52 results about "Consciousness Disorders" patented technology

The invention relates to the technical field of biological consciousness state monitoring, in particular to an emergency patient consciousness state monitoring system based on a bio-electricity signal, and the system obtains a theta rhythm signal, an alpha wave signal and a gamma wave oscillation signal through an electroencephalogram signal acquisition module and a signal extraction module; obtaining an effective phase amplitude coupling event and an occurrence time position thereof through a phase amplitude coupling event time position positioning module; based on a patient consciousness state monitoring module, obtaining a patient consciousness state monitoring index, and fitting the patient consciousness state monitoring index with the time sequence to complete patient consciousness state monitoring. By means of the method, the theta-gamma cross-brain region phase amplitude coupling (PAC) event can be accurately positioned, the limitation that space-time positioning of the PAC event is fuzzy and dynamic tracking lags in the traditional technology is broken through, millisecond-level high-resolution monitoring of the consciousness state of an emergency patient is achieved, and a quantifiable neural electrophysiology basis is provided for real-time intervention of disturbance of consciousness.

The invention discloses a multi-modal data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring multi-modal neural signal data corresponding to a target object; extracting consciousness characteristic parameters from the multi-modal neural signal data; the consciousness feature parameters are converted into consciousness feature vectors, the consciousness feature vectors are fused into a target feature vector, and the target feature vector is a vector used for representing the consciousness state of the target object; inputting the feature vector into a pre-training model of consciousness state classification, and outputting a consciousness state category corresponding to the target object; the pre-training model of consciousness state classification is a model which is obtained through training based on historical sample data of a plurality of patients and is used for determining the consciousness state classification of the to-be-tested person. According to the embodiment of the invention, the problem of low classification accuracy of consciousness states of patients with disturbance of consciousness in the prior art can be solved.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

The invention discloses a disturbance of consciousness electroencephalogram discrimination method and system adaptive to channel deletion. According to the method, on the basis of an electroencephalogram classification model of a deep network, automatic discrimination of disturbance of consciousness (MCS and UWS) is carried out with high accuracy according to resting-state electroencephalogram energy of a patient. The model utilizes a time domain double-branch CNN module and a space-time Transform module to learn features in electroencephalogram data hierarchically; and a model architecture with variable channel dimensions is introduced, an inter-channel association learning method fusing electrode position information and a training strategy of channel random discarding are introduced, so that the model can adapt to a scene in which part of channels are missing, and the clinical applicability of the method is improved.

The invention discloses a method for dynamically evaluating the degree of disturbance of consciousness under multiple modes, and the method comprises the steps: S1, obtaining a physiological signal of a detected person under multiple modes; s2, preprocessing the physiological signal to obtain a preprocessed signal; s3, calculating features of the physiological signals based on the preprocessed signals to obtain multi-dimensional features; s4, preprocessing the multi-dimensional features to obtain preprocessed data; s5, fusing the preprocessed data to obtain fusion features; and S6, inputting the fusion features into a mode recognition output module to obtain a disturbance of consciousness assessment score of the tested person. According to the method, the degree of disturbance of consciousness is evaluated in multiple modes and multiple dimensions, so that the result is more accurate.

The invention discloses a brain-computer interface manipulator motion detection system for disturbance of consciousness assessment, which belongs to the technical field of disturbance of consciousness assessment and specifically comprises an electroencephalogram acquisition device for acquiring original electroencephalogram signals when a patient executes motor imagery tasks containing different hand action instructions; the dynamic feature extraction module generates a motor imagery activation map by calculating the energy distribution change of a preset frequency band; the driving instruction generation module calculates the space-time matching degree of the map and a preset action instruction and generates a manipulator driving instruction; the manipulator control module is used for controlling and executing a composite action positively correlated with the instruction strength, and the action is formed by combining basic grasping and multi-joint coordination actions in proportion; the motion capture device records track changes, and the ratio of the number of the joints exceeding the threshold value to the motion duration serves as a consciousness activity index; the disturbance of consciousness grading module determines a grading result according to the fluctuation range of the index in the continuous period; the method provides an objective and accurate quantification means for disturbance of consciousness assessment.

