727 results about "Cerebral stroke" patented technology

The invention belongs to the field of cerebral apoplexy rehabilitation training, and provides a cerebral apoplexy upper limb dynamic rehabilitation method and system based on vagus nerve electrical stimulation, the excitement degree of a cortical spinal cord pathway of a patient is measured and evaluated by using motor evoked potential to obtain a motor function evaluation result, and an initial rehabilitation training task is set according to the motor function evaluation result; the training motion intention of the patient is determined by monitoring the electroencephalogram signal and the electromyographic signal of the patient, and percutaneous ear vagus nerve electrical stimulation is triggered; after percutaneous ear vagus nerve electrical stimulation, performing rehabilitation training analysis on basic clinical characteristics and multi-modal signals of the patient to determine intermuscular coordination, brain region connectivity and brain muscle coupling of the patient; the training condition of the patient is evaluated according to the rehabilitation training analysis result, and parameters of percutaneous ear vagus nerve electrical stimulation and training tasks are adjusted based on the evaluation result for iterative rehabilitation training; and stopping iteration until the rehabilitation training analysis result of the patient reaches a preset standard, and completing rehabilitation training.

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses an intelligent assessment method and device for body rehabilitation of a stroke patient, and relates to the technical field of rehabilitation intelligent assessment, and the method comprises the steps: connecting a medical information management system, and obtaining the stroke medical record information of the current patient; performing identification according to the stroke medical record information; the multi-modal sensing module is connected with the rehabilitation device, performs rehabilitation motion sensing on the limb on the affected side according to the multi-modal sensor, and outputs a multi-modal sensing data set; collecting a multi-modal sensing sample set of a healthy limb corresponding to the limb on the affected side; a rehabilitation evaluation model is constructed, the rehabilitation evaluation model evaluates the multi-modal sensing data set, and rehabilitation level indexes are obtained; the rehabilitation level indexes are fed back to the rehabilitation device for training mode optimization. The technical problems that in the prior art, body rehabilitation evaluation depends on subjective judgment, the limb function recovery condition of the patient is difficult to comprehensively reflect, rehabilitation evaluation efficiency is poor, and the result is inaccurate are solved, and the technical effect of improving rehabilitation efficiency and evaluation accuracy is achieved.

The invention discloses a cerebral apoplexy risk and prognosis prediction system and method, and relates to the field of intelligent medical treatment, and the system comprises a data processing and knowledge construction layer which is used for extracting, cleaning and constructing a space-time multi-modal knowledge graph and structured clinical features from multi-source heterogeneous medical data; the feature engineering and fusion layer is used for deeply fusing dynamic semantic information in the space-time multi-modal knowledge graph and the structured clinical features through a graph embedding and attention mechanism to generate a fusion feature vector for a cerebral apoplexy prediction task; and the prediction model and output layer is used for performing cerebral apoplexy risk and prognosis prediction based on the fusion feature vector to obtain a prediction result, and generating a decision result for assisting a doctor in understanding the model through an interpretable mechanism. The method provided by the invention can improve the accuracy of stroke recurrence, bleeding transformation or function prognosis prediction, and provides a new way for accurate stroke management.

The invention relates to a mechanical exoskeleton rehabilitation training system and method based on a brain-computer interface. The system comprises electroencephalogram acquisition equipment, a preprocessing unit, a feature extraction unit, a motion intention decoding unit, a mechanical exoskeleton control unit, a muscular tension state monitoring unit and a self-adaptive functional electrical stimulation feedback unit. The method comprises the following steps: preprocessing electroencephalogram and electromyographic signals; electroencephalogram and myoelectricity time-frequency features are obtained through feature extraction; an electroencephalogram decoding model is used for decoding to obtain the movement intention of the patient, and an exoskeleton control instruction is generated to control a mechanical exoskeleton control unit to drive the limb movement of the rehabilitation patient for rehabilitation training; meanwhile, the muscular tension state of the patient is analyzed according to the time-frequency characteristics of electroencephalogram and myoelectricity, functional electrical stimulation of different intensities is applied in a self-adaptive mode, stimulation feedback is enhanced, and the muscular tension state of the patient is adjusted. According to the invention, deep fusion of brain-controlled exoskeleton training and low-frequency nerve electrical stimulation adjustment can be realized, and the rehabilitation effect of a stroke patient is improved.

