353 results about "Stroke patient" patented technology

The invention discloses a post-stroke cognitive impairment prediction method based on multi-modal feature fusion. The method comprises the steps that firstly, multi-modal information of a stroke patient is collected, wherein the multi-modal information comprises a three-dimensional brain MRI image, an EEG electroencephalogram signal and clinical medical record information; secondly, converting MRI into a tensor, and inputting the tensor into a multi-scale spatial-temporal feature extraction backbone network to obtain MRI modal features; the EEG electroencephalogram signals are subjected to electroneurographic signals and are combined with Transform, and EEG modal features are obtained; the clinical medical record information of the stroke patient is converted into semantic sentences, the semantic sentences are input into a two-channel semantic encoder for encoding extraction, and clinical medical record information features are obtained. And finally, inputting the MRI modal features, the EEG modal features and the clinical medical record information features into a three-modal fusion device to obtain fusion features, and outputting probability prediction through a classifier. The post-stroke cognitive impairment prediction method achieves accurate prediction of post-stroke cognitive impairment, and significantly improves robustness and medical interpretation.

The invention discloses an apoplexy patient management method and system based on a multi-mode RAG and LLM model, and the method comprises the steps: obtaining the standardized electronic medical record data of a patient based on an FHIR standard electronic medical record; performing feature extraction on the electronic medical record data to obtain multi-modal data; integrating and representing a time-space relationship among the multi-modal data, and obtaining a dynamically updated dynamic knowledge graph; analyzing and integrating the electronic medical record data to obtain integrated information; constructing a stroke risk causal network based on an LLM model in combination with a causal reasoning mechanism; obtaining a personalized health report based on the dynamic knowledge graph and a stroke risk causal network; and based on a personalized report result and feedback data of the patient, a treatment scheme and a management strategy of the patient are optimized in real time. According to the application, the causal reasoning mechanism is embedded into the Transform architecture of the LLM, so that the modeling and prediction capabilities of the stroke risk causal relationship are enhanced.

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.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

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 belongs to the field of rehabilitation medical treatment and intelligent computing, and particularly discloses a cerebral apoplexy rehabilitation personalized treatment recommendation system based on artificial intelligence. Comprising a multi-modal data acquisition module, a feature extraction and clustering modeling module, a personalized path making module and a recommended path generation and feedback optimization module, and can realize dynamic monitoring and intelligent analysis of rehabilitation data of a stroke patient; a multi-modal aggregation neural network model is constructed by fusing multi-dimensional data such as images, biomarkers, clinical behaviors and cognitive emotions, a rehabilitation path is formulated according to a medical data clustering result, and a recommended path is dynamically adjusted and optimized in combination with treatment feedback of a patient; compared with a traditional method, the method has the advantages that scientificity and adaptability of personalized treatment are improved, the method has the advantages of real-time updating of rehabilitation paths, closed-loop management of a system, high model robustness and the like, rehabilitation intervention efficiency and accuracy can be remarkably improved, and technical support is provided for intelligent development of rehabilitation medicine.

The invention provides a stroke patient gait walking analysis method and device. The stroke patient gait walking analysis method comprises the steps that pressure data and inertial data generated in the gait walking process of a patient are collected based on a plantar pressure sensing module and an inertial measurement module; performing data preprocessing on the pressure data and the inertial data; constructing a double-flow neural network to respectively extract pressure features and inertial features of the pressure data and the inertial data, performing feature fusion, and calculating step length, stride frequency, gait asymmetry index and gait stability index key indexes based on the fused features so as to realize gait evaluation; an evaluation result is transmitted to the flexible OLED touch screen, and gait parameters, rehabilitation progress, abnormal early warning and training guidance information are displayed in real time through the flexible OLED touch screen. According to the method, the end-to-end process from original sensor data to clinical application is realized, manual monitoring is avoided, the accuracy and clinical practicability of evaluation are improved, and the method is particularly suitable for family rehabilitation monitoring of stroke patients and precise rehabilitation evaluation of medical institutions.

