34 results about "Autistic disorders" patented technology

The invention relates to the technical field of artificial intelligence and data processing, in particular to an execution type intervention effect evaluation method based on deep learning, and the method specifically comprises the following steps: recording a heart rate signal of an autistic child during intervention task training through wearable heart rate monitoring equipment; the collected heart rate signals are preprocessed; constructing a data set and dividing the data set into a training set, a verification set and a test set; performing dynamic time segmentation and adaptive noise suppression on each segment of sample data in the data set; constructing an executive intervention effect evaluation model based on deep learning, inputting the denoised training set into the model for training, and verifying performance indexes of the model through a verification set; and inputting data in a test set into the trained model to obtain a final evaluation result. The heart rate signals reflecting psychological load, cognitive participation and emotional fluctuation are analyzed and processed, the situation of the intervention effect can be objectively estimated, and manual judgment is assisted.

Provided is a Bifidobacterium lactis. The Bifidobacterium lactis is Bifidobacterium animalis subsp. lactis BL-11, and the preservation number of the Bifidobacterium animalis subsp. lactis BL-11 is CGMCC No. 20847. The provided Bifidobacterium animalis subsp. Lactis can improve intestine metabolism disorder and normalize the composition of intestine microbiome so as to improve height, suppresses obesity, promote infant and youth physical development and mental development, and prevent and treat mental disorders, said mental disorders including anxiety, depression, attention-deficit / hyperactivity disorder, autism, schizophrenia, hepatic encephalopathy, anorexia, Tourette syndrome, and Asperger syndrome.

The invention discloses an autistic child emotion regulation system integrating electroencephalogram feedback and art, and relates to the technical field of medical intelligent rehabilitation. An emotional state decoding module; a dynamic generation type art interaction guiding module; a closed-loop feedback regulation and control module; and an adaptive optimization module. According to the method, deep fusion of physiological signal monitoring and artistic emotion expression is realized, scientificity and effectiveness of emotion regulation intervention of the autistic children are remarkably improved, participation willingness and compliance of the children are effectively improved, personalized iteration and accurate regulation of the intervention process are realized, limitation of traditional single-mode intervention is broken through, and the method is suitable for popularization and application. Not only is the objectivity of emotion recognition and the pertinence of art guidance enhanced, but also an individualized rehabilitation path is formed through long-term memory map construction, and an intelligent, quantifiable and sustainable new emotion regulation normal form is provided for autistic children.

The invention discloses an autistic child emotional learning assistance method based on generative artificial intelligence. The method comprises the steps of obtaining user information; setting an interaction initial parameter according to the to-be-learned emotion of the user; and according to the user information, constructing an interaction model under a large language model framework, wherein the interaction model comprises a context generation module, an interaction module, an expression extension module, a feedback generation module and an evaluation module. The invention further provides an autistic child emotion learning auxiliary system. According to the method provided by the invention, more personalized and diversified auxiliary learning contents can be provided, so that the learning enthusiasm of the user is improved, and the emotional learning effect is further improved.

FIELD: neurology; psychiatry.SUBSTANCE: used as a non-pharmacological therapy for autism spectrum disorders in children. The method includes a set of procedures: interval hypoxic-hyperoxic training (HHT), hyperbaric oxygenation and ozone therapy. HHT consists of alternating inhalations of a hypoxic mixture with an oxygen content of 9 to 16 vol.% and a hyperoxic mixture with an oxygen content of 32 to 40 vol.% for a total duration of 45-50 minutes under the control of cardiovascular system parameters. Hyperbaric oxygenation is performed in a pressure chamber under a pressure of 1.3 atmospheres of 100% oxygen mixture, flow of 10 liters per minute, for 45 minutes using a mask. Ozone therapy is performed by rectal insufflation with an ozone-oxygen mixture with an ozone concentration of 85 μg / ml in a volume of 20–30 ml or by intravenous administration of a physiological solution enriched with ozone with an ozone concentration of 85 μg / ml at a rate of 200 ml per 100 ml of physiological solution. The procedures are carried out daily for 12 days.EFFECT: method is effective and has a positive impact on the patient’s condition.1 cl, 1 dwg, 3 ex

The invention relates to the crossing field of target detection and emotion recognition, in particular to an autistic child facial expression recognition method based on deep learning. Comprising the following steps of: embedding three-dimensional attention (Tri-attention) behind a C3k2 module of a YOLOv11 backbone network so as to model an interactive relationship among three dimensions of height, width and channel in parallel and enhance response to key expression areas such as eyes, mouth corners and the like; three-dimensional attention (Tri-attention) is introduced into the multi-scale feature output end of the neck network again, and effective positioning and background noise suppression of a small-scale face under a complex background are achieved; respectively training and verifying the YOLOv11-TA model by using the public data set and a self-built autistic child data set, and outputting a detection frame of each face and an expression category thereof; by comparing various indexes of the original YOLOv11 and YOLOv11-TA on various data sets, the accuracy and robustness of expression recognition of the autistic children are remarkably improved by the method according to actual verification.

