93 results about "Autism" patented technology

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 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 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 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 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 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 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 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.

This application relates to the field of biomedical technology, and in particular to a method for evaluating the effectiveness of autism intervention and its application. The method includes the following steps: collecting a test sample and a control sample; the test sample is a urine sample from an autism patient, and the control sample is a urine sample from a healthy volunteer; processing the test sample and the control sample separately to obtain NMR samples; testing the NMR samples using an NMR spectrometer to obtain NMR spectra; performing multivariate statistical analysis on the NMR spectra to construct a urinary metabolite model; and using the urinary metabolite model to screen and evaluate autism patients. This application provides a method for evaluating the effectiveness of autism intervention and its application, constructing a urinary metabolite model through NMR metabolomics technology, providing practical evidence for establishing methods for early screening and treatment process evaluation of autism.

The application discloses an autism recognition and biomarker screening method based on deep feature guidance. The method comprises the following steps: acquiring multi-channel task-state electroencephalogram signals of ASD children and TD children in a four-direction visual guidance selection reaction task, and synchronously recording behavioral indicators to construct a labeled electroencephalogram dataset; pre-processing the multi-channel task-state electroencephalogram signals; extracting hand-crafted features and deep features based on the pre-processed multi-channel task-state electroencephalogram signals in parallel; inputting the hand-crafted features and the deep features into a deep guidance attention fusion module for fusion to obtain final feature weights, and inputting the hand-crafted features after recalibration based on the final feature weights into a classifier to obtain a two-classification result of ASD and TD; and based on the final feature weights, extracting a group-level biomarker set from the hand-crafted features through a triple screening mechanism of discriminativeness, stability and functionality. The application improves the accuracy of autism recognition and the reliability of biomarker screening.

The invention relates to the technical field of medicinal chemistry, and particularly discloses application of echinacoside in preparation of a medicine for improving autism social and cognitive impairment, and the application comprises the following steps: constructing a recombinant saccharomyces cerevisiae strain, and knocking out a precursor competition pathway gene; performing metabolic flow reprogramming fermentation on the recombinant strain; centrifuging the fermentation liquor at 7000-9000 r / min and pretreating the fermentation liquor with an 8-12 kDa ultrafiltration membrane; carrying out dual-targeting affinity chromatography purification on the pretreatment liquid; the preparation method comprises the following steps: preparing echinacoside into a liposome containing pH sensitive poly-histidine; and adding a freeze-drying protective agent into the suspension to carry out gradient pre-freezing and vacuum freeze-drying. According to the application, a mode of combining recombinant saccharomyces cerevisiae strain construction and metabolic flow reprogramming fermentation is adopted, targeted enrichment of echinacoside is realized through affinity chromatography coupled with autism brain region specific double ligands, then the echinacoside is prepared into the liposome containing pH sensitive poly-histidine, the double ligands are modified, and the effects of precise targeting and efficient effect are achieved.

The present application relates to the technical field of early screening of children's autism, in particular, the present application relates to a child autism recognition method and system based on fNIRS signal and wearable device, through the fNIRS intelligent helmet, the multi-band brain blood flow signal of the forehead of children and the head movement data are collected, the motion artifact is eliminated by using the edge calculation chip, and the noise reduction fNIRS signal is obtained through the adaptive filtering, the time domain signal slope and the frequency domain wavelet energy entropy and the inter-channel functional connection matrix are extracted, with the aid of double-flow neural network, the time-frequency characteristics are processed by 1D convolution branch, and the topological characteristics are processed by graph convolution branch, after the fusion characteristics, the autism risk probability value is output by the channel attention weighted output, and the screening grade report is generated again and transmitted to the mobile terminal, the system contains a signal acquisition unit, an edge processing unit, an intelligent analysis unit and an interactive output unit, adapts to the daily scene of children, and improves the screening accuracy.

This invention discloses an MDGA2-targeting polypeptide and its applications, which consists of 8-55 consecutive amino acids of the RPS23RG1 protein and contains QHNSX1CMR, where X1 is F or L. The MDGA2-targeting polypeptide and fusion protein of this invention can significantly inhibit the lysosomal degradation of MDGA2, thereby increasing MDGA2 levels. Therefore, it can be used to treat diseases associated with low MDGA2 activity (such as autism and other neurological disorders and gastric cancer), and has significant clinical value.

