30 results about "Conscious State" patented technology

The invention discloses an awareness perception quantum gravitational nerve acceleration unified computing platform and method based on formalized verification, and belongs to the field of high-performance computing, medical AI and quantum information processing. A quantum gravitational coupling neural processing unit (QGCNPU); a consciousness information integral calculation core (phi ICC); formalizing a receipt chain verifier (FRCV); and the mirror reflection symmetry correction module (MRSCM) is used for eliminating the continuous projection mistake and the discrete Boolean mistake through topological equivalence verification of a local observer and a global GodCam visual angle. The system adopts an FPGA + ASIC + quantum processor three-layer heterogeneous architecture, and supports real-time pathological modeling (precision gt; gt) of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and ALS; 99.7%), drug target discovery (acceleration by 50 times) and consciousness state monitoring (time resolution lt; and meanwhile, the mathematical proving performance and the clinical auditing performance of the calculation process are ensured, and the requirements of the global 152 billion dollar neural science and technology market and the 89 billion dollar quantum calculation industry are met.

This invention discloses an intelligent shore-based rescue method, system, and computer-readable medium. The method involves a first device determining the respective rescue routes of a drone and an unmanned vessel based on a user's distress call. The drone conducts a search and rescue operation according to its route, generating the current location of the person in the water. The first device detects the consciousness state of the person in the water based on real-time image data collected by the drone at the current location. Then, based on the detection results, different surface rescue tasks are performed on the person in the water according to the determined rescue method. Therefore, this embodiment determines different rescue methods based on the consciousness state of the person in the water, which not only enables rapid rescue but also improves the success rate of rescue. This provides an automated rescue solution with intelligent perception, precise positioning, and rapid response, solving the technical problem of low rescue rates for people in the water due to mismatched rescue methods, inaccurate positioning, or untimely rescue in existing technologies.

The invention discloses a medical comprehensive assessment method for old people and related equipment, and relates to the field of health management, and the method comprises the steps: obtaining the basic information of the to-be-assessed old people; carrying out body function state evaluation on the human body to obtain a body function state evaluation result related to the life self-care ability and the falling risk; performing nutrition state evaluation on the old people based on the nutrition evaluation scale to screen the risk of malnutrition; performing psychological state assessment on the old people to obtain psychological state assessment results including cognitive functions, emotional states and consciousness states; and comprehensively integrating the basic information, the body function state evaluation result, the malnutrition evaluation result and the psychological state evaluation result to form an old people comprehensive evaluation report. The evaluation report can reflect the health risk characteristics of the old people from multiple dimensions, and provides a scientific basis for the formulation of subsequent medical schemes, nursing measures or intervention strategies, thereby facilitating the improvement of the pertinence and effectiveness of the health management of the old people.

The invention belongs to the field of flight personnel state monitoring, and discloses a meta-consciousness ability training method and system based on a neural biofeedback technology. The system can obtain neural activity data directly related to the distraction state in a real and continuous task situation, hysteresis and unreliability caused by dependence on subjective reports or behavioral expressions are avoided, and the accuracy and objectivity of distraction monitoring are improved from the source. The decoding model is embedded into a closed-loop neural feedback training system, so that an acoustic feedback signal can be triggered in time when a distraction state occurs, a trainee is prompted to perceive attention deviation, and the self consciousness state is autonomously regulated and controlled without external guidance. The closed-loop feedback can strengthen a conditioned reflex mechanism paying attention to a regression task, and the self-monitoring and self-adjusting capability of meta-awareness is remarkably improved.

The application provides a prognosis prediction method and system for chronic Doc patients, relates to the technical field of clinical prognosis evaluation, and collects multi-modal data such as neuroimaging, neuroelectrophysiological data, biochemical indexes and clinical data, carries out standardized processing and feature extraction, and fuses spatial features, time sequence features and key features.Combining a trained consciousness state grading evaluation model and a prognosis prediction model, a feature level data set is generated, and the consciousness state grading and prognosis development trend of the patient are accurately predicted.The application effectively integrates the complementary information of multi-modal data, improves the accuracy and reliability of prognosis prediction, and provides scientific support for clinical decision-making, individualized treatment and medical resource optimization.