The present invention is directed to the use of antiviral agents, in particular valomaciclovir stearate and its polymorph Form A, as well as H2G (omaciclovir) to treat multiple sclerosis in combination with other agents, as well as the use of these agents to treat diseases and conditions such as chronic mononucleosis, long COVID, chronic fatigue syndrome, fibromyalgia, Crohn's disease, ulcerative colitis, rheumatoid arthritis, systemic lupus erythematosus, Graves' disease, Alzheimer's disease, mesial temporal lobe seizures, Epstein-Barr-virus-linked autism, or an Epstein-Barr-virus-linked cancer.

The present invention relates to the field of artificial intelligence and data processing technology, and in particular to a method for evaluating the effect of an executive intervention based on deep learning, which is as follows: recording the heart rate signals of autistic children during intervention task training through a wearable heart rate monitoring device; preprocessing the collected heart rate signals; constructing a data set and dividing it into a training set, a validation set, and a test set; performing dynamic time segmentation and adaptive noise suppression on each sample data in the data set; constructing an executive intervention effect evaluation model based on deep learning, inputting the denoised training set into the model for training, and verifying the performance indicators of the model through the validation set; inputting the data in the test set into the trained model to obtain the final evaluation results. The present invention can objectively estimate the effect of the intervention and assist manual judgment by analyzing and processing the heart rate signals reflecting psychological load, cognitive engagement, and emotional fluctuations.

The invention discloses a method for improving autism behaviors through Npas4 expression and overexpression in a cat chat syndrome model, and belongs to the technical field of animal model construction. Selecting SD wild rats and CdCS model rats, and normally feeding and breeding the rats; the method comprises the following steps: measuring the protein concentration by adopting a BCA method, then carrying out Western Blot, then carrying out brain stereotactic injection, and finally carrying out behavioral testing. According to the invention, a CdCS rat model is established by using a CRISPR-Cas9 technology, and the model shows cognitive and social behavior disorders and medial prefrontal cortex (mPFC) neuron dendritic abnormality; in addition, an abnormally expressed gene is found in the model mouse, the research focuses on Npas4, and a new insight is possibly provided for disease treatment.

The present invention discloses an artificial intelligence-based rehabilitation training management method and system for autistic children, and relates to the technical field of autism rehabilitation training, including conducting rehabilitation training for autistic children through VR technology, collecting activity data of autistic children; judging the abnormal behavior of autistic children based on the collected data, setting an early warning mechanism, issuing a warning and terminating the training; calculating the matching degree between the attention of autistic children and the training content based on the collected data, and constructing a fusion feature matrix. The present invention uses VR technology to conduct rehabilitation training for autistic children, provides a richer and more interactive training experience for autistic children, uses a deep learning model, analyzes eye movement data and interaction point data, intuitively evaluates and judges the rehabilitation training effect of the current VR rehabilitation training on autistic children, and screens and adjusts the content of VR rehabilitation training, while making abnormal judgments.

The present application relates to the cross field of target detection and emotion recognition, and particularly relates to a facial expression recognition method for autistic children based on deep learning. The method comprises the following steps: embedding a three-dimensional attention (Tri-Attention) after a C3k2 module of a YOLOv11 backbone network to model the interaction relationship among height, width and channel dimensions in parallel, and enhancing the response to key expression regions such as eyes and corners of the mouth; introducing the three-dimensional attention (Tri-Attention) again at the multi-scale feature output end of the neck network to realize effective positioning of small-scale faces in a complex background and suppression of background noise; training and verifying the YOLOv11-TA model by using a public data set and a self-built autistic children data set respectively, and outputting the detection frame and expression category of each face; and comparing various indexes of the original YOLOv11 and the YOLOv11-TA on multiple data sets, and actually verifying that the method significantly improves the accuracy and robustness of the expression recognition of autistic children.

The application discloses a kind of adolescent autism intestinal microorganism marker and application thereof, belong to the field of biological medicine.The microorganism marker includes one or more of Ruminococcus gnavus, Streptococcus thermophilus, Streptococcus salivarius, Butte's intestinal bacillus, pet big anaerobic clavulanic acid bacteria and Prevotella pittmanniae.The application provides a kind of product (such as kit), including detection reagent for detecting the relative abundance of the microorganism marker.The product provided by the application has good feasibility and accuracy, and can effectively evaluate whether adolescent has the risk of autism, provides a new tool for clinical diagnosis.

