204 results about "Epilepsy seizure" patented technology

The invention discloses an epilepsy recognition method and device based on electroencephalogram characteristics, a medium and a product, and relates to the technical field of epilepsy detection and recognition. The method comprises the steps that original electroencephalogram signals of multiple leads are collected and preprocessed, and the signal-to-noise ratio of the preprocessed signals is calculated; selecting a plurality of lead signals with the highest signal-to-noise ratio from each brain functional area to obtain high-quality lead signals, performing time-frequency domain conversion, extracting time domain features and frequency domain features, and combining the time domain features and the frequency domain features to generate feature vectors; segmenting the feature vector according to a preset time interval, and performing attack period and non-attack period labeling on segmented data to obtain a model training sample; constructing a neural network model, and performing model training by using the model training sample to obtain an epilepsy recognition model; and determining a to-be-detected feature vector of the to-be-detected electroencephalogram signal, and inputting the to-be-detected feature vector to the epilepsy recognition model to obtain an epilepsy recognition result. According to the invention, the epileptic seizure condition can be accurately identified based on the electroencephalogram characteristics.

The invention relates to an epileptic seizure detection method and system based on self-attention mechanism and GRU-LSTM fusion, and the method comprises the following steps: (1) carrying out the preprocessing of original electroencephalogram signal data, and extracting time domain and nonlinear features; (2) randomly dividing the data set into a training set, a test set and a verification set; (3) constructing a deep learning model fusing a self-attention mechanism, a gating circulation unit and a long short-term memory network; (4) extracting fusion features from the trained deep learning model, and inputting the fusion features into a support vector machine classifier to perform epileptic seizure and non-seizure classification; and (5) outputting a classification result through multi-modal feature fusion, long and short term dependence modeling and adaptive feature selection. The method has the advantages that a self-attention mechanism, a gating circulation unit, a long-short-term memory network and a support vector machine classifier are combined, deep features of electroencephalogram signals are extracted through a multi-stage processing flow, and finally epileptic seizure and non-seizure classification is carried out.

The technical scheme of the invention discloses an epilepsy diagnosis system based on electroencephalogram data channel selection and a KAN convolutional spiking neural network, which is characterized by comprising a data processing module, a channel feature extraction module, a self-adaptive channel selection module and a feature classification module. According to the method, an effective solution is provided by introducing KAN convolution, channel selection and a spiking neural network. The introduced attention mechanism allows the model to dynamically pay attention to the most relevant EEG channel and signal characteristics, so that personalized channel selection is realized. Through the mechanism, the model can distribute different weights for different individuals, only key signals most related to epileptic seizure are selected, the calculation burden of redundant channels is avoided, and meanwhile the classification precision is improved. Finally, the artificial neural network is converted into the pulse neural network, an event-driven calculation mode is adopted, energy consumption is effectively reduced, and the method is particularly suitable for being applied to portable equipment.

The invention discloses an epileptic seizure auxiliary prediction and cognitive function evaluation system and method based on multi-modal data fusion and intelligent adaptive optimization. All the modules work cooperatively, and intelligent optimization from multi-dimensional electroencephalogram signal, motion data and physiological data collection, intelligent preprocessing and feature deep extraction and fusion to epileptic seizure prediction and cognitive function evaluation is achieved. Through the combination of neural architecture search and self-supervised comparative learning, the network structure can be automatically optimized on the basis of multi-modal data, and the ability of learning and identifying an epileptic seizure mode and a cognitive state is effectively improved. The system realizes high-accuracy epileptic seizure prediction and cognitive state evaluation through adaptive optimization, and remarkably reduces the missing report rate and the false report rate. The system has the characteristics of high efficiency, intelligence and accuracy, can provide timely early warning for epilepsy patients, provides scientific cognitive function intervention suggestions for doctors, and has wide application prospects.