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 relates to the technical field of medical equipment, provides a non-intrusive closed-loop ear vagus nerve stimulator for improving cerebral apoplexy and a use method, and aims to improve the symptoms of a patient by stimulating the ear vagus nerve in a conchae area. The device integrates an electrical stimulation electrode, a host and a closed-loop feedback module, monitors brain blood flow changes and other physiological parameters of a patient in real time, and automatically adjusts stimulation parameters through an intelligent algorithm to realize personalized treatment. A microprocessor, a multi-mode monitoring unit and a data fusion analysis unit are arranged in the host, and accurate and efficient treatment is ensured. In addition, the equipment supports remote intelligent monitoring and guidance, a treatment plan can be remotely adjusted, and the treatment effect is improved. A user interaction interface is friendly, and operation of patients and medical staff is facilitated. The stimulator is light in design, the conductive silica gel electrode is adopted, through real-time monitoring and intelligent adjustment, accurate and efficient treatment is provided for a patient, the comfort level and treatment convenience of the patient are ensured, and a safe and effective rehabilitation scheme is provided for the stroke patient.

The invention discloses a method and a related device for closed-loop optimization of a lower limb motor imagery experiment normal form through dynamic mind training, and relates to the field of rehabilitation medicines.The method comprises the steps that electroencephalogram signals of a testee when the testee executes a mind fusion lower limb motor imagery task are collected and preprocessed, and preprocessed electroencephalogram signal data are obtained; based on a dynamic feature extraction technology of a sliding window, calculating electrophysiological indexes in real time; performing weighted fusion on the motor imagery definition, the concentration degree and the correctness degree through predefined weights to generate a comprehensive state score; on the basis of the comprehensive state score, dynamically adjusting a normal feeling and task stimulation parameter applied to the testee; based on the collected electroencephalogram signals, extracting optimal perception motion rhythm and cross-frequency coupling characteristics; and inputting into the trained two-dimensional time convolution network decoding model to obtain the lower limb motor imagery intention of the testee. According to the method, the evoked rate and the stability of the electroencephalogram characteristics of the stroke patient in the lower limb motor imagery task can be improved.

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 relates to an assessment method and system for a stroke recovery stage, and belongs to the technical field of medical assessment, the method comprises the following steps: obtaining demographic information of a stroke patient, and obtaining motion video data of the stroke patient, the motion video data comprising upper limb motion data, lower limb motion data and / or hand motion data; key points are extracted from the motion video data of each type based on a key point recognition algorithm, corresponding motion posture data in the motion video data of each type are calculated based on the positions of the key points, and the motion posture data comprise angle data and displacement data; and determining preset motion attitude data based on a Brunnstrom six-stage theory and demographic information, comparing the motion attitude data with the preset motion attitude data, and obtaining a recovery stage of the stroke patient based on a comparison result. Real-time monitoring and intelligent evaluation of the rehabilitation process of the stroke patient are realized, and the evaluation accuracy and diagnosis efficiency are improved.

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 provides a remote rehabilitation system for a stroke patient, relates to the technical field of stroke rehabilitation, and aims to remotely diagnose the patient, periodically generate a personalized rehabilitation scheme for the patient, and timely adjust and update a training scheme according to the exercise effect of the patient. The system comprises a data acquisition terminal, a data transmission terminal, a control terminal, a doctor monitoring terminal and a patient terminal, a personalized rehabilitation training scheme generation algorithm is applied, multi-dimensional data is fused to form a comprehensive feature vector, and rehabilitation potential and requirements are evaluated. Personalized schemes covering exercise, cognition and psychological intervention are customized for the patient, dynamic adjustment is conducted according to feedback, and precise rehabilitation is achieved. The system has the functions of real-time monitoring, intelligent reminding and abnormity early warning, the rehabilitation effect is optimized, the medical burden is relieved, the life quality of patients is improved, and the system is suitable for medical institutions at all levels.