The application discloses a semantic context-related auditory brain-computer interface consciousness detection system, uses a brain-computer interface and a deep learning technology to carry out consciousness detection, designs a speech context-related auditory brain-computer interface paradigm, through using animal sounds as auditory stimulation, adds a semantic context related to the stimulation to improve the attention of a subject to the paradigm, thereby improving the quality of an event-related potential induced by the paradigm and improving the performance of the brain-computer interface. Through improvement of an EEG-Inception model for feature extraction and classification, the EEG-Inception model can extract more effective electroencephalogram signal features and also has a better classification effect under a small sample size. Through innovation and improvement of the brain-computer interface paradigm and a detection and classification algorithm, the application improves the performance of the auditory brain-computer interface and solves the problem that some patients with consciousness disorders cannot use a visual brain-computer interface for consciousness detection because they cannot control eye movement.

This invention discloses a method for classifying consciousness disorders in EEG signals based on dynamic graph convolution and channel attention mechanisms, relating to the field of EEG signal recognition technology. According to the method provided in the embodiments of this invention, a complete closed loop is achieved, covering uploading, preprocessing, artifact removal, segmentation, feature extraction, and model prediction. A dynamic graph convolution modeling method with a trainable adjacency matrix is ​​used to adaptively learn functional connections between EEG channels, overcoming the poor generalization problem of static adjacency matrices. Simultaneously, a joint modeling framework of explicit connectivity (PLV) + implicit connectivity (dynamic graph convolution) is used to more robustly capture cross-channel synchronization patterns under low signal-to-noise ratio conditions.

In MRI examinations of patients with acute ischemic stroke, it is often difficult for patients to fix and hold their heads due to impaired consciousness or limb weakness. Therefore, this invention provides a tool that is structurally rational, easy to operate, and effectively enhances the head fixation effect. [Solution] A tool to support MRI examination, comprising a base 1, a head rest member 2 with adjustable anterior-posterior position, a lateral limiting member 3 with an elastic buffer layer, and an adjustment mechanism 4. The head rest member slides relative to the base to adapt to the length of the patient's neck. The lateral limiting member is biased inward by a compression coil spring and flexibly holds both sides of the head with an elastic buffer layer, absorbing and suppressing involuntary movements. The adjustment mechanism uses a single reverse-threaded adjustment rod to synchronously and symmetrically move the left and right limiting plates, enabling precise adjustment according to the head circumference. This effectively limits multi-directional micro-movements of the head during imaging and improves image stability.

The present application relates to a kind of automatic sleep staging method, its steps include: the EEG original data is processed to obtain EEG feature map, while the EOG original data is processed to obtain EOG feature map;EEG feature map and EOG feature map are multiplied and fused, and highlight feature map is obtained;Highlight feature map is directly added with EEG feature map and EOG feature map and fused to obtain initial fusion feature map;Initial fusion feature map is processed to obtain the most weight feature map by weight adjustment;Initial fusion feature map and the most weight feature map are multiplied and fused to obtain the final fusion feature map with weight information;According to the weight information of final fusion feature map, each sleep stage prediction probability is output.The automatic sleep staging method described in the present application is further trained and optimized, does not need too much artificial intervention, and can be simply and effectively applied in sleep staging of healthy individuals and patients with consciousness disorders.

The invention discloses a Web-based disturbance of consciousness electroencephalogram data analysis system, which relates to the technical field of medical data processing, and comprises a data acquisition and storage layer, which comprises four levels of front-end acquisition, data buffering, original data storage and a system database, and is used for realizing real-time acquisition, efficient caching, safe storage and rapid retrieval of original electroencephalogram data; extracting obstacle features including spectrum features, complexity indexes, continuity indexes and ERP evoked potentials; the obstacle feature analysis model is used for screening obstacle features, eliminating redundant or low-distinction-degree data, determining key feature data, establishing a key feature data relation network, and combining real-time input data changes and the key feature data relation network to determine the consciousness obstacle state. According to the method, the key feature data relation network is established by obtaining the key feature data, accurate recognition and classification of the consciousness disorder state are achieved, and the consciousness disorder state of the patient is determined by combining with the real-time data.

The present invention relates to the technical field of biological consciousness state monitoring, and specifically to an emergency patient consciousness state monitoring system based on bioelectric signals. The system of the present invention uses an EEG signal acquisition module and a signal extraction module to acquire θ rhythm signals, α wave signals, and γ wave oscillation signals; uses a phase amplitude coupling event time position positioning module to acquire effective phase amplitude coupling events and their occurrence time positions; and uses a patient consciousness state monitoring module to obtain patient consciousness state monitoring indicators, and fits the patient consciousness state monitoring indicators with a time series to complete patient consciousness state monitoring. The present invention can accurately locate θ-γ cross-brain region phase amplitude coupling (PAC) events, and overcome the limitations of traditional technologies in fuzzy spatiotemporal positioning of PAC events and delayed dynamic tracking, thereby achieving millisecond-level high-resolution monitoring of emergency patient consciousness states and providing a quantifiable neuroelectrophysiological basis for real-time intervention in consciousness disorders.

