314 results about "Epilepsy" patented technology

The disclosure provides methods for treating a subject in need thereof comprising administering to the subject a therapeutically-effective dose of psilocybin. The methods described herein may be used to treat a variety of diseases, disorders, and conditions. For example, the methods may be used to treat neurocognitive disorders (e.g., Alzheimer's disease, Parkinson's disease), ADHD, Epilepsy, Autism, Sleep-wake disorders, Chronic pain, Inflammatory Disorders, IBD, Stroke, ALS, and / or Multiple Sclerosis.

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 relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

The invention discloses an epilepsy region positioning method and system based on brain power supply imaging and a dynamic brain network, and the method comprises the steps: obtaining T1 weighted magnetic resonance imaging data of a user, and constructing an individual three-dimensional head model through a boundary element method; acquiring scalp electroencephalogram data of a user, and preprocessing the scalp electroencephalogram data; based on an individual three-dimensional head model, performing inverse problem solving on the preprocessed scalp electroencephalogram data by using a standardized low-resolution brain power source imaging algorithm to obtain source current density signals of 68 brain regions; decomposing into six frequency bands, calculating the power spectrum density of each brain region and carrying out normalization processing, and screening effective frequency bands; based on the source current density signals of the 68 brain regions of the effective frequency band, information flow directions and intensities of different brain regions are calculated by adopting a directional transfer function method, a directional transfer function matrix of the effective frequency band is formed, and a directed brain network is constructed; and calculating a graph theory index and / or an epilepsy index of each brain region, carrying out maximum value normalization analysis, and determining an epilepsy region positioning result.

The invention 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 provides the scheme for solving the problems of graph structure immobilization and the like in the prior art. The method comprises the following steps: an electroencephalogram signal acquisition and preprocessing step; constructing a hybrid EEG graph; optimizing a self-adaptive residual image; node specific diffusion convolution is carried out; modeling time sequence characteristics; and performing classified output. The system comprises a data acquisition module, a mixed graph construction module, a self-adaptive mapping module, a node specific convolution module, a time sequence modeling module and a classification output module. When the system runs, the steps of the method are executed, so that the electroencephalogram epilepsy detection function based on the node adaptive graph neural network is realized. The method has the technical advantages that (1) graph structure self-learning is carried out; (2) carrying out brain region personalized modeling, and strengthening region feature expression; and (3) combining space-time dependence modeling, and completely depicting the epilepsy dynamic process.

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 discloses an epilepsy signal identification method and system based on multi-modal information of wearable equipment. The method comprises the following steps: firstly, preprocessing acquired multi-modal physiological signal data; calculating a time-frequency feature through a sub-band analysis coefficient, extracting a time domain feature, a frequency domain feature and a nonlinear feature after reconstruction, and constructing a multi-modal feature matrix; obtaining an action feature matrix through a time-frequency feature threshold classifier; performing T test, correlation analysis and recursive feature elimination, and screening to obtain a final training set and an optimal feature list; respectively training an LSTM attack detection model and a random forest motion detection model, and constructing a serial ensemble learning framework; and finally, outputting a risk value based on a sliding window, triggering an alarm when the risk value continuously exceeds a threshold value, and starting a misinformation suppression mechanism in a non-response period. According to the method, non-attack motion interference is filtered through a designed serial integrated learning framework, the false alarm rate of the system is remarkably reduced, meanwhile, high detection sensitivity and accuracy are kept, and the real-time processing efficiency and practicability of the system are improved.

The invention discloses an intelligent pre-warning system for Parkinson / epilepsy recognition based on a multi-modal sensor and a bracelet, and relates to the technical field of biosensors. The intelligent early warning system for Parkinson / epilepsy recognition based on the multi-modal sensor comprises a multi-modal acquisition interference judgment module, a multi-modal interference processing optimization module and a multi-modal fusion input early warning module. According to the method, whether the multi-modal interference processing optimization is carried out or not is judged according to the multi-modal interference early warning result, if yes, the multi-modal fusion instruction is sent after the multi-modal interference processing optimization, and if not, multi-modal biological data fusion is directly executed, and the multi-modal fusion early warning result is obtained. Finally, whether multi-modal fusion input early warning is carried out or not is judged based on the multi-modal fusion early warning result, the effect of carrying out physical condition early warning more accurately is achieved, and the problem that in the prior art, in the process of carrying out physical condition early warning through wearable equipment, multi-modal biological data collection and fusion association are not sufficient is solved.

