316 results about "Epileptic seizure" patented technology

The invention relates to a disturbance of consciousness disease course monitoring system. The multi-dimensional data acquisition module is used for acquiring electroencephalogram (EEG), heart rate variability (HRV) and skin conductance (EDA) physiological signal data of a patient in real time through a plurality of sensors arranged on the body of the patient, and preprocessing the acquired data through edge computing equipment; the self-adaptive disease course prediction model is based on the physiological signal data acquired by the multi-dimensional data acquisition module, combines the historical disease course data of the patient, trains a personalized disease course prediction model by utilizing a self-adaptive algorithm, and adaptively updates model parameters through a machine learning algorithm in the disease course development process of the patient, so that the disease course prediction accuracy is improved. The dynamic prediction of the progress of the disturbance of consciousness is realized; the continuous disease course monitoring module continuously obtains physiological signals of a patient through a high-frequency sampling technology, recognizes key events in the disease course in real time through an anomaly detection algorithm, the key events comprise sudden consciousness decline and epileptic seizure, generates early warning signals and triggers an emergency response mechanism.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

The invention provides a full hydrogel neural electrode which is composed of four layers of hydrogel, namely a PSFD hydrogel layer, an LPPDMEAs layer, a PSFD hydrogel layer and an MHD layer. The invention further provides a preparation method and application of the full-hydrogel neural electrode. According to the invention, a PEDOT: PSS hydrogel conductive path with ultrahigh conductivity is provided, so that the recording capability of the PEDOT: PSS hydrogel conductive path is achieved compared with that of a metal electrode; according to the full-hydrogel neural electrode disclosed by the invention, due to tight interlayer combination, excellent interface compliance is ensured by introducing dopamine into each layer of the full-hydrogel neural electrode, so that the full-hydrogel neural electrode can be attached to a complex geometrical shape of a soft nervous tissue, and interface mismatching among a conductive layer, a packaging layer, a shielding layer and a tissue-electrode interface is avoided; the natural polymer packaging layer improves the compatibility, and the electromagnetic shielding layer is used for improving the signal-to-noise ratio. The full-hydrogel neural electrode can provide artificial electrical stimulation for the cerebral cortex of a rat during epileptic seizure, and the prepared full-hydrogel neural electrode has the potential to become a valuable tool for better understanding the development and functions of brain diseases and neurodegenerative diseases and treating the neurodegenerative diseases.

The invention discloses a system and method for classifying and recognizing epilepsy in electroencephalogram signals based on a time sequence. The system comprises a cross-domain mixed self-supervised learning module, a multi-scale electroencephalogram feature learning module, an epileptic seizure guided self-attention learning module and a classifier. The cross-domain hybrid self-supervised learning module comprises a time domain context prediction task unit, a channel information reconstruction task unit and a frequency spectrum masking identification task unit; the cross-domain hybrid self-supervised learning module reconstructs an input electroencephalogram signal according to a time domain, a space domain and a frequency domain to obtain an electroencephalogram representation model; the time domain context prediction task unit is used for capturing dynamic change characteristics before and after epileptic seizure by learning a time domain evolution rule of electroencephalogram signals; the channel information reconstruction task unit is used for reconstructing missing electroencephalogram signal channel data and enhancing spatial information representation of an electroencephalogram representation model; the frequency spectrum masking identification task unit trains the electroencephalogram characterization model to characterize epilepsy nerve oscillation through a random masking part electroencephalogram signal frequency component in a frequency domain; the multi-scale electroencephalogram feature learning module captures local and global level epilepsy features of an electroencephalogram representation model through different resolutions to obtain a first epilepsy electroencephalogram feature model; the epileptic seizure guided self-attention learning module optimizes the first epileptic electroencephalogram feature model by fusing time sequence and spatial features to obtain a second epileptic electroencephalogram feature model; and the classifier classifies and outputs the electroencephalogram features of the second epileptic electroencephalogram feature model based on the long short-term memory network to determine whether the epileptic seizure occurs. The invention provides an efficient, accurate and robust solution for automatic detection and clinical auxiliary diagnosis of the epileptic seizure.