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 discloses an intelligent early warning bracelet for epilepsy monitoring and a computing device, and the bracelet comprises a first processing module which is used for analyzing physiological signal data of a patient, and generating preliminary result data of epileptic seizure; the second processing module is used for identifying a potential epileptic seizure mode in the first data structure and generating a second data structure; the prediction analysis module is used for combining the prediction probability, the epileptic seizure risk score and the event triggering time into a third data structure; the risk grading module is used for carrying out risk grading on the epileptic seizure risk score to generate a fourth data structure; the early warning information generation module is used for generating a fifth data structure according to the size and the resolution ratio of the bracelet display screen; and the bracelet display module is used for sending the fifth data structure to a nurse wearing bracelet for vibration and flickering prompt. Warning of different degrees is given according to different risk levels, nurses can take measures in time, and epilepsy danger of patients is avoided.

The invention provides an epilepsy detection device, and relates to a medical equipment technology. The epilepsy detection device comprises a wearable device, the wearable device comprises a data acquisition module, a data processing unit, a communication module, a software algorithm and a system platform, and the data acquisition module comprises an electromyogram sensor, a heart rate monitoring sensor, an accelerometer, a gyroscope and a skin electrical activity sensor. The data processing unit comprises a microprocessor and a memory, the communication module comprises a Bluetooth / Wi-Fi module and a mobile application / cloud platform, and the software algorithm comprises a signal processing algorithm and an optimization algorithm. By combining medical history data of a patient, including epileptic seizure history, seizure type, frequency, duration time, preamble before seizure, symptoms after seizure and the like of the patient, genetic history and medical history of epilepsy or other related diseases in a family, a more personalized model is trained, and more accurate monitoring and early warning are provided for the patient.

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 present disclosure relates to the use of cannabidiol (CBD) for the treatment of seizures in patients aged about 1-2 years old. In particular the CBD appears particularly effective in reducing seizures in patients suffering with etiologies that include: Lennox-Gastaut Syndrome; Tuberous Sclerosis Complex; Dravet Syndrome; Doose Syndrome; Aicardi syndrome; CDKL5 and Dun15q.

The invention relates to an epilepsy time-frequency joint feature extraction and detection method and device based on covariance decomposition, electronic equipment and a storage medium. The method comprises the following steps: carrying out data preprocessing on collected electroencephalogram data to generate electroencephalogram data; decentralizing the electroencephalogram data and then calculating a covariance matrix, decomposing eigenvalues, extracting features and performing stretching processing to generate a time domain eigenvector; frequency spectrum feature extraction and frequency domain covariance feature extraction are carried out respectively, and a frequency spectrum feature vector and a frequency domain covariance feature vector are generated respectively; generating a time-frequency joint feature based on a preset feature fusion strategy; and based on a preset dichotomy identification method, carrying out classification identification on the time-frequency joint features to complete epilepsy prediction. According to the method, the time-frequency multi-scale feature information is extracted and fused, so that the manual data marking time of medical workers can be effectively shortened, the marking efficiency of epileptic seizure events is improved, and a new way and a new method are provided for clinical application of epilepsy detection.

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.

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.

Verbal memory impairment is a common symptom of temporal lobe epilepsy (“TLE”). The ability to encode and recall verbal memories can be probed with the classic free recall task. This study had three overarching hypotheses: 1) increased seizure frequency will lead to decreased verbal memory, 2) stimulation will modulate memory performance, and 3) stimulation will modulate the underlying neural correlates of memory. The study investigated these hypotheses in patients receiving chronic DBS to bilateral ANT. Recordings were obtained from individuals with drug resistant mesial TLE implanted with an investigational MEDTRONIC SUMMIT RC+S™ sensing and stimulation device. As patient seizure diaries are notoriously inaccurate, the patient's continuous local field potential (“cLFP”) was scored for seizures using a validated seizure classifier with trained epileptologist review, resulting in a reliable seizure diary. The subjects completed free recall memory tasks in their home environment with cLFP and behavioral data streamed to a handheld device and cloud repository.Through implementation of generalized linear mixed models (GLMM), the study was able to determine that ANT stimulation modulates memory performance, but changes in the seizure rate were not predictive of changes in memory performance. Analyzing the continuous local field potential recordings from these tasks, the study found that spectral power in the theta frequency band in the left ANT and hippocampus correlated with acute memory performance and chronic therapeutic stimulation frequency received.Not all patients are candidates for devices with chronic sensing capabilities, and this study indicates that changes in verbal memory performance can provide a suitable metric of therapy effectiveness in lieu of a reliable, objective seizure diary. But for those who do receive these and similar devices, thalamic-hippocampal spectral activities can be used to track and predict memory performance acutely and chronically, as well as predict the effect of therapeutic deep brain stimulation. Our study demonstrates chronic improvement of verbal memory with a new biomarker-based technology for remote task administration and modulation of the associated neural activities.

