168 results about "Epilepsy" patented technology

The invention relates to an epilepsy prediction method based on adaptive sparse attention and a hierarchical graph convolution network, and the method comprises the steps: carrying out the time domain convolution, spectrum transformation and Haar wavelet down-sampling of an electroencephalogram signal, respectively generating time domain, spectral domain and fidelity down-sampling features, and fusing the features into a low-level feature set; on the basis of a sparse attention mechanism, constructing and applying a multi-level sparse mask to adaptively screen and weight-aggregate key discriminative features in the feature set to obtain screened features; on the basis of the feature, by constructing a local channel graph and a global frequency band graph and respectively executing graph convolution, capturing local spatial correlation of each channel in a single frequency band and global cross-frequency-band spatial dependence among different frequency bands, and fusing the local spatial correlation and the global cross-frequency-band spatial dependence into an embedded feature; and inputting the embedded features into a classifier to obtain a state probability, and triggering an alarm based on the state probability. Therefore, the problems of key information loss, insufficient time-space spectrum dependent modeling and feature redundancy are solved, and the accuracy, stability and real-time performance of epilepsy prediction are improved.

The invention discloses an epilepsy abnormal brain network identification method based on a multi-scale static-dynamic fusion network, and belongs to the field of brain image analysis. The method comprises the following steps: firstly, constructing a static function connection weighted graph and a dynamic function connection graph; and fusing the static and dynamic representations by adopting a cross attention module. In order to describe a multi-scale spatial relationship, performing lexical meta-processing on brain connection according to anatomical partition and a functional network; and the local-global fusion module is used for integrating the fine granularity and the macroscopic relationship, so that the brain region with diagnostic significance is highlighted. In the training stage, cross entropy, reverse contrast loss and sparse regularization based on contrast graph adjacency matrix entropy are jointly used. The method is verified on multi-center functional magnetic resonance data, compared with other mainstream depth models, the classification accuracy, generalization and interpretability are remarkably improved, an abnormal brain region consistent with an epilepsy network can be positioned, and brain image markers with biological significance can be connected and recognized.

The invention relates to the technical field of magnetic stimulation therapy, in particular to a magnetic convulsion therapy coulometric titration method and system based on individualized epilepsy prediction.Based on epilepsy prediction, intervention time periods are divided, electroencephalogram is collected to extract the phase change trend, and magnetic stimulation applying modes consistent in space direction are matched and screened; stimulation is executed, nerve electrical activity evolution is monitored, nerve response area distribution is concluded, a time period sequence, a response area form and an application trend are synthesized, a stimulation energy configuration range is divided and limited, and a magnetic convulsion treatment electricity titration result is formed. According to the method, epilepsy risks are described in a staged mode, time sequence correlation is established, intervention is endowed with dynamic distinguishing capacity, the stimulation direction is restrained according to phase propagation characteristics, the stimulation path is kept consistent with the dominant direction of neural activity, the energy range is limited in combination with neural response distribution, energy and state mapping is established, and the intervention matching degree and regulation controllability are improved; the invalid stimulation probability is reduced, and the treatment adaptation capability is enhanced.

This invention provides a novel GABA structure with better efficacy, reduced side effects, and greater safety for clinical use. A This invention relates to receptor agonists, specifically a phenol derivative, its preparation method, and its use in the central nervous system. The phenol derivative provided by this invention offers more and better drug options for inducing or maintaining anesthesia in animals or humans, promoting sedation and hypnosis, and treating and / or preventing anxiety, nausea, vomiting, migraines, seizures, epilepsy, neurodegenerative diseases, and other central nervous system-related disorders.

The present invention relates to a system and method configured for analysing acoustic parameters of speech to detect, diagnose, predict and / or monitor progression of a condition, disorder, or disease, and more particularly, any of paediatric and adult neurological and central nervous system conditions including but not limited to low back pain, multiple sclerosis, stroke, seizures, Alzheimer's disease, Parkinson's disease, dementia, motor neuron disease, muscular atrophy, acquired brain injury, cancers involving neurological deficits, paediatric developmental conditions and rare genetic disorders such as spinal muscular atrophy. The system and method extracts a first formant data set from words spoken by an individual and uses these to classify the vowels in the words on a first computing device, such as a mobile smart phone equipped with a microphone into which an individual speaks. The system stores at least some of these frequencies for the vowel formants in a second formant data set as a recorded file and provides the second formant data set as input to acoustic metrics to generate score data from which an assessment is made to determine the articulation level of the vowels in the words spoken by the individual, allowing allow for detection, diagnosis, prediction and / or monitoring progression of the condition, disorder, or disease.