The invention discloses an automatic marking system for an abnormal wave band of an epilepsy electroencephalogram image, and relates to the technical field of epilepsy electroencephalograms, the system carries out frequency domain analysis and time domain feature extraction on an electroencephalogram signal through Fourier transform and convolution operation, abnormal waveforms such as sharp waves, ratchet waves and slow waves can be accurately identified, and the accuracy of the abnormal wave band marking is improved. The sensitivity of the system to fine abnormal wavebands is enhanced, highly similar abnormal wavebands can be combined into a complete abnormal event, redundant annotation and repeated event annotation are avoided, further refinement is performed through calculation of the spectrum entropy and the spectrum entropy difference value, setting of a reasonable threshold value and combination of the ratio difference value, and the abnormal event annotation accuracy is improved. The system can identify and label different epilepsy stages more accurately, the system optimizes labeling through multi-dimensional feature differences such as frequency spectrum entropy difference and ratio difference values of different wavebands, seizure, precursor and normality of epilepsy in different stages can be distinguished more accurately, and the multi-level labeling method effectively improves the epilepsy prediction capacity.

The invention belongs to the technical field of electroencephalogram signal detection, and particularly discloses an epilepsy electroencephalogram signal detection system and method based on a diffusive attention model.The system comprises an acquisition unit, a signal processing unit, a signal processing unit and a signal processing unit, the reconstruction unit is configured to be used for inputting the preprocessed electroencephalogram signals into a distraction attention model for denoising and extracting features related to epileptic seizure at the same time; wherein the distraction attention model reconstructs a signal by using a diffusion model through step-by-step denoising, and introduces an adaptive gradient correction term to iteratively optimize the signal; in the reconstruction process of each stage, the extraction of epileptic seizure related features is enhanced through a self-adaptive attention mechanism; and the detection unit is configured to use a classifier to classify the epileptic seizure related features to obtain an epileptic seizure detection result. According to the method, the feature extraction capability of the high-dimensional noise electroencephalogram signals is improved, and the accuracy of epileptic seizure detection is remarkably improved.

The invention provides an intracranial neuron electrical stimulation system for treating epilepsy and other brain diseases involving epileptic seizure, which can immediately apply intracranial neuron electrical stimulation to the interseizure epilepsy activity in a personalized manner after detecting the interseizure epilepsy pattern each time. And the stimulation is activated by intracranial electroencephalogram (EEG) detection. The intracranial stimulation for epileptic activities in the epileptic seizure period continuously acts along with time, the forming process of an epileptic network can be blocked, the neuronal super-synchronization risk is reduced, and therefore the control effect on epileptic seizure is improved. The invention also includes the use of nerve adjustment biomarkers, thereby dynamically optimizing the detectability and providing more accurate overall assessment for the patient's treatment progress. Finally, according to the scheme, by detecting the intracranial EEG mode and the high-risk state mode (such as the epileptic state and the sudden accidental death risk in epilepsy) in the attack period, an instant alarm is given to the patient / nursing personnel, and it is ensured that safe treatment measures are taken for the patient in time.

The invention discloses a closed-loop deep brain regulation and control system for epilepsy treatment, and the system comprises an electroencephalogram signal collection module which collects neural electrophysiological signals in real time by implanting a recording electrode in a cerebral cortex or hippocampal brain area; the electroencephalogram signal real-time analysis module is used for outputting the epileptic seizure period prediction probability xi through a seven-layer full-connection neural network based on the 33-dimensional feature vector so as to realize epileptic seizure early warning; when the zeta is greater than or equal to a threshold value, the electrical stimulation regulation and control module is used for triggering electrical stimulation, filtering out a frequency band required to be regulated and controlled according to filtering, calculating and predicting electrical stimulation intensity and then correcting and outputting pulse stimulation, so that personalized electrical stimulation regulation and control are carried out on a specific frequency band, and detection is continuously carried out until the frequency band becomes normal; and the medicine intervention module is used for triggering a micro-injection pump to carry out medicine infusion through a pulse signal. According to the system, epilepsy treatment is improved from empirical parameter adjustment to accurate regulation and control based on real-time nerve physiological signals, and a safer and more effective treatment choice is provided for a drug-refractory epilepsy patient.