APRIL research shows that in a selected stroke patient, infusion of 0.2 mg / kg of ApTOLL (SEQ ID NO: 1) in combination with intravascular treatment is safe, and the death rate of 90 days is reduced. ApTOLL has proved to limit intracranial hemorrhage and reperfusion injury which may occur after recanalization, and also improve early nerve injury and long-term disability. ApTOLL molecules are provided for reducing the risk of intracranial or secondary bleeding after acute ischemic stroke, reducing nerve injury, and improving nerve recovery in a subject.

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 belongs to the technical field of assessment of dysphagia of stroke patients, and provides a stroke patient dysphagia risk prediction and personalized intervention recommendation system based on R language construction, which comprises a data processing module, a prediction model module, an intervention recommendation module and an output module, the data processing module receives and processes an input predictive factor value, the predictive model module constructed based on a large amount of case data is used for calculating a dysphagia risk score, then the intervention recommendation module intelligently recommends personalized intervention measures, and the personalized intervention measures are displayed to a user in a visual form through the output module; according to the system, accurate prediction and personalized intervention recommendation of the dysphagia risk of the stroke patient are realized, the prediction accuracy and reliability are improved, meanwhile, the pertinence and effectiveness of intervention measures are enhanced, and the life quality and rehabilitation effect of the patient are improved.

The invention provides an in vitro method for determining the prognosis of a patient with stroke comprising: (i) determining, in a sample isolated from the patient, the expression level of at least one miRNA selected from the group consisting of miR-376c-3p, miR-199a and combinations thereof, (ii) comparing the level of expression obtained in step (i) with a corresponding reference value, wherein if the expression level determined in step (i) is higher than the corresponding reference value, the patient is considered to have a bad prognosis. The invention also provides an in vitro method for deciding or recommending whether to initiate a medical regimen in a patient with stroke, and an in vitro method for determining the response of a patient already diagnosed with stroke, to a medical regimen for treating stroke. The invention further provides a miRNA inhibitor of a miRNA selected from the list consisting of miR-376c- 3p, miR-199a and combinations thereof, for use in the treatment or prevention of stroke in a patient. The invention also provides a combination of miRNAs comprising said miRNA inhibitor, and a pharmaceutical composition for use in the treatment or prevention of stroke.

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 utility model discloses a walking rehabilitation training device for a stroke patient, and relates to the technical field of rehabilitation training. Comprising a base, a storage cavity is formed in the base, a plurality of lead screws are rotationally arranged in the storage cavity, the lead screws are movably sleeved with lifting frames, supporting plates are hinged to the tops of the lifting frames, and replacement plates are detachably connected to the tops of the supporting plates. According to the liftable replacement plate, the multiple supporting plates can form a stair state, a patient can simulate walking stair-climbing training, the training difficulty is increased, the angle of the angle-adjustable replacement plate can be adjusted, the multiple replacement plates can simulate a slope surface so that the patient can conduct walking training, the training difficulty is greatly increased, uphill training can be conducted, and downhill training can also be conducted; by selecting the replacement plates of different table tops, walking on different road surfaces can be simulated, the training difficulty is further increased, the structure is simple and compact, economic benefits are met, and the application prospect is wide.

The invention belongs to the technical field of medical care informatics, particularly relates to a method and a system for constructing a refined model of cerebral apoplexy dysfunction based on an ICF theory, and aims to construct a refined model capable of accurately describing a dysfunction area of a cerebral apoplexy patient through dynamic optimization and a self-adaptive adjustment mechanism. In order to improve the accuracy and efficiency of dysfunction assessment, multi-modal assessment data, including pathological information, imaging data and biochemical indexes, of a patient is continuously collected and analyzed through a dynamic scoring mechanism, then, in combination with an ICF theory, feature parameters of a barrier region are automatically extracted and optimized through feature matching and a deep learning algorithm, and the accuracy and efficiency of dysfunction assessment are improved. According to the method, a three-dimensional model capable of accurately evaluating and positioning an obstacle area is constructed, and the model not only can update and adaptively adjust parameters in real time, but also can provide personalized rehabilitation suggestions and treatment schemes according to individual differences of different patients.