The invention discloses a cerebral apoplexy multi-mode early screening intelligent evaluation system based on a large model, and relates to the technical field of medical health information, the cerebral apoplexy multi-mode early screening intelligent evaluation system comprises an intelligent management platform, and the intelligent management platform is in communication connection with the following modules: a multi-source heterogeneous data fusion engine, the data integration module is used for integrating multi-modal data including clinical data and terminal health data and constructing a health portrait of a patient; and the cerebral apoplexy knowledge graph construction platform is used for constructing a cerebral apoplexy domain knowledge graph in combination with evidence-based medical knowledge. By combining the digital twinning technology and the intelligent risk assessment engine, the influence of different intervention schemes on the cerebral apoplexy risk can be simulated, personalized intervention suggestions are generated, a patient is helped to reduce the cerebral apoplexy risk and change from passive prediction to active intervention, the patient is helped to take effective measures earlier, the health condition is improved, and the patient experience is improved. The occurrence of cerebral apoplexy is prevented, so that the disability rate and the death rate caused by cerebral apoplexy are reduced.

The invention discloses a method and a system for evaluating movement of two lower limbs of a stroke patient, and relates to the technical field of movement function evaluation. The invention discloses a stroke patient double-lower-limb movement evaluation system which comprises a task guide module, a movement acquisition module, a movement scoring module, an interference evaluation module, a trend analysis module, a combination analysis module and a result output module. According to the method, the scoring model based on crowd category feature matching is constructed, exercise ability scoring is performed by adopting a machine learning algorithm or a fixed weighted combination, personalized evaluation is realized according to the group features of different patients, and the generalization ability and prediction accuracy of the scoring model are improved; group modeling is carried out by introducing features such as patient age, gender and motion level, and a scoring strategy is automatically switched according to the number of samples, so that the scoring mechanism also has reliability and clinical consistency in a family scene with limited data volume.

The invention discloses a cerebral apoplexy high risk group positioning evaluation system based on multi-modal data, and relates to the technical field of medical information science, the cerebral apoplexy high risk group positioning evaluation system comprises a cerebral apoplexy prevention and control management platform, and the cerebral apoplexy prevention and control management platform is in communication connection with the following modules: a multi-modal data acquisition and integration module, the multi-modal data collection module is used for collecting multi-modal data related to cerebral apoplexy from multiple channels and carrying out preprocessing operation on the collected multi-modal data. By integrating clinical data, image data, omics data and terminal health data, multi-dimensional information related to the cerebral apoplexy can be comprehensively captured, particularly, cerebral vessel digital twin is utilized to simulate hemodynamic characteristics, a plurality of data sources are fused in combination with a graph neural network, high-risk groups can be recognized more accurately, and the accuracy of cerebral apoplexy recognition is improved. The accuracy and reliability of risk prediction are remarkably improved, the problem of missing detection caused by dependence on a single data source in a traditional method is solved, and more powerful support is provided for early intervention.

The invention relates to the technical field of medical data analysis and rehabilitation, in particular to a cerebral apoplexy patient rehabilitation exercise analysis system based on medical big data, which comprises a data acquisition module used for acquiring electromyographic signals, exercise data and behavior data of a patient to form a data set; the data processing and fusion module is used for preprocessing the electromyographic signals in the data set, normalizing motion and behavior data and fusing the motion and behavior data into a multi-modal feature vector; the hierarchical analysis module is used for performing motion intention recognition according to the preprocessed electromyographic signals and outputting an intention classification result, recognizing the motion intention and predicting a rehabilitation effect through a double-flow Transform encoder, and evaluating a behavior risk level in combination with a risk factor formula; the optimization module is used for dynamically adjusting the mirror image treatment intensity according to the risk level and optimizing the prediction model by adopting incremental learning; and the interaction module is used for generating a thermodynamic diagram superposition rehabilitation curve in the X-Y coordinate system, and marking doctor intervention suggestions to complete visual interaction.

The invention relates to a cerebral apoplexy patient gait analysis and personalized rehabilitation correction method and system based on a big data model, and relates to the technical field of medical rehabilitation, a patient feature data set is generated by integrating multi-modal data including gaits, electroencephalogram, myoelectricity and the like, a rehabilitation critical period is determined by using real-time nerve regulation and control and brain-computer interface data, and the rehabilitation accuracy is improved. The system constructs a personalized rehabilitation scheme according to the optimized feature data set and rehabilitation training parameters, the rehabilitation effect is tracked through a rehabilitation progress evaluation module, the scheme relates to data fusion, dimension reduction, correlation analysis and dynamic adjustment of the rehabilitation scheme, the personalization and effectiveness of rehabilitation training are ensured, and the rehabilitation effect is improved. The invention aims to improve the rehabilitation efficiency and life quality of stroke patients.