The embodiment of the invention provides a dynamic function network adaptive construction method for physiological signal analysis and related equipment, and belongs to the technical field of biomedical signal processing and computer-aided diagnosis. The method comprises the following steps: inputting a multi-channel physiological signal and extracting a preset frequency band; performing time sequence segmentation on the signal by adopting a sliding window; calculating a phase locking value matrix among all channels for each time window; carrying out personalized sparsification processing on each connection matrix by adopting a self-adaptive threshold value method; and constructing a dynamic graph sequence based on a sparse result, wherein node features adopt weighting centrality. According to the method, the time-varying information of the brain function network is completely reserved through the dynamic graph sequence, the noise robustness and personalized processing are realized through the adaptive threshold, the learning efficiency of the subsequent graph neural network is enhanced through the structure-related node features, the accuracy of disturbance of consciousness classification is remarkably improved, and the classification efficiency of the disturbance of consciousness is improved. And the method has the universality of expanding to other physiological signal analysis and industrial monitoring fields.

The application relates to a closed-loop nerve regulation system and method based on heart-brain interaction, which comprises four modules of multi-modal physiological signal acquisition, cross-modal feature fusion evaluation, Bayesian parameter optimization and median nerve electric stimulation output, can synchronously collect signs, fuse features to give scores, optimize stimulation parameters and apply electric stimulation, and forms a closed loop. Through the whole-link design of multi-modal signal synchronous acquisition, cross-modal feature intelligent fusion, stimulation parameter dynamic optimization and closed-loop electric stimulation intervention, the closed-loop nerve regulation system based on heart-brain interaction according to the embodiment of the application can effectively solve the core pain points of traditional consciousness disorder patient wake-up treatment, such as lack of real-time feedback, one-sided single-index evaluation, insufficient use of autonomic nervous signals and non-intelligent control of non-invasive stimulation, realize precise evaluation and individualized intervention, improve the wake-up efficiency, reduce the risk, and promote the data-driven change of nerve regulation.

The application discloses a consciousness disorder patient emotion recognition system based on electroencephalogram microstate analysis, comprising: an electroencephalogram data acquisition module, which is used for presenting emotional stimuli to a consciousness disorder patient according to a preset emotional stimulus paradigm and collecting multi-channel electroencephalogram data; a data preprocessing module, which is used for preprocessing the electroencephalogram data to obtain pure electroencephalogram data; a feature extraction module, which is used for extracting features from the pure electroencephalogram data to obtain microstate features related to positive and negative emotions; a feature fusion module, which is used for fusing the microstate features related to positive and negative emotions to construct a feature representation capable of comprehensively reflecting the emotional state of the patient; and an emotion classification module, which is used for training and classifying the fused features by using a classification model to obtain a patient emotion recognition result. The application realizes objective recognition of the emotions of the consciousness disorder patient, thereby providing an objective neurophysiological index for clinical consciousness judgment and prognosis evaluation.

The invention discloses application of a molecular target Ptchd1 in nerve synaptic function and consciousness regulation and related diseases. Whole genome screening finds that Ptchd1 is a key molecular target for consciousness regulation, and animal experiments prove that the target has an important regulation effect on neuronal synaptic vesicle release and consciousness disappearance caused by general anesthesia drugs. In view of the fact that Ptchd1 can regulate and control drug-induced awareness disappearance and neuronal synaptic delivery, related treatment with Ptchd1 as a target spot has significant potential values in the following fields: i) application in preparation of reagents for regulating and controlling a neuronal synaptic function and awareness regulation and control; the invention also relates to application of the pharmaceutical composition in preparation of drugs for promoting awakening after anesthesia and application of the pharmaceutical composition in preparation of drugs for promoting awakening of diseases related to disturbance of consciousness, wherein the diseases related to the disturbance of consciousness comprise vegetative people, micro-consciousness or brain injury coma.

