87 results about "Epilepsy seizure" patented technology

The invention relates to the technical field of epileptic seizure prediction, and provides an epileptic seizure prediction method based on a space-time attention dynamic pulse neural network. The method comprises the following steps: firstly, a preprocessing module is used for preprocessing collected electroencephalogram to obtain purified time window electroencephalogram; secondly, a pulse sequence generation module generates a sparse pulse sequence, a space-time pulse sequence and multi-scale space-time channel attention characteristics based on a sparse compression coding method, a space-time structure pulse mapping method and a multi-scale space-time channel attention method; thirdly, obtaining a space-time attention pulse sequence based on a pulse similar coupling smoothing method; and finally, constructing a residual pulse neural network, and training the network based on the space-time attention pulse sequence so as to realize epileptic seizure prediction. According to the method, the space-time attention dynamic pulse neural network is constructed based on the space-time dynamic complexity of the electroencephalogram signals, so that high-precision epileptic seizure prediction is expected to be realized.

A method of predicting a seizure occurrence or a surgical outcome includes monitoring a patient using an electroencephalography (EEG) system, including recording EEG data that indicates a seizure, and extracting a peri-ictal segment of the EEG data that includes a pre-ictal period immediately preceding the seizure, or a post-ictal period immediately following the seizure. The method also includes processing the peri-ictal segment, including generating an electrode-wise power spectral density (PSD) feature across a frequency band defined by at least one of a delta frequency range, a theta frequency range, an alpha frequency range, a beta frequency range, and a gamma frequency range. The method also includes inputting the PSD feature into a model, wherein the model predicts a seizure occurrence or a surgical outcome of the patient.

The present invention relates to the use of cannabidiol (CBD) in the treatment of Sturge Weber syndrome. CBD ap-pears particularly effective in reducing all types of seizures and non-seizure symptoms in patients suffering with Sturge Weber syndrome. Preferably the CBD used is in the form of a highly purified extract of cannabis such that the CBD is present at greater than 98% of the total extract (w / w) and the other components of the extract are characterised. In particular the cannabinoid tetrahydrocan-nabinol (THC) has been substantially removed, to a level of not more than 0.15% (w / w) and the propyl analogue of CBD, cannabidivarin, (CBDV) is present in amounts of up to 1%. Alternatively, the CBD may be a synthetically produced CBD.

The invention relates to the technical field of epilepsy detection, in particular to a real-time epilepsy behavior detection and analysis method and system. The method comprises the following steps: acquiring first view data in a first time period; identifying one type of typical behaviors in the first view data by adopting a preset epilepsy prediction model; updating the early warning level of the epilepsy prediction model according to the class of typical behaviors; wherein the class of typical behaviors comprise a first class of behaviors and / or a second class of behaviors; the typical attack time of the first type of behaviors is before the typical attack time of the second type of behaviors; when it is identified that the first type of behaviors and the second type of behaviors exist, the occurrence time difference of the first type of behaviors and the second type of behaviors is calculated; and when the second-class typical behavior is identified, the epilepsy seizure risk of the to-be-analyzed object is determined by the epilepsy prediction model according to the first-class typical behavior and the second-class typical behavior. According to the invention, the accuracy and efficiency of epilepsy behavior identification can be greatly improved.

The invention provides an epilepsy prediction system based on multivariate weighted joint recursion and a graph attention network. The epilepsy prediction system can be applied to the technical field of biomedical signal processing and artificial intelligence. The system comprises a brain function imaging module which is configured to obtain electroencephalogram signal data of a target object under the condition that the target object is authorized; the processor comprises a multivariate weighted joint recursion processing unit which is configured to obtain a phase-space trajectory vector of each channel of the electroencephalogram signal data according to the electroencephalogram signal data, construct a recursion plot according to the phase-space trajectory vector of each channel, and calculate the phase-space trajectory vector of each channel based on the recursion plots of any two channels. Obtaining a channel correlation coefficient between any two channels, and according to the channel correlation coefficient between any two channels, constructing a weighted adjacency matrix representing the brain function of the target object; and the image attention network processing unit is configured to perform spatial-temporal feature processing according to the weighted adjacency matrix and the electroencephalogram signal data and output an epileptic seizure prediction result.

