154 results about "Autism" patented technology

The invention discloses a method for identifying an autism brain image through multi-mode brain image information fusion, is applied to the field of image identification, and aims to solve the problems that multi-mode nuclear magnetic resonance image data are not fully utilized and the model interpretability is insufficient in the prior art. The method comprises the following steps: respectively extracting brain region features from three modal data of a structural magnetic resonance image, a functional magnetic resonance image and a phenotype, then carrying out multi-modal fusion, and constructing a brain region map structure; designing a drawing convolution module and a loss function to learn how to extract brain map depth features; on the basis of brain map depth features, a multi-layer perceptron classifier is trained through ABIDE data set label data, and better performance indexes such as accuracy, sensitivity and heterogeneity can be obtained; the trained multi-mode brain image fusion identification autism model can be used for assisting autism diagnosis and identification.

The invention relates to the technical field of data processing, and discloses a child autism behavior recognition method and system based on data analysis and a medium. The method comprises the steps of collecting and preprocessing child multi-modal behavior data; extracting nonlinear features to obtain a time sequence feature matrix; identifying a repeated behavior mode through a three-dimensional convolutional network; analyzing behavior time sequence change by using a time convolutional network; fusing a plurality of feature representations to obtain a comprehensive feature vector; a multi-classifier system is applied to identify autism behavior types and evaluate severity. By extracting the nonlinear time sequence features, constructing the deep spatial-temporal feature extraction network and designing a feature fusion mechanism and a multi-classifier integration system, the method can overcome the limitations of a single mode, a linear feature and a single algorithm in the prior art, and improves the accuracy and interpretability of autism behavior recognition.

The invention discloses an autism identification method based on a graph enhanced path convolutional network, relates to the technical field of biomedical information, and provides a new GNN framework which is used for classifying brain diseases in an end-to-end rs-fMRI mode. According to the method, a path convolutional network is used for learning features of multiple paths, a Transform is used for capturing dynamic spatio-temporal information to strengthen the features, a relation between non-imaging information and a subject is brought into a framework, singular value decomposition, RES + SE enhanced data and channel influence of the enhanced data are provided, and the multi-center problem of ABIDE is alleviated. According to the method, the overall performance of the model is remarkably improved, a gradient method is proposed to find the importance of features and the important features learned by R-pool, and the two are mutually verified, so that the reliability of the method is proved.

The invention discloses a brain region correlation analysis system and method for autistic children. The system comprises a brain region division module used for dividing the cerebral cortex into a plurality of brain regions; the training data set construction module is used for constructing a training data set, and each training sample comprises a sensor space function connection matrix and a source space function connection matrix corresponding to the sensor space function connection matrix; the signal preprocessing module is used for acquiring a real electroencephalogram signal and calculating a real value of a corresponding sensor space function connection matrix; the deep learning mapping module is used for learning a mapping relation from the sensor space function connection matrix to the source space function connection matrix and outputting a predicted value of the source space function connection matrix; and the brain region correlation analysis module is used for calculating the correlation between the brain regions. The method can be used for accurately analyzing the correlation between the brain areas of the autism children.

The invention discloses an autism behavior modeling and dynamic optimization system based on digital twinning and AI, and relates to the technical field of intelligent digital diagnosis and treatment. According to the technical scheme, the digital twinborn model for reflecting the state of the autism patient in real time is constructed by collecting the multi-modal data of the autism patient, and the learning corpus is collected for training to obtain the generative AI evaluation model for evaluating the state of the autism patient; and importing digital twinborn model data of a to-be-evaluated autism patient into the generative AI evaluation model, generating an evaluation result of the current state of the patient, further generating a personalized diagnosis and treatment scheme suitable for the patient, and applying intervention measures in the diagnosis and treatment scheme to the to-be-evaluated autism patient, so that the evaluation result of the current state of the patient is obtained. And obtaining reactions of a patient under different intervention measures by using the constructed digital twinborn model, classifying the intervention measures, and feeding back and importing the intervention measures into a generative AI evaluation model to optimize a formulated personalized diagnosis and treatment scheme.