The application discloses a method for constructing a social novelty defect model through catechol exposure of pregnant mice, and belongs to the technical field of disease animal models. The method comprises the following steps: performing catechol exposure treatment on the pregnant mice, feeding the pregnant mice until childbirth, and waiting for the obtained offspring mice to grow to a required age, so as to obtain the social novelty defect model. The method is capable of precisely matching a key time window of embryonic neural development, and adopts a single catechol exposure mode, so that the method is simple to operate, has a high model formation rate, and has a high survival rate of the mother mice and the offspring. In addition, the induced social novelty defect model of the offspring mice is stable in phenotype and strong in specificity, can simulate the cross-generation influence of maternal pregnancy environment exposure on abnormal social functions of offspring young, and is suitable for mechanism research, drug screening and diagnosis marker development of social impairment diseases such as autism.

This invention provides a method and system for recommending autism interventions based on knowledge graphs and enhanced retrieval, comprising: receiving multimodal data of individuals with autism; performing feature extraction and semantic analysis on the multimodal data to abstract it into structured evidence entities; utilizing multi-source medical knowledge information in the autism domain to perform structured transformation and entity representation, constructing a knowledge graph in the autism domain, introducing a scale alignment mechanism, calculating the semantic similarity of assessment items in different assessment scales, constructing a unified representation space across scales, and incorporating the alignment results as a component of the knowledge graph; using the evidence entities as the starting point for reasoning, performing knowledge retrieval under evidence constraints in the knowledge graph, filtering target strategies that match individual characteristics from candidate intervention strategies through multi-evidence fusion and matching degree calculation, and generating autism intervention recommendation results including intervention goals, implementation methods, and knowledge sources by combining a large language model.

This invention relates to the field of medical information processing and intelligent decision-making technology, and particularly to an autism decision assessment system based on multivariate data and knowledge graphs. The autism decision assessment system based on multivariate data and knowledge graphs includes the following components: a time-locked random calculation module: collecting endogenous and exogenous data, and determining time lock and random variables based on the endogenous and exogenous data; an arousal graph correction module: obtaining the endogenous arousal level based on the time lock and random variables, constructing a knowledge graph based on the endogenous and exogenous data and combining the endogenous arousal level, and correcting the knowledge graph; and a decision assessment output module: performing autism decision assessment based on the corrected knowledge graph. This invention integrates physiological and environmental time-series data, and through mutual information phase-locked detection, randomized causal graph construction, and individualized extreme value modeling, decouples endogenous and exogenous confusion, improving the accuracy and interpretability of autism assessment.

The invention relates to a time-space diagram neural network autism classification method based on dynamic function connection and dynamic effective connection feature fusion, which can realize accurate classification of autism by using resting state functional magnetic resonance imaging data. The method comprises the following steps: firstly, constructing a dynamic function connection matrix and a dynamic effective connection matrix to respectively extract brain network diagram characteristics, and under the guidance of a dynamic effective connection network, forming fused brain network space-time connection characteristics by adopting a space-time fusion position Transform based on a cross attention mechanism; introducing a multi-layer perceptron to extract high-order image features in the brain network, embedding the high-order image features as node representation of a population graph, constructing edges of the population graph by using demographic information, realizing fusion of the magnetic resonance image features and the demographic information, and finally learning node embedding through a graph convolutional network to obtain the demographic information of the population graph. And autism classification is realized based on a linear classifier. Experimental results show that the provided method has excellent performance in autism diagnosis tasks, and the accuracy and robustness of diagnosis are remarkably improved.