The application provides a traditional Chinese medicine preparation for treating or improving cerebral hemorrhage and a preparation method thereof, and relates to the technical field of traditional Chinese medicine pharmacy. The traditional Chinese medicine preparation is composed of nacre, antelope horn powder, dragon's blood, Herba Schizonepetae, Prunella vulgaris, Stink-bug, Smilax riparia, bamboo juice, dried tangerine or orange peel, clubmoss, radix puerariae, Zizania aquatica and leaf of Chinese pear. The preparation has a significant therapeutic effect on cerebral hemorrhage, can obviously improve the neurological deficit of cerebral hemorrhage rats, reduce the water content of brain tissue to alleviate brain edema, simultaneously alleviate the inflammatory response in the body, thereby playing a neuroprotective effect on cerebral hemorrhage rats. In addition, the preparation can obviously improve the consciousness state of cerebral hemorrhage patients, alleviate the degree of neurological deficit, relieve the related TCM syndromes of the patients, simultaneously effectively improve the daily life ability of the patients, significantly improve the total effective rate of clinical treatment, and no obvious adverse reactions are observed in the treatment process, and the safety is good, thereby providing a safe and effective treatment scheme for the conservative treatment of cerebral hemorrhage.

The invention provides a traditional Chinese medicine preparation for treating or improving cerebral hemorrhage and a preparation method thereof, and relates to the technical field of traditional Chinese medicine pharmacy, the traditional Chinese medicine preparation is composed of nacre mother of pearl, antelope horn powder, dragon's blood, motherwort fruit, selfheal, aspongopus, radix actinidiae chinensis, bamboo juice, pericarpium citri reticulatae, selaginella tamariscina, radix puerariae, zizania aquatica and folium eriobotryae. The traditional Chinese medicine composition can obviously improve neurological impairment of cerebral hemorrhage rats, reduce the water content of brain tissues to relieve encephaledema and reduce inflammatory response in vivo, thereby playing a role in protecting the nerves of the cerebral hemorrhage rats. Besides, the preparation can obviously improve the consciousness state of a cerebral hemorrhage patient, relieve the degree of neurological impairment, relieve related traditional Chinese medicine syndromes of the patient, effectively improve the daily living ability of the patient and obviously improve the total effective rate of clinical treatment, no obvious adverse reaction is observed in the treatment process, and the preparation is good in safety and free of toxic and side effects. And a safe and effective treatment scheme is provided for the conservative treatment of cerebral hemorrhage.

The application provides a method, system, device and medium for determining anesthesia consciousness, which comprises: obtaining a target electroencephalogram signal of a target object; determining consciousness state information of the target object based on the target electroencephalogram signal through a consciousness determination model, wherein the consciousness state includes a conscious state, a light anesthesia state and an unconscious state; wherein the consciousness determination model is a machine learning model, and the consciousness determination model is obtained based on training samples, wherein the training samples include sample electroencephalogram signals, and the sample electroencephalogram signals include sample conscious state data, sample light anesthesia state data and sample unconscious state data. Through the trained consciousness determination model, the change in anesthesia depth of the target object during the operation and the smooth transition thereof can be accurately tracked on the basis of accurately determining the unconscious state or the non-unconscious state of the target object, so that the regulation of the amount of anesthetic drugs for the target patient during the operation is more accurate.