The application discloses an electroencephalogram epilepsy detection method and system based on a node adaptive graph neural network, relates to a computer system based on a biological model, and is proposed in view of problems such as fixed graph structure in the prior art. The method comprises the following steps: an electroencephalogram signal acquisition and preprocessing step; mixed EEG graph construction; adaptive residual graph optimization; node-specific diffusion convolution; time sequence feature modeling; and classification output. The system comprises a data acquisition module, a mixed graph construction module, an adaptive graph construction module, a node-specific convolution module, a time sequence modeling module, and a classification output module; when the system is running, the method steps are executed, so that the electroencephalogram epilepsy detection function based on the node adaptive graph neural network is realized. Technical advantages include: (1) graph structure self-learning; (2) brain region personalized modeling, and strengthened regional feature expression; and (3) combined space-time dependence modeling, and complete description of the epilepsy dynamic process.

The invention provides a brain wave epilepsy detection method based on Transform. The method comprises the following steps: acquiring EDF data of a multi-channel electroencephalogram from a database, processing the EDF data, generating a CSV file, and performing data preprocessing on the generated CSV file; defining a plurality of machine learning models, and independently training the models on the EEG feature data to obtain the classification performance of the models; predicting a probability vector by using a machine learning model to construct Transform model input data; the attention of the Transform model is used for dynamic training, and a trained Transform fusion model is obtained; and outputting a prediction result, and storing the trained Transform fusion model. According to the method, more efficient model fusion is realized through dynamic fusion, and the accuracy and generalization ability of epilepsy detection are improved.

The invention relates to the technical field of medical health, in particular to an epilepsy auxiliary evaluation method and device based on heart rate variability, equipment and a medium. The method comprises the following steps: acquiring at least two target physiological states in a current acquisition combination, wherein the target physiological states comprise a resting state and one or two stimulation states; based on the target physiological state, electrocardiosignals of the subject are collected; based on a preset signal processing algorithm, heart rate variability analysis is conducted on each group of electrocardiosignals, a corresponding resting state HRV feature set and at least one stimulation state HRV feature set are obtained, and each HRV feature set comprises a plurality of feature items; determining a group of target feature items suitable for the current collection combination according to the independent judgment efficiency, the correlation judgment efficiency and / or the global judgment efficiency of each feature item on epilepsy; and generating a screening report for assisting epilepsy risk assessment based on the target feature item. And the operation efficiency is improved while the evaluation accuracy is guaranteed.

A fall detection device and method are provided. In at least some embodiments, the system can utilize sensing devices such as smartwatches or smartphones (e.g., using iOS, Android, or Pebble operating systems) to read a user's vital signs and apply algorithms to interpret these signs. If the patient is interpreted as having a fall, tremor, or seizure, a notification is sent to a designated caregiver via a reporting process. In at least some embodiments, a doctor or other party can log into a protected dashboard and review patient data in real time. Also in at least some preferred forms, the doctor or other party can analyze the patient's medical history. In at least some embodiments, the user / patient can also use the data to track the onset and progression of falls, tremors, or seizures. Embodiments of the invention can be applied, for example, to situations where the patient / user experiences a condition such as epilepsy that makes the patient / user susceptible to falls and related events.

The present disclosure provides an operating method of a prediction computing system, which includes steps as follows. The magnetic resonance imaging (MRI) of a child's brain is pre-processed to obtain a pre-processed MRI; a region of interest is found from the pre-processed MRI; multiple radiomic features are obtained based on the region of interest; and a machine learning based on the multiple radiomic features is performed to obtain an epilepsy prediction model.

The present disclosure relates to methods of electroconvulsive therapy (ECT) for a patient's brain based on determining an optimal individualized current amplitude for a patient by (1) dividing an optimal E-field strength (optimal Ebrain) by a baseline E-field strength (baseline Ebrain) of the patient's brain, or (2) performing an initial treatment to determine a patient's amplitude titrated seizure threshold (STa), followed by use of a multiplier value multiplied by the STa for an individualized amplitude for subsequent treatments. Methods of ECT using optimal individualized current amplitude provide an antidepressant effect and reduce adverse cognitive effects on the patient's brain.

