17 results about "Seizure activity" patented technology

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.

Neonatal epileptic seizures differ from adult epileptic seizures, and many antiepileptic drugs that are effective in adults often fail to treat neonatal epilepsy. Here we show that gluconic acid, a natural organic acid abundant in fruit and honey, and the glucose oxidase enzyme effectively suppress neonatal epilepsy both in vitro and in vivo. Sodium gluconate is demonstrated to suppress epileptiform burst activity in cell culture and protect neurons from kainic acid-induced cell death. Sodium gluconate also suppresses epileptiform burst activity in brain slices in a manner far more effective in neonatal animals than in aged animals. Sodium gluconate suppresses seizure activity in neonatal animals in a manner far more effective than in adult animals. Consistently, in vivo EEG recordings also revealed that sodium gluconate suppresses seizure activity in neonatal animals in a manner far more effective than in adult animals. Mechanistically, sodium gluconate suppresses voltage-dependent CLC‑3 C1 in neuronal cultures and hippocampal slices ‑ Together, these data suggest a new class of antiepileptic drugs, gluconate, that blocks CLC‑3 C1 ‑ The channel effectively suppresses neonatal epileptic seizures.

A device for measurement of neurological and / or cognitive function of a user's brain to is disclosed, as well as a system for analyzing the measured activity to notify a user of such neurological and / or cognitive function for measuring brain capacity, output, tracking learning methods, seizure activity, sleep, and / or other related events to provide better understanding, efficiency, and predictability of a user's brain function.

CASK-related disorders are caused by mutations in an important regulatory gene in the brain, calcium / calmodulin-dependent serine protein kinase (CASK) and can result in microcephaly with pontine and cerebellar hypoplasia (MICPCH), in addition to intellectual disability, and seizure activity. CASK-related Intellectual Disabilities (CASK-ID) predominantly affect the female population and are classified as X-linked neurodevelopmental disorders. CASK-ID is typically the result of a de novo loss of function mutation. As females have two Xchromosomes, disease causing mutations present with a 50 / 50 expression of mutant and wildtype, due to the mosaicism caused by random X-chromosome inactivation (XCI). This mosaicism presents an interesting therapeutic option of reactivating the intact allele in affected neurons. Disclosed herein are gene editing systems that can enhance in vivo CASK expression.

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

The present invention relates to a method for determining the onset time and excitability of brain regions that are not observed as recruited or non-recruited during seizure activity in the brain of an epilepsy patient. The method according to the present invention comprises the steps of: providing a dynamic model of the propagation of an epileptic seizure in a brain network; providing a statistical model that defines the probability of the dynamic model generating a set of observations of the state of the brain network; using the statistical model and a data set of observations of a training cohort to train the dynamic model of the propagation of the epileptic seizure; and inverting the trained dynamic model and using the statistical model to infer the onset time and excitability of a third region from the onset times observed for a first and a second region.

A computer-implemented method can include receiving data indicative of brainwave activity over a particular time period. The method can also include determining, based on the data, at least one metric associated with at least one sleep state for a subject. Additionally, the method can include determining, based on the at least one metric, a risk level associated with seizure activity for the subject. The method can further include generating an output indicating the risk level.

Methods and apparatus for preventing seizure activity in an environment using a computer vision system deployed within the environment are described. One example method includes detecting a seizure trigger within the environment via one or more camera devices deployed in the environment. At least one user associated with the environment that is susceptible to the seizure trigger is identified. Information associated with the seizure trigger is transmitted to a computing device associated with the at least one user.

Methods, systems, and apparatus for determining probabilistic measures of seizure activity (PMSA) values based on a plurality of seizure detection algorithms and / or body signals used as inputs by the seizure detection algorithms. Use of the PMSA values to detect seizure activity based on a consensus of the algorithms and / or body signals, and / or warn, log, administer a therapy, or assess the efficacy of a therapy.

Methods, systems, and apparatus for determining probabilistic measures of seizure activity (PMSA) values based on a plurality of seizure detection algorithms and / or body signals used as inputs by the seizure detection algorithms. Use of the PMSA values to detect seizure activity based on a consensus of the algorithms and / or body signals, and / or warn, log, administer a therapy, or assess the efficacy of a therapy.

Methods and apparatus for preventing seizure activity in an environment using a computer vision system deployed within the environment are described. One example method includes detecting a seizure trigger within the environment via one or more camera devices deployed in the environment. At least one user associated with the environment that is susceptible to the seizure trigger is identified. Information associated with the seizure trigger is transmitted to a computing device associated with the at least one user.

A method of estimating the probability of a seizure in a subject, the method comprising: receiving historical data associated with epileptic events experienced by the subject over a first time period, the historical data comprising physiological data associated with each epileptic event and a time at which each epileptic event occurred; generating a temporal probability model of future epileptic events based on the time of each of the epileptic events, the temporal probability model representing a probability of a future seizure occurrence in each of a plurality of time windows; generating a probabilistic model based on the physiological data associated with each epileptic event; weighting the probabilistic model based on the temporal probability model to generate a weighted probabilistic model of future seizure activity; and outputting an estimate of seizure probability in the subject using the weighted probabilistic model.

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.

A device for measurement of neurological and / or cognitive function of a user's brain to is disclosed, as well as a system for analyzing the measured activity to notify a user of such neurological and / or cognitive function for measuring brain capacity, output, tracking learning methods, seizure activity, sleep, and / or other related events to provide better understanding, efficiency, and predictability of a user's brain function.

Provided herein is a method of treating seizure activity by administering to a subject in need of treating seizure activity a therapeutically effective amount of a cellular composition that includes pluripotent stem cell-derived, MGE-type inhibitory (e.g., GABAergic) neuron cells. In some embodiments, at least 90% of the cells of the composition are post-mitotic cells. In some embodiments, at least 50% of the cells of the composition express LHX6, ERBB4, and GAD1.

A computer-implemented method may include receiving data indicative of brain wave activity over a particular time period. The method may also include determining at least one metric associated with at least one sleep state of the subject based on the data. In addition, the method may include determining a risk level associated with seizure activity of the subject based on the at least one metric. The method may also include generating an output indicative of the risk level.

Provided herein is a method of treating epileptic seizure activity by administering to a subject in need thereof a therapeutically effective amount of a cell composition comprising pluripotent stem cell-derived, MGE-type inhibitory (e.g., GABAergic) neuronal cells. In some embodiments, at least 90% of the cells in the composition are post-mitotic cells. In some embodiments, at least 50% of the cells in the composition express LHX6, ERBB4, and GAD1.