The embodiment of the invention provides an inquiry method and device and a storage medium. The method comprises the steps that a target medical expression corresponding to the chief complaint content of a patient and consciousness state data of the patient are acquired; determining at least one target red flag symptom from a plurality of preset red flag symptoms according to the target medical expression and the consciousness state data; determining a plurality of inquiry targets according to the target medical expression and the obtained target red flag symptom, and determining an inquiry strategy and an inquiry sequence of each inquiry target according to the consciousness state data; performing interrogation on the patient according to the interrogation strategy and the interrogation sequence of each interrogation target; and outputting the inquiry medical history of the patient according to the answers of the patient to the plurality of inquiry targets. According to the embodiment of the invention, patients with unconsciousness can be accurately inquired.

The invention discloses an intelligent terminal and system for evaluating the consciousness state of an unconscious patient based on neurology department monitoring, and belongs to the technical field of medical treatment. According to the method, feature sampling is carried out on the collected neurology department monitoring real-time data of the coma patient, the preset evaluation index of the patient is obtained and analyzed, the key consciousness state risk evaluation parameter is marked as the patient consciousness state basic score of the current monitoring period, and the comprehensive evaluation result of the consciousness state of the coma patient is obtained. An evaluation model is constructed by combining monitoring data collection with key feature extraction, a convolutional neural network of a multi-feature extraction layer and a recurrent neural network of time sequence learning, weights are dynamically corrected by combining individual factors, intelligence and individuation of consciousness state evaluation are achieved, feature vectors of four types of indexes are optimized through the multi-feature extraction layer, and the evaluation accuracy of the consciousness state evaluation is improved. Dynamic correction is carried out by combining the age of the patient, the cause of coma and the monitoring period, so that the evaluation result can reflect the overall consciousness state and highlight the key risk point of the individual.

The invention discloses a critical patient brain function assessment method and system based on electroencephalogram analysis. The method comprises the steps that 1, EEG signals are collected; 2, a data buffering and windowing step; 3, a sub-window data preprocessing step; 4, a brain function prediction step; and 5, a data visualization and alarm step. The evaluation system comprises an EEG signal acquisition device, an EEG signal acquisition module, a data processing module, a brain function prediction module and a visualization module. The critical patient brain function assessment method and system based on electroencephalogram analysis have the advantages that subtle changes of the consciousness state can be captured in time clinically, and objective, continuous and real-time prediction of the brain function state of an ICU patient is achieved.

The application provides a distributed computing and communication system with brain-like consciousness and an implementation method thereof, comprising: a plurality of computing nodes and a consciousness state management module; each computing node comprises a computing unit, a storage unit and a communication unit; the communication units constitute a network on chip (NoC) as a whole; the computing unit and the storage unit are connected with nodes of the NoC, and the cooperation and data sharing between the computing nodes are completed through an efficient communication protocol; and the consciousness state management module is configured on each computing node to constitute a consciousness management system of a CTM, that is, a brain-like model of the CTM, so that the mutual cooperation between each node dynamically adjusts the computing strategy to complete a complex computing task. Each computing node of the application is configured with a consciousness state management module, can perceive the real-time state of the node, and accepts the input from other nodes; through analyzing the data, the module can adjust the computing strategy of the node in real time, and realize the formation and dynamic adjustment of the consciousness.

The application discloses a kind of to the consciousness level evaluation system for wounded person, it is related to personnel consciousness evaluation grading technical field, including the acquisition module for obtaining the electroencephalogram signal and the photoplethysmogram signal of two channels of brain of wounded person, the digitization processing module of corresponding module electric signal digitization conversion, feature extraction module extracts time domain feature, frequency domain feature and entropy from electroencephalogram digital signal, from the ratio of pulse wave digital signal, calculate blood oxygen saturation and calculate blood perfusion index, consciousness evaluation module passes through fusion feature vector input consciousness state assessment model, output continuous consciousness state score, and with multiple threshold mapping obtains consciousness grade.The system of the application carries out time domain, frequency domain and nonlinear analysis respectively in the feature extraction of electroencephalogram signal and pulse wave signal, and blood oxygen and blood perfusion degree calculation, realizes the quantitative description and grading determination to the consciousness level of wounded person.

