91 results about "Lowered consciousness" 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 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 an interactive nursing teaching system for nurses and patients, relates to the technical field of nursing teaching, and aims to solve the problems that the traditional teaching is difficult to create a highly tense and complex emergency room atmosphere, students cannot deeply experience the pressure caused by time urgency, and serious consequences possibly caused by slightly poor information transmission. The nursing teaching system comprises a scene simulation module, an interaction module, an intelligent feedback module and a case management module. The scene simulation module utilizes virtual reality and augmented reality technologies to restore the environment of an emergency room and dynamically simulate patient condition changes, and the condition changes comprise dynamic adjustment of the heart rate, the blood pressure, the respiratory rate, the pain degree and the consciousness state of a patient in real time according to different emergency diseases; the interaction module comprises a voice interaction device and an action interaction device. The system has the advantages that the adaptation period of students entering clinical practice, especially emergency room work, is effectively shortened, and the nursing work efficiency and quality are guaranteed.

The invention relates to the technical field of emergency triage, in particular to an intelligent pre-examination triage method and system for an emergency child patient, and the method comprises the steps: collecting the vital sign data, symptom complaint information and past medical history of the emergency child patient in real time through a medical Internet of Things device; an illness crisis degree evaluation model is constructed, an illness crisis coefficient K of the child patient is calculated based on a multivariate parameter fusion algorithm, and the parameters comprise a respiratory rate abnormal value delta R, a blood oxygen saturation degree deviation degree S, a consciousness state classification C and a pain index P; according to the method, the predicted available time T of the doctor can be calculated according to the coefficient eta of the doctor resource dynamic evaluation matrix, and the predicted available time T is matched with the illness state critical coefficient K of the child patient, so that reasonable distribution of the child patient and the doctor is realized; therefore, doctors with high professional ability and low workload can be distributed to the child patients with critical illness, the treatment efficiency and quality are improved, and meanwhile, the doctor-patient problem caused by the fact that the child patients with serious illness cannot be treated in time is also avoided.

The invention relates to the technical field of medical data processing, in particular to a multi-feature recognition based postoperative anesthesia patient state quantitative evaluation method. According to the method, the anesthesia postoperative state is divided into a deep anesthesia stage, a superficial consciousness recovery stage and a cognitive recovery stage based on clinical observation. In the deep anesthesia stage, physiological feature data are collected at high frequency, and a physical sign health index is obtained by calculating the standardized deviation between the physiological feature data and a normal reference value; in the shallow consciousness recovery stage, physiological feature data and unconsciousness behavior data are collected, a weighted model is constructed in combination with the physical sign health index, and an unconsciousness behavior response index is obtained; in the cognitive recovery stage, physiological feature data and subjective feature data are collected, and short-term trend prediction is performed on a cognitive recovery index based on a grey prediction model. Furthermore, multi-source feature data of the three stages are fused to construct a dynamic trajectory prediction model, the postoperative recovery trend and state change of the patient are comprehensively analyzed, and quantitative evaluation of the postoperative delirium risk is achieved.

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 an anesthesia state evaluation method and device suitable for patients of different ages and a medium, and belongs to the technical field of neural signal analys.The method specifically comprises the steps that electroencephalogram response signals triggered by stimulation input events corresponding to the patients of different ages in different consciousness states are collected; extracting nerve conduction delay time and frequency domain energy characteristics based on the electroencephalogram response signal, and constructing a delay characteristic vector based on the nerve conduction delay time and the electroencephalogram signal frequency domain energy characteristics; inputting the age data and the delay feature vector of each training patient into a preset training model, and generating a state recognition model; the method comprises the following steps: analyzing nerve conduction velocity migration features of patients of different ages under different consciousness states, acquiring the age and delay feature vectors of a current patient, inputting the age and delay feature vectors into a state recognition model, and judging the anesthesia state of the patient. And prediction and accurate evaluation of anesthesia states of patients of different ages are realized.

The invention provides a wounded consciousness state evaluation system based on face recognition, and relates to the technical field of health evaluation. Comprising a facial information extraction module, a facial feature analysis module, a consciousness state calculation module, an anomaly detection module and a consciousness state evaluation module. According to the invention, the infrared camera carefully captures the fine movement of the muscles around the eyes, the mouth corner and the frontal part, the accuracy and comprehensiveness of data acquisition are improved, the accuracy and dimension of evaluation are improved by evaluating the blinking frequency, the mouth corner displacement and the frontal muscle stretching amplitude, and by utilizing the extraction of the deformation characteristics and the calculation of the movement amplitude gradient, the accuracy and the comprehensiveness of the evaluation are improved. The time sequence analysis is combined, the evaluation of the consciousness state becomes more sensitive and accurate, potential changes of consciousness can be found in time, and therefore, rich data interpretation is provided for diagnosis and monitoring through quick response, and the density and distribution characteristic analysis of sudden change points, so that the consciousness state can be quickly and accurately evaluated under the condition that a wounded person is not contacted.