The invention provides a fall detection algorithm model for a wearable device of a stroke patient, and relates to the technical field of health monitoring and posture detection, and the technical key points are as follows: a construction method of the model is as follows: S1: obtaining posture information of a wearer by using an inertial sensor IMU in the wearable device, after data collection is completed, performing low-pass filtering processing on original data to obtain sensor data; s2, decomposing the inertial sensor data obtained at the t moment into a trend component, a season component and a residual component by using LOESS according to an STL (Standard Template Library) cyclic trend decomposition method, and then carrying out normalization processing on the data; and S3, performing time sequence modeling on each inertial sensor component by using LSTM (Long Short Term Memory). According to the fall detection algorithm model for the wearable device of the stroke patient, collaborative optimization of multi-physical-quantity coupling feature decoupling and time sequence dynamic modeling is realized, and medical-level fall monitoring performance is realized under resource constraints of the wearable device.

The invention discloses an intelligent generation and maintenance system and method for a dynamic cerebral apoplexy knowledge graph, and relates to the field of intelligent medical treatment, and the system comprises a data access and preprocessing module which is used for accessing and retrieving cerebral apoplexy related information from a multi-source data source; the knowledge extraction module based on LLM is used for acquiring candidate knowledge fragment streams; the knowledge graph structuring and filling module is used for integrating the candidate knowledge fragment flow into a formal knowledge graph structure stored in a graph database; the LLM-driven verification and refinement module is used for detecting the candidate knowledge fragment stream and the formal knowledge graph structure to obtain a detection result; and the dynamic updating and maintaining module is used for realizing dynamic fusion and version control of the formal knowledge graph structure. Through modular architecture design, a high-performance knowledge extraction and verification mechanism of a large language model and a dynamic processing capability oriented to continuous updating are fused, and high automation, standardization and intelligentization of a medical knowledge graph construction process are realized.

The invention relates to the technical field of intelligent medical auxiliary diagnosis and neural function automatic evaluation, in particular to an NIHSS score automatic evaluation system for a stroke patient. Comprising a multi-mode induction and perception unit which is used as a front-end data entry and is used for collecting patient response in real time to generate a video stream containing depth and color information and a synchronous audio stream; the dynamic reference calibration unit is used for extracting kinematic characteristics to construct an individualized nerve reference template; the neural motion spectrum decomposition unit is used for generating a spectrum pathological feature vector for distinguishing myasthenia and ataxia; the opposite-side image rejection analysis unit is used for generating compensation and driving confidence for representing a real nerve driving intention; the cross-modal reflection analysis unit is used for generating a sensory pathway integrity index according to the nerve conduction velocity difference; and the collaborative scoring decision engine is used for mapping the multi-modal features into standardized NIHSS scores. According to the method, the interference of age and basic physique on scoring is effectively eliminated, and a high-precision comparison reference can be provided for subsequent abnormal judgment of the affected side.

The invention provides a construction method and device of a cerebral apoplexy early diagnosis model, electronic equipment and a storage medium, which are applied to the field of model construction, and in a transformer training process, channel reduction or channel expansion is performed on a feedforward network based on an activation matrix and a current width of the feedforward network of transformer to adjust the width of the feedforward network, so that the accuracy of the cerebral apoplexy early diagnosis model is improved. On the premise of not sacrificing the model performance, the parameter quantity and video memory occupation are reduced, the computing resource overhead of a cerebral apoplexy early diagnosis system is reduced, and the adaptability is enhanced.

The invention discloses a cerebral apoplexy focus detection method and system, and belongs to the technical field of medical image detection. Extracting a fusion feature map of the brain image; for each region type, obtaining a representative feature which has the highest similarity with the feature at each pixel point position in the fused feature map in the class prototype set and carries a corresponding region type label, and further determining the region type to which each pixel point position in the fused feature map belongs so as to obtain a corresponding pseudo-label map; the class prototype set comprises representative features of different region types and is obtained in the training process of the system, and feature distribution of different region types in the memory bank is calculated through a Gaussian mixture model; for each region type, sampling is carried out based on feature distribution of the region type, and a plurality of representative features are obtained; the prototype-like set in the cerebral apoplexy detection method has real global context perception ability, can clearly distinguish the focus and various complex background structures, and can accurately realize cerebral apoplexy detection.

