11 results about "Epilepsy seizure" patented technology

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.

Disclosed are methods and compositions relating to antisense therapy for treating epilepsy, such as a focal epilepsy and temporal lobe epilepsy, in a subject in need thereof by targeting GRIK2 mRNA. In particular, the disclosure provides methods for treating symptoms (e.g., seizures) of epilepsy in a subject by administering a particular dose in a defined volume and in a specific route of administration of an inhibitory ribopolynucleotide or adeno-associated viral vector encoding the same, which is capable of inhibiting expression of GRIK2.

Disclosed are methods and compositions relating to antisense therapy for treating epilepsy, such as a focal epilepsy and temporal lobe epilepsy, in a subject in need thereof by targeting GRIK2 mRNA. In particular, the disclosure provides methods for treating symptoms (e.g., seizures) of epilepsy in a subject by administering a particular dose in a defined volume and in a specific route of administration of an inhibitory ribopolynucleotide or adeno-associated viral vector encoding the same, which is capable of inhibiting expression of GRIK2.

The application provides a method and system for seizure detection and automatic annotation based on electroencephalogram data, comprising the following steps: step 1, acquiring electroencephalogram records with the possibility of seizures and pre-processing; step 2, building an SC-LSTM model based on deep learning, including two parallel feature extraction modules and a classification module, for feature extraction, feature selection fusion and seizure detection and classification of electroencephalogram. The deep learning technology used in the application can automatically capture effective features from EEG signals and realize end-to-end seizure detection. The application of deep learning technology to seizure detection can reduce the workload of neurologists, improve work efficiency and improve detection accuracy.

This invention discloses the application of the scorpion venom polypeptide BmK AS in the treatment of chronic epilepsy. BmK AS exerts potent anti-epileptic and neuroprotective effects through a confluence mechanism. In a lycine-induced mouse model, BmK AS significantly shortened seizure duration, reduced seizure frequency, prolonged seizure latency, and improved cognitive and mental outcomes. Electrophysiologically, BmK AS nonlinearly inhibited multiple VGSC subtypes, with a particularly significant effect on Nav1.6, reducing peak sodium current by 43% at a concentration of 5 nM. Furthermore, BmK AS alleviated hippocampal neuroinflammation by inhibiting the NLRP1 inflammasome pathway and related pyroptosis. This invention establishes BmK AS as a promising multi-mechanism therapeutic candidate, highlighting the value of a dual therapeutic strategy of Nav1.6 blockade and neuroinflammation suppression for epilepsy treatment.

The application discloses a kind of epilepsy closed loop intelligent processing method based on micro machine learning.The method comprises: the pre-processing of input electroencephalogram signal;Lightweight one-dimensional convolutional neural network model is used to analyze and judge the electroencephalogram feature after processing, and when the seizure state is identified, the treatment control module is triggered to output pulse signal, guides nerve stimulation device to intervene epilepsy, and realizes closed loop treatment.The present application is based on embedded platform, with the advantages of fast response, efficient calculation, wearable, etc., suitable for real-time epilepsy monitoring and intervention in family and mobile scene, effectively improve the intelligent and personalized level of epilepsy management.

This invention discloses a method for detecting neonatal epilepsy using electroencephalography (EEG) based on multimodal spatiotemporal feature fusion, comprising: acquiring EEG signals through electrode pairs; preprocessing the acquired EEG signals to obtain preprocessed EEG signals; and inputting the preprocessed EEG signals into a trained epilepsy EEG detection model to obtain detection results. The preprocessed EEG signals include three-dimensional time-domain signals, three-dimensional frequency-domain signals, one-dimensional time-domain signals, and one-dimensional frequency-domain signals. The epilepsy EEG detection model includes: a first convolution branch for convolutional operations on the three-dimensional time-domain signals, a second convolution branch for convolutional operations on the three-dimensional frequency-domain signals, a third convolution branch for convolutional operations on the one-dimensional time-domain signals, a fourth convolution branch for convolutional operations on the one-dimensional frequency-domain signals, and a fusion decision layer. This invention extracts spatial-temporal-frequency-domain features, achieving accurate identification of neonatal epilepsy seizures under low signal-to-noise ratio conditions.

The application relates to a memristor reserve pool computing circuit and a prediction method for real-time epilepsy prediction, and belongs to the technical field of electroencephalogram signal processing. The circuit comprises a plurality of levels of series-connected reserve pool computing architectures. Each level of architecture is used for receiving a multichannel EEG signal or a multichannel binary coded signal, and after high-dimensional feature mapping, nonlinear time feature extraction and feature fusion are sequentially performed on the multichannel EEG signal or the multichannel binary coded signal by a mask circuit, a reserve pool module based on a volatile memristor and a multiply-accumulate circuit comprising a cross array based on a non-volatile memristor in the architecture, the parallel output road fusion signal is output to a feature binaryzation circuit in the architecture, signal binaryzation processing is performed on the parallel output road fusion signal, a multichannel binary coded signal is output in parallel as an input of a next level of architecture, and finally, a binary coded signal output by a last level of architecture is output as an epilepsy prediction result. The application provides a hardware efficient solution for real-time low-power epilepsy seizure prediction.

This invention provides the application of GSK484 in the preparation of anti-status epilepsy drugs, offering a new approach to the treatment of status epilepsy. The results of this invention show that GSK484 can significantly prolong the latency of Pilo-induced status epileptic seizures in mice, reduce the seizure severity in a Pilo-induced status epileptic model, and significantly reduce the duration of Pilo-induced status epileptic seizures. GSK484 can significantly inhibit neutrophil activation and extracellular trap formation in status epilepsy, and it does not affect mouse body weight. Therefore, GSK484 is an effective treatment for status epilepsy with good safety and minimal toxic side effects.

The present application relates to a kind of vagus nerve electric stimulation efficacy prediction method based on deep learning and multi-modal data, belong to medical artificial intelligence cross technical field.The method of the present application includes: collecting the electroencephalogram signal of epilepsy patient before vagus nerve electric stimulation and multi-dimensional clinical features;The electroencephalogram signal is preprocessed, and the multi-dimensional electroencephalogram static timing feature is obtained by using electroencephalogram feature extraction model automatic feature extraction;Based on the multi-dimensional electroencephalogram static timing feature, corresponding dynamic brain network timing feature is constructed, and is fused with the multi-dimensional clinical features of patient to obtain multi-modal timing feature;Based on multi-modal timing feature, the seizure reduction rate of patient after vagus nerve electric stimulation is predicted by using deep learning regression model.The present application constructs multi-modal timing feature including dynamic brain network timing feature and multi-dimensional clinical features, realizes the prediction of curative effect by using sample level and patient level two-stage modeling, improves the prediction accuracy, improves the clinical reference value of prediction result.

This invention relates to a group of synthetic cannabinoid compounds having structural formula I as defined herein. The invention also relates to compositions comprising these compounds, methods for preparing these compounds, intermediates that can be used to prepare these compounds, and the use of these compounds as medicines, particularly for treating conditions associated with seizures such as epilepsy.