The application discloses a kind of consciousness disorder evaluation method and system based on electroencephalogram and language processing response, visual face image and hierarchical auditory sentence of self-reference are presented to subject simultaneously, construct multimodal audio-visual mixed stimulation paradigm, and P300 event-related potential and hierarchical language tracking features are simultaneously induced.Electroencephalogram signal is collected and preprocessed, time-domain P300 features and frequency-domain hierarchical language tracking features are extracted and multi-domain feature fusion is carried out;Cross-subject meta-learning classification model is constructed, and the feature is adaptively calibrated using hypernetwork, and the instruction following ability and language processing ability grading are output;Finally, combined with online statistical test and offline hierarchical analysis, cross-validation output consciousness state comprehensive evaluation result is carried out.The application realizes the cross-subject precision discrimination under small sample condition, and provides an objective, bed-side deployable evaluation tool for patients with consciousness disorder.

The invention discloses a spinal cord electrical stimulation prognosis prediction system for a patient with disturbance of consciousness based on neurovascular coupling, and belongs to the technical field of brain-computer interfaces. The system synchronously acquires electroencephalogram and cerebral blood flow signals of the patient through electroencephalogram acquisition equipment and transcranial Doppler ultrasound; brain network connections are calculated by using weighted pairwise phase congruency indexes, neurovascular coupling parameters are calculated based on a phase-amplitude coupling method, and finally results are presented through a display module to predict prognosis. The system can solve the problem of clinical lack of prognosis evaluation indexes of SCS treatment consciousness disorder patients, has the advantages of simple acquisition equipment, reusability and the like, and can assist doctors in providing personalized clinical schemes and reducing the treatment cycle; the system relates to a data acquisition, processing and result display module, a key parameter calculation process is clear, and the system is suitable for prognosis evaluation after SCS implantation of a consciousness disorder patient.

This invention discloses an intelligent terminal interactive awakening system and method for patients with impaired consciousness based on Traditional Chinese Medicine (TCM) rules, belonging to the field of intelligent medical technology. The system includes a data acquisition module that acquires multidimensional clinical data of patients with impaired consciousness. Through data cleaning and standardization, an initial patient clinical information matrix is ​​constructed to obtain a structured dataset of impaired consciousness features. An intervention plan generation module generates a preliminary intervention intensity curve based on the structured dataset. For this preliminary intervention intensity curve, boundary condition constraint technology is used to set upper and lower limits for intervention intensity and time. Simultaneously, a smoothness weight allocation method is used to adjust the importance parameters of time points to obtain a smooth intervention execution plan. This intelligent terminal interactive awakening system and method for patients with impaired consciousness based on TCM rules achieves precise and personalized intervention for these patients, improves treatment outcomes, and provides new ideas and methods for clinical practice.

The invention discloses a digital integrated brain injury nerve regulation diagnosis and treatment platform, and relates to the technical field of brain injury diagnosis and treatment, and the technical scheme is characterized in that the platform comprises a multi-mode brain function monitoring module used for collecting brain function data of a brain injury consciousness disorder patient in real time; the multi-sensory nerve regulation and control stimulation module is used for cooperatively applying multi-sensory stimulation to the patient; and the main control and data processing module receives and processes the multi-modal signals transmitted by the multi-modal brain function monitoring module, generates a personalized nerve regulation scheme by extracting brain function characteristics, and controls the multi-sensory nerve regulation stimulation module to execute the personalized nerve regulation scheme at the same time. According to the method, multi-sensory stimulation is cooperatively applied to the patient, the problem of pain points of traditional distributed non-standard stimulation is solved, the core requirement of the patient for multi-channel sensory input awakening is met, meanwhile, a complete closed loop of monitoring-analysis-intervention-feedback is formed, and dynamic evaluation and accurate intervention on the brain function state of the patient with acute brain injury consciousness disorder are achieved.

The invention provides an electroencephalogram coding method and system for communication of patients with disturbance of consciousness, and relates to the technical field of electroencephalogram coding. The method comprises the following steps: constructing a codebook of multi-dimensional stimulation and response dimensions, mapping information units into code words, and setting separability and length constraints; a frame structure comprising a synchronization preamble, a code element area, a verification and error correction area and a confirmation and retransmission area is adopted for transmission; collecting and weighting multi-channel electroencephalogram signals under time sequence constraints, and decoding and outputting confidence coefficients based on observation signals; when the confidence coefficient is lower than a threshold value, adaptive retransmission and redundancy lifting are executed; evaluating a load index in real time, dynamically adjusting frame and strategy parameters, and switching a sensory mode; and a result is output and feedback is combined to complete closed-loop transmission, so that high-precision, stable and tolerable alternating current is realized.