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 consciousness level recognition device based on electroencephalogram data and a computer readable storage medium. The device can perform the following operations: obtaining multi-lead scalp electroencephalogram data of a patient to be recognized; inputting the multi-lead scalp electroencephalogram data into a trained neural network model for consciousness level recognition operation, to output a response vector composed of response intensities of each lead, the response intensity representing the correlation degree between the electroencephalogram feature of the lead and the target consciousness level; calculating the distance between the response vector and the average response vector of the target consciousness level group; and determining the consciousness level recognition result of the patient to be recognized according to the distance. The consciousness level recognition scheme based on electroencephalogram data can improve the accuracy of distinguishing between the vegetative state and the weak consciousness state, and realize objective and accurate evaluation of the consciousness level.

The invention belongs to the technical field of electroencephalogram signal processing, and particularly relates to a subthreshold unconscious electroencephalogram signal acquisition system which is composed of a masking material automatic generation module, a stimulation induction module, an electroencephalogram signal acquisition and analysis module and an optical stereoscope hardware platform. Subthreshold unconscious electroencephalogram signals are collected by acquiring initial resolution, establishing a visual experiment engine, generating Mondrian masking pictures of color blocks at different positions, exploring influence factors of consciousness breakthrough and brain network function connection conditions and analyzing differences of brain dominant brain areas and brain function connection conditions. The accuracy of subthreshold stimulation, the repeatability of the experiment and the induction effectiveness of an unconscious state are ensured, a Mondrian picture with a high masking effect is automatically generated, the accurate adjustment of an experiment interface is ensured, and the accuracy and the signal-to-noise ratio of electroencephalogram signal acquisition are improved.

The invention relates to the technical field of consciousness state monitoring, in particular to a consciousness state real-time monitoring system and method based on multi-modal physiological signal fusion, and the system comprises a server side and a medical care side; the server comprises a physiological index data acquisition module used for acquiring current physiological index data of a target patient, and a feature extraction module used for performing feature recognition extraction on the acquired current physiological index data to form corresponding feature data; the consciousness state recognition module is used for taking the extracted feature data as input data, inputting the input data into a pre-trained consciousness state classification model, and outputting consciousness state type data corresponding to the target patient; and the feedback module is used for feeding back the consciousness state type data corresponding to the target patient to the medical care terminal of the medical care personnel.

The invention relates to an intelligent mind and pupil monitoring method and device, and the method comprises the steps: collecting a brain heat radiation signal, a micro-motion feature and a pupil video sequence of a patient, and obtaining the cerebral cortex neural activity data of the patient; inputting the pupil video sequence into a deformed pupil positioning model, and extracting a pupil contour, a pupil diameter, a light reflex incubation period and a contraction speed curve; inputting the brain heat radiation signal, the micro-motion characteristic and the cerebral cortex neural activity data into a time-frequency fusion neural network model, and identifying the consciousness state of the patient; according to the pupil contour, the pupil diameter, the light reflex incubation period, the contraction speed curve and the consciousness state of the patient, a comprehensive neural state index report of the patient is generated. According to the method, sub-pixel-level pupil contour segmentation is achieved, the pupil diameter is accurately extracted, the method has higher adaptability to light reflection latency and contraction speed curves, pupil deformation, shielding and illumination changes, and finer changes of the consciousness state can be captured.

The invention relates to the technical field of biomedical signal processing, and discloses a whole brain consciousness level visual analysis method and equipment, and the method comprises the steps: obtaining resting state electroencephalogram data and task state electroencephalogram data of a target object; performing resting state power spectrum density typing based on the resting state electroencephalogram data to obtain a resting state electroencephalogram channel quantitative score; task state feature quantitative analysis is carried out based on the task state electroencephalogram data, and a task state negative wave function score is obtained; performing weighted fusion based on the resting state electroencephalogram channel quantitative score and the task state negative wave function score to obtain a local consciousness comprehensive index; and carrying out whole-brain consciousness visual imaging based on the local consciousness comprehensive index. According to the method, the consciousness state of each local brain region of the whole brain can be comprehensively, accurately and intuitively presented.