The embodiment of the invention provides a cerebral apoplexy focus detection and scoring method and device based on a multi-modal medical image. The method comprises the following steps: acquiring medical images of various different modals of a suspected acute ischemic cerebral apoplexy patient; performing registration fusion and focus labeling on the medical images of different modalities based on an attention mechanism to obtain a labeled first modal image; training a target detection model of a YOLOv8 framework based on Swin-Transform improvement by using the labeled first modal image, and carrying out target detection on the to-be-detected image based on the trained target detection model to obtain an infarction region detection result; training a semantic segmentation model by using the labeled ASPECT scoring and partitioning brain atlas, performing scoring region segmentation on the to-be-detected image based on the trained semantic segmentation model, and outputting the ASPECT scoring and partitioning brain atlas; and mapping the infarct area detection result to the ASPECT score partition brain map, and calculating to obtain an ASPECT score.

The invention relates to a self-supervised cerebral apoplexy focus segmentation method based on sparse stripe sampling and a lightweight encoder. The invention relates to the technical field of cerebral apoplexy image segmentation, and the method comprises the steps: carrying out the preprocessing of input MRI image data, and carrying out the sparse fringe patch sampling of the preprocessed data; establishing a lightweight encoder, and carrying out self-supervision pre-training; based on the trained lightweight encoder, performance optimization and task adaptation are realized through a lightweight fine tuning mode, and optimization of the encoder is completed; and according to the optimized lightweight encoder, encoding and decoding the area after sparse fringe patch sampling, and outputting a segmentation result. According to the method, local convolution and global attention modeling are comprehensively considered, and the structural recognition capability of the focus with the complex form and the fuzzy boundary is effectively improved.

The invention discloses a cloud edge collaborative early warning method for early recognition of cerebral apoplexy, and the method comprises the steps: collecting the posture data and voice data of an edge end, and carrying out the preprocessing of the data; on the edge end, lightweight model reasoning is performed on the attitude data and the voice data based on a sub-modal processing and feature fusion strategy, and finally the cerebral apoplexy risk probability is output; and cooperative and multi-terminal linkage early warning is carried out based on the cloud and the edge terminal. According to the method, active capture of the cerebral apoplexy risk is realized through a framework of edge-end multi-modal real-time processing, cloud collaborative optimization and multi-end linkage early warning, the mode is converted from passive help calling to active early warning-rapid linkage, and the early warning precision and the response speed are improved.

The invention provides application of active decapeptide in preparation of a medicine for preventing or treating cerebral apoplexy, and belongs to the technical field of biological medicine, the active decapeptide with the amino acid sequence as shown in SEQ ID NO.1 is applied to prevention or treatment of cerebral apoplexy for the first time, the active decapeptide can effectively inhibit formation of cerebral thrombosis of zebra fish with ischemic cerebral apoplexy, and the active decapeptide can be used for preventing or treating cerebral apoplexy. The brain blood flow supply is recovered; meanwhile, the active decapeptide can significantly reduce the hemorrhagic area and hemorrhagic rate of the zebra fish brain with hemorrhagic stroke, repair brain vascular injury and inhibit the occurrence and development of hemorrhagic stroke. In addition, the active decapeptide has small toxic and side effects, and is of great significance for improving the clinical curative effect of cerebral apoplexy and reducing the medication risk when being used for research and development of novel drugs for resisting cerebral apoplexy.

The invention discloses a multi-modal feature integrated risk assessment method and system for a stroke dangerous group, and relates to the technical field of telemedicine collaboration, a telemedicine collaboration network platform is built, and multi-modal data of a stroke high-risk group is collected and processed; analyzing the relation between risk factors and a cerebral apoplexy pathological mechanism by applying bioinformatics and medical knowledge, and defining a key action path; and based on an analysis result of the key action path, integrating multi-modal data by taking a pathological mechanism as an axis, and constructing a dynamic risk knowledge network based on a knowledge graph. According to the method, multi-modal data are integrated, a comprehensive patient individual feature matrix is constructed, a dynamic risk knowledge network is combined, and the association between patient individual features and a cerebral apoplexy pathological mechanism and the dynamic change of risk factors are accurately analyzed, so that an accurate risk score is calculated, the cerebral apoplexy risk of a cerebral apoplexy risk crowd is analyzed, and the cerebral apoplexy risk of the cerebral apoplexy risk crowd is analyzed. A powerful basis is provided for prevention of the cerebral apoplexy, and the occurrence rate of the cerebral apoplexy is reduced.