This invention provides a collaborative control method based on consciousness impairment assessment and monitoring and synchronous neural stimulation. The method includes: an eye-tracking monitoring and analysis system extracting eye-tracking feature data from eye-tracking signals using image preprocessing and eye-tracking feature extraction algorithms; a server-side management system combining scale assessments and eye-tracking feature data, utilizing a multi-source data fusion assessment algorithm to output the patient's consciousness state assessment results and stimulation parameter information; a neural correlation electrical stimulator receiving the stimulation parameter information and using a timing synchronization control algorithm to synchronously initiate neural electrical stimulation with the eye-tracking monitoring and analysis system; and the server-side management system dynamically adjusting the consciousness state assessment results and stimulation parameter information based on post-stimulation eye-tracking response characteristics and stimulation parameter feedback, reusing the multi-source data fusion assessment algorithm to form a closed-loop control. This invention achieves timing synchronization and command closed-loop through a collaborative linkage algorithm, ensuring efficient collaborative operation of multiple modules and adapting to complex clinical scenarios such as ICUs and rehabilitation departments.

The application discloses a kind of consciousness disorder stimulation regulation systems and methods of fusion electroencephalogram connection identification, including simulation electroencephalogram signal data acquisition stage, connection identification analysis stage, stimulation parameter optimization stage and executable stimulation instruction conversion stage;The application has the following advantages and effects: generate whole brain electrical activity distribution map and identify key connection area by simulation electroencephalogram signal data acquisition stage, generate brain function connection atlas in connection identification analysis stage using functional connection analysis network and phase synchronization algorithm, establish the space-time association of whole brain electrical activity distribution map and brain function connection atlas in stimulation parameter optimization stage and generate optimized stimulation parameter set by fusion network using multi-objective optimization algorithm, finally executable stimulation instruction is converted based on adaptive control model in executable stimulation instruction conversion stage, so as to significantly improve the precision and adaptive ability of electroencephalogram signal stimulation regulation.

The invention discloses a brain monitoring method and system for a patient with disturbance of consciousness, and belongs to the technical field of medical monitoring. A brain monitoring system for a patient with disturbance of consciousness comprises a patient brain monitoring module and a patient demand recognition module. By continuously acquiring the electroencephalogram activity signals and combining with the effective connection analysis algorithm, the brain monitoring information flow graph of the patient with the disturbance of consciousness can be constructed, signal transmission and causal intensity between different brain function areas can be accurately reflected, the brain activity of the patient with the disturbance of consciousness can be monitored in real time, and accurate evaluation of the brain state of the patient is ensured; by combining the electroencephalogram activity signal and the body movement monitoring signal, the physiological state of the patient can be comprehensively analyzed, the body movement data of the patient during unconscious activity is captured, and the monitoring comprehensiveness and accuracy are further improved.

The invention belongs to the field of medicine, and relates to application of overexpressed PSAP in preparation of medicine for improving secondary brain injury after subarachnoid hemorrhage. HuPSAP is adopted for overexpressing PSAP, the HuPSAP is combined with a Gpr37 receptor on the surface of an oligodendrocyte cell membrane, downstream mTOR and ERK signal channels are activated, myelin sheath related gene expression is enhanced, mitochondrial dependent cell apoptosis is inhibited, and the effect of treating secondary brain injury after subarachnoid hemorrhage is achieved. Therefore, the conduction function of the CL-mPFC loop after SAH is protected, and disturbance of consciousness is reduced. Experimental basis and theoretical basis are provided for treatment of secondary brain injury after SAH, especially disturbance of consciousness, by the HuPSAP.

The utility model discloses a urine bag special for a patient with disturbance of consciousness. The urine bag comprises a bag body, a butt joint mechanism, a non-return mechanism, a sensor and a beeper. The bag body is provided with a drainage opening matched with urine to flow in, and the butt joint mechanism is arranged on the outer surface of the bag body, communicated with the drainage opening and used for fixing the bag body to a patient; the non-return mechanism is arranged between the bag body and the butt-joint mechanism, so that urine flows into the bag body from the butt-joint mechanism and is prevented from flowing back into the butt-joint mechanism from the bag body; the sensor is arranged at the bottom of the bag body and electrically connected with the pager, and the sensor can detect urine in the bag body to start the pager. Compared with the prior art, the special urine bag for the patient with the disturbance of consciousness not only can prevent urine from flowing back and overflowing, but also can remind medical staff to replace the bag body in time, so that the comfort level of the patient lying in bed is greatly improved.

