factors, each case rehabilitation factor comprises at least one case rehabilitation set, and each case rehabilitation set is matched with at least one consciousness rehabilitation scheme; the case rehabilitation factors include the following factors: the priorities of the rehabilitation factors of the cases are arranged from large to small according to different time periods of the occurrence of the injury position of the brain region, the injury type of the brain region, the injury position of the cortex and the illness state, and the higher the priorities are, the higher the weight is; each case rehabilitation factor comprises at least one case rehabilitation set, wherein the brain region injury position comprises cortex injury, axonal injury and brain stem injury; the brain region injury type comprises brain trauma, cerebral ischemia and anoxia, cerebral hemorrhage and cerebral infarction; the injury site of the cortex comprises a rear cortex wound and a front cortex wound; different time periods of the disease include 1 month after the disease, and 3 months after the disease;
the case feature weight optimization module is matched with the corresponding case rehabilitation set based on the final consciousness level grade and the weight value;
the data extraction and analysis module is used for extracting at least one consciousness rehabilitation scheme which is not disabled through the case feature and rehabilitation measure matching module when the final consciousness level grade in the consciousness assessment report is UWS, LIS or EMCS;
the data extraction and analysis module is used for extracting consciousness rehabilitation schemes in the corresponding case rehabilitation sets through the case characteristic weight optimization module when the final consciousness level in the consciousness assessment report is MCS, MCS-or MCS+; the rehabilitation scheme execution module combines the real-time electroencephalogram monitor based on the selected consciousness rehabilitation scheme to form a rehabilitation result;
in the executing process of the consciousness rehabilitation scheme, the monitored brain data are transmitted back to the consciousness assessment system, the consciousness assessment system re-assesses and judges the consciousness level grade of the patient again, the consciousness assessment system re-generates a consciousness assessment report and a case analysis report, the final consciousness level grade in the consciousness assessment report is extracted through the data extraction and analysis module, the case analysis assessment result in the case analysis report is obtained, and the consciousness rehabilitation scheme is readjusted through the consciousness rehabilitation scheme decision system.
2. The AI-based consciousness disturbance assessment and advanced consciousness rehabilitation instruction scheme decision system according to claim 1, wherein the consciousness level assessment module is configured to further determine, in combination with PET/FMRI data, to obtain a final consciousness level of the patient when the initial consciousness level of the patient is UWS;
the consciousness level assessment module is used for defining the preliminary consciousness level as a final consciousness level when the preliminary consciousness level of the patient is not obtained as the UWS.
3. The AI-based consciousness disturbance assessment and progress-type consciousness rehabilitation guidance scheme decision-making platform is characterized by comprising a cloud server and at least one clinical base, wherein the clinical base is matched with at least one handheld hardware device, the cloud server is provided with the AI-based consciousness disturbance assessment and progress-type consciousness rehabilitation guidance scheme decision-making system according to any one of claims 1 to 2, and the cloud server is used for carrying out data interaction with the clinical base.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310515745.0A CN116543873B (en) | 2023-05-08 | 2023-05-08 | DOC evaluation and progress type consciousness rehabilitation guidance scheme decision-making system and platform based on AI |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310515745.0A CN116543873B (en) | 2023-05-08 | 2023-05-08 | DOC evaluation and progress type consciousness rehabilitation guidance scheme decision-making system and platform based on AI |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116543873A CN116543873A (en) | 2023-08-04 |
| CN116543873B true CN116543873B (en) | 2023-12-12 |
Family
ID=87455572
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310515745.0A Active CN116543873B (en) | 2023-05-08 | 2023-05-08 | DOC evaluation and progress type consciousness rehabilitation guidance scheme decision-making system and platform based on AI |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116543873B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335728A (en) * | 2018-02-07 | 2018-07-27 | 广州贝方医疗设备有限公司 | A kind of name of calling out for disturbance of consciousness patient promotees system of waking up |
| CN109431499A (en) * | 2018-12-12 | 2019-03-08 | 杭州师范大学 | Auxiliary system and householder method are nursed by plant person family |
| CN209107350U (en) * | 2018-08-31 | 2019-07-16 | 华南理工大学 | The detection of the biofeedback type disturbance of consciousness and wake-up system |
| CN112768066A (en) * | 2021-03-03 | 2021-05-07 | 王明光 | Neurosurgical patient comprehensive diagnosis and treatment method and system |