The invention discloses an autism identification method based on electroencephalogram cross-band coupling, and relates to the technical field of neuroscience and medical science, and the method comprises the steps: obtaining original electroencephalogram data of an autism patient and a normal person, carrying out the preprocessing, setting labels for the autism patient and the normal person, and carrying out the identification of the autism patient and the normal person; constructing a training sample set for the processed electroencephalogram data and labels; calculating cross-band coupling characteristics of the preprocessed electroencephalogram data; constructing an autism recognition model based on electroencephalogram cross-band coupling, and constructing a classifier by using the cross-band coupling features and labels of the training set and using a deep learning method; and putting the electroencephalogram data features with unknown tags into the classifier to obtain tags corresponding to the electroencephalogram data features, thereby completing the process of identifying autism patients. The cross-band coupling feature of the electroencephalogram reflects the balance state of nerve excitation suppression in the brain, and the autism classification and recognition model constructed based on the balance state has a better classification effect.

The invention provides an asynchronous anthropomorphic communication method and system based on voice transfer, which are suitable for various terminals such as wearable equipment, earphones, dolls, bolsters and the like. The method comprises the steps of user voice input, edge or cloud recognition, playback confirmation, content translation and voice synthesis, asynchronous transmission and broadcast and the like. The system does not depend on a specific hardware form, emphasizes a user confirmation mechanism and anthropomorphic voice broadcast and supports multilingual translation and personalized voice styles, the communication process is bound based on a device ID or nickname identity, social account login is not needed, and interaction privacy security is guaranteed. The method is widely applicable to various asynchronous social application scenes such as children, old people, lovers, autism rehabilitation and the like. The system supports nickname binding and friend relationship establishment, users can complete social connection through voice instructions or two-dimensional codes, and controllability and interestingness of communication interaction are enhanced.

The invention relates to an intelligent screening system for children with autism. The intelligent screening system comprises a data acquisition module, wherein the data acquisition module acquires face videos of children completing various normal form tasks; the data preprocessing module acquires basic data related to sight drop points, facial expressions and head postures of the children according to the facial video; the feature extraction module adopts a deep learning technology to extract sight drop point features, facial expression features and head posture features from the basic data obtained in the data preprocessing module; the feature fusion module fuses the sight drop point features, the facial expression features and the head posture features; the classification module comprises an LSTM (Long Short Term Memory) model, integrates the fused features into time sequence data and inputs the time sequence data into the LSTM model for classification analysis; according to the arrangement, the feature extraction module and the feature fusion module are used for integrating extrinsic behavior features under various typical paradigms, time sequence analysis is carried out on the features through the LSTM model, and accurate classification of ASD children is achieved.

The invention discloses an autism symptom recognition system based on multivariate emotion path analysis, and the system comprises a multi-modal data collection module which is used for collecting the multi-modal data of a participant, and the multi-modal data comprises skin electrical activity data, eye movement fixation data, electroencephalogram activity data and facial expression data; the data feature extraction module is used for extracting multi-modal features from the multi-modal data; the emotion cognition mode analysis module is used for establishing a path relation model of the multi-modal data and the emotion cognition stage variables, establishing a path relation model of the emotion cognition stage variables and the emotion cognition ability, and calculating to obtain a path coefficient representing each modal; and the deep learning module is used for carrying out orthogonal fusion and autism identification on the mapping features of the multiple modes in an inverted order mode according to the path coefficients corresponding to the multiple modes. According to the method, multi-modal data are systematically collected, orthogonal fusion is carried out on multi-modal mapping features in a reverse order mode, and autism identification can be better carried out.

The invention belongs to the field of medicines, and discloses application of a protein polymer in preparation of a medicine for treating autism. The inventor finds that a protein polymer generated after mesenchymal stem cells are stimulated by a stress environment, such as ultraviolet radiation, has a certain treatment effect on autism, and provides possibility for treatment of autism.

The invention relates to the technical field of knowledge graph construction, in particular to a method for constructing a knowledge graph of autism multi-source heterogeneous big data. Original data is obtained from an electronic medical record system, a gene database, a research literature library and a social media multi-source heterogeneous data source in a real-time or timing mode; 2, cleaning, denoising, missing value completion and de-identification are carried out on the original data, and standardized mapping is carried out on terms based on a unified medical language system, the fifth edition of 'Mental Diagnostic and Statistical Handbook' and a human phenotype ontology medical ontology; 3, on the basis of the medical ontology, autism related concept subsets are expanded, version control is carried out, and a special autism ontology is formed. According to the method, high-precision fusion of multi-source entities is achieved, and the technical bottlenecks that standardization mapping is difficult, extraction precision is low, fusion alignment is difficult and the like are fundamentally solved.

