69 results about "Consciousness Disorders" patented technology

The invention relates to a brain function evaluation system for a patient with disturbance of consciousness based on a neural multi-modal monitoring technology, and belongs to the technical field of biomedicine, and the system comprises a multi-modal data acquisition module which is used for acquiring multi-modal data of an evaluated person, the multi-modal data comprises clinical behavioral data, cerebral blood flow data, brain image data and neural electrophysiological data, the neural electrophysiological data comprises electroencephalogram and evoked potential data, and the brain image data comprises brain structure images and brain function images; the multi-modal data evaluation module is used for evaluating the clinical behavioral data, the cerebral blood flow data, the brain image data and the neural electrophysiological data to obtain an evaluation value of each modal data; and the comprehensive evaluation module is used for processing the plurality of evaluation values by using a disturbance of consciousness awakening prediction model to obtain an evaluation result. The method has the effects that the brain function of the patient with the disturbance of consciousness is evaluated by using a multi-mode neural function monitoring technology, and the evaluation accuracy is improved.

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 disturbance of consciousness auxiliary communication system fusing eye movement tracking and electroencephalogram, comprising a data acquisition device connected with a multi-mode BCI communication system and used for collecting scalp electroencephalogram signals and eye movement signals; the multi-modal BCI communication system can identify the answer (yes / no) of the subject by detecting and analyzing the multi-modal data collected from the communication normal form, so as to realize online binary communication; the multi-modal BCI communication system comprises a multi-modal target identification network, and the multi-modal target identification network specifically comprises a data preprocessing module used for preprocessing and synchronizing different modal data; the double-flow feature extraction module is composed of two independent multi-scale convolutional neural networks which are respectively used for EEG and eye movement data so as to carry out feature extraction on data of different modals; according to the invention, a novel BCI alternating normal form integrating P300 potential and eye movement tracking is designed; the P300 detection and the eye movement tracking detection have irreplaceability and complementarity.

The invention provides a noninvasive deep time domain interference electrical stimulation individualized electrode site determination method and device for treating disturbance of consciousness. The method comprises the steps that a brain region segmentation model is generated according to brain structure image data of a patient; the brain region segmentation model is used for representing structures of different brain regions in the brain; a front lead field matrix is calculated according to the brain region segmentation model, the front lead field matrix is used for representing the influence of neurons at different positions on the electric signals measured by each electrode site, and each electrode site corresponds to a plurality of neurons; determining a brain region stimulation scheme according to the front lead field matrix; determining a target electrode site according to the electroencephalogram data related to the left thalamus or the right thalamus of the patient and a brain region stimulation scheme; the target electrode site is used for time domain interference electrical stimulation. According to the embodiment of the invention, the recognition accuracy of the electrode sites for electrical stimulation treatment of disturbance of consciousness can be improved, and the improvement of the treatment effect of disturbance of consciousness is facilitated.

The invention relates to the technical field of waking-up training monitoring systems, in particular to a disturbance of consciousness waking-up training monitoring system, which is characterized in that a brain wave response analysis module is used for analyzing and obtaining related information of brain waves of a patient; the action response analysis module is used for analyzing and obtaining related information of response after the patient completes the action; the control module obtains a patient state factor; the training intensity judgment module obtains information about whether the patient adapts to wake-up training or not; the brain wave monitoring module obtains total brain wave power and delta wave power; the stimulation intensity analysis module obtains intensity adjustment information; the vagus nerve electrical stimulation module starts power-off stimulation according to the information whether the patient adapts to waking-up training, and the electrical stimulation intensity is improved, reduced or maintained according to the vagus nerve electrical stimulation module. The system can more comprehensively reflect the state of the patient in the training process, provides data support for personalized treatment, and is beneficial to improving the overall flexibility.

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.

Provided in the present application are an arousal electrical stimulation system for rehabilitation of disorders of consciousness and a method. The system comprises: a program control module, used for setting a plurality of stimulation point combinations and a plurality of stimulation parameter combinations; an implanted spinal cord stimulation module, used for sequentially outputting stimulation signals according to the plurality of stimulation point combinations and the plurality of stimulation parameter combinations; an acquisition module, used for acquiring electroencephalographic signals and electromyographic signals when the stimulation signals act on a patient suffering from disorders of consciousness; and a processing module, used for processing the electroencephalographic signals and the electromyographic signals to obtain a human body index, and, on the basis of the human body index, determining an optimal stimulation point combination and an optimal stimulation parameter combination from among the plurality of stimulation point combinations and the plurality of stimulation parameter combinations. The present invention uses the cooperation of in-vitro electroencephalographic and electromyographic sampling devices and a spinal cord electric stimulation device implanted into the human body, and performs calculation by means of the processing module, so as to obtain the optimal electric stimulation electrode point combination and the optimal electric stimulation parameter combination, thereby improving the arousal efficiency.