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 invention discloses a system for monitoring the consciousness response of a heat stroke patient based on an intelligent sensor, relates to the technical field of heat stroke, and aims to solve the problem that the condition monitoring of the heat stroke patient is inaccurate. According to the method, the difference between the illness state and the physical condition of each heat stroke patient is fully considered in the personalized adjustment stage, medical staff are allowed to carry out personalized adjustment on the treatment scheme according to the real-time response and the specific condition of the patient, and the personalized treatment strategy is beneficial to improving the pertinence and the effectiveness of treatment; by reasonably configuring the response frequency of the emergency response point, it can be ensured that limited medical resources can be fully utilized under the emergency situation, and the area where the emergency situation possibly occurs can be predicted in combination with the consciousness state change simulation diagram, so that preventive measures are taken in advance, and risks are reduced.

Disclosed are systems and methods that provide a novel computerized framework for a closed-loop, decision-intelligence (DI)-based computerized framework for automatically and dynamically managing and controlling a medical procedure, inclusive of administered medication and / or anesthesia to a patient and an intraoperative level of consciousness of the patient. The disclosed framework provides an improved electroencephalography (EEG) indices that adapts to specific patient needs, and dynamically adapts to factors of an ongoing procedure to ensure that the proper levels of anesthesia are administered, required and / or maintained. This provides computerized capabilities to maintain safe levels of the patient's consciousness, such that post-operative patient health is preserved and maintained. Thus, the disclosed framework provides an effective anesthesia management framework that can be leveraged to safely manage a patient's health during and after a medical procedure for which anesthesia is used.

The invention provides an electroencephalogram consciousness monitoring data analysis method and system applied to anesthesia management. The method comprises the following steps: acquiring EEG signal entropy sequence data of an anesthesia object, carrying out dynamic entropy change analysis on the EEG signal entropy sequence data to obtain an entropy change feature vector of the anesthesia object, carrying out time domain phase coupling modeling on the EEG signal entropy sequence data to obtain a phase synchronization feature matrix of the anesthesia object, and according to the entropy change feature vector and the phase synchronization feature matrix, carrying out dynamic entropy change analysis on the EEG signal entropy sequence data to obtain the EEG signal entropy sequence data of the anesthesia object. Generating consciousness state fusion features of the anesthesia object; according to the consciousness state fusion features, anesthesia state prediction is conducted on the anesthesia object, and the EEG signal entropy of the sampling time sequence nodes of the anesthesia object in the second time period is obtained. According to the method, accurate prediction of the EEG signal entropy of the sampling time sequence node of the anesthetic object in the second time period can be achieved, the anesthetic state of the anesthetic object can be monitored more accurately, the anesthetic dosage can be adjusted in time, and the safety and effectiveness of the operation process are ensured.

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 present invention provides a system for assessing the state of consciousness of injured persons based on facial recognition, which relates to the field of health assessment technology. It includes a facial information extraction module, a facial feature analysis module, a state of consciousness calculation module, an anomaly detection module, and a state of consciousness assessment module. In the present invention, an infrared camera is used to carefully capture the subtle movements of the muscles around the eyes, corners of the mouth, and forehead, thereby improving the accuracy and comprehensiveness of data collection. By evaluating the blinking frequency, displacement of the corners of the mouth, and stretching amplitude of the forehead muscles, the accuracy and dimension of the assessment are improved. By extracting deformation features and calculating the gradient of motion amplitude, combined with time series analysis, the assessment of the state of consciousness becomes more sensitive and accurate, and is able to detect potential changes in consciousness in a timely manner, thereby responding quickly. The density and distribution feature analysis of mutation points provide rich data interpretation for diagnosis and monitoring, so that the state of consciousness of the injured person can be quickly and accurately assessed without contacting the injured person.