The invention relates to the technical field of medical health information services, in particular to a cerebral apoplexy knowledge question-answering system construction method and system based on a knowledge graph and a large language model. Natural language input of a user is analyzed through a query agent, a query intention and constraint conditions are recognized, and a structured execution plan is generated; a user state management tool is forcibly activated, a static clinical portrait and a dynamic rehabilitation log are loaded, and a personalized context is constructed; a plurality of tools such as knowledge graph query, authoritative literature retrieval and rehabilitation plan generation are scheduled, and accurate retrieval and reasoning of heterogeneous knowledge are completed; through double verification of fact consistency and clinical risks, error or high-risk suggestions are intercepted and replaced with risk early warning. The problems of'illusion 'risk, insufficient individuation, poor interpretability and the like of a traditional single model are solved to a large extent, high-credibility, individuation and traceable rehabilitation knowledge service can be provided for the stroke patient and a caregiver of the stroke patient, and rehabilitation safety and effect are guaranteed.

The invention provides a multi-modal cerebral arterial thrombosis medical image segmentation method, device and equipment, and the method comprises the steps: extracting the independent features of different modal medical images through combining a ViT encoder branch and a CNN encoder branch which are finely adjusted by a hybrid expert as a multi-modal image double-branch coding network; further integrating complementary information of different modes by using a mode missing adaptive fusion network, and performing global-local information interaction between CNN features and ViT features by using an encoder branch interaction network, so that specific features and cross-mode invariant features of different available modes can be decoupled under the condition of mode missing; and meanwhile, the advantages of different types of features are fully utilized, and the value information of the multi-modal image features is deeply mined, so that accurate multi-modal cerebral arterial thrombosis medical image segmentation and imaging are realized, and the method is high in reliability, good in accuracy and good in practicability.

The invention discloses an early recognition and early warning system for acute ischemic stroke symptoms, and relates to the technical field of health detection. Comprising an image acquisition module, a voice acquisition module, a data processing module, a display module and an alarm module, the voice acquisition module is used for acquiring user audio signals; the data processing module is used for processing the collected signals to generate corresponding face judgment signals, limb judgment signals and audio judgment signals; the display module is used for displaying sentences needing to be read by the user; and the alarm module is used for judging whether the user has an acute ischemic stroke early symptom or not according to the face judgment signal, the limb judgment signal and the audio judgment signal, and if so, dialing 120 and a contact person through the mobile phone. The method can significantly improve the early warning and prevention efficiency of cerebral apoplexy, is expected to play a greater role in the field of cerebral apoplexy prevention and treatment, and improves the prognosis and life quality of patients.

The invention belongs to the field of rehabilitation training, and particularly relates to a training method for preventing a cerebral apoplexy patient from falling down, which comprises the steps of dynamic risk assessment, graded training starting, double-task execution, self-adaptive adjustment and environment simulation training. The bottleneck of balance function training is broken through, and the problem of cognition-motion separation is solved; three-level cognitive interference quantitative control is adopted (vision: the light spot moving frequency is 0.1-3 Hz; auditory sense: N-back task difficulty is self-adaptive at 1-4 levels; tactile sense: sole asymmetric vibration), double-task load ratio dynamic adjustment, and radically cure insufficient individual adaptation; and the four-level risk grading model is matched with an adaptive adjustment mechanism. Through a multi-modal closed-loop training system, the problems of subjective evaluation, single training, protection lag and the like in the field of cerebral apoplexy falling prevention are solved, the falling risk reduction effect is good, and remarkable technical breakthrough and market irreplaceability are achieved.

The embodiment of the invention discloses a cerebral apoplexy upper limb rehabilitation training system and method based on dynamic reward feedback. A data acquisition module is used for acquiring physiological signals, motion signals, emotional state data and training performance data; the motion intention decoding module is used for performing motion intention feature extraction and reliability evaluation on the physiological signals according to an improved MREE-Net + + fusion algorithm, performing weighted fusion after feature weights are adjusted according to a reliability result, obtaining a unified motion intention feature vector, performing motion intention decoding, and obtaining a motion intention intensity index and a motion intention vector; the dynamic reward decision module is used for processing the facial micro-expression data according to an improved VGG-Face model to obtain an emotion titer, and obtaining an emotion awakening degree according to the voice signal; a reward action is generated according to an improved deep reinforcement learning algorithm; and the adaptive training regulation and control module is used for generating a personalized virtual training scene according to the generative adversarial network and regulating and controlling the training intensity according to the fatigue index. The rehabilitation training effect can be improved.