The application relates to the field of disease prediction, and particularly relates to an epilepsy prediction system based on electroencephalogram-electrogastrogram signals collected synchronously, which comprises the following modules: a data acquisition module, which is used for acquiring data, wherein the data comprises electroencephalogram data and electrogastrogram data; a first analysis module, which is used for obtaining average electrode inconsistency indexes of first channels and second channels based on a first model according to time sequence data; and a prediction module, which is used for comparing the average electrode inconsistency indexes of a subject with preset evaluation thresholds, so as to predict epilepsy patients. The system provided by the application can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be applied to the preliminary screening of epilepsy for a large population (for example, a community or a physical examination center) as an auxiliary diagnosis tool.

The present invention discloses a compound of formula (I); specifically, the invention relates to a compound shown as a formula (I) or pharmaceutically acceptable salt, stereoisomer, prodrug, deuterated derivative or solvate, and application of the compounds in drugs for treating or preventing pain, gout, epilepsy, insomnia, anxiety disorder, depression, Parkinson's syndrome and other related diseases.

Methods of treating epilepsy are provided. Accordingly, there is provided a method of treating epilepsy in a subject in need thereof, the method comprising: (a) administering into a thalamus nucleus of the subject a therapeutically effective amount of a polynucleotide encoding a bistable type II opsin attached to a heterologous ER export signal and / or membrane trafficking signal which enables trafficking of said bistable type II opsin to axonal presynaptic terminals; and (b) exposing a neural region of said subject to light in a wavelength that activates said bistable type II opsin, wherein said neural region comprises a cell body and / or an axon of said thalamus nucleus.

The invention provides an epileptic post-hospital rehabilitation management method and system based on remote electroencephalogram monitoring. The method comprises the following steps: acquiring a remote electroencephalogram monitoring data stream generated by an epileptic in a target scene, synchronously recording an activity type and a time stamp when the epileptic executes a preset rehabilitation activity, analyzing a specific electroencephalogram mode from the remote electroencephalogram monitoring data stream, associating the specific electroencephalogram mode with the activity type and the time stamp, and establishing an induction relationship; target risk activity links meeting preset conditions are screened out from preset rehabilitation activities, activity sequences and intensity are rearranged and graded, individualized dynamic rehabilitation regulations are generated, adjustment instructions are sent to a patient, electroencephalogram data fed back by the patient are received, the evoked relation is updated, and the rehabilitation activity is obtained. The individualized dynamic rehabilitation regulation is optimized, and the optimized individualized dynamic rehabilitation regulation is generated. According to the technical scheme provided by the invention, the accuracy of rehabilitation management and the adaptive ability of the system to the state change of the patient are improved.

The application discloses a cistema brain-computer interface-minimally invasive whole brain nerve signal acquisition and closed-loop control system based on a cerebrospinal fluid channel and a method thereof. The system comprises a flexible cistema interface array, an ultra-wideband wireless energy and data synchronous transmission module, a nerve signal analysis and decoding module, a self-adaptive closed-loop control module and a system integrated control platform. The flexible cistema interface array is implanted in a cerebrospinal fluid cavity in a minimally invasive manner to acquire multi-brain region nerve signals; and the ultra-wideband transmission module realizes energy supply and high-speed bidirectional communication between an implanted body and an extracorporeal device. The application solves the problems of a large trauma, limited coverage and poor signal quality of an existing invasive interface, has the advantages of minimally invasive, wide area coverage, high signal quality and good integration, and is suitable for diagnosis and treatment of neurological diseases such as epilepsy suppression and post-stroke motor function reconstruction.

Methods and compositions are provided for treating or preventing a neurological disease or disorder using an inhibitor of Glyoxalase 1 (GLO1). In some embodiments, the inhibitor is a small molecule. In certain embodiments, the disease or disorder is a sleep disorder, a mood disorder such as depression, epilepsy, an anxiety disorder, substance abuse, substance dependence or substance such as an alcohol withdrawal syndrome.

The invention discloses an epilepsy medical knowledge graph construction method and system based on rhodic acid C, and relates to the technical field of knowledge graph construction. The method comprises the following steps: acquiring initial medical text information; preprocessing the initial medical text information to obtain non-standard medical text information; performing term conversion on the non-standard medical text information according to a medical term relationship knowledge base to obtain standardized medical text information; extracting medical information related to a mode of action of a rhodic acid C molecule and epilepsy diagnosis and treatment from the standardized medical text information; and adding the medical information into a knowledge graph database. According to the method, the non-standard medical text information can be converted into the standardized medical text information in combination with the medical term relationship knowledge base, so that the knowledge graph is constructed, and the data recognition accuracy and the knowledge graph quality are improved.