This invention discloses a method for locating epileptogenic zones based on high-frequency oscillations and connectivity, comprising the following steps: 1. Acquiring SEEG data and selecting representative channels; 2. Filtering to acquire 54-200Hz signals and selecting baseline and target data; 3. Acquiring normalized high-frequency energy; 4. Calculating time and energy coefficients to obtain a high-frequency epileptogenicity index; 5. Filtering to acquire 12-45Hz signals; 6. Calculating nonlinear regression analysis; 7. Calculating total intensity; 8. Defining and calculating the connectivity high-frequency epileptogenicity index and evaluating performance. This invention can accurately locate epileptogenic zones in patients with different seizure patterns, and has potential application value in epilepsy treatment.

The application provides a wireless and battery-free brain nerve stimulator and a preparation method. The brain nerve stimulator can be driven by a mobile phone loudspeaker, and a resonator designed can effectively amplify a piezoelectric signal. The brain nerve stimulator comprises two parts: a base provided with implantable nerve stimulation electrodes and a resonant cavity device connected through magnetic coupling. The design scheme of the brain nerve stimulator provides a general method for inhibiting epilepsy. The brain nerve stimulator can be extended to target brain deep layers or surface areas and peripheral nervous systems. A stimulation mode can be wirelessly transmitted and controlled. It is proved by electroencephalogram recording of free-moving mice that the wireless nerve stimulator can effectively relieve seizure events of the epileptic mice in vivo.

The invention discloses an unbalanced classification method for electroencephalogram data in epilepsy detection, and relates to the technical field of data optimization and big data processing. According to the method, firstly, covariance matrixes of majority class samples and minority class samples are calculated, linear transformation is achieved through matrix decomposition, and the minority class samples inherit global distribution characteristics of the majority class samples; and then, in the transformed feature space, sorting samples based on mahalanobis distance and performing partition pairing, selecting sample pairs with large difference to generate convex combination synthesis samples, and ensuring sample diversity and boundary consistency. Experimental results show that on a CHB-MIT electroencephalogram data set, the method effectively solves the problems that a traditional oversampling technology is prone to expanding minority class decision boundaries and generated samples are lack of diversity, and the reliability of epileptic seizure detection is remarkably improved.

The present disclosure relates to a method and device for extracting and detecting time-frequency joint features of epilepsy based on covariance decomposition, an electronic device and a storage medium. The method comprises: performing data preprocessing on collected electroencephalogram (EEG) data to generate EEG data; calculating a covariance matrix, eigenvalue decomposition, feature extraction and stretching processing on the EEG data after decentralization to generate a time-domain feature vector; performing spectral feature extraction and frequency-domain covariance feature extraction respectively to generate a spectral feature vector and a frequency-domain covariance feature vector; generating time-frequency joint features based on a preset feature fusion strategy; and classifying and identifying the time-frequency joint features based on a preset binary classification method to complete epilepsy prediction. The present disclosure extracts and fuses time-frequency multi-scale feature information, which can effectively shorten the manual data labeling time of medical workers, improve the labeling efficiency of epilepsy seizure events, and provide a new approach and method for clinical application of epilepsy detection.

Disclosed are methods and compositions relating to antisense therapy for treating epilepsy, such as a focal epilepsy and temporal lobe epilepsy, in a subject in need thereof by targeting GRIK2 mRNA. In particular, the disclosure provides methods for treating symptoms (e.g., seizures) of epilepsy in a subject by administering a particular dose in a defined volume and in a specific route of administration of an inhibitory ribopolynucleotide or adeno-associated viral vector encoding the same, which is capable of inhibiting expression of GRIK2.

