126 results about "Epilepsy seizure" patented technology

The invention relates to the technical field of deep learning, and discloses an epileptic seizure prediction method and device based on a diffusion model and computer equipment, and the method comprises the steps: carrying out the bimodal feature extraction of epileptic electroencephalogram data, and obtaining epileptic features and corresponding semantic features; performing multi-step noise adding operation on the epilepsy features and the semantic features in sequence to generate a noise-containing 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 an epileptic seizure prediction module based on the clean epileptic features to obtain a trained epileptic seizure prediction module; and inputting the test sample set of the obtained epileptic electroencephalogram data into the trained epileptic seizure prediction module to obtain an epileptic seizure state prediction result. According to the method, noise information in epilepsy electroencephalogram signals can be effectively modeled, gradual purification of epilepsy features is realized under the guidance of semantic information, and the prediction accuracy is remarkably improved.

The invention relates to the technical field of medical health monitoring, in particular to an epileptic seizure cycle HRV feature mining and early warning system and method.Multi-channel electrocardiosignals are collected through wearable equipment, RR intervals are extracted in a layered mode, a high-dimensional manifold and a dynamic graph are constructed, quantum state attention and chaos pooling are combined, and key nodes and attractor modes are recognized; the method comprises the following steps: extracting epileptic risk dynamic characteristics, generating multi-dimensional risk scores and dynamically calibrating, finally outputting graded early warning and intervention suggestions, realizing intelligent prediction and management of epileptic seizure, revealing inherent geometric characteristics of HRV data through a manifold mapping technology, and compared with a traditional Euclidean space analysis method, the method provided by the invention has the advantages that the efficiency is high; the real distance between different physiological states can be measured more accurately, and the accuracy of feature characterization is improved.

The invention discloses an epilepsy early warning system based on electroencephalogram signals, and relates to the technical field of nervous systems, the epilepsy early warning system comprises an information acquisition module, a data division module, a data preprocessing module, a model training module and a real-time reasoning module, the information acquisition module is used for determining a few-channel electroencephalogram signal acquisition position and acquiring electroencephalogram signal data; the data division module is used for recording the total number of times of epileptic seizure events and defining the interval of seizure, the early stage of seizure and the period of seizure for each epileptic seizure event, and the data preprocessing module is used for data denoising and dynamically adjusting the overlapping proportion of a data window by adopting a sliding window-based adaptive overlapping data slicing method so as to obtain the data of the epileptic seizure event. The model training module is used for completing a preprocessing process to relieve the problem of data imbalance, and is used for constructing and training an early warning model based on a deep learning model of time-frequency feature fusion; the complexity and wearing burden of the equipment are effectively reduced, and efficient epilepsy early warning is realized.

The invention relates to the technical field of electroencephalogram signal processing, and discloses a focal epileptic seizure feature level identification method and system. The method specifically comprises the steps that electroencephalogram data are obtained, time sequence electroencephalogram data are generated, the electroencephalogram data comprise electroencephalogram signals, focal epileptic seizure feature tags and seizure interval tags, and in the subsequent feature extraction process, the type of seizure which each seizure specifically belongs to can be judged; and focal epileptic seizure characteristic wavebands with short duration and small range can be accurately captured. And on the basis, performing feature extraction on the time sequence electroencephalogram data by using a feature extraction model integrated with a strategy network, a residual neural network and a recurrent neural network. Wherein the policy network can optimize the data processing flow and improve the real-time response speed of the system. The residual neural network can deeply mine the dynamic characteristic relation in the complex electroencephalogram signals, and it is ensured that focal epilepsy characteristics can still be efficiently recognized under the complex background.

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.

Disclosed electroencephalogram (EEG) monitoring and detection systems and methods can detect differentiated electrographic seizure characteristics of different seizure types across a diverse group of patients. Disclosed systems and methods can utilize EEG data collected by discrete wireless EEG sensors positioned on a scalp of a patient.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of SCN2A RNA in a cell or subject, and in certain instances reducing the amount of SCN2A protein in a cell or subject. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a disease or disorder associated with a voltage-gated sodium channel protein, such as, for example, a Developmental and Epileptic Encephalopathy, an intellectual disability, or an autism spectrum disorder. Such symptoms and hallmarks include, but are not limited to seizures, hypotonia, sensory integration disorders, motor development delays and dysfunctions, intellectual and cognitive dysfunctions, movement and balance dysfunctions, visual dysfunctions, delayed language and speech, gastrointestinal disorders, neurodevelopmental delays, sleep problems, and sudden unexpected death in epilepsy.

The present disclosure relates to the use of cannabidiol (CBD) for the treatment of atonic seizures. In particular the CBD appears particularly effective in reducing atonic seizures in patients suffering with etiologies that include: Lennox-Gastaut Syndrome; Tuberous Sclerosis Complex; Dravet Syndrome; Doose Syndrome; Aicardi syndrome; CDKL5 and Dup15q in comparison to other seizure types. The disclosure further relates to the use of CBD in combination with one or more anti-epileptic drugs (AEDs).

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.

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.

A system for detection of onset and ictal phases of an epileptic seizure comprises at least one motion sensor, at least one intermediate device and a sensing data interpretation device, embedded with an application program that, after execution, detects onset and ictal phases of an epileptic seizure, and configured to mark a feature or a reference on, or normalize sensing data of a motion sensing data file, wherein the reference comprises at least one of a phase of an epileptic seizure, a sensor position and a sensing time.

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.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of KCNT1 RNA in a cell or subject, and in certain instances reducing the amount of KCNT1 protein in a cell or subject. These compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a neurological condition. Such symptoms and hallmarks include seizures, encephalopathy, and behavioral abnormalities. Non-limiting examples of neurological conditions that benefit from these compounds, methods, and pharmaceutical compositions are epilepsy of infancy with migrating focal seizures (EIMFS), autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE), West syndrome, and Ohtahara syndrome.