The invention discloses an EEG signal spine wave detection method and system based on KAN feature fusion, and the method comprises the steps: carrying out the preprocessing of electroencephalogram data, and obtaining a training set and a test set; performing parallel feature extraction to obtain multi-KAN parallel features, and performing splicing and dimension reduction to obtain mixed features; performing gating dynamic selection on the obtained mixed features to obtain fusion features of comprehensive multi-scale analysis of the EEG signals; focusing the key period features based on a double attention mechanism to obtain weight fusion features; inputting the obtained weight fusion features into a classifier, and training a prediction model based on a cross entropy loss function; evaluating the performance of the model, and outputting a classification result. The epileptic seizure risk prediction is realized by extracting the electroencephalogram heterogeneous features, and inputting the electroencephalogram heterogeneous features into the classifier after gating dynamic fusion and double attention time sequence compression. The feature expression capability is obviously improved, the focusing capability in a key period is enhanced, and the classification performance is optimized.

The invention relates to the field of intelligent health management, and provides an epileptic seizure early warning method and system, which uses a data processing technology based on deep learning to perform noise reduction processing and feature extraction on an electroencephalogram signal of a user to be monitored in a predetermined time period, and performs operation state feature extraction on a time queue of motion data at the same time. And intelligently identifying whether the to-be-monitored user has an epileptic seizure risk or not according to the cross-modal aggregation representation between the motion state time sequence coding feature and the electroencephalogram signal feature, and generating a corresponding epileptic seizure early warning prompt. Therefore, more accurate epileptic seizure early warning can be realized by comprehensively capturing subtle modes in different user data and fully considering the interrelation between the features.

The invention provides an application of Ly93 in preparation of an anti-epileptic seizure drug, and provides a new thought for an epileptic seizure treatment drug. Research results show that the Ly93 can significantly reduce the grade of epileptic seizure in a KA-induced mouse epileptic seizure model, can significantly reduce the duration of PTZ-induced epileptic seizure, can significantly inhibit neuroinflammation in epileptic seizure, and does not affect the weight of a mouse. Therefore, the Ly93 serving as the epileptic seizure treatment medicine is remarkable in effect, relatively good in safety and relatively small in toxic and side effects.

This invention is eyewear (e.g. eyeglasses) with electrodes (e.g. EEG sensors) which collect brain activity data (e.g. electroencephalographic data) which can be used to predict and / or detect health events (e.g. epileptic seizure, stroke, or heart attack) or function as a BCI (Brain-to-Computer Interface) for communication. There can be anterior and posterior electrodes on the eyewear temple and the temple can have an upward and / or inward curving wave or arm. A posterior electrode can have multiple electroconductive protrusions to help penetrate between strands of hair.

The invention relates to the field of epilepsy, in particular to application of alprazolam gel particles in preparation of a medicine for treating the state of epilepsy. The invention provides application of alprazolam gel particles in preparation of a medicine for treating and / or preventing epileptic seizure of a subject, and is characterized in that the alprazolam gel particles are prepared by the following steps: S101, dropwise adding an alprazolam solution into a first solution to form an internal phase solution; s102, forming a preliminarily cured pre-crosslinked liquid drop; s103, forming cured particles; and S104, obtaining the alprazolam gel particles. The blood brain barrier transmittance of the alprazolam gel particles provided by the invention can reach 1.5-2 times that of the prior art, and the alprazolam gel particles are high in effect taking speed, have dual properties of quick release and continuous release, and are suitable for effective and timely treatment of epileptic seizure patients.