This invention discloses the application of the inflammasome NLRP6 as a target in screening drugs for treating epilepsy, and the application of NLRP6 expression inhibitors in the preparation of drugs for treating epilepsy. This invention reveals for the first time the role of the inflammasome NLRP6 in epileptic neuroinflammation. This invention identifies NLRP6 as a key regulator of neuroinflammation in epilepsy and investigates its role in activating the caspase-1 / IL-1β / IL-18 signaling pathway. Knockdown of NLRP6 can improve the damaging effects of epilepsy on neurons, thereby improving seizures; overexpression of NLRP6 may exacerbate seizures, neuronal damage, and neuroinflammatory responses. This invention provides a new potential target for epilepsy treatment, offering new research ideas and directions.

An oral film in an individual unit dose for delivery of one or more actives is disclosed herein, the film having a precisely calculated and controlled active dissolution profile. A wide variety of actives may be used, including, for example, clobazam, diazepam, or riluzole. Also disclosed are methods of treating a variety of diseases and conditions, for example, epilepsy and seizures, by administering the oral film disclosed herein.

A system can include a processing circuit. The processing circuit can receive an electroencephalogram signal from at least one electrode to sense brain activity of a neonatal patient. The processing circuit can execute a non-linear energy operator (NLEO) using the electroencephalogram signal to generate an NLEO signal. The processing circuit can generate at least one trajectory of a metric from the NLEO signal. The processing circuit can detect that the electroencephalogram signal indicates a seizure responsive to the trajectory of the metric matching a trajectory pattern.

The application provides an epilepsy prediction system based on multi-element weighted joint recursion and graph attention network, which can be applied to the fields of biomedical signal processing and artificial intelligence technology. The system comprises a brain function imaging module configured to acquire electroencephalogram signal data of a target object under authorization of the target object; a processor comprising a multi-element weighted joint recursion processing unit configured to obtain a phase space trajectory vector of each channel of the electroencephalogram signal data according to the electroencephalogram signal data, construct a recursion graph according to the phase space trajectory vector of each channel, obtain a channel correlation coefficient between any two channels based on the recursion graph of each channel, and construct a weighted adjacency matrix representing the brain function of the target object according to the channel correlation coefficient between any two channels; and a graph attention network processing unit configured to perform spatiotemporal feature processing according to the weighted adjacency matrix and the electroencephalogram signal data and output an epilepsy seizure prediction result.

The application relates to the technical field of deep learning, and discloses a seizure prediction method and device based on a diffusion model and computer equipment, the method comprising the following steps: performing bimodal feature extraction on epilepsy electroencephalogram data to obtain epilepsy features and corresponding semantic features; performing multi-step noise adding operations on the epilepsy features and the semantic features in sequence to generate a noisy epilepsy feature sequence and a semantic feature sequence; training an iterative denoising module based on the noisy epilepsy feature sequence and the semantic feature sequence to obtain clean epilepsy features; training a seizure prediction module based on the clean epilepsy features to obtain a trained seizure prediction module; and inputting a test sample set of the acquired epilepsy electroencephalogram data into the trained seizure prediction module to obtain a seizure state prediction result. The application can effectively model noise information in epilepsy electroencephalogram signals, and realize step-by-step purification of epilepsy features under the guidance of semantic information, thereby significantly improving the prediction accuracy.

The application discloses a system and method for synchronously detecting epilepsy attack and type identification, and the system comprises an electroencephalogram signal processing module, a multi-scale time-space-frequency feature electroencephalogram fusion module, an epilepsy attack detection task branch, a cross-task attention interaction module, an epilepsy attack type classification branch and a multi-task learning optimization module; the cross-task attention interaction module realizes bidirectional feature sharing between the task branches by establishing an information interaction mechanism at a feature level, so that the epilepsy attack detection task can utilize fine-grained structural information related to attack types to improve detection precision, and meanwhile, the attack type classification task can utilize attack time positioning and context information provided by the detection task; the application realizes collaborative modeling of epilepsy attack detection and attack type classification, improves the accuracy, robustness and clinical application value of epilepsy attack identification by sharing feature representation and cross-task information interaction, while ensuring the calculation efficiency.