The invention provides a cyclic peptide used for treating, improving or preventing nervous system injury of mammals or diseases or pains caused by the injury, neurodegenerative diseases, anxiety or epilepsy or used as a neuronal protective agent, a conjugate containing the cyclic peptide, a pharmaceutical composition containing the cyclic peptide or the conjugate and application of the cyclic peptide and the conjugate.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention discloses an ear nerve stimulator, and belongs to the technical field of biomedical engineering and nerve regulation and control. The device mainly comprises a plurality of flexible patches attached to the ear, ear electrodes arranged according to ear acupuncture points and embedded in the flexible patches, and a stimulator adopting a cross-ear design. And the flexible patch has biocompatibility and adhesion, so that comfortable and stable wearing is ensured. A control unit and a stimulation module are contained in the stimulator shell, and stimulation current parameters can be generated and accurately regulated and controlled. Through flexible fitting and precise acupoint electrode design, the defects that a traditional electrode is uncomfortable to wear, and a stimulation area is fixed and inaccurate are overcome. And the equipment is light and stable due to the ear-crossing structure. A selectable air pump and air bag mechanism further ensures the reliability of electric contact. The device can realize safe, accurate and comfortable ear vagus nerve stimulation, is suitable for treatment and rehabilitation of epilepsy, depression and other diseases, and has a wide application prospect.

The invention provides a heterocyclic compound and a pharmaceutical composition and application thereof, and belongs to the technical field of pharmaceutical chemistry. The series of heterocyclic compounds prepared by the invention have efficient sedative, hypnotic and / or anesthetic effects, can control the state of epilepsy, also have an analgesic effect, and have great significance for clinically preparing drugs with the analgesic effect and drugs with anesthetic, sedative, hypnotic and / or capable of controlling the state of epilepsy. The invention provides a new choice for the medicine which has the effects of anesthesia, sedation and hypnosis and / or can control epilepsy persistence and also has an analgesic effect.

The invention relates to the technical field of epilepsy detection, in particular to a self-supervised graph neural network epilepsy detection method based on Transform. The method comprises the following steps: S1, preprocessing original EEG data, and constructing an EEG graph; s2, building a graph neural network based on DCTran, and respectively capturing a space-time dependency relationship of the EEG signal through diffusion convolution and a Transform structure; and S3, training the model through spatio-temporal joint complementary double-branch pre-training based on the self-supervised prediction pre-training task and the mask reconstruction pre-training task. According to the epilepsy detection method based on the self-supervised graph neural network of the Transform, provided by the invention, EEG data is modeled into a graph structure, and a diffusion convolution space-time network DCTran based on the Transform is provided; the diffusion convolution accurately captures a complex spatial relationship between the electrodes through multi-order neighbor information propagation; and meanwhile, global context information of the EEG signal is fully utilized, and DCTran is introduced into a Transform structure to model a time sequence, so that the long-distance time dependency relationship in the signal is effectively captured.

The invention relates to the technical field of medical instruments, in particular to a multi-mode biological feedback ear vagus nerve self-adaptive stimulation device and method.The device comprises a host, a stimulation electrode, a signal collection bracelet and an electroencephalogram collection module, and the host comprises core hardware configuration and a power management system; the signal acquisition bracelet comprises an ECG module, a PPG module, a GSR module, a six-axis acceleration sensor and the like. The ear vagus nerve stimulation parameter can be dynamically adjusted through real-time analysis of multi-mode data such as electrocardio, electroencephalogram, heart rate variability, respiration-heart rate synchronism and emotional state, personalized precise treatment is achieved, meanwhile, the wearing comfort and the durability of the device are improved, and the application range is wide. The traditional Chinese medicine composition can be used for auxiliary treatment of various diseases such as inflammatory bowel disease, epilepsy, chronic insomnia, depression, chronic pain, anxiety disorder, post-traumatic stress disorder and autonomic nerve dysfunction, and the functions of a nervous system are adjusted and the symptoms of a patient are improved by accurately stimulating the vagus nerve in the auricular concha area.

The present invention relates to the field of disease prediction, in particular to a gastrointestinal electrical signal-based epilepsy prediction system and a construction method therefor. The present invention provides an epilepsy prediction system, comprising: a database configured for storing data, wherein the type of the data comprises gastrointestinal electrical signal data, and the gastrointestinal electrical signal data comprise a gastric lead time difference before meal, an intestinal lead time difference before meal, an intestinal main power ratio before meal, an intestinal normal slow wave percentage before meal, a gastric waveform average frequency after meal, an intestinal lead time difference after meal, and a difference value of gastric main power ratios before and after meal; and a prediction module configured for predicting the probability at which a subject has epilepsy. The present invention also provides a construction method for the described epilepsy prediction system. The epilepsy prediction system provided by the present invention only requires the described gastrointestinal electrical signal data of the subject, is non-invasive, and has a simple program and low price, thereby facilitating the popularization of large-scale epilepsy screening and providing an auxiliary diagnosis basis for epilepsy diagnosis.

The invention relates to a multistage self-anchoring flexible deep brain electrical stimulation electrode and a preparation method thereof, and relates to the technical field of medical instruments. A bionic root system-octopus whisker composite framework including a main electrode base, a second-stage fractal arm and a third-stage self-anchoring tail end is adopted, intelligent materials such as carbon nano tube / PDMS composite fibers, temperature-sensitive PNIPAAm hydrogel and a degradable PLGA-gelatin composite material are fused, and a mechanical lock catch and biological fusion dual-anchoring mechanism is constructed. The distributed electrode array comprises platinum-iridium alloy, graphene / PDMS and a titanium nitride nano electrode, and cross-scale stimulation from the nuclear group level to the single cell level is achieved. The intelligent regulation and control system solves the problems of brain tissue displacement and chronic inflammation through a pressure feedback degradation and flexible interconnection technology. Compared with a traditional product, the contact area of the electrode is increased by 5-8 times, the stimulation precision reaches the single cell level, and the electrode is suitable for long-term deep brain stimulation treatment of nerve diseases such as Parkinson's disease and epilepsy and has remarkable clinical application value.