The invention discloses a method for detecting and classifying electroencephalogram events of electroencephalogram signals by using a target detection model DINO, and belongs to the technical field of deep learning. The method mainly comprises three stages of data set acquisition, data preprocessing, feature extraction and detection classification evaluation. According to the method, a complete electroencephalogram data file can be input, a tedious post-processing process is not needed, many defects of post-processing are overcome, an end-to-end model is constructed, original data are directly input, and an event detection result is output; meanwhile, many limitations existing in manual detection are overcome, a universal automatic electroencephalogram event detection framework is established by using a deep learning method, a doctor can be assisted to play a role in tasks such as epileptic seizure detection, sleep staging and artifact detection in various clinical application scenes, the electroencephalogram event labeling efficiency is improved, meanwhile, the burden of the clinician is reduced, and the workload of the clinician is reduced. And the pressure of medical resources is relieved.

The present disclosure provides a method and system for forecasting the likelihood of seizure onset within an individual. Specifically, the present disclosure relates to an intracranial implant comprising one or more electrodes attached thereto and an associated method for delivering active perturbation patterns to brain regions which includes: sending, via the intracranial implant, an electrical stimuli into a brain, wherein the electrical stimuli is sent in accordance with a stimuli pattern; measuring an output from the brain in response to the electrical stimuli; feeding the output into a forecasting model; and detecting, via the forecasting model, outputs that indicate a high likelihood of seizure. The intracranial implant and associated method may further apply via the brain-contacting electrodes, a prophylactic treatment to the brain to prevent a seizure from occurring, based on detecting an output indicating a high likelihood of seizure.

The present invention relates to a composition of cannabinoid compounds having a structural formula I as defined herein. The invention also relates to compositions comprising these compounds, to processes for preparing these compounds, to intermediates useful for preparing these compounds and to the use of these compounds as medicaments, in particular for the treatment of conditions associated with epileptic seizures, such as epilepsy.

The invention discloses an EEG-based epilepsy automatic detection method and system, and belongs to the field of disease automatic detection. The method comprises the steps of collecting multi-channel electroencephalogram EEG signals in real time, conducting signal preprocessing, time-frequency feature extraction, deep learning analysis and multi-modal prediction, achieving accurate detection and prediction of epileptic seizure, generating real-time risk assessment of epileptic seizure through a prediction module in combination with multi-modal feature information, and achieving real-time risk assessment of epileptic seizure. The epilepsy early warning system sends an early warning signal to a user or a medical worker, starts corresponding intervention measures, has high detection precision and real-time response capability, and can provide effective early warning and management for epilepsy patients.

The invention belongs to the field of biological medicine, and discloses a blood marker for identifying epilepsy convulsion and application thereof. The inventor finds that the LRG1 is obviously increased during epileptic convulsion, which indicates that the increase of the blood LRG1 is a blood marker during the acute attack stage of epileptic convulsion. The invention proposes and verifies that the LRG1 is the peripheral blood marker in the acute attack stage of epilepsy convulsion for the first time, and can be used as a clinical auxiliary diagnosis and treatment target.

The invention discloses a concomitant epilepsy depression onset risk prediction system and device, and the method comprises the steps: constructing an array graph model based on multivariable factors, such as marriage conditions, onset age, epileptic seizure frequency, drug treatment types, SEEG epilepsy origin regions, daily life activity ability assessment scale ADL scores, and the like; through the array graph model, the onset risk of concomitant depression of epilepsy patients can be accurately predicted, so that the diagnosis rate of concomitant depression of epilepsy is improved, and a high-risk patient group of depression can be clinically recognized earlier, so that intervention is performed in time, serious consequences caused by depression onset are avoided in time, and the risk of concomitant depression of epilepsy patients is reduced. And a theoretical basis can be provided for clinically and actively intervening the depressive mood of the epilepsy patient.

The invention discloses a phase synchronization sequence parameter epileptic seizure prediction method and equipment based on KL divergence enhancement, and belongs to the field of electroencephalogram epileptic seizure prediction. After filtering preprocessing is carried out on epilepsy EEG data, an instantaneous phase of each channel is extracted based on Hilbert transformation, a phase locking value is calculated, and a phase synchronization matrix is obtained. According to the method, PLV values of all channel pairs in the network are extracted, arithmetic averaging is carried out on the values, global synchronization sequence parameters are calculated, and phase consistency of brain intervals is visually reflected. Then, empirical distribution and von Mises distribution are constructed to obtain a KL divergence matrix, and a global KL divergence synchronization index is calculated; experimental results show that the amplification and significance of the synchronous sequence parameter based on the KL divergence in the same stage are more prominent, higher sensitivity is shown in the recognition of the mutation of the synchronous mode, and an important reference basis can be provided for subsequent diagnosis and treatment.