Disclosed are methods and systems for treating epilepsy by stimulating a main trunk of a vagus nerve, or a left vagus nerve, when the patient has had no seizure or a seizure that is not characterized by cardiac changes such as an increase in heart rate, and stimulating a cardiac branch of a vagus nerve, or a right vagus nerve, when the patient has had a seizure characterized by cardiac changes such as a heart rate increase.

The application provides an epilepsy seizure prediction method and system based on electroencephalogram power spectrum and phase spectrum, comprising: an electroencephalogram signal acquisition module configured to acquire and preprocess multi-channel electroencephalogram signals; a power spectrum and phase spectrum extraction module configured to divide the electroencephalogram signals into multiple segments of a set time, transform each segment of the set time, and extract power spectrum and phase spectrum based on the transformation; a model construction module configured to construct a double-flow convolutional neural network model, wherein the model comprises a first network flow and a second network flow; a feature fusion module configured to splice output features of the first network flow and the second network flow in the channel dimension, fuse power and phase information, and obtain a fused feature map; and a prediction module configured to convert the fused feature map into a one-dimensional feature vector through further processing, and calculate the one-dimensional feature vector to output the probability of epilepsy seizure and non-seizure.

An oral film in an individual unit dose for delivery of one or more actives is disclosed herein, the film having a precisely calculated and controlled active dissolution profile. A wide variety of actives may be used, including, for example, clobazam, diazepam, or riluzole. Also disclosed are methods of treating a variety of diseases and conditions, for example, epilepsy and seizures, by administering the oral film disclosed herein.

The application discloses an online self-adaptive seizure prediction method and system, relates to the technical field of medical signal processing and artificial intelligence, and comprises the following steps: acquiring multi-channel electroencephalogram signals of a target user; constructing an online self-adaptive seizure prediction model according to spatially constrained independent component analysis, brain function network and a transfer learning mechanism; inputting the multi-channel electroencephalogram signals into the online self-adaptive seizure prediction model, identifying and predicting a pre-seizure state, and obtaining a prediction result; and performing online early warning judgment according to the prediction result, and triggering an alarm if early warning conditions are met. The application solves the problem of unstable early warning effect caused by the variability of electroencephalogram data of epilepsy, realizes online seizure prediction with clinical accuracy and rapidness, and provides a basis for the treatment of intractable epilepsy and the research on the seizure mechanism of epilepsy.

The invention relates to a method for inferring epileptogenicity of a brain region not observed to be recovered or not observed to be not recovered in a seizure activity of a brain of an epilepsy patient, comprising the steps of: providing a computerized model modeling individual regions of a primate brain and connectivity between said regions; providing said computerized model with a model capable of reproducing the dynamics of a seizure in a primate brain; providing structural data of a brain of an epilepsy patient and using said structural data to individualize the computerized model in order to obtain a virtual epilepsy patient (VEP) brain model; translating a state space representation of the virtual epilepsy patient (VEP) brain model into a probabilistic programming language (PPL) using probabilistic state transitions in order to obtain a probabilistic virtual epilepsy patient brain model (BVEP); and acquiring electroencephalogram or magnetoencephalogram data of the brain of the patient and fitting the probabilistic virtual epilepsy patient brain model against said data in order to infer the epileptogenicity of said brain region not observed.

The application discloses a kind of based on mixed dynamic causal model's epilepsy excitability and inhibitory balance calculation method, mainly includes four parts of establishing neuron cluster model module, establishing power spectral density function calculation module, establishing mixed simulated annealing principle module and excitability and inhibitory balance calculation, neuron cluster model module uses cPBM model simulation epilepsy seizure each stage EEG signal power spectral density function;Power spectral density function calculation module generates predicted power spectral density function according to state space equation and calculates the sampling power spectral density function of real EEG signal;Mixed simulated annealing principle module introduces simulated annealing algorithm in dynamic causal model, proposes a kind of including heating and cooling mixed annealing scheme, for improving the accuracy of model parameter estimation;Excitability and inhibitory balance calculation uses C5 and C8 in model parameter estimation result, obtains E pf , E pf From the interictal period to the seizure period, the increase is about 190%.