The invention discloses a PID (Proportion Integration Differentiation) electrical stimulation balance rehabilitation method based on a damage synergistic effect. According to the method, firstly, myoelectricity and IMU joint movement data of a healthy subject on a disturbance platform are collected, non-negative matrix factorization (NMF) is conducted on the myoelectricity data, and the muscle synergistic effect of the healthy subject under a disturbance normal form is solved; myoelectricity and IMU signals of stroke patients in the same disturbance normal form are collected, nonnegative matrix factorization is carried out on the myoelectricity of the patients, and similarity analysis is carried out on the health synergistic effect and the patient synergistic effect to determine the injury synergistic effect. The injury synergistic effect is applied to adjustment of electrical stimulation pulse width parameters and used for conducting electrical stimulation on lower limb muscles, joint angle data are collected in real time, PID electrical stimulation control based on the injury synergistic effect is achieved, and therefore a patient can track a joint angle track of a healthy subject under a disturbance normal form. The patient is assisted to recover the injury synergistic effect under the disturbance normal form, the balance capacity is improved, and a new path is developed for rehabilitation treatment of the stroke patient.

The invention discloses a cerebral apoplexy patient postoperative neural function real-time monitoring method. The method comprises the steps that brain MRI image data and clinical information of a to-be-monitored patient and post-operation electroencephalogram data collected in real time are collected and preprocessed; respectively extracting neurophysiological features and radiomics features of the patient to be monitored based on the preprocessed data; performing significant feature screening on the clinical information, the neurophysiological features and the radiomics features, performing preprocessing on the screened data, and combining minimum absolute contraction and selection operator regression analysis to obtain quantitative electroencephalogram data feature indexes and radiomics scores; inputting the screened clinical information, the quantitative electroencephalogram data characteristic index and the radiomics score of the to-be-monitored patient into a trained prediction model, and predicting a risk index of early neurological deterioration of the to-be-monitored patient; the problem that the evaluation result is inaccurate due to the fact that a single clinical feature is adopted to evaluate the neural function in a traditional method is solved.

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 Doppler ultrasonic diagnosis system for dysphagia detection, and belongs to the technical field of Doppler ultrasonic diagnosis. The method comprises the following steps: respectively measuring the geniolingual muscle movement, swallowing time, tongue movement and the distance between the tongue bone and the thyroid cartilage of a stroke patient in a B-type ultrasonic mode and an M-type ultrasonic mode, recording test data after multiple swallowing tests, calculating to obtain an average value of the geniolingual muscle movement, the swallowing time, the tongue movement and the distance between the tongue bone and the thyroid cartilage, and calculating the average value of the geniolingual muscle movement, the swallowing time, the tongue movement and the distance between the tongue bone and the thyroid cartilage. Then the measurement results of the swallowing time and the distance between the tongue bone and the thyroid cartilage are used as evaluation and diagnosis bases for diagnosis, so that objective and accurate diagnosis bases are provided for the treatment and rehabilitation process of the swallowing dysfunction of a stroke patient, and nursing personnel can evaluate and diagnose the swallowing dysfunction after the stroke by using a simple B-ultrasonic instrument at the bedside; and the diagnosis accuracy of medical personnel is improved.