The application relates to the technical field of biomedical information processing, in particular to an autism classification system based on resting-state electroencephalogram signals, which comprises an EEG signal acquisition and preprocessing unit and an EEG signal classification unit, the EEG signal classification unit is used for classification by using a trained Rest-HGCN network model; the Rest-HGCN network model comprises a resting-state mixed graph network module, an attention learning module and a classification module; the resting-state mixed graph network module comprises a cognitive graph branch and a data-driven graph branch and is used for extracting corresponding feature mappings; the attention learning module is used for fusing the feature mappings extracted by the resting-state mixed graph network module to obtain a final feature mapping; and the classification module is used for classifying the final feature mapping to obtain a classification result. Through the classification system, the problems of ASD patient EEG feature extraction difficulty and low recognition rate in the prior art can be effectively solved, and only a small amount of features are needed to achieve the purpose of more efficient ASD classification recognition.

1. The name of the design product: emotional blocks (autism healing teaching aid). 2. The use of the design product: the design product is used for autism healing teaching of emotion recognition and emotion matching. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: component 1 combined state reference figure 1.

This invention relates to the field of autism identification technology, and more specifically, to an autism identification method and system based on dynamic and sparse coding. The invention constructs an identification model, first building a feature extraction template, then training the identification model to obtain a trained model. The trained model is then used to process fMRI brain signal data from G patients to be identified, yielding G identification results. Considering the differences in certain specific brain functions between autistic patients and normal individuals, the identification model constructed in this invention selects brain functional connections with significant differences to form feature selection when extracting features from fMRI brain signal data. It also uses the feature extraction template to extract feature vectors with high F-scores, thereby reducing the amount of subsequent feature processing and achieving better efficiency with less computation. This invention solves the problem of low efficiency in existing models.

Provided is an application of a protein polymer in the preparation of a drug for treating autism. Research has found that protein polymers derived from mesenchymal stem cells stimulated under stress conditions, such as ultraviolet irradiation, have a certain therapeutic effect on autism, providing a possibility for the treatment of autism.

The invention relates to application of NADH (nicotinamide adenine dinucleotide) in preparation of drugs for preventing or treating autism, and further provides a liposome nanoparticle oral preparation for preventing or treating autism, the NADH can reduce central nervous system inflammation, and tests prove that the NADH can remarkably reduce neural immune inflammation of autism model rats, and can be used for preventing or treating autism. The method has the advantages that the method is simple and convenient to operate and has neuroprotection and progeny rat behavioral correction capacity, social experiment defects of progeny rats are overcome, the engraving behaviors of the progeny rats are reduced, the influence of autism-related behaviors of the progeny rats is reversed, and a new scheme is provided for prevention or treatment of autism.

The application provides a network architecture search method and system for electroencephalogram signal processing, and realizes effective migration from high-channel EEG data to low-channel EEG data. In particular, for children with autism, it is difficult for them to cooperate for a long time to collect EEG signals, which may result in incomplete data. The application searches for the optimal neural network architecture by using the multi-agent deep deterministic policy gradient algorithm to search for the neural network architecture, so that a high classification accuracy is maintained in the process of migrating from high-channel data to low-channel data.

The invention discloses a pupil light reflex auxiliary diagnosis detector based on a smart phone and a detection method. According to the pupil light reflex autism auxiliary diagnosis detector based on the smart phone and the detection method, combined optimization of stimulation light source spectrums is achieved through intelligent program control multi-wavelength light stimulation of a mobile phone OLED screen, and single-shot majority synchronous image analysis based on the smart phone is designed. The auxiliary diagnosis kit is convenient to use, quick in auxiliary diagnosis result, easy in technology implementation, low in cost, convenient to popularize and friendly to users, and the convenience of auxiliary diagnosis and detection of the PLR of the autism children is improved.

The system according to this embodiment aims to enable individuals with autism to live independently and enrich their leisure time. [Solution] The system according to this embodiment comprises a personalized assistant unit, a social skills training unit, an emotion recognition unit, an independent living support robot unit, and a communication support tool unit. The personalized assistant unit assists with daily schedule management and reminder setting. The social skills training unit provides training to learn dialogue simulations and appropriate responses according to the situation, as provided by the personalized assistant unit. The emotion recognition unit recognizes emotions provided by the social skills training unit and provides appropriate feedback. The independent living support robot unit assists with simple tasks within the home, as provided by the emotion recognition unit. The communication support tool unit provides communication support using speech recognition and generation technology provided by the independent living support robot unit.