The invention discloses a resveratrol-loaded Prussian blue / tetrahedral framework nucleic acid nanocomposite and application thereof in preparation of a medicine for treating epilepsy. The nano-composite (R-tFNAs (at) PB) takes Prussian blue (PB) nano-enzyme as a core, and tetrahedral framework nucleic acid (tFNAs) loaded with resveratrol (Res) is anchored on the surface of the nano-composite (R-tFNAs (at) PB). The nanocomposite has excellent blood-brain barrier (BBB) penetrating ability and focus targeting ability, and can specifically gather in epilepsy hippocampus focus. In mechanism, the compound enhances the oxidation resistance of mitochondria by synergistically removing reactive oxygen species (ROS) and activating an SIRT3 / SOD2 signal channel, further inhibits activation of NLRP3 inflammasomes, and promotes polarization of microglial cells from a pro-inflammatory M1 type to an anti-inflammatory M2 type. In-vivo and in-vitro experiments show that the compound can significantly reduce neuron damage, reduce epileptic seizure frequency and severity, and significantly improve cognitive impairment caused by epilepsy, and has good biological safety.

The application belongs to the technical field of electroencephalogram signal detection, and specifically discloses an epilepsy electroencephalogram signal detection system and method based on a diffusion attention model. The system comprises: an acquisition unit configured to acquire an electroencephalogram signal to be detected and perform preprocessing; a reconstruction unit configured to input the preprocessed electroencephalogram signal into a diffusion attention model to perform denoising and simultaneously extract epilepsy seizure-related features; wherein the diffusion attention model uses a diffusion model to reconstruct the signal by gradually denoising and introduces an adaptive gradient correction term to iteratively optimize the signal; in the reconstruction process at each stage, the adaptive attention mechanism is used to enhance the extraction of epilepsy seizure-related features; and a detection unit configured to use a classifier to classify the epilepsy seizure-related features to obtain an epilepsy seizure detection result. The application improves the feature extraction capability of high-dimensional noisy electroencephalogram signals and significantly improves the accuracy of epilepsy seizure detection.

To provide a new compound useful for treatment, prevention and / or diagnosis of seizure or the like in diseases accompanied by epileptic seizure or convulsive seizure (including multidrug-resistant seizure, intractable seizure, acute symptomatic seizure, febrile seizure and status epilepticus).SOLUTION: There are provided a compound represented by formula [I] and a salt thereof. Ring C is selected from pyridazine, pyrimidine, indole, pyrrolopyridine, indazole, pyrazolopyridine, imidazopyridine, imidazopyrazine, imidazopyridazine, triazolopyridine, pyrazolopyrimidine, imidazopyrimidine, triazolopyrimidine, isoquinoline, naphthyridine, quinazoline, quinoxaline, benzodioxole, oxazine, oxazepine, benzothiazole, and triazolopyridazine, with the proviso that pyrimidine-2, 4-dione and dihydropyrimidine-2, 4-dione are excluded.SELECTED DRAWING: None

The application relates to an epilepsy monitoring and electric stimulation system and method, wherein the epilepsy monitoring and electric stimulation system comprises a multi-modal signal acquisition module, an electric stimulation module, a control module and a set of ring-shaped common electrodes distributed on a human head; in the multi-modal signal acquisition module, an electric impedance acquisition unit obtains impedance characteristics when the common electrodes are in a first acquisition state, and an electroencephalogram acquisition unit obtains electroencephalogram characteristics when the common electrodes are in a second acquisition state; the control module is used for evaluating an epilepsy pre-onset time, a lesion site and a brain edema degree based on the impedance characteristics, combining the two characteristics to evaluate an epilepsy grade, then setting electric stimulation parameters according to the brain edema degree and the epilepsy grade, selecting an electric stimulation electrode pair according to the lesion site, and controlling the electric stimulation module to apply electric stimulation with the parameters to the electrode pair before the epilepsy pre-onset time, and adjusting the parameters according to real-time signals in the stimulation. Through the application, the problems of low positioning accuracy and inability to intervene in time before epilepsy onset in the related art are solved.