The invention relates to the technical field of image analysis, in particular to a training set expansion method for cerebral apoplexy patient recurrence risk assessment, and the method comprises the steps: obtaining an initial training set composed of historical images under different recurrence risk levels; clustering is carried out based on similar conditions among different historical images, and a recurrence risk level corresponding to each target cluster is determined; dividing the historical images in each target cluster; determining a target core degree corresponding to each core possible image in each target cluster; determining a feature satisfaction degree corresponding to each core possible image in each target cluster; and screening out a target core image from each target cluster according to the target core degree and the feature satisfaction degree corresponding to all the core possible images in each target cluster, and expanding the initial training set based on all the target core images. According to the method, the historical images are analyzed, so that the training set extension is realized, and the rationality of the training set extension is improved.

The invention relates to the technical field of signal processing, and discloses a stroke patient motor imagery electroencephalogram signal decoding method and device, and the method comprises the steps: obtaining an original electroencephalogram signal; the original electroencephalogram signals are preprocessed; the preprocessed original electroencephalogram signals are input into a pre-established feature extraction model, diversified feature extraction is carried out, a feature set is obtained, and the feature set is used for motor imagery classification; wherein the feature extraction model comprises a channel attention module and a space attention module which are arranged in parallel; wherein the channel attention module is used for adaptively distributing a channel attention weight to each channel in an original electroencephalogram signal; and the channel attention module is used for adaptively allocating a space attention weight to each space in the original electroencephalogram signals. According to the method, weight distribution can be carried out on different channels and spatial positions of a stroke patient in a self-adaptive mode, local channel and spatial feature responses are strengthened, and accurate recognition of stroke rehabilitation motor imagery electroencephalogram signals is achieved.

A pharmaceutically acceptable salt of a polypeptide and a use thereof. According to the pharmaceutically acceptable salt of the polypeptide, the half-life of a drug is prolonged by means of a study on the structure-activity relationship and structural modification, the in-vivo exposure is increased, the number of times of administration is reduced, the side effect in animal experiments is lower, and the therapeutic window of the drug is larger. The pharmaceutically acceptable salt of the polypeptide can be used for treatment of ETBR receptor-related diseases, such as rehabilitation treatment of the acute phase and the recovery phase of a cerebral arterial thrombosis patient, promoting better recovery of the stroke patient.

The invention relates to a lower limb function rehabilitation evaluation method based on a multi-dimensional feature fusion network, and belongs to the technical field of intelligent evaluation of lower limb function rehabilitation conditions. The method comprises the following steps: acquiring multichannel surface electromyogram signals of a stroke patient and preprocessing the multichannel surface electromyogram signals to construct a data set; constructing a lower limb function rehabilitation evaluation model which comprises a time domain feature extraction branch, a frequency domain feature extraction branch, a first cross attention module, a second cross attention module and a classifier; inputting samples in the data set into a lower limb function rehabilitation evaluation model, and training the model; optimizing the model by adopting a loss function, updating parameters by using an AdamW optimizer, and minimizing loss through a back propagation algorithm until the model converges to obtain a trained model; and preprocessing the to-be-evaluated surface electromyogram signal, and inputting the preprocessed to-be-evaluated surface electromyogram signal into the trained model to obtain an evaluation result The accuracy of lower limb function rehabilitation evaluation can be improved.

The invention discloses a paralytic nursing intelligent early warning system and method based on the Internet of Things technology. The system comprises a multi-modal physiological parameter acquisition module, an edge calculation preprocessing unit, an intelligent data transmission module, a multi-scale time sequence feature extraction module, a space-time diagram convolutional network module, a cross-modal attention fusion module, a multi-task risk prediction module and a model training and optimization module. According to the system, multi-mode data such as electrocardio, blood pressure, blood oxygen, eye movement tracks and voice are collected, preprocessed at an edge end and then transmitted to a cloud end; a multi-scale convolutional network is adopted to extract time sequence features, parameter association is modeled through space-time diagram convolution, cross-modal data fusion is realized by using an attention mechanism, and finally risk classification, anomaly detection and trend prediction are completed through a multi-task network. The early-stage, accurate and explainable early warning of the stroke risk is realized, and the early warning accuracy and clinical practicability are remarkably improved.