The invention provides an autistic patient dietary behavior associated data analysis method and system, relates to the technical field of data analysis, and aims to effectively identify potential trigger events and establish a behavior stability benchmark by integrating multi-dimensional time sequence data, establishing a behavior stability benchmark and performing multi-dimensional difference comparison during behavior deviation. And potential triggering factors inconsistent with the safety mode are identified when the behavior deviates, so that the accuracy and the reliability of analysis are improved.

The present invention comprises an arrangement with an EEG device, in particular a QEEG device and a head part which has at least two applicators which generate a low-frequency electromagnetic field of an application frequency in a frequency range of 1 Hz to 100 Hz and with a field strength below 20 mT, for use in the treatment of dementia, depression, HRV regulation, Raynaud's disease, Asperger's syndrome, migraine, dizziness, sleep disorders, burnout, ADHD, muscular and / or joint pain or autism.

The present invention relates to the field of auxiliary diagnosis and treatment, and in particular to an analysis method for auxiliary diagnosis and treatment of autistic children. The present invention performs pre-detection on a target object and stores detection data to analyze the target object's motion feature representation, determine the target object's motion feature feedback degree, and perform auxiliary diagnosis and treatment analysis, including: adjusting the image acquisition accuracy of the image acquisition device for the target object, calling the video record in the target object's review case, identifying emotional gaze features, limb movement amplitude, and the change ratio of feature matching parameters relative to the pre-detection based on the video record, and comparing them with expected control data determined based on a pre-stored intervention diagnosis and treatment plan to determine whether the feature matching standard is met; generating a recommended number of re-examinations based on the motion feature representation, and outputting the results of the auxiliary diagnosis and treatment analysis through an output end. The data analysis results are accurate and reliable, can provide doctors with auxiliary diagnosis and treatment data support, and improve diagnosis and treatment efficiency.

A targeted acupuncture treatment method for children with autism belongs to the field of medical care, and comprises a sound induction ear acupuncture method, a long-acting eye stimulation opposite pressing acupuncture method and a liver-spleen-kidney simultaneous acupuncture method. The system can improve the core symptoms of the autistic children (3-12 years old), such as not listening to instructions, eye contact parallax, inattention, diet singleness, emotion awareness, hyperactivity and the like, realizes targeted intervention through multi-module collaborative acupuncture, and is suitable for pediatrics of medical institutions, children rehabilitation centers and professional acupuncture clinics.

Modulators of intracellular chloride concentration are disclosed. The present invention relates in particular to compounds of Formula Ia, Ib and Ic, pharmaceutical compositions comprising the compounds of Formula Ia, and their use in the treatment or prevention of pathological conditions associated with the delivery of depolarized GABA, including, for example, Down's disease and autism.

The application discloses a kind of composite nutrient composition and its application in intervention autism and mental retardation, the composite nutrient composition is composed of composite vitamin, mineral, enterobacterium, apple fruit powder and the substance for improving taste;Wherein, the procyanidin B1 content in the apple fruit powder is more than 30mg / kg, the composite nutrient composition of the application can effectively improve the repetitive stereotyped behavior of autistic children, the degree of anxiety, the degree of cognition, and social skills, and effectively improve the intestinal flora imbalance phenomenon of autistic children.It can also effectively improve the cognitive ability of children with mental retardation.

A machine learning model based on neural data (e.g., electroencephalogram (EEG) data, magnetoencephalogram (MEG), and / or magnetic resonance imaging (MRI) data) may be used to predict the effectiveness of future diagnoses and / or interventions (e.g., early interventions) of autistic (autistic) spectrum disorders (ASD) in an individual child. The training of the model may be based on experimental data obtained in a child who previously accepts an intervention.

The present invention relates to compositions for use in the treatment, management or amelioration of FMRI mediated autism and Fragile X Syndrome (FXS), wherein the composition comprises one or more non-nucleoside reverse transcriptase inhibitors (NNRTIs). The invention also relates to one or more NNRTIs for use as medicaments for treating a range of autistic diseases, diseases having an autistic component and behavioural disorders.

The invention discloses an intelligent autistic child diagnosis system based on a resting state near infrared spectrum. The intelligent autistic child diagnosis system comprises a data import module, a matrix calculation module, an intelligent diagnosis training module and an intelligent diagnosis prediction module. Through loading 52 channel resting state near infrared spectrum data, correlation coefficients of relative concentration changes of hemoglobin between channels are calculated after preprocessing, and a 52 * 52 functional connection matrix is constructed. An improved CNN-Attention neural network is adopted for training and prediction, the CNN part captures local channel correlation, the self-attention mechanism layer highlights important channel pairs, and finally the diagnosis probability that whether autistic children suffer from hyperactivity or not is output. The method is low in children adaptability requirement, efficient in diagnosis process, high in operation speed and high in classification accuracy, supports new data addition and model retraining, and provides a scientific and visual diagnosis result.