The application discloses a kind of based on multi-channel atlas structure feature mining's epilepsy attack prediction method, belong to medical image processing technical field.The application includes: signal pre-processing is carried out to electroencephalogram signal, obtains the multi-channel time-frequency domain atlas of electroencephalogram signal based on non-negative Tucker decomposition dimension reduction, contour sampling and contour profile feature extraction are carried out to it to obtain electroencephalogram signal feature, then automatic epilepsy preictal prediction is carried out based on the feature, simultaneously also include automatic identity recognition to the feature that epilepsy preictal is identified.The application small storage overhead, can assist epilepsy patient to be prepared to cope with epilepsy attack;Using non-negative Tucker to reduce the amount of calculation of data dimension reduction;Using the profile of power contour as the multi-channel atlas structure feature of time-frequency domain, the coupling relationship of time, frequency and energy can be well described;Directly using the feature that epilepsy prediction has acquired carries out identity recognition, can simplify system module, accelerate identity recognition response speed.

The application provides a severe encephalitis patient seizure automatic detection device based on brain network optimization, characterized by comprising an acquisition module, a preprocessing module and a detection module, the acquisition module acquires original electroencephalogram signals of the encephalitis patient to be detected; the preprocessing module inputs the detected electroencephalogram signals after preprocessing into a recognition model in the detection module to obtain a seizure detection result; the recognition model in the detection module integrates multiple brain networks and multiple features, more comprehensively reflects the relationship between each node in the brain network from multiple related dimensions; and the network layer and feature layer optimization based on the improved genetic algorithm realizes the optimal determination of multiple network weighting coefficients and multiple network weighting coefficients, takes the optimal network layer and feature layer as machine learning input, thereby greatly improving the accuracy of epilepsy recognition.

The invention discloses a pharmaceutical salt of lamotrigine, a pharmaceutical composition, a preparation method and application. Through a medicine-medicine combination strategy, lamotrigine and lipoic acid are combined in a salifying or mixture form, in epilepsy treatment, lamotrigine fragments can inhibit abnormal discharge and nerve over-excitation in the brain, and lipoic acid fragments can enhance the resistance of the brain to epileptic seizure, so that the epilepsy treatment effect is improved, and the epilepsy treatment effect is improved. The damage to the brain after the epileptic seizure is relieved, a multi-channel treatment effect with a synergistic effect is exerted, and the disease development is delayed. Meanwhile, the formed salt also has better physical and chemical stability, dissolution, pharmacokinetics and pharmacodynamic properties, is more beneficial to medicine formation on the whole, and provides an effective medicine for treating epilepsy.

The invention provides an epilepsy characteristic waveform detection method, device and equipment applied to epileptic seizure prediction, a medium and a product, and is applied to the technical field of medical device.The method comprises the steps that electroencephalogram signals of a target in different frequency bands are obtained; based on weight data learned by the frequency spectrum information, performing frequency spectrum processing on the electroencephalogram signals of each frequency band to obtain time domain signals of the target in different frequency bands; respectively carrying out time feature extraction on the time domain signal of each frequency band on different scales to obtain time feature data; inputting the time characteristic data into a constructed dynamic space-time diagram convolutional network, and capturing functional connection change of each region of the brain in the epileptic seizure process and / or propagation change of an epileptic characteristic waveform on a time axis to obtain space-time characteristic data; and performing classification prediction about epilepsy feature waveforms on the spatial-temporal feature data to obtain a waveform detection result. According to the invention, the problem of low epilepsy characteristic waveform detection precision in the prior art is solved.