The invention discloses a classification method and device for multi-level feature fusion based on autism electroencephalogram signals, relates to the field of electroencephalogram signal analysis, and solves the problem that an existing deep learning method only pays attention to high-level features and neglects low-level features in autism electroencephalogram signal classification. The classification method comprises the following steps: acquiring an electroencephalogram signal of an autism patient, and executing a nonlinear multi-dimensional preprocessing step on the electroencephalogram signal; performing time domain-frequency domain double-domain joint feature extraction on the preprocessed electroencephalogram signals; constructing a hierarchical fusion and refinement module with decomposition-fusion-refinement three-stage processing capability; and based on the extracted hierarchical fused features, constructing a multi-level feature fusion model with an adaptive feature weight distribution mechanism, and training the multi-level feature fusion model based on autism electroencephalogram signals by adopting a self-supervised training strategy to realize classification of autism and non-autism samples. The method is also suitable for the field of deep learning electroencephalogram signal feature fusion.

The invention relates to a traditional Chinese medicine for treating children autism, which can effectively solve the medication problems of good curative effect and convenience for treating children autism. The preparation method comprises the following steps: disinfecting and sterilizing radix bupleuri, radix paeoniae alba, fructus aurantii, rhizoma nardostachyos, rhizoma atractylodis macrocephalae, poria cocos, codonopsis pilosula, cassia twig, rhizoma acori graminei, cistanche, rhizoma alismatis, peach kernel, safflower, scutellaria baicalensis, pinellia ternate, honey-fried licorice root, rock candy and maltose, soaking, boiling with strong fire, decocting with slow fire, filtering, heating to prepare a traditional Chinese medicine concentrated solution, adding rock candy and maltose, decocting into thick paste, pouring into a mold, shaping at room temperature to form granules, and packaging. The traditional Chinese medicine composition is rich in raw materials, easy to operate in preparation method and scientific and reasonable in compatibility according to the primary and secondary relationship of monarch, minister, assistant and guide, has the effects of soothing the liver, strengthening the spleen, tonifying the kidney, replenishing essence, inducing resuscitation and promoting intelligence, is effectively used for treating autism of children, and has huge economic and social benefits.

The invention discloses an infantile autism three-early evaluation method and rehabilitation training cloud platform based on child visual size perception, and the method comprises the steps: constructing a multi-core ability dimension question bank based on Delphi expert scoring and an analytic hierarchy process, and dividing the question bank into a plurality of hierarchies according to size perception ability factors, each hierarchy comprises a plurality of test tasks for testing the size perception ability of the user, dynamically matching an initial test hierarchy through ASQ development month age, realizing cross-layer question insertion by utilizing a dynamic dominant layer polling mechanism, recording behavior and eye movement characteristics of the user, and automatically collecting the behavior and eye movement characteristics of the user as evaluation indexes; evaluating the big and small feeling perception ability of the user by comprehensively calculating the evaluation indexes, obtaining an evaluation result, and adaptively executing intervention measures according to the evaluation result. According to the invention, through fusion of a seven-factor dominant layer polling mechanism and eye movement-behavior bimodal data, accurate identification, scientific diagnosis and targeted intervention of visual perception abnormity of autistic children are realized.

The invention relates to the technical field of rehabilitation medical treatment, in particular to an autism assessment system and method.The system comprises a data acquisition module, a data processing module and a data analysis module, and the method comprises the steps that assessment data of the assessment item execution process of a patient is obtained to obtain feature data values; calculating an evaluation value based on the feature data value; generating a first feature set and a second feature set based on a comparison result of the feature data value and a standard feature data value; determining a data extraction proportion of a feature set based on the evaluation value, and determining a correction coefficient according to the emotion fluctuation frequency of the patient to generate an intervention strategy; and determining whether a single intervention target meets the standard, determining whether the intervention strategy meets the standard based on the number proportion, and determining an adjustment data extraction proportion or a correction coefficient based on the number proportion difference of the intervention targets under the condition that the intervention strategy does not meet the standard. The accuracy of the intervention strategy generated according to the evaluation result is improved.