| WO2021203719A1 (en) * | 2020-04-08 | 2021-10-14 | 江苏贝泰福医疗科技有限公司 | Acoustic-electric stimulation neuromodulation therapy and apparatus combining electroencephalogram testing, analysis and control |
| CN114557708A (en) * | 2022-02-21 | 2022-05-31 | 天津大学 | Device and method for detecting somatosensory stimulation consciousness based on electroencephalogram dual-feature fusion |
| CN115670484A (en) * | 2022-11-11 | 2023-02-03 | 杭州师范大学 | Consciousness disturbance patient consciousness detection method based on language paradigm and electro-oculogram indexes |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2001250844A1 (en) * | 2000-03-15 | 2001-09-24 | Stephen Faris | Apparatus for and method of assessing, monitoring, and reporting on behavioral health disorders |
| WO2011160222A1 (en) * | 2010-06-22 | 2011-12-29 | National Research Council Of Canada | Cognitive function assessment in a patient |
| EP3850639A1 (en) * | 2018-09-11 | 2021-07-21 | ICM (Institut du Cerveau et de la Moelle Épinière) | System and methods for consciousness evaluation in non-communcating subjects |
-
2023
- 2023-05-08 CN CN202310515745.0A patent/CN116543873B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335728A (en) * | 2018-02-07 | 2018-07-27 | 广州贝方医疗设备有限公司 | A kind of name of calling out for disturbance of consciousness patient promotees system of waking up |
| CN209107350U (en) * | 2018-08-31 | 2019-07-16 | 华南理工大学 | The detection of the biofeedback type disturbance of consciousness and wake-up system |
| CN109431499A (en) * | 2018-12-12 | 2019-03-08 | 杭州师范大学 | Auxiliary system and householder method are nursed by plant person family |
| WO2021203719A1 (en) * | 2020-04-08 | 2021-10-14 | 江苏贝泰福医疗科技有限公司 | Acoustic-electric stimulation neuromodulation therapy and apparatus combining electroencephalogram testing, analysis and control |
| CN112768066A (en) * | 2021-03-03 | 2021-05-07 | 王明光 | Neurosurgical patient comprehensive diagnosis and treatment method and system |
| CN114557708A (en) * | 2022-02-21 | 2022-05-31 | 天津大学 | Device and method for detecting somatosensory stimulation consciousness based on electroencephalogram dual-feature fusion |
| CN115670484A (en) * | 2022-11-11 | 2023-02-03 | 杭州师范大学 | Consciousness disturbance patient consciousness detection method based on language paradigm and electro-oculogram indexes |
Non-Patent Citations (4)
| Title |
|---|
| 严重意识障碍患者康复治疗方案的临床研究;吴军发;吴毅;胡永善;李放;白玉龙;王海珏;姜鹭春;宋凡;路微波;;中国康复医学杂志(第10期);第910-912页 * |
| 儿童重症脑损伤后意识障碍预后影响因素分析;刘华等;中国康复;第38卷(第1期);第41-44页 * |
| 周士枋等.实用康复医学.东南大学出版社,1998,第831页. * |
| 慢性意识障碍康复中国专家共识;张皓等;中国康复理论与实践;第29卷(第2期);第125-139页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116543873A (en) | 2023-08-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mudgal et al. | Brain computer interface advancement in neurosciences: Applications and issues | |
| Babo-Rebelo et al. | Neural responses to heartbeats distinguish self from other during imagination | |
| JP5259766B6 (en) | Method for classifying and treating brain physiological imbalances using quantitative EEG | |
| Subramaniam et al. | Automated nociceptive pain assessment using physiological signals and a hybrid deep learning network | |
| KR102211647B1 (en) | Artificial Intelligence Sleep Enhancement Noninvasive Brain Circuit Control Therapy System | |
| Pun et al. | Brain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Geneva | |
| JP2003528679A (en) | Method and apparatus for objectively measuring pain | |
| CN114343573A (en) | Closed-loop digital medicine system, equipment and medium for medicine addicts | |
| Atlas et al. | Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learning | |
| Xue et al. | Symptomatic responses elicited by electrical stimulation of the cingulate cortex: study of a cohort of epileptic patients and literature review | |
| Mahmood et al. | Introduction to Non-Invasive Biomedical Signals for Healthcare | |
| Kurgaev et al. | Rehabilitation According to the Biological Feedback | |
| CN116543873B (en) | DOC evaluation and progress type consciousness rehabilitation guidance scheme decision-making system and platform based on AI | |
| Bressi et al. | Robotic treatment of the upper limb in chronic stroke and cerebral neuroplasticity: A systematic review | |
| Hadoush et al. | Modulation of resting-state brain complexity after bilateral cerebellar anodal transcranial direct current stimulation in children with autism Spectrum disorders: a randomized controlled trial study | |
| Guan et al. | BCI facilitates the improvement of cognitive functions in children and elderly | |
| Wu et al. | [Retracted] Complexity Assessment of Chronic Pain in Elderly Knee Osteoarthritis Based on Neuroimaging Recognition Techniques | |
| Ngamsomphornpong et al. | Development of Hybrid EEG-fEMG-based Stress Levels Classification and Biofeedback Training System | |
| Hassan et al. | An overview of brain machine interface research in developing countries: Opportunities and challenges | |
| Rockstroh et al. | The postimperative negative variation following ambiguous matching of auditory stimuli | |
| JP6749777B2 (en) | Rehabilitation equipment | |
| Li et al. | Decoded EEG Neurofeedback-Guided Cognitive Reappraisal Training for Emotion Regulation | |
| Wang | Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS) | |
| Jarjees | The causality between Electroencephalogram (EEG) and Central Neuropathic Pain (CNP), and the effectiveness of neuromodulation strategies on cortical excitability and CNP in patients with spinal cord injury | |
| Khaleghi et al. | EEG-based functional connectivity analysis of brain abnormalities: A review study |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |