4 results about "Seizure disorder" patented technology

The present application provides a kind of potassium channel regulator, its preparation method, pharmaceutical composition and application. Specifically provided are compounds as shown in formula V or pharmaceutically acceptable salts thereof. The potassium channel regulator compound of the present application has one or more of the following effect advantages: (1) novel structure; (2) good KCNQ2 / 3 channel opening activity; (3) weak KCNQ4, KCNQ5 opening activity, high KCNQ2 / 3 selectivity; (4) good in vivo efficacy, good protection effect on seizures; (5) low neuro-motor toxicity; (6) high brain-blood ratio; (7) high plasma, brain tissue concentration.

Systems and method for seizure detection. A seizure detection device utilizes frequency discrimination, time series features, and machine learning clustering algorithms to distinguish between the EEG signal of those experiencing a seizure compared to those who are not experiencing a seizure.

The present invention relates to a brain dysfunction and seizure detector monitor and system, and a method of detecting brain dysfunction and / or seizure of a subject. Preferably, the present invention also includes one or more seizure detection algorithms. The analysis method is specifically optimized to amplify abnormal brain activity and minimize normal background activity yielding a seizure index directly related to the current presence of ictal activity in the signal. Additionally, a seizure probability index based on historical values of the aforementioned seizure index, is derived for diagnostic purposes. The seizure probability index quantifies the probability that the patient has exhibited abnormal brain activity since the beginning of the recording. These indexes can be used in the context of emergency and / or clinical situations to assess the status and well-being of a patient's brain, or can be used to automatically administer treatment to stop the seizure before clinical signs appear.

The application discloses a cross-patient seizure detection method based on a graph-enhanced pulse state space model, first, the brain electrical signal data of a patient is acquired and an electrode space topology graph is constructed; then, a pulse state space model is used to extract the time sequence features of physiological channels, and a pulse graph convolution network is used to perform message aggregation between adjacent nodes in a discrete pulse domain across time steps; finally, feature aggregation is performed through a global average pooling operation at a graph level, and a two-class prediction probability is output through a fully connected classifier. The application follows the design logic of "first time and then space", the pulse state space model models the potential brain state dynamics of the time sequence change, and realizes stable long context feature processing through the cyclic updating mode of the adaptive hardware; the pulse graph neural network completes the information transmission and fusion between channels based on the electrode correlation graph, can capture the spatial dependence relationship which is crucial for the representation of seizures, reduces the calculation energy consumption, and improves the overall performance.