183 results about "Epileptic seizure" patented technology

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 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 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 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 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 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.

The invention belongs to the technical field of biomedical signal processing, and discloses an SEEG attack detection method based on a time-frequency space-dynamic graph attention network, which comprises the following steps: constructing an epilepsy brain network sequence, defining SEEG lead points as nodes of a graph, and calculating directed connection edge weights among the nodes through a multiband directed transfer function; constructing a node feature matrix based on epilepsy induced epilepsy index features; arranging the multiband epilepsy brain network according to a time sequence to form a dynamic sequence; extracting space-spectrum correlation by using a GATv2 dynamic attention mechanism, and dynamically weighting node connection weight through a learnable attention matrix; and in combination with a time sequence convolutional network and a time attention mechanism, key time sequence features are extracted, identified and focused. According to the method, a good dynamic propagation characteristic learning capability is obtained, an SEEG pathological connection mode is disclosed, a model mechanism corresponds to an epileptic seizure propagation mechanism, and the method has a good application prospect in the aspects of epileptic seizure detection and epileptic seizure auxiliary positioning.

The present invention relates to the use of selective aquaporin inhibitors, e.g., of aquaporin-4 or aquaporin-2, e.g., certain phenylbenzamide compounds, for the prophylaxis, treatment and control of aquaporin-mediated conditions, e.g., diseases of water imbalance, for example edema (particularly edema of the brain and spinal cord, e.g., following trauma or ischemic stroke, as well as the edema associated with glioma, meningitis, acute mountain sickness, epileptic seizures, infections, metabolic disorders, hypoxia, water intoxication, hepatic failure, hepatic encephalopathy, diabetic ketoacidosis, abscess, eclampsia, Creutzfeldt-Jakob disease, and lupus cerebritis, as well as edema consequent to microgravity and / or radiation exposure, as well as edema consequent to invasive central nervous system procedures, e.g., neurosurgery, endovascular clot removal, spinal tap, aneurysm repair, or deep brain stimulation, as well as retinal edema), as well as hyponatremia and excess fluid retention, and diseases such as epilepsy, retinal ischemia and other diseases of the eye associated with abnormalities in intraocular pressure and / or tissue hydration, myocardial ischemia, myocardial ischemia / reperfusion injury, myocardial infarction, myocardial hypoxia, congestive heart failure, sepsis, and neuromyelitis optica, as well as migraines, as well as to novel assays for identifying aquaporin inhibitors.

The invention discloses an epileptic seizure video detection method based on a memory enhanced double-branch auto-encoder. The method comprises the following steps: firstly, preparing and preprocessing data; secondly, constructing an ME-DBAE model which comprises a query generation module, a multi-head memory module and a double-branch decoder module; training the model by using training set data, finally inputting a to-be-detected video into the trained model to obtain an epileptic seizure score, and judging whether the video belongs to an epileptic seizure sample or not through a threshold value. By designing the multi-head memory module, diversified normal motion prototypes are stored in parallel, and the limitation that a single memory structure is difficult to adapt to complex normal actions is broken through; and by constructing a double-branch decoder architecture, double tasks of input reconstruction and future frame prediction are cooperatively realized, and meanwhile, space structure and time trend characteristics of normal motion are captured, so that epileptic seizure actions and normal actions are effectively distinguished.

The invention discloses an epileptic seizure automatic detection method, system and device based on an electroencephalogram and a medium. The method comprises the steps of preprocessing a to-be-detected one-dimensional electroencephalogram signal of a user to obtain a plurality of electroencephalogram signal sequences, inputting the electroencephalogram signal sequences into a pre-trained synchronous learning network to obtain a classification result, and mapping the classification result back to the to-be-detected one-dimensional electroencephalogram signal of the user to obtain a time positioning result of epileptic seizure. The synchronous learning network comprises a multi-scale convolution module, a feature extractor, a batch processing self-attention module and a classification module. According to the invention, the multi-scale convolution module and the batch processing self-attention module in the synchronous learning network make full use of the information in the samples and between the samples, the discrimination of the extracted features is improved without any additional data, the generalization is strong, and the positioning result is accurate.

The invention discloses an epileptic seizure prediction method based on a multi-scale mixed graph attention convolutional network. The epileptic seizure prediction method comprises the following steps: 1, performing data processing on electroencephalogram signals; 2, processing an electroencephalogram signal space sequence through graph construction and convolution; 3, the global correlation of the electroencephalogram signal time sequence is processed through an iTransform (Inverted Transform) model, and an element sequence is output by a decoder; 4, processing electroencephalogram signal time sequence local correlation through a multi-scale time adaptive network, and predicting epileptic seizure; and 5, classifying the epilepsy types of the discriminated seizures through a classifier. Compared with a traditional electroencephalogram diagnosis method, an iTransform model is introduced, input is converted into segment sequence level features from time node level features, and representation of time sequence features is optimized through an adaptive attention mechanism and a feedforward network. In addition, the method also adopts a multi-scale time adaptive network, so that the accuracy and timeliness of epileptic seizure prediction are improved.

The invention discloses an epileptic seizure detection and positioning system and method combining time convolution and Transform network, and the system comprises a data obtaining module which is configured to be used for obtaining a to-be-detected multi-channel electroencephalogram signal through an electrode, a multi-channel electroencephalogram amplifier and an A / D converter which are connected in sequence, dividing the multi-channel electroencephalogram signal into a plurality of multi-channel electroencephalogram signal segments according to time; the data processing module is configured to be used for extracting time-frequency characteristics of each multi-channel electroencephalogram signal segment through S transformation and performing dimension reduction processing; the epileptic seizure detection module is configured to be used for inputting the multi-channel electroencephalogram signal fragments subjected to dimension reduction processing into the trained TCN-Transformer model to obtain an epileptic seizure detection result; and the epilepsy positioning module is configured to calculate the weight of each channel based on the epilepsy seizure probability score when the epilepsy seizure is detected, generate the final activation vector of each channel based on the weight, and realize the positioning and visualization of the epilepsy seizure based on the activation vector. According to the method, the multi-scale time-frequency-space characteristics from the multi-channel EEG data can be more comprehensively captured, so that the accuracy and robustness of epileptic seizure detection and focus positioning are improved.

The invention provides an epileptic seizure early warning and help seeking system and method, and relates to the technical field of medical assistance. The system comprises a neck ring body, an electroencephalogram signal acquisition module, a respiration-heart rate acquisition module, a blood pressure value acquisition module, a blood oxygen value acquisition module, an electromyographic signal acquisition module, a muscle pressure value acquisition module, a first chip, a second chip, Bluetooth, a wireless transmitting coil, an early warning and help seeking unit and an upper computer. The electroencephalogram signal acquisition module and the electromyographic signal acquisition module are both connected with the first chip, the first chip, the respiration-heart rate acquisition module, the blood pressure value acquisition module, the blood oxygen value acquisition module, the muscle pressure value acquisition module and the Bluetooth are all connected with the second chip, and the wireless transmitting coil and the early warning and help seeking unit are both connected with the Bluetooth. And the wireless transmitting coil is wirelessly connected with an upper computer. According to the invention, the danger caused by epileptic seizure and the probability of accidental death can be reduced.

Described herein are systems and methods for seizure detection. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a seizure detection module in communication with the data module configured to process and classify the data to detect various types of seizure activity using multiple classifiers. A control policy may be employed to determine a seizure burden on the aggregated seizure activity data and / or classifications. When the seizure burden is equal to or exceeds a threshold, a notification may be generated. The notification may be usable by a healthcare practitioner to assess whether the subject is having a seizure or at risk of having a seizure.