An apparatus for determining a brain consciousness state of a patient, the apparatus includes one or more processors, the apparatus configured to: determine sensor data associated with an electrode arrangement, the sensor data indicative of a change in a corneo-retinal standing potential of an eye of the patient caused by ocular microtremor; and determine, based on the sensor data, the brain consciousness state of the patient.

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 application relates to the technical field of workflow, in particular to a rule-based non-BPMN standard process flow engine, which comprises a current consciousness and a meta, wherein the current consciousness is composed of a neurotransmitter, a consciousness switch and a meta signal processor; the neurotransmitter is used for transmitting information and context in the non-BPMN standard process flow engine, and the neurotransmitter can contain data and information in a business process; the consciousness switch is used for controlling the start and shutdown of a consciousness state; and the meta signal processor is responsible for receiving and processing a meta signal, the meta signal is triggered by an internal event, the meta signal processor captures the meta signal and triggers corresponding operation and process transfer. Compared with the prior art, the application has the advantages that: the complexity of a license and implementation is reduced, and the cost is lowered; the application can adapt to the requirements of various different industries and fields, and has flexibility; the application can describe a process flow by defining rules, and has ease of use; the application can quickly and accurately execute tasks, and improves process execution efficiency.

The invention relates to a disturbance of consciousness auxiliary diagnosis method and model based on facial dimension emotion estimation. The method belongs to the cross technical field of deep learning and clinical medicine. According to the method, facial expression videos of a patient under resting, name calling stimulation and pain stimulation are collected, and global features and local five-sense-organ patch features are extracted by using RetinaFace, MaxViT and HRNet models. A global branch captures overall time sequence dependence through a time sequence convolutional network and a Transform encoder, a local branch and multi-head attention modeling local subtle change through a residual network, and a wake-up degree sequence is output through dynamic weighted fusion. And calculating three indexes, namely, a calling name / resting wake-up degree difference, a pain / resting wake-up degree difference and a wake-up degree standard difference under pain, based on the sequence. And comparing each index with a preset threshold, and judging the consciousness state category of the patient. The method is simple and convenient to operate, low in cost and capable of providing objective quantitative basis for clinical diagnosis and reducing the misdiagnosis rate.

The invention relates to a method and a device for improving the cognitive state of a motor vehicle driver (1) when solving tasks while driving in a motor vehicle (2), in particular a commercial vehicle, wherein the behavior of the motor vehicle driver (1) with regard to his cognitive state or state of consciousness, in particular his attention, is continuously determined by means of an interior monitoring device (3) arranged in the motor vehicle (2), wherein tasks to be solved are presented to him during the journey by means of an output device of the motor vehicle (2), wherein these tasks to be solved are conveyed to him when an under-challenge in solving driving-related tasks is detected, wherein the difficulty of solving the tasks to be solved depends on the degree of attention determined, wherein the difficulty of solving is lower the lower the degree of attention.

The application discloses a method and a medium for sharing contribution of ERP components in consciousness state evaluation. The method comprises the following steps: introducing a preset disturbance in a time window corresponding to a target ERP component to obtain multi-lead scalp electroencephalogram data after shielding, for multi-lead scalp electroencephalogram data to be analyzed; inputting the multi-lead scalp electroencephalogram data to be analyzed and the multi-lead scalp electroencephalogram data after shielding into a trained neural network model respectively, performing a consciousness state evaluation operation, and outputting a first prediction result and a second prediction result composed of prediction probabilities of each lead; calculating an absolute difference value between the first prediction result and the second prediction result as a prediction difference; and determining the contribution of the target ERP component in the consciousness state evaluation based on the prediction difference. By using the scheme of the application, quantitative analysis of the contribution of each ERP component in the consciousness state evaluation can be realized, and the accuracy of the consciousness state evaluation is improved.