The invention relates to the technical field of intensive care equipment, and discloses intensive care equipment which comprises an intensive care bed support, a bed tail support is fixed to the top of the intensive care bed support, and a first adjusting handle is installed on one side of the intensive care bed support in a matched mode; the device is provided with an auxiliary turning-over monitoring equipment placing mechanism, a thigh adjusting plate can conveniently turn over the side of a patient who cannot turn over independently due to deep consciousness disturbance, and the turning-over angle and speed can be accurately controlled; the thigh adjusting plate assists the patient to turn on one side and the bedside monitoring equipment lifting supporting sleeve drives the bedside monitoring equipment to ascend and descend synchronously, and intensive care is facilitated; the device is provided with the side plate mechanism, the protective side plates shield the two sides of the patient, the protective side plates do not affect the turning operation of the patient, particularly the situation that the patient accidentally falls off when the patient is in a half-turning state is prevented, operation is convenient, and lifting locking and unlocking can be conveniently achieved.

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 a modeling and intervention simulation method and system for children with difficulty in reading and writing Chinese based on a digital twin brain technology. A data acquisition module acquires behavior data and brain imaging data of children with difficulty in reading and writing Chinese and normal contrast children; constructing a Chinese read-write digital twin base model by using a model construction module, and simulating function shunting and consciousness formation dynamics in a brain read-write process; the individualized modeling module adopts a double constraint optimization method to carry out individualized fine tuning on the Chinese read-write digital twinborn base model and establish an individualized digital twinborn model; the intervention simulation module carries out verification and mechanism analysis on the individualized digital twinborn model, simulates different intervention strategies, predicts the improvement effect of the intervention strategies on individual read-write behaviors and brain characterization, and recommends and outputs an individualized intervention scheme; aiming at individual digital twinning modeling with difficulty in reading and writing in Chinese, an exclusive individual digital twinning model is constructed for each tester, and individual heterogeneity with difficulty in reading and writing is analyzed.

The utility model relates to an epileptic incidence early warning device which comprises a bottom plate and a pressing plate arranged above the bottom plate, an elastic supporting piece is arranged between the pressing plate and the bottom plate, and when the elastic supporting piece is in a natural stretching state, a gap exists between the pressing plate and the bottom plate; a sliding block is slidably connected to the top face of the bottom plate in the length direction, a transmission assembly is arranged between the sliding block and the pressing plate, and the sliding block is driven to slide through the transmission assembly when the pressing plate and the bottom plate are relatively close to each other or away from each other; a sound production part is further arranged on the top face of the bottom plate, the sound production part is located on the sliding track of the sliding block, and the sound production part can produce sound when collided by the sliding block. The problems that in the prior art, due to the fact that epileptic seizure is sudden and is accompanied by loss of consciousness, a patient is difficult to call for help, the body movement amplitude of the patient is small during spasmodic convulsion, sound generated by collision is small, the propagation distance is limited, and family members or medical staff of the patient cannot find the situation in time are solved.

This invention discloses a heatstroke prevention and monitoring system based on conscious cognitive behavioral characteristics, belonging to the field of intelligent medical technology. This invention addresses the problems of existing technologies lacking intelligent analysis and exhibiting a certain degree of subjectivity, making it difficult to ensure the effectiveness of heatstroke prevention and monitoring. It analyzes the patient's physiological signals and behavioral data using a linear discriminant analysis model to predict whether the patient has heatstroke. After predicting heatstroke, a weighted average method is used to further calculate the patient's risk index, and different prevention and monitoring strategies are implemented based on the risk index. Based on this, it can effectively identify whether a patient has heatstroke and the risk index, thereby enabling targeted preventative measures to avoid further disease progression. Simultaneously, through intelligent analysis and judgment, it can effectively reduce the incidence of heatstroke, reduce the consumption of medical resources, and lower the socioeconomic burden.

The invention belongs to the technical field of medical instruments, particularly relates to a supporting assembly and a post-anesthesia awakening bed of a medical health big data sharing platform, and solves the problems of single traditional awakening mode, inconvenient monitoring and the like. The awakening bed is provided with the supporting assembly, a bed plate I and a bed plate II are arranged in a bed frame, a driving structure can enable the bed plate II to incline, a bracelet is arranged, and a monitoring structure is arranged on the bracelet; a hollow lead screw penetrates through a bed plate I in a threaded mode, an ejector rod pushes a piston plate to inject air into an annular rubber air bag through a hose, a detection structure is made to be tightly attached to the skin, and the physical condition of a patient is monitored in real time and transmitted to a platform; the knocking structure enables the knocking plate to intermittently knock the arch of the foot of the patient, consciousness recovery of the patient is promoted in multiple modes, real-time monitoring and data uploading are achieved, and doctors can conveniently observe the illness state.