A system and method for distinguishing the type of seizures in a human patient, such as an epileptic seizure (ES), or a functional or dissociative seizure (FDS). The system and method use a diagnostic analytical platform that gets heart rate variability (HRV) analytical metrics from a ECG and uses an analytical diagnostic algorithm to determine if an ES or FDS has occurred in the patient. The diagnostic analytical platform can create a model for distinguishing that a predetermined type of seizure has occurred from the HRV analytical metrics.

The invention discloses a multi-scale epileptic seizure cross-object detection method based on power spectral density. The method comprises the following steps: acquiring multi-channel electroencephalogram signal data of an epileptic and preprocessing the multi-channel electroencephalogram signal data; carrying out regularization processing on the preprocessed electroencephalogram signal data; constructing self-adaptive power spectrum density; according to the power spectral density characteristics, balancing the electroencephalogram signal data by using an SMOTE oversampling method to form a balanced data set; building and training a multi-scale space-time deep convolutional network model; dividing data in the balanced data set into a training set and a verification set, and inputting the training data and the verification data into the multi-scale space-time deep convolutional network model for training. According to the method, the dependence on a large amount of marked data can be reduced, the complexity and the calculation cost of the model are reduced, and meanwhile, the applicability of the model among different patients is improved.

The invention relates to the technical field of nerve regulation and control, and discloses a nerve regulation and control method based on dynamic frequency band analysis, which is used for improving the accuracy of epileptic seizure judgment. The neural regulation and control method based on dynamic frequency band analysis comprises the following steps: acquiring a local field potential signal of a target brain region in a preset time period; performing dynamic frequency band analysis on the local field potential signal to obtain a pathological wave frequency band, a high-frequency oscillation band, a harmonic coupling band and a suppression wave band; calculating the energy ratio and the cross-band phase coupling strength of each feature sub-band, and generating a multi-dimensional feature vector; the epileptic seizure probability is calculated according to the multi-dimensional feature vector; and when the epileptic seizure probability is greater than a preset threshold value, selecting a corresponding stimulation parameter group according to the dominant characteristic sub-band type, and outputting an electrical stimulation pulse with a frequency band matching characteristic.

The invention relates to an epilepsy electroencephalogram detection method adopting multi-modal feature fusion and an attention mechanism, and belongs to the technical field of medical signal processing and artificial intelligence. According to the method, automatic detection of epilepsy is realized through three core modules: firstly, preprocessing and time-frequency transformation are performed on electroencephalogram signals, a time-frequency graph is generated, and power spectrum characteristics of different frequency bands are extracted; the method comprises the steps that firstly, a time-frequency diagram and a function connection diagram are processed at the same time through a multi-modal feature combined extraction network, and finally fusion enhancement and classification decision making of multi-modal features are achieved through a self-adaptive feature fusion and classification network in combination with a learnable frequency band attention mechanism and a Transform encoder. According to the method, the time-frequency characteristics, the brain region connection relation and the long-range dependency relation of the electroencephalogram signals can be captured at the same time, the limitation of a traditional method in the aspects of feature expression and mode recognition is solved, and the accuracy of epilepsy detection is remarkably improved.

A system may be configured for treating a patient with epileptic seizures. The system may include a therapy delivery system configured to use a set of electrodes positioned adjacent to a spinal cord or dorsal column nuclei and a stimulation configuration to deliver neuromodulation energy to at least one target neural pathway in or near the spinal cord. The at least one target neural pathway includes the dorsal column nuclei or axons projecting to the dorsal column nuclei. The dorsal column nuclei and the axons are somatotopically organized and project to at least one of a cerebellum, a thalamus or a brainstem, which include somatotopically organized nuclei and project axons to different areas of a cortex. The at least one target neural pathway corresponds to at least one of the different areas of the cortex in which the epileptic seizures were located.