The embodiment of the invention relates to the technical field of big data analysis, and provides an autism identification method, device and equipment, a storage medium and a product, and the method comprises the steps: obtaining medical record scale data which is determined by scanning a paper medical record and a paper quality table of a target identification object; preprocessing the medical record scale data to obtain preprocessed medical record scale data; performing feature extraction on the preprocessed medical record scale data to obtain medical record scale features; and inputting the medical record scale features into a pre-trained autism recognition model to obtain an autism recognition result of the target recognition object. According to the embodiment of the invention, autism identification efficiency and accuracy can be improved.

The invention relates to an autism classification method based on a multi-scale residual image neural network, and aims to cope with the challenge of crowd autism classification in multi-modal medical data. The method comprises the following steps: firstly, providing a new function connection feature construction method, extracting second-order function connection features by using tangent Pearson embedding to capture a high-order interaction relationship between brain intervals, and then adopting a maximum independent domain to adaptively minimize statistical dependence between the features and acquisition sites, and combining F-score to screen the features with the most discriminative ability, so as to obtain the feature with the most discriminative ability. And redundancy is effectively removed. Secondly, a multi-modal edge weight calculation method fusing imaging information and non-imaging information is provided, so that noise interference is effectively suppressed while a key discriminant relation is reserved. And finally, expanding a node receptive field layer by layer by stacking multiple layers of Chebyshev convolutions with residual errors on the subject graph so as to capture multi-level relation characteristics, and performing weighted modeling and adaptive fusion on convolution output of each layer by using a multi-head self-attention mechanism, so that effective integration of multi-scale information is realized, and accurate classification of autism is realized. The method has excellent performance in the aspect of autism classification, and an innovative, feasible and effective solution is provided for solving the autism classification task in the multi-modal medical data.

The invention discloses an autism detection method based on multi-mode collaborative embedding, and belongs to the technical field of medical image analysis and artificial intelligence. The method comprises the following steps: firstly, obtaining resting state functional magnetic resonance imaging data and non-imaging data of a subject; a Markov transition field is utilized to encode the time sequence into an image so as to retain dynamic features, and feature extraction is carried out through an efficient multi-scale attention module; then realizing effective fusion and semantic alignment of multi-modal information by adopting a three-level fusion architecture and a joint loss function; and then adaptively constructing a graph structure based on the fusion features, dynamically learning a node relationship by using a graph attention network, and completing a classification decision. According to the method, the defects of a traditional method in the aspects of dynamic feature modeling, multi-modal fusion and heterogeneous graph structure processing are effectively overcome, the autism detection accuracy and robustness are remarkably improved, and a reliable tool is provided for clinical intelligent diagnosis.

The invention discloses an autism classification algorithm based on combination of dynamic and static brain networks. A static brain network encoder effectively extracts global brain network features and generates functional connections with richer semantic information; a dynamic brain network encoder independently encodes each subsequence in the dynamic connection and fuses all codes according to an attention mechanism; and the graph classifier recombines the results of the first two modules into a high-order function connection brain graph and performs classification based on a GCN algorithm. According to the method, deep features in a static function connection matrix are effectively learned by introducing the static brain network encoder, and the dynamic brain network encoder module is introduced, so that the method has the capabilities of learning time dynamics of a brain network and integrating dynamic function connection, and the brain fitting capability of the model is greatly improved; and finally, through representation learning of a graph classifier, the model can deeply learn classification features of the brain to diseases from two aspects of a dynamic brain network and a static brain network, so that the classification performance of the model can be improved.

The invention discloses an autism early screening automatic scoring system and method based on artificial intelligence multi-modal data processing, which is an autism early screening auxiliary scheme based on BeDevice toolkit analysis and scoring and comprises a multi-modal data conversion module, an intelligent desensitization module, a dialogue analysis module and an intelligent scoring module. The functions of audio and video transcription, privacy desensitization and automatic scoring are integrated, and single-file or batch processing is supported; the accuracy and flexibility of desensitization are balanced by combining a mixed desensitization form of an AI mode and a rule mode; during audio and video transcription, a multi-engine fault-tolerant mechanism is adopted, standby services are automatically switched, and robustness is improved; the cloud data leakage is avoided by depending on the local FFmpeg and the self-defined module, and the data security is improved; the unstructured scoring standard is analyzed through the DeepSeek model, different scales are adapted, the method adapts to subjects of different age groups, and the application range is wide.