This application provides a method and system for analyzing EEG consciousness monitoring data applied to anesthesia management. The method includes: acquiring EEG signal entropy sequence data of anesthetized subjects; performing dynamic entropy change analysis on the EEG signal entropy sequence data to obtain the entropy change feature vector of the anesthetized subjects; performing temporal phase coupling modeling on the EEG signal entropy sequence data to obtain the phase synchronization feature matrix of the anesthetized subjects; generating consciousness state fusion features of the anesthetized subjects based on the entropy change feature vector and the phase synchronization feature matrix; predicting the anesthesia state of the anesthetized subjects based on the consciousness state fusion features; and obtaining the EEG signal entropy of the anesthetized subjects at sampling time points in a second time period. This application can accurately predict the EEG signal entropy of anesthetized subjects at sampling time points in a second time period, helping to more accurately monitor the anesthesia state of anesthetized subjects so as to adjust the dosage of anesthetic drugs in a timely manner and ensure the safety and effectiveness of the surgical procedure.

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.

Disclosed are systems and methods that provide a novel computerized framework for a closed-loop, decision-intelligence (DI)-based computerized framework for automatically and dynamically managing and controlling a medical procedure, inclusive of administered medication and / or anesthesia to a patient and an intraoperative level of consciousness of the patient. The disclosed framework provides an improved electroencephalography (EEG) indices that adapts to specific patient needs, and dynamically adapts to factors of an ongoing procedure to ensure that the proper levels of anesthesia are administered, required and / or maintained. This provides computerized capabilities to maintain safe levels of the patient's consciousness, such that post-operative patient health is preserved and maintained. Thus, the disclosed framework provides an effective anesthesia management framework that can be leveraged to safely manage a patient's health during and after a medical procedure for which anesthesia is used.

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 invention provides an emotion support art treatment platform for depression patients, which comprises a patient residence, and a medicine supply device, an art platform and a control system arranged in the patient residence, and the control system comprises a computer of a networked medical institution terminal in the residence. The cloud server is networked with a medical institution terminal or an intelligent mobile terminal held by a physician, and the medicine supply device is controlled by the control system and intervenes medicine in a water source or food so that patients can drink or eat the medicine respectively. According to the treatment platform, drug supply and mental support are integrated depending on the residence of the patient, the patient is prevented from being reminded of the behavior of the pregnant woman to realize the illness condition of the pregnant woman in the diet aspect through regular door-to-door drug supplement of the medical institution and the technical performance personnel, anxiety is reduced, and communication is performed in an art performance form while door-to-door. And dual treatment from pharmacology and psychology is realized.

The utility model discloses an old-age nursing bed with a protection function, which comprises an old-age nursing bed body, the lower surface of the old-age nursing bed body is fixedly connected with rollers, and the inner surface of the old-age nursing bed body is connected with a sliding rod in a sliding manner. By arranging the motor, the transmission shaft, the first conical teeth, the lead screw and other structures, overall lifting of the sliding rod, the guardrail and the connecting plate can be achieved, so that the old can be effectively prevented from accidentally falling off during sleeping or resting while getting up of the old is not hindered, a physical barrier is provided especially at night or when the old are unconscious, and the safety of the old is improved. By arranging a guide rod, a sliding seat and a table plate, the old can directly use the table plate to have dinner, read, write or use electronic equipment on the bed, so that the convenience of daily activities is improved, and by arranging a rotating shaft, a gear, a limiting assembly and other structures, the table plate can be folded and stored when not used, so that the safety of the old is improved. And the practicability is improved.

The invention discloses an emotion recognition, consciousness evaluation and auxiliary rehabilitation method and device based on closed-loop olfactory stimulation, and a medium, and relates to the technical field of neural function diagnosis and artificial intelligence, and the method comprises the steps: obtaining a first face image sequence of a patient under odor stimulation; inputting the first facial image into a facial action unit intensity estimation model, and obtaining a plurality of facial action unit recognition results output by the model online; determining the facial expression change intensity of the patient based on the recognition results of the plurality of facial action units; and under the condition that the facial expression change is lower than a preset threshold value, adjusting the concentration and the type of the odor stimulation in real time based on a preset concentration gradient, and re-collecting the image for evaluation. Through multiple expression intensity results, the consciousness level of the patient is determined, the olfactory consciousness brain network remaining condition is judged, a rehabilitation training scheme is formulated, and longitudinal tracking is implemented. The emotion and consciousness level of the patient can be accurately evaluated in real time, and the method is applied to olfactory stimulation assisted rehabilitation.

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 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.