The invention provides a cerebral apoplexy medical treatment seeking recommendation method and system based on intelligent screening and remote collaboration, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the body index information, medical history information and diagnosis and treatment agreement confirmation information of a to-be-diagnosed user, sending the first information to a preset screening model for feature extraction, and obtaining the first information of the to-be-diagnosed user; constructing disease risk index data containing multiple types of variables; then, inputting the index data into an intelligent cerebral apoplexy risk prediction model, performing risk assessment through a random forest, XGBoost and DNN neural networks, and fusing prediction results to obtain a cerebral apoplexy risk level of the user; and on the basis of the prediction result, generating an electronic referral sheet by combining the geographic position of the user and the medical resource distribution condition through an intelligent referral model, and recommending an optimal receiving hospital, thereby realizing personalized medical seeking recommendation. According to the method, the screening accuracy of high-risk people suffering from cerebral apoplexy and the collaborative efficiency of remote medical treatment are remarkably improved.

The invention relates to the technical field of medical rehabilitation, in particular to a post-stroke upper limb function recovery training system combined with a vr technology, which comprises a fixed base, and a motor is fixedly connected to the center of an inner cavity of the fixed base. The upper limb rehabilitation training device has the advantages that the angle adjusting and rotating functions are achieved, a personalized training plan can be made according to the specific conditions of a patient, and therefore upper limb rehabilitation training of the patient is more effectively promoted; through cooperative use of a fixed base, a motor, a fixed column, a supporting rod, a fixed sleeve, a rotating rod, an adjusting rod, an electric push rod, a lifting block, a seat and a sliding rod, the rotating and angle adjusting functions of the seat can be achieved, it is ensured that a patient can obtain the best comfort degree and experience feeling in the training process, the angle is flexibly adjusted through the seat, and the training efficiency is improved. A patient can experience a more real training scene in an immersive manner, and the brain is stimulated to generate strong neural feedback, so that the movement function recovery of the upper limbs is effectively promoted.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention relates to a stroke gait phase recognition method and system based on a multi-stage model, and the method specifically comprises the following steps: employing a self-adaptive sliding window algorithm based on index-standard deviation fusion, carrying out the dynamic fragment interception of a gait signal of a stroke patient, and obtaining the original stroke gait data; preprocessing the original stroke gait data to obtain original gait features; constructing an integrated feature enhancement unit (IFEU), inputting original gait features, and extracting time context features of the stroke gait signals; constructing a hierarchical gait feature aggregation unit HGFAU, inputting time context features, performing gait feature extraction of each level, and obtaining multiple fusion features after fusion; and inputting the multiple fusion features into a linear layer classifier, and classifying the input features to obtain a stroke gait phase recognition result. According to the invention, gait phase recognition can be realized, and objective walking state evaluation and abnormity monitoring can be carried out on rehabilitation treatment assistance of a patient.

The invention discloses a potential stroke risk assessment method and system after coronary artery recanalization, and the method comprises the steps: obtaining the heart and cerebral vessel image data of a patient, and constructing a complete heart and cerebral artery blood vessel geometric model; based on the cardio-cerebral artery blood vessel geometric model, numerical simulation is conducted on the flowing conditions of blood flow in coronary arteries and cerebral vessels by means of a computational fluid mechanics method, and the pressure ratio before and after coronary artery stenosis and the partial pressure ratio of the cerebral vessels are calculated; according to the pressure ratio and the partial pressure ratio, the potential risk of the cerebral arterial thrombosis is quantitatively evaluated, and a cerebral blood flow partial pressure ratio numerical value and a cerebral arterial thrombosis risk evaluation report are generated. According to the cerebral apoplexy risk quantitative evaluation method, by integrating coronary artery CTA and cranial CTA or MRA data, reconstructing a complete heart and cerebral vessel geometric model and simulating heart and cerebral blood flow changes before and after a coronary artery recanalization operation, and particularly by calculating coronary artery FFR and cerebral blood flow partial pressure ratio FPR, an evaluation report is generated, and a basis for quantitative evaluation of the cerebral apoplexy risk in the operation is provided for clinical doctors.