The embodiment of the present application provides a device for assisting oral muscle training for children with autism, the device includes an inflatable airbag, a shell and a jump piece. By blowing air into the second air port, the airflow enters the inner cavity of the connecting part from the second air port, and makes a sound under the action of the airflow when passing through the whistle opening; the airflow flows to the first air port, and enters the inflatable airbag through the first air port. The inflatable airbag swells after being inflated, and when it swells, a force is formed on the jump piece, and the jump piece bounces upward under the action of the inflatable airbag. Compared with the existing oral muscle auxiliary training equipment for children with autism, which is more expensive, the device provided by the embodiment of the present application has a simple structure, low cost, and is suitable for mass production, which can effectively reduce the economic burden of the families of children with autism. It effectively solves the technical problem of the high cost of using professional equipment in the prior art and the inability to popularize auxiliary training tools for use in families.

The invention discloses a children autism risk assessment method and system based on multi-modal collaborative reasoning and a vertical domain large model, and the method comprises the steps: collecting the audio data of a to-be-assessed child and a parent in a parent-child interaction link, and collecting the video data through collection equipment; analyzing and marking the collected audio data, and generating a sounding record and a transcription text with a timestamp; performing real-time analysis and labeling on the collected video data, and generating sight line, expression and action recognition records with timestamps; performing independent analysis on the preprocessed multi-modal data, performing feature fusion and collaborative analysis on the multi-modal data such as audio, text and video by using a multi-modal collaborative reasoning method, and generating a scientific data index for autism risk assessment; training a vertical domain large model special for autism risk assessment; and inputting the generated data indexes into a vertical domain large model, and outputting targeted assessment suggestions by the large model in combination with daily information of the children to be assessed obtained by interviews and conversations with parents.

The invention relates to the technical field of early screening of child autism, in particular to a child autism recognition method and system based on fNIRS signals and wearable equipment, and the method comprises the steps: collecting forehead multi-band cerebral blood flow signals and head movement data of a child through an fNIRS intelligent helmet; the method comprises the following steps: eliminating motion artifacts by using an edge computing chip, obtaining a noise-reduced fNIRS signal through adaptive filtering, extracting a time-domain signal slope, a frequency-domain wavelet energy entropy and an inter-channel function connection matrix, and processing time-frequency features and topological features by using a 1D convolution branch and a graph convolution branch by means of a double-current neural network, thereby obtaining a noise-reduced fNIRS signal. After features are fused, an autism risk probability value is output through channel attention weighting, a screening grade report is generated and transmitted to a mobile terminal, the system comprises a signal acquisition unit, an edge processing unit, an intelligent analysis unit and an interactive output unit, the system is adaptive to daily scenes of children, and the screening precision is improved.

The invention discloses application of Trolox in preparation of a medicine for treating autism, and belongs to the technical field of biological medicine. The invention finds that Trolox has the effect of preventing and / or treating autism for the first time, and further, animal experiments prove that Trolox prevents and / or treats autism by relieving repeated engraving behaviors of autism rats, rescuing social novelty disorders of the autism rats and reducing anxiety behaviors of the autism rats. The invention provides a theoretical basis and a new drug source for prevention and / or treatment of autism.

The invention relates to an autism early screening system and device based on MRMR feature selection, and the system integrates a data preprocessing module, a data coding module, a data imbalance processing module, a feature filtering module, a feature selection module and a model construction module. Text is converted into numerical values through coding, and then a data type proportion is balanced by applying a strategy. Subsequently, the feature filtering and selection process ensures that only highly correlated and low-redundancy features are retained, forming an optimized feature set. And finally, based on the feature set, systematically constructing an autism prediction model integrating a plurality of prediction classifiers, and realizing accurate autism prediction by training each classifier and selecting the optimal classifier. The invention aims to improve the accuracy and efficiency of early screening of autism and provide powerful support for early intervention treatment of child patients.