The present disclosure relates generally to alkyne containing pharmaceutical agents, and in particular, to phenylethynyl-thiophene based compounds. More particularly, the present disclosure provides a class of compounds that can inhibit and / or attenuate apoptosis via caspase 3 for the treatment of various degenerative disorders. Additionally, the present disclosure relates to methods for treating specific degenerative disorders such as amyotrophic lateral sclerosis (ALS), Huntington's disease, epilepsy, spinal cord injury, complication due to diabetes, multiple sclerosis (MS), muscular dystrophy (MD), Parkinson's disease (PD), irritable bowel syndrome (IBS) and Alzheimer's disease (AD) in a patient comprising administering to the patient an effective amount of a present compound.

The present application relates to the technical field of intelligent wearable medical equipment, in particular to an intelligent bracelet for monitoring neurological diseases, which comprises a wristband body and an electronic monitoring system, the electronic monitoring system comprising a multi-modal physiological signal acquisition module, a microprocessor control unit, a disease characteristic analysis module, an early warning feedback module, a wireless communication module and a power management module. By collecting multi-dimensional physiological signals, Parkinson's disease targeting features, epilepsy targeting features and double disease auxiliary features are extracted, then through the basic differentiated contribution weight of the convolution long short-term memory network model and the dynamic output distribution weight adjustment strategy triggered by the pathological threshold, the spatio-temporal fusion analysis is realized and the risk probability of the two diseases and the normal state probability are output, the early warning feedback module outputs differentiated prompts according to the risk probability, the wireless communication module synchronizes data or sends emergency alerts through dual-mode communication, and the power management module realizes power supply and low power reminder.

The invention provides an epileptic information management method and system, and relates to the technical field of medical information management. The method comprises the following steps: firstly, performing standardization processing on multi-source heterogeneous data of a patient, and constructing a multi-dimensional patient portrait containing static, dynamic and time sequence features; secondly, performing multi-level clustering on historical patients based on treatment response and disease course evolution similarity; generating a management suggestion according to the association degree of the to-be-managed patient and the clustering cluster and the current management scene; and finally, dynamically updating the portrait and the clustering affiliation based on treatment feedback. According to the invention, by mining time sequence features and establishing a closed loop feedback mechanism, precise classification and dynamic personalized management of epileptics are realized, and the accuracy of clinical treatment recommendation is significantly improved.

The invention relates to the technical field of artificial intelligence, and provides a brain abnormal discharge detection method and device, a readable storage medium and equipment.The brain abnormal discharge detection method comprises the steps that a to-be-detected electroencephalogram signal is obtained; the electroencephalogram signal comprises a plurality of electroencephalogram signal fragments, each electroencephalogram signal fragment comprises a plurality of continuous sub-fragments, and the duration of each sub-fragment is smaller than or equal to a preset duration threshold value; inputting the electroencephalogram signals into a pre-trained abnormal discharge detection model, and outputting a first detection result used for indicating the overall abnormal state of each electroencephalogram signal segment and a second detection result used for indicating the local abnormal state of each sub-segment through the abnormal discharge detection model; and fusing the first detection result and the second detection result to generate an abnormal discharge detection result for the electroencephalogram signal. According to the invention, through coarse and fine granularity combined modeling and multi-task learning, the detection accuracy and clinical interpretability of epilepsy abnormal discharge can be significantly improved.

Subject matter of the present invention is an angiotensin-receptor-agonist and / or a precursor thereof for use in the treatment of a disease in a subject, • wherein said disease is selected from the group comprising heart failure, chronic heart failure, acute heart failure (AHF), myocardial infarction (MI), stroke, liver failure, burn injuries, traumatic injuries, severe infection (microbial, viral (e.g. AIDS), parasitic diseases (e.g. Malaria)), SIRS or sepsis, cancer, acute kidney injury (AKI), CNS disorders (e.g. seizures, neurodegenerative diseases), autoimmune diseases, vascular diseases, hypotension and shock, and • wherein said subject has an amount of DPP3 protein and / or DPP3 activity in a sample of bodily fluid that is above a predetermined threshold.

