421 results about "Epilepsy" patented technology

The invention relates to the field of disease prediction, in particular to an epilepsy prediction system based on synchronously acquired electroencephalogram-gastric electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and gastrointestinal electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict an epileptic. The system provided by the invention can more accurately and efficiently distinguish epilepsy patients from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of epilepsy of relatively large people (such as communities and physical examination centers).

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 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 intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

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 relates to the field of medicines. Specifically, the invention relates to a voltage-gated calcium channel alpha2delta subunit ligand containing a polycyclic gamma-aminobutyric acid structure as shown in a general formula I, a preparation method of the voltage-gated calcium channel alpha2delta subunit ligand and application of the voltage-gated calcium channel alpha2delta subunit ligand to treatment of chronic neuropathic pain, epilepsy and anxiety. # imgabs0 #

The invention discloses an all-directional monitoring and emergency treatment system for patients in the emergency department, which belongs to the technical field of diagnosis and monitoring of emergency surgery and comprises a data input and acquisition module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the data input and acquisition module is used for acquiring emergency treatment features including external stimulation inducements and ages of a current patient before emergency treatment; the monitoring and evaluation module is used for acquiring an epilepsy risk evaluation value, a comprehensive evaluation value and a feedback adjustment value, and collecting all-dimensional monitoring characteristics of a current patient, including amygdalia activeness, a brain activity reference level, abnormal discharge intensity, heart rate, respiratory rate, systolic pressure value and diastolic pressure value, and the monitoring and evaluation module is used for acquiring and outputting the epilepsy risk evaluation value, the comprehensive evaluation value and the feedback adjustment value; the early warning module is responsible for carrying out early warning and inputting an adjusted first-aid strategy to the first-aid execution module when the epilepsy risk assessment value, the comprehensive assessment value and the feedback adjustment value are in a continuous monitoring rising trend on the visualization module, and comprehensive and real-time monitoring and accurate assessment of the illness state of a patient and timely adjustment of first-aid measures are achieved.

The invention discloses a method, a system and equipment for evaluating the treatment effect of CGRP on epilepsy or brain injury caused by epilepsy. It is found for the first time that CGRP inhibits expression of P2RX7 by inhibiting a JAK-STAT1 pathway so as to play a role in treating epilepsy or brain injury caused by epilepsy. On the basis, the invention provides a method, a system and equipment for evaluating the treatment effect of the CGRP on the epilepsy or the brain injury caused by the epilepsy for the field, so that a doctor is assisted in evaluating the treatment effect of a patient suffering from the epilepsy or the brain injury caused by the epilepsy, and a clinician is guided to formulate an individualized treatment scheme for the patient.

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 relates to a self-management behavior auxiliary system for gestation and epilepsy patients, and belongs to the technical field of medical monitoring and diagnosis. The auxiliary system comprises a detection unit used for obtaining physiological parameters, related to pregnancy combined epilepsy, of a patient; the processing unit is used for analyzing and processing the data obtained by the detection unit so as to evaluate the health trend of the patient, and the physiological parameters obtained by the detection unit comprise the blood concentration, the liver enzyme activity and the blood sugar level. The processing unit can calculate and obtain a gestational epilepsy management index used for representing the health trend of the patient based on the physiological parameters so as to judge the epilepsy risk condition of the patient in the current gestational period.

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 discloses a PET-based epilepsy postoperative prognosis information generation system and method, and the system comprises a preprocessing module, a target training data generation module, a training module and an application module which are sequentially connected. Through processing the whole brain health PET image, the preoperative whole brain PET image and the postoperative whole brain magnetic resonance imaging, the obtained target training data focuses on the metabolic connection characteristics of the focus area and the whole brain, a new view angle is provided for the postoperative prognosis prediction, so that the target training data is adopted to train the deep learning model, and the prognosis prediction accuracy is improved. A deep learning model focusing on the metabolic connection characteristics of the focus area and the whole brain can be obtained, so that the postoperative whole brain magnetic resonance imaging is accurately processed, and prognosis information is obtained.

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 invention relates to particular prodrugs and analogs of (3α,5α)-3-hydroxy-21-(1H-imidazol-1-yl)-3-methoxymethyl)-pregnan-20-one, in free or pharmaceutically acceptable salt and / or substantially pure form as described herein, pharmaceutical compositions thereof, and methods of use as sedatives, hypnotics, anxiolytics, and / or anesthetics, and methods for treatment of depression, anxiety, insomnia, epilepsy, and other central nervous system disorders, as well as to combinations with other agents.

The invention discloses an epilepsy detection method, system and equipment based on electroencephalogram signals and a medium, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: preprocessing multichannel original electroencephalogram signals of a detector, and constructing a graphic structure for describing spatial features of the electroencephalogram signals; the graphic structure comprises nodes and connecting lines, the nodes represent channels, and the connecting lines represent correlation coefficients between the channels; determining a final epilepsy detection result according to the graphic structure and an epilepsy detection model; the epilepsy detection model comprises a graph attention network, a Transform network and a Softmax classifier which are connected in sequence; wherein the graph attention network is used for aggregating spatial features between channels, the Transform network is used for extracting time features, and the Softmax classifier is used for carrying out binary epilepsy classification. The detection accuracy can be improved.

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.

The present application provides methods of treating a CNS disorder (such as glioblastoma and epilepsy) in an individual, comprising systemically (e.g., intravenously or subcutaneously) administering to the individual an effective amount of a composition comprising nanoparticles comprising an mTOR inhibitor (such as a limus drug, such as sirolimus or a derivative thereof) and an albumin, optionally further comprising administering a second agent (such as an anti-VEGF antibody, a proteasome inhibitor, or an alkylating agent).

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.