The invention discloses an induction type program control electric guiding method and device, and relates to the technical field of medical instruments. According to the inductive program-controlled electric guiding method and device, neural electrophysiological signals of an epilepsy origin area are acquired and detected in an inductive mode, and the neural electrophysiological signals of the epilepsy origin area can be monitored in real time; establishing an epilepsy prediction model based on potential difference characteristics, and calculating an epilepsy identification prediction result according to an epilepsy identification rule; meanwhile, by arranging the subscalp low potential generation module, the stability of the potential difference of the neural electrophysiological signals in the epileptic origin area is achieved, the problems that in-vitro low potential is unstable and the potential difference is not large enough during epileptic seizure are effectively solved, and the treatment effect of epileptic seizure intervention is greatly improved.

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.

The invention relates to the technical field of medical instruments, and discloses an epileptic tongue bite prevention device for the emergency department, which comprises an anti-bite mechanism for preventing a patient from bite the tongue during epileptic seizure; the distraction mechanism is located on the upper side of the bite-proof mechanism and used for distraction of the oral cavity of the patient; the rotating mechanism is located on the lower side of the anti-bite mechanism and used for assisting medical staff in better pushing the jaw of the patient aside; the pushing-aside mechanism is located on the upper side of the rotating mechanism and used for pushing aside lips and teeth of the patient; the opening mechanism comprises a first fixing block and a second fixing block, and the first fixing block and the second fixing block are fixed to the upper side of the bite-proof mechanism. Through cooperation of the anti-biting mechanism, the opening mechanism, the rotating mechanism, the pushing-aside mechanism, the clamping mechanism and the like, a patient can be effectively prevented from biting the tongue, oral safety is guaranteed, convenient, accurate and stable supporting is provided for medical care operation, the device adapts to individual differences, single-person operation is easy, multi-person cooperation is efficient, and it is guaranteed that oral diagnosis and treatment are smoothly carried out.

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 invention provides an epileptic seizure prediction method of a self-adaptive attention mechanism fusion network, and the model is composed of a self-adaptive feature extraction module and a multiple attention fusion mechanism, and aims to improve the epileptic seizure prediction performance. The method comprises the following steps: firstly, carrying out preprocessing and timely frequency domain conversion on an epilepsy data set, and constructing and combining an adaptive feature extraction neural network to carry out feature selection; then constructing a triple attention mechanism to carry out feature fusion; and then constructing a seizure state classification network, and adopting a focal loss training strategy to alleviate the problem of sample imbalance. According to the method, by introducing the adaptive feature extraction technology, the limitation that a traditional method depends on static parameters and a fixed analysis mode to process EEG data is broken through, the dynamic change and complexity of a sample space are effectively dealt with, in addition, effective feature fusion can be guaranteed by introducing a triple attention mechanism of residual connection, and the prediction reliability is further improved.

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 disclosure provides a method for enriching epileptiform discharges and predicting SOZ during epilepsy interictal period, including the following steps: obtaining stereotactic electroencephalogaphy signals of patient during the epilepsy interictal period; preprocessing the stereotactic electroencephalogaphy signals to obtain processed stereotactic electroencephalogaphy signals; dividing the processed stereotactic electroencephalogaphy signals into a training set and a test set, dividing the training set into a plurality of signal segments by using a sliding window, and performing a self-supervised reconstruction training on the signal segments based on a Transformer encoder model to obtain a trained Transformer encoder model; inputting the test set into the trained Transformer encoder model, obtaining reconstructed values of each of the signal segments, comparing the reconstructed values of each of the signal segments with values of the stereotactic electroencephalogaphy signals to obtain deviation values of each of the signal segments from the background signals.

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).

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 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.