The present application provides an intelligent interaction-based auxiliary system and method for the intervention and treatment of autistic children, which relates to the field of intelligent data analysis. It uses AI-based data processing technology to extract audio features from the audio data of autistic children, and at the same time extracts facial features from the facial expression images of autistic children. Based on this, it can intelligently judge whether the behavior state of the autistic children is an attempt to communicate based on the multi-modal fine-grained joint representation between the audio feature encoding features and the facial image feature encoding features. In this way, through continuous data collection, it is possible to more accurately capture the extremely subtle changes of autistic children and ensure that no behavior occurring at any moment is missed. Moreover, by combining facial expression analysis and audio feature extraction, the judgment of the behavior state of autistic children can be made more accurate, and then more targeted personalized intervention feedback can be generated.

A pharmacological method for treating the expressive language deficit in an autistic human child or adult is provided. A therapeutically effective dose of a succinimide anticonvulsant, e.g., ethosuximide, methsuximide, phensuximide, or a pharmaceutically acceptable salt thereof, is administered to a patient suffering from expressive language deficit, preferably over an extended period, e.g., six months or longer. Language gains are retained even after treatment is discontinued.

The invention discloses a language barrier person intention analysis system based on multi-modal perception and dynamic interaction, which realizes high-precision demand inference through a non-intrusive multi-modal perception matrix and dynamic credibility fusion algorithm. The system supports two deployment forms: 1) an independent unit (spherical equipment comprises a mechanical arm); (2) an embedded module (a standard interface is adaptive to various devices); the innovativeness comprises throat vibration spectrum analysis, thermal imaging emotion recognition and federal learning personalized modeling. According to the embodiment of the invention, the intention recognition accuracy is high and the response speed is higher than that of a traditional scheme in a family scene-autistic child assistance, a public place-apoplexy patient navigation, disaster rescue-aphasia trapped person communication and other scenes. The system supports independent deployment of minimum units or is used as a module to be integrated to equipment such as robots, household appliances and public facilities, and is suitable for families, medical treatment and public rescue scenes.

The present invention relates to dosing regimens and methods of using LY3154207, also described as 2-(2,6-dichlorophenyl)-1-[(1S,3R)-3-(hydroxymethyl)-5-(3-hydroxy-3-methylbutyl)-1-methyl-3,4-dihydroisoquinolin-2 (1H)-yllethanone, and / or pharmaceutical compositions thereof, for treatment of dopaminergic central nervous system disorders. Dopaminergic CNS disorders of the present dosing regimen methods include Parkinson's Disease, Alzheimer's Disease, Lewy body dementia (LBD), Vascular Dementia, Schizophrenia, ADHD, Depression, Autism, chronic musculoskeletal pain, fibromyalgia, cognitive impairment disorders, sleep disorders, excessive daytime sleepiness, narcolepsy, shift work disorder, traumatic brain injury, chronic traumatic encephalopathy, obesity and appetite regulation, mood disorders, lethargy, apathy, and addiction disorders.

This application relates to the field of data processing technology, and discloses a method, system, and medium for identifying autistic behaviors in children based on data analysis. The method includes: collecting and preprocessing multimodal behavioral data of children; extracting nonlinear features to obtain a time-series feature matrix; identifying repetitive behavioral patterns using a three-dimensional convolutional network; analyzing temporal changes in behavior using a temporal convolutional network; fusing multiple feature representations to obtain a comprehensive feature vector; and applying a multi-classifier system to identify autistic behavior types and assess severity. By extracting nonlinear time-series features, constructing a deep spatiotemporal feature extraction network, designing a feature fusion mechanism, and integrating a multi-classifier system, this invention overcomes the limitations of existing technologies involving single modality, linear features, and single algorithms, thereby improving the accuracy and interpretability of autistic behavior identification.

The invention belongs to the field of autistic child emotion regulation, and provides a wearable device-based autistic child emotion recognition and regulation method and system, and the method comprises the following steps: carrying out the data processing of the physiological data and facial expression of an autistic child, inputting the processed data into a hybrid multilayer neural network, and obtaining an emotion recognition result; if the emotion is recognized as the negative emotion, the inflatable vest is inflated to relieve the negative emotion of the child patient. According to the method, the emotion of the self-closing child is recognized by using the multi-modal data, the real emotion characteristics of the child patient can be fully mined, and the emotion recognition accuracy is improved. Meanwhile, by means of the sensitivity of physiological signals to emotional fluctuation, the effects of emotion early warning and advanced intervention are achieved. In addition, various wearable devices are systematically integrated, and the emotion of the autistic children can be continuously, intelligently and effectively adjusted.