The invention provides a single-channel epileptic seizure early warning method based on deep double-cross metric learning. Wherein a double-crossover graph attention network is designed, and a dependency relationship between a multi-scale time feature and a multi-rhythm spectrum feature is disclosed; a routing Transform network is used to extract key routing features with epileptic seizure potential; and using a metric learning strategy based on hard triple optimization to optimize intra-class and inter-class distances of routing features in the inter-attack period and the early-attack period in an iteration mode. In one embodiment of the invention, verification is carried out on an intracranial single-channel electroencephalogram data set containing 10 epilepsy patients, the average early warning sensitivity (Sn) reaches 89.1%, and the rate of false alarm per hour (FPR) is lower than 0.104. Compared with an existing method, the average early warning sensitivity is improved by more than 4%, it is indicated that the model can provide more effective epileptic seizure early warning for individual patients, and the method has important significance on clinical epileptic seizure early warning and intracranial single-channel nerve regulation and control.

The invention provides an epileptic seizure prediction method and system based on a power spectrum and a phase spectrum of an electroencephalogram, and the system comprises an electroencephalogram signal obtaining module which is configured to obtain a multi-channel electroencephalogram signal and carry out the preprocessing of the multi-channel electroencephalogram signal; a power spectrum and phase spectrum extraction module configured to divide the electroencephalogram signal into a plurality of segments of set time, transform each segment of set time, and extract a power spectrum and a phase spectrum based on the transform; the model construction module is configured to construct a double-flow convolutional neural network model, and the model comprises a first network flow and a second network flow; the feature fusion module is configured to splice the output features of the first network flow and the second network flow in a channel dimension, fuse power and phase information, and obtain a fused feature map; and the prediction module is configured to convert the fused feature map into a one-dimensional feature vector through further processing, and calculate the probability of epileptic seizure and non-seizure according to the one-dimensional feature vector to output the probability of epileptic seizure and non-seizure.

The invention provides a portable and adjustable head-mounted epilepsy monitoring instrument, and relates to the technical field of epilepsy monitoring. External connection plates are fixedly mounted at the two ends of the flexible headband, a controller is arranged in the flexible headband, and a wireless communication module is connected in the controller and is in data connection with a remote terminal; an air pump is fixed on the elastic airbag ring, and a charging power supply is arranged in the flexible headband; an electroencephalogram sensor and a myoelectricity sensor are integrated on the inner side of the flexible head band; the alarm sensitivity and the false alarm rate are balanced through hierarchical logic of the alarm module, the safety of a patient is improved through an emergency automatic help seeking function, and an air pump is started to enable an elastic airbag ring to expand to form a head protection layer during epileptic seizure; the elastic airbag ring is convenient to maintain and replace quickly; the problems that a signal acquisition mode of a traditional head-mounted epilepsy monitoring instrument is prone to causing discomfort of a patient and inaccurate data, an alarm mechanism is difficult to balance sensitivity and false alarm rate, automatic help seeking and rapid head protection are difficult in emergency, safety is insufficient, the structure is complex, and parts are inconvenient to replace are solved.

The invention discloses an intracranial electroencephalogram epileptic seizure early warning method based on multipath map brain network modeling, and belongs to the technical field of electroencephalogram analysis. The method comprises the following steps: providing a multi-path map brain network modeling framework for epileptic seizure prediction; the multi-channel brain map is constructed based on three cross-frequency coupling (CFC) relationships in neuroscience, a frequency band in each channel of the electroencephalogram signal forms nodes of the map, and the three CFC relationships form edges of the map; and designing a multi-path brain map learning network with a joint fusion module to learn modes in the multi-path brain map to form a final representation. According to the method, the iEEG signals can be analyzed, the modes in the iEEG signals can be mined by utilizing the designed multi-channel brain map learning network, uniform characterization is formed, and the accuracy of epileptic seizure prediction is improved.