The invention discloses a magnetoencephalogram-based epilepsy lesion positioning method and system and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a structure image of a patient, carrying out the registration with the magnetoencephalogram of the patient, carrying out the grid division of a cerebral cortex region based on the preprocessed structure image and a registration result, and obtaining a forward model of the patient; performing preprocessing and spine wave detection on the magnetoencephalogram to obtain a spine wave time point sequence; based on the forward model and the ratchet wave time point sequence, using a magnetic dipole algorithm to calculate a source coordinate and a source direction of a ratchet wave time point, and classifying the ratchet wave time point sequence according to the source coordinate and the source direction to obtain a clustering result; calculating the source coordinate and the source direction of the class center of each class in the clustering result by using a magnetic dipole algorithm; generating a clinical report of the patient; according to the positioning method, the problems of large epilepsy diagnosis difference and error proneness caused by level difference of different doctors are solved, and the epilepsy focus position can be quickly determined.

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 a method for classifying epilepsy electroencephalogram (EEG) based on an iterative graph convolutional neural network, calculates the node similarity and distance similarity of epilepsy EEG data as an original graph structure, introduces a multi-head graph attention mechanism for node similarity measurement learning, iteratively optimizes the parameters of the graph structure and the graph convolutional neural network, finds the optimal graph structure and achieves the optimal epilepsy EEG classification effect. Experiments are conducted on TUEP bipolar and unipolar montage datasets and TUAB and MPI LEMON joint datasets to verify the effectiveness of the method. The method has better epilepsy EEG classification effect and obtains more accurate electroencephalogram structure.

The invention discloses a brain nerve cell targeted gene protein co-loaded nano delivery system and a preparation method thereof, phenylboronic acid modified polyethyleneimine is synthesized through substitution reaction, then the polyethyleneimine is compounded with DNA through electrostatic interaction to form a PEI-PBA / DNA nano compound, bovine serum albumin is loaded through nitrogen-boron complexation, and the brain nerve cell targeted gene protein co-loaded nano delivery system is obtained. The preparation method comprises the following steps: preparing a gene-protein co-loaded nano delivery system PPDB, further endowing the nano delivery system with a function of crossing a blood brain barrier, and introducing glycerophosphorylcholine through the formation of a borate bond, so as to prepare the cerebral nerve cell targeted gene-protein co-loaded nano system PPDBG. The preparation method disclosed by the invention is simple, realizes simultaneous loading of genes and proteins with completely different physical structures, and has very important research significance on brain-related diseases such as epilepsy and the like. Meanwhile, the introduction of the boric acid ester bond endows the nano delivery system with the on-demand plug-and-play characteristic, so that the spectral applicability of the nano delivery system is realized.

Disclosed herein are imidazole compounds and compositions useful in the treatment of a disease or disorder associated with sodium channel mediated activity, such as epilepsy, having the structures of Formula (I) wherein the R groups, A, and L are as defined in the detailed description. Methods of inhibition of disease or disorder associated with sodium channel mediated activity in a subject are also provided.

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 present invention relates to compounds, compositions and methods for deinhibiting a RE1 silencing transcription factor (REST) target gene. In particular, disclosed is a peptide having the sequences TEDLEPPEPPLPKEN (SEQ ID NO: 1) and EDLEPPEPPLPK (SEQ ID NO: 15) or a reverse sequence (reverse inversion, RI) consisting of D-amino acids nekplppeppeldet (SEQ ID NO: 16) and kplppeppelde (SEQ ID NO: 17), for use in the inhibition of REST activity. The peptides can be used for treating, preventing or alleviating diseases 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 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 invention discloses a method, a device and a system for planning an SEEG electrode implantation path. The method comprises the following steps: processing medical image data of the brain of an epileptic to obtain a target brain anatomical image; matching and searching a target experience module in an experience database according to the clinical description data of the epileptic; the experience database is established according to prior experience, a plurality of experience modules are arranged in the experience database, and each experience module comprises a plurality of combinations of a first target range and a second target range of a fixed electrode; and determining a target path of each electrode based on the target brain anatomy image and the target experience module. According to the method, the experience database containing a plurality of experience modules is established, the target experience module is screened in combination with the clinical description data of the patient, the target path of each electrode is further screened in combination with the target brain anatomy image of the patient, the proper path of the SEEG electrode is accurately and efficiently planned for the individual patient, and the accuracy of the SEEG electrode is improved. The method provided by the invention can reduce the learning and use threshold of doctors, and has high popularization value.