The invention relates to the technical field of data processing, and discloses a multi-dimensional data-based lonely risk prediction method, device and equipment and a medium, and the scheme provides accurate and comprehensive data support for social risk assessment by collecting multi-dimensional social behavior data of a target object. By calculating the social network features, a quantitative basis is provided for lonely risk assessment. And analyzing the voice data through an emotion analysis model, and capturing voice emotion features of the target object in social interaction. According to the method, social network features and voice emotion features are comprehensively analyzed based on an autism risk assessment model, an autism risk prediction value is generated, the possibility that a target object is in an autism state is visually reflected, an autism intervention strategy is formulated according to an autism risk level, intervention can be effectively performed according to the actual autism risk condition of the target object, and the risk assessment efficiency is improved. And the intervention accuracy and timeliness of the social health of the target object in the home-based care service in the medical field are obviously improved.

The invention discloses an autism early prediction method and system based on multi-modal feature fusion and a bidirectional attention mechanism, and the method comprises the steps: carrying out the parallel extraction of an original video, carrying out the data enhancement through a segmentation recombination technology, and obtaining a training set, which comprises the enhanced behavior and physiological time sequence signals; double-flow deep feature learning is carried out on the training set by using a parallel flow frame, spatial-temporal features and time-frequency features are obtained, and the parallel flow frame comprises spatial-temporal feature extraction flow and time-frequency feature extraction flow; performing deep interaction and adaptive weighting on the spatial-temporal features and the time-frequency features by using a bidirectional cross attention mechanism to obtain fusion features; and inputting the fusion features into a classifier to obtain a final autism risk prediction probability. According to the method, the defects in the prior art are effectively overcome, and the accuracy and reliability of early prediction of autism are remarkably improved.

The invention discloses an equestrian-based children autism treatment intervention method, and relates to the technical field of children autism treatment intervention methods, and the method comprises the steps: employing a comprehensive evaluation and data analysis method to analyze the physical condition, cognitive level and psychological demand of a child, and obtaining the personalized rehabilitation demand of each child; a specific equestrian training plan is made by adopting a method based on the personalized rehabilitation requirements, and a personalized equestrian rehabilitation scheme of each child is obtained; refining the personalized equestrian rehabilitation scheme by adopting an interactive experience design method to obtain an interactive experience activity arrangement; monitoring physiological and psychological states of children participating in interactive experience activity arrangement by adopting a real-time monitoring and dynamic adjustment method to obtain reaction data in different activities of the children, and marking the reaction data as a progress report; a family and social support network construction method is adopted to feed back the progress report to parents, and a parent-child equestrian experience course and a remote guidance application program are provided.

The invention relates to the technical field of electroencephalogram signal monitoring and processing, in particular to a user category determination method based on an auditory brainstem reaction normal form and source localization, and the method comprises the following steps: obtaining auditory brainstem reaction data and electroencephalogram data when a target user is stimulated by external sound; based on a feature extraction module, feature extraction is carried out on source estimation data corresponding to the auditory brainstem response data and the electroencephalogram data; performing feature fusion on the basis of the features extracted from the auditory brainstem reaction data and the features extracted from the source estimation data, and determining fusion features; and determining the category of the target user based on the fusion features and a user category prediction model. According to the scheme for carrying out early diagnosis and screening on the autism infants based on the auditory brainstem reaction data and the electroencephalogram data, early autism (ASD) screening of the infants can be finally achieved under the support of the electroencephalogram equipment and auditory brainstem reaction testing.

The invention discloses an array type acupoint electrical stimulator based on autism patient emotion regulation, and belongs to the technical field of electrical stimulators, the array type acupoint electrical stimulator comprises a fixing band fixed on the body of a patient, an instrument controller is fixedly installed in the middle of the fixing band, sliding grooves are symmetrically formed in the fixing band, and the instrument controller is fixedly installed in the sliding grooves. Adjusting mounting bases are symmetrically arranged on the fixing belt in a sliding mode, a fixing base is fixedly installed on the adjusting mounting bases, a movable connecting piece is rotatably installed on the fixing base, an adjustable attaching base is fixedly connected to the movable connecting piece, and an elastic film body is fixedly connected to the adjustable attaching base in a sealed mode. Therapeutic electrode plates are attached to the outer side of the elastic film body in an array mode. According to the array type acupoint electrical stimulator based on the emotion regulation of the autism patient, the electrode plates distributed in an array mode can be attached to the outside of the patient when the array type acupoint electrical stimulator is worn and used, multimode positioning can be achieved, it is ensured that the action position is accurate, and the electrical stimulation treatment effect is effectively improved.