The application belongs to the technical field of epilepsy detection, and particularly relates to an epilepsy prediction method based on comprehensive analysis of multiple brain regions. The application provides an epilepsy prediction method based on an epilepsy prediction system based on comprehensive analysis of multiple brain regions, and the method can accurately predict whether a subject has epilepsy.

The application discloses application of YL-0919 in treatment of epilepsy or brain injury caused by epilepsy. The application proves through animal experiments that YL-0919 has a good effect of treating epilepsy or brain injury caused by epilepsy. The research result of the application provides a new method and thought for clinical treatment.

The application belongs to the technical field of medicines, and relates to an N-H aporphine nitrogen-containing heterocyclic derivative, a preparation method and application thereof.The N-H aporphine nitrogen-containing heterocyclic derivative comprises a compound shown in a general formula M, isomers thereof and pharmaceutically acceptable salts or hydrates thereof.The application introduces a nitrogen-containing heterocyclic ring into an aporphine derivative, and the disclosed N-H aporphine nitrogen-containing heterocyclic derivative can be applied in the preparation of medicines for preventing and / or treating obesity, urinary incontinence, depression, anxiety, obsessive-compulsive disorder, epilepsy, schizophrenia, pain, diabetes and drug addiction.

The invention relates to compounds, compositions, and methods for derepressing RE1 silencing transcription factor (REST) target genes are provided. In particular, a peptide having the sequence TEDLEPPEPPLPKEN (SEQ. ID NO: 1) and EDLEPPEPPLPK (SEQ. ID NO: 15), or the reversed sequences made of D-amino acids (retro inverted, RI) nekplppeppeldet (SEQ ID NO: 16) and kplppeppelde (SEQ ID NO: 17), are disclosed for inhibiting REST activity. The peptides are useful to treat, prevent, or ameliorate conditions such as traumatic brain injury, epilepsy, dementia, Huntington's Disease (HD), chronic pain, brain cancer (including glioblastoma multiforme), pancreatic cancer; diabetes, and peripheral nerve injury

The present application relates to the technical field of intelligent medical treatment, and particularly relates to a seizure prediction system based on multi-modal biological images and an image data processing method. The present application first collects multi-modal images, then extracts features from the multi-modal images respectively, then aggregates image information by using an attention pooling mechanism, then weights and fuses feature vectors of different modes by using a fusion module based on a gated attention mechanism to generate a unified multi-modal feature vector as an input of a prediction module, so that the seizure can be predicted based on the multi-modal feature vector. Through the fusion of multi-modal information, the complementary features of eye images, tongue images and face images are fully utilized, the accuracy and robustness of prediction are significantly improved, and the present application has a good clinical application prospect.

The application discloses an evaluation system for an epilepsy electroacupuncture treatment target based on an amygdala nerve response prediction, relates to the technical field of nerve regulation and brain function modeling, and realizes the multi-modal synchronous collection of brain region potentials, blood oxygen and metabolic activities through high-density EEG, electrophysiological sensors, fMRI and PET, and constructs a standardized nerve response dataset and a brain region function mapping matrix. An amygdala prediction response index is calculated and compared with a response threshold, a stimulation response compliance judgment and parameter correction are realized. The nerve response propagation delay, cooperative coupling coefficient and epilepsy wave front tracing time are calculated, a comprehensive target evaluation coefficient is obtained, and effective target points are screened. Based on the treatment scheme implementation of the effective target point set, the nerve feedback data in the continuous treatment cycle is monitored in real time, and a nerve plasticity correction index is calculated, compared with a plasticity stability threshold, an adaptive feedback regulation closed loop is formed, and precise prediction and dynamic optimization control of epilepsy electroacupuncture targets are realized.

The invention relates to a reversible adhesive flexible electrode material based on zinc ion activation, which is characterized in that polydimethylsiloxane is used as a main chain, and functionalization capable of responding to metal ions is realized through sulfydryl-alkene click reaction and subsequent imidazolation modification. The invention also provides a preparation method and application of the electrode material. The elastomer material disclosed by the invention has remarkably enhanced adhesive force, and can be stably attached to the surfaces of soft tissues such as cerebral cortex and spinal cord; under the action of Zn or after a competitive ligand is introduced, the adhesive force is reduced, and mild stripping is realized. The material is simple in preparation process and excellent in biocompatibility, can be used for constructing a large-area flexible electrode, is suitable for acute and chronic nerve electrophysiological signal acquisition, and is particularly suitable for epilepsy lesion positioning and brain function research.