The application discloses a personalized exoskeleton rehabilitation system and method based on consciousness and motor disorder patient grading, which comprises a comprehensive analysis platform, an electroencephalogram interface device, an electromyogram interface device and a modular exoskeleton combination. When applied, the patient wears the required device, connects the electroencephalogram signal collector through the electroencephalogram interface device, connects the electromyogram signal collector through the electromyogram interface device, sends the collected electroencephalogram signal and electromyogram signal to the comprehensive analysis platform for analysis, sends the control instruction to the controller according to the analysis result, and controls the modular exoskeleton combination to execute the corresponding personalized action combination. Through the integration of the comprehensive physiological signals of the patient and the dynamic evaluation mechanism, the accurate identification of the consciousness state and the motor ability of the patient is realized, the full-grade patient group from complete unconsciousness to basic autonomy can be covered, and the individual adaptability of the rehabilitation system is greatly improved. The application has good clinical popularization and productization potential.

The invention discloses a wounded consciousness state interpretation method of an intelligent robot based on voiceprint recognition, relates to the technical field of voiceprint recognition, and solves the problems of multi-band frequency spectrum partitioning, stable feature segment screening and invalid clutter elimination of voice data. Sound frequencies of different wave crests in each frequency spectrum band are deeply analyzed through a unique stable frequency spectrum band selection mode; in the invalid clutter elimination process, wave bands are strictly screened according to frequency change characteristics, and invalid clutters with the frequencies not obviously changed or the change degree deviating greatly are accurately removed. The accuracy of judging the consciousness state of the wounded person is greatly improved through a series of rigorous processing procedures; compared with a traditional method for judging the consciousness state by means of simple voice volume and intonation, the method can effectively avoid misjudgment and missed judgment caused by factors such as environmental noise interference and different pronunciation habits of the wounded.

The invention discloses an artificial consciousness activation and white-box evaluation method based on a DIKWP semantic map and an IIT axiom, and belongs to the technical field of artificial intelligence and cognitive computing. The method comprises the steps that firstly, a semantic intention cluster (SIC) is defined in a DIKWP five-layer semantic map to serve as a minimum consciousness unit, and a semantic integration degree and information density index is proposed based on an integrated information theory (IIT) to quantify the artificial consciousness level; secondly, designing a DIKWP consciousness activation path from top to bottom, and realizing semantic communication and feedback adjustment from intention to data; a visual interface and a semantic auditing module are further constructed, and dynamic monitoring and man-machine consensus verification of the AI consciousness state are achieved; and finally, introducing a consciousness-driven intention reconstruction and anti-fact prediction mechanism, and improving the self-adaption and causal reasoning capability of the AI in an open environment. According to the method, constructability, quantizability, interpretability and controllability of artificial consciousness are realized, and a white-box evaluation framework with cognitive self-consistency, semantic security and value alignment capability is provided for an advanced AI system.

The personalized exoskeleton rehabilitation system comprises a comprehensive analysis platform, an electroencephalogram interface device, a myoelectricity interface device and a modular exoskeleton combination, during application, a patient wears a required device, the device is connected with an electroencephalogram signal collector through the electroencephalogram interface device, and the electroencephalogram signal collector is connected with the modular exoskeleton combination through the myoelectricity interface device; the electromyographic interface device is connected with the electromyographic signal collector, sends the collected electroencephalogram signals and electromyographic signals to the comprehensive analysis platform for analysis, sends a control instruction to the controller according to an analysis result, and controls the modular exoskeleton combination to execute the corresponding personalized action combination. By integrating the comprehensive physiological signals of the patient and a dynamic evaluation mechanism, accurate recognition of the consciousness state and the exercise ability of the patient is achieved, full-level patient groups from completely unconsciousness to basically autonomous can be covered, and the individual adaptability of the rehabilitation system is greatly improved. The method has good clinical generalization performance and productization potential.