The invention discloses an intelligent terminal disturbance-of-consciousness interactive waking-up system and method based on traditional Chinese medicine rules, and relates to the technical field of intelligent medical treatment, the system comprises a data acquisition module for acquiring clinical multi-dimensional data of a disturbance-of-consciousness patient, constructing an initial patient clinical information matrix through data cleaning and standardization processing, and sending the initial patient clinical information matrix to an intelligent terminal; the intervention plan generation module is used for obtaining a preliminary intervention intensity curve aiming at the structured disturbance of consciousness characteristic data set, setting upper and lower limits of intervention intensity and time by adopting a boundary condition constraint technology aiming at the preliminary intervention intensity curve, and meanwhile, obtaining an intervention plan through a smoothness weight distribution method; adjusting importance parameters of the time points to obtain a smooth intervention execution plan; according to the intelligent terminal disturbance of consciousness interaction awakening system and method based on the traditional Chinese medicine rules, precise and personalized intervention of patients with disturbance of consciousness is achieved, the treatment effect is improved, and a new thought and method are provided for clinical practice.

The invention discloses a method, a system and equipment for adjusting the circadian rhythm of a patient with disturbance of consciousness based on a Bayesian fusion algorithm and reinforcement learning. The method comprises the following steps: acquiring real-time physiological data of the patient; the real-time physiological data at least comprises heart rate, electroencephalogram signals, electrodermal response and body temperature; inputting the real-time physiological data of the patient into the circadian rhythm regulation model to obtain a corresponding intervention scheme, feeding back the physiological data at the next moment, and updating the circadian rhythm regulation model; the circadian rhythm adjustment model is constructed based on a Bayesian fusion algorithm and a reinforcement learning model. According to the method and the device, dynamic real-time intervention on the circadian rhythm of the patient with the disturbance of consciousness is realized, the adaptive ability of circadian rhythm adjustment is improved, and the awakening efficiency of the patient with the disturbance of consciousness is further improved.

The embodiment of the invention provides a disturbance of consciousness classification method based on a dynamic graph neural network and related equipment, and belongs to the technical field of crossing of medical artificial intelligence and neural engineering. The method comprises the steps that resting-state electroencephalogram signals of a patient are collected and preprocessed; calculating a phase locking value through a sliding window, and constructing a dynamic brain function network diagram sequence; the dynamic graph sequence is input into a dynamic graph neural network model for processing, the model is composed of a plurality of dynamic graph convolution units which are connected in a time sequence mode, and each unit extracts space-time dynamic characteristics of the brain network in a combined mode through a coupled graph convolution network and a gating circulation unit; and based on the final hidden state output by the model, obtaining a classification result of the micro-consciousness state or the plant state through a classifier. According to the method, the problems that an existing static analysis method cannot capture brain network dynamic characteristics and a model and a data structure are mismatched are solved, higher-precision automatic classification of disturbance of consciousness is achieved, and the method has good clinical auxiliary diagnosis value.

The utility model relates to the field of oxygen devices, and provides a plateau oxygen inhalation device which comprises a bottle body, a cavity arranged in the bottle body, oxygen filled in the cavity, a bottle opening arranged on the bottle body, an oxygen outlet device arranged on the bottle opening, an air outlet channel arranged in the oxygen outlet device, an air outlet arranged at the end of the air outlet channel and communicated with the cavity. A flow velocity electromagnetic valve is arranged on the air outlet channel and connected with a power source, a human body sensor and an airflow sensor are arranged on the oxygen outlet device, and an oxygen mask is arranged on the bottle body. The flow velocity solenoid valve can accurately control opening and closing and flow velocity of oxygen, the problem that a traditional hand-pressing switch needs to be pressed and inhaled synchronously is solved, oxygen waste is avoided, the service life of the device is prolonged, meanwhile, the device can be conveniently used by special groups such as old people, children, people with disturbance of consciousness, mentally retarded people and low-culture-level people, the use crowd range is expanded, and the practicability is high. The technical problems that when an oxygen inhalation device is used, waste is easily caused, potential safety hazards exist, and use of special crowds is limited are solved.