A model may be generated for characterization of a fluctuation in an arousal level based on EEG data for a cohort of subjects. Changes in gamma and delta waves can be used to detect arousal units (AUs) for cortical periods. One or more metrics relating at least in part to gamma and theta waves can be determined for each AU. Centroids for the cortical periods can be used to train the model to generate an arousal index. The model can be used to determinate arousal level in one or more patients. For example, EEG data can be acquired for a subject using a set of sensors. AUs in the EEG data can be detected, and a characterization of a fluctuation in an arousal level of the subject can be generated using the model. The characterization may be used in evaluating and / or treating a health condition, such as a consciousness disorder.

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 invention belongs to the technical field of diagnostic data processing, and particularly relates to a percutaneous ear vagus nerve electrical stimulation and disturbance of consciousness correlation qualitative analysis method and system. The consciousness state of the patient can be reflected more comprehensively by constructing the feature information of the standard consciousness evaluation index data distribution data, in addition, influence factors of drug therapy and diseases and lesions can be identified and eliminated more accurately through corresponding verification of the electrical stimulation signal abnormal fluctuation value, and the accuracy of the diagnosis result is improved. Therefore, the accuracy and effectiveness of pathogenesis analysis of the patient are improved, and medical resources can be reasonably distributed and used; the invention further provides a percutaneous ear vagus nerve electrical stimulation and disturbance of consciousness correlation qualitative analysis system and electronic equipment.

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 invention discloses an intelligent disturbance of consciousness assessment system based on electroencephalogram, and relates to the technical field of disturbance of consciousness assessment. Comprising an audio acquisition module, a voice feature extraction module, a voice simulation module, a voice generation module, a speech generation module, a picture acquisition module, a picture generation module, a video generation module, a background setting module, a character model generation module and a character feature extraction module. According to the intelligent assessment system for disturbance of consciousness based on electroencephalogram, pictures and videos are played through the display to stimulate a patient, audios are played through the earphone to stimulate the patient and the tactile sense of the patient, and electroencephalogram signals of the patient in the testing process are collected; the electroencephalogram signals of the patient are analyzed through electroencephalogram signal analysis software, the disturbance of consciousness of the patient is recognized and evaluated according to the electroencephalogram signal analysis result of the patient, and therefore compared with an experimental result obtained through a traditional testing mode, the accuracy and objectivity are higher.

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 relates to the field of medical care, and particularly discloses a cerebral cortex state detection method and system based on a disturbance of consciousness detection model, and the method comprises the steps: dividing a cerebral cortex region according to different functional regions of the cerebral cortex, and obtaining different cerebral cortex monitoring sub-regions; the corresponding relation between the cerebral cortex monitoring sub-areas and the data acquisition point density is obtained in a cloud medical data server, and a data acquisition point distribution scheme of each cerebral cortex monitoring sub-area is obtained; the data acquisition point data management module acquires initial monitoring data of each cerebral cortex monitoring sub-region according to the data acquisition point distribution scheme; and comparing the monitoring data sequence of the cerebral cortex monitoring sub-region sequence of the corresponding stimulation type with the initial monitoring data sequence of the corresponding cerebral cortex monitoring sub-region one by one to complete the state detection of the cerebral cortex state. According to the invention, the neural activity state of the cerebral cortex can be monitored and evaluated in real time.

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 present invention discloses a consciousness level detection system integrating ASSR and P300, including: a data transmission module for receiving and marking EEG signal data; a data processing module for importing EEG signal data and performing preprocessing operations; an experimental paradigm module for presenting a stimulation paradigm to a subject; a model training module for importing EEG signal data, extracting features and training a P300 model and an ASSR model as well as a hybrid model integrating the two components of P300 and ASSR; an online test module for processing the EEG signal data received from the EEG signal acquisition software while the subject is undergoing the experiment, and inputting the processed data into the model saved by the model training module, and feeding back the prediction results to the subject. The present invention makes full use of the superiority of brain-computer interface technology to improve the diagnosis level of the consciousness state of patients with consciousness disorders, and at the same time, provides a certain degree of explainability for the diagnosis results, thereby enhancing the credibility of the diagnosis results.

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.