108 results about "Epilepsy seizure" patented technology

In an embodiment, a seizure monitor provides intelligent epilepsy seizure detection, monitoring, and alerting for epilepsy patients or people with seizures. In an embodiment, the seizure monitor may be a wearable, non-intrusive, passive monitoring device that does not require any insertion or ingestion into the human body. In an embodiment, the seizure monitor may include several output options for outputting the accelerometer/gyro or other motion sensor data and video data, so that the data may be immediately validated and/or remotely viewed. The device alerts and communicated to the outside care givers via wirelessly or wired medium. The device may also support recording of accelerometer or other motion sensor data and video data, which can be reviewed later for further analysis and/or diagnosis. The device and invention is also used and applicable for other body motion disorders or detection and diagnostics.

The present invention provides a method for predicting seizures and for detecting seizures, and other potentially injurious or life-threatening events, optionally in conjunction with a mobile device. In an embodiment, a portable device provides epilepsy seizure prediction, detection, monitoring, analysis and alerting for epilepsy patients or other people afflicted with seizures. In an embodiment, the portable device may alert and communicate information to health care providers, caregivers and family members, emergency services, and the like. Collected data may be reviewed and analyzed for use in developing specific criteria for predicting and detecting seizures and other potentially injurious or life-threatening events.

The present disclosure relates to the use of cannabidiol (CBD) for the treatment of atonic seizures. In particular the CBD appears particularly effective in reducing atonic seizures in patients suffering with etiologies that include: Lennox-Gastaut Syndrome; Tuberous Sclerosis Complex; Dravet Syndrome; Doose Syndrome; Aicardi syndrome; CDKL5 and Dup15q in comparison to other seizure types. The disclosure further relates to the use of CBD in combination with one or more anti-epileptic drugs (AEDs).

The invention discloses an epilepsy seizure prediction method based on a semi-supervised deep generative adversarial network. The method comprises the following steps: carrying out segmentation windowing processing on each EEG(electroencephalography) data of N original EEG samples through a sliding window in specified length, and during segmentation, carrying out superposition expansion processingon adjacent sub-windows to obtain S sections of sub-signals; carrying out short-time Fourier transform on the S sections of sub-signals to obtain spectrum signals, and converting the obtained spectrum data into an image for expression; inputting the processed picture information to a discriminator in the semi-supervised deep generative adversarial network for training and classification accordingto labeled training samples, training sample labels and unlabeled training samples; and inputting Z-dimensional noise data into a generator, and generating a pseudo-data image similar to distributionof the unlabeled data through the generator. The method gradually releases dependence of epilepsy seizure prediction on the labeled data through a semi-supervised learning mode, and obtains better classification accuracy.

Disclosed are methods and systems for treating epilepsy by stimulating a main trunk of a vagus nerve, or a left vagus nerve, when the patient has had no seizure or a seizure that is not characterized by cardiac changes such as an increase in heart rate, and stimulating a cardiac branch of a vagus nerve, or a right vagus nerve, when the patient has had a seizure characterized by cardiac changes such as a heart rate increase.

The invention discloses a wearable epilepsy brain-electricity seizure brain area positioning device and method. The device comprises an acquiring electrode, a brain-electricity acquiring chip, a conditioning circuit, a micro-controller, a Bluetooth transmitting module and a Bluetooth receiving module. The method comprises the following steps: transmitting acquired signals to the conditioning circuit by the brain-electricity acquiring chip; carrying out amplification and filtering preprocessing on the acquired brain-electricity signals by the conditioning circuit, and transmitting the brain-electricity signals to a microprocessor; synchronously transmitting data to an mobile phone APP and displaying the data in real time by using a Bluetooth technology, and transmitting the data to a cloud; downloading brain-electricity signals of various channels of a patient by using a terminal of an upper computer from the cloud, extracting features of the received brain-electricity data by using a feature recognition algorithm, and recognizing abnormal epileptic waves. By the wearable epilepsy brain-electricity seizure brain area positioning device and method, brain-electricity change conditions before and after epilepsy seizure of the patient are acquired, transmitted and monitored in real time, and an epileptic focus is positioned accurately; and a Bayesian linear discriminant analysis algorithm is adopted to fulfill a function of automatically examining and positioning epilepsy brain-electricity signals.

A fosphenytoin sodium injection in the form of freeze dried powder for preventing and treating epilepsy is prepared from fosphenytoin sodium, pharmacologically acceptable auxiliary, and water for injection through mixing fosphenytoxin sodium with water for injection and aminobutanetriol, heating while stirring, regulating pH value 8.0-10.0, adding charcoal for injection, heating while stirring, removing charcol, sterilizing and freeze drying.

A classification system of epileptic EEG signals based on non-linear dynamics features includes a preprocessing module, a feature extraction module, a feature sorting module, a feature selection module and a classification module: the preprocessing module uses discrete wavelet transformation to remove noise in the EEG data and obtain effective EEG signal data without noise; the feature extraction module uses multiple entropy algorithms to calculate the non-linear dynamics features of each EEG signal; the feature sorting module sorts features with analysis of variance; the feature selection module selects the optimal feature subset that has the most significant impact on the accuracy of the model uses a uses a forward sequential feature selection algorithm; the classification module transforms the judgment of EEG during the period of epilepsy and EEG during the interval period of epilepsy into a binary classification problem by use of a least squares support vector machine algorithm.

The invention discloses an epilepsy detection method based on a generative adversarial network. According to the method, EEG signals during an epilepsy seizure are generated based on the generative adversarial network, experimental data samples are expanded, the expanded data samples are input into a CNN for training, then the real data samples are used for testing, and the aim of improving accuracy of epilepsy detection is achieved. The epilepsy detection method includes the specific steps which can be described as: step one, data are cut, downsampled and filtered, and processed data fragments are divided into a disease seizure class and a normality class; step two, the generative adversarial network is trained; step three, the trained generative adversarial network is used for generatingepilepsy seizure EGG data; and step four, a training set and a testing set are divided, the CNN is constructed for training and testing, and detection results are analyzed.

The invention relates to an epileptic seizure prediction system and method, and belongs to the technical field of epilepsy seizure prediction. The epileptic seizure prediction system comprises an electroencephalogram signal acquisition module, an electroencephalogram signal segmentation module, a feature information extraction module, a feature filtering processing module, a feature matrix generation module and an epileptic seizure prediction module. According to the epileptic seizure prediction system, the electroencephalogram feature information of the epileptic in the epileptic seizure period is generated through the generative adversarial network of the feature matrix generation module, so that the prediction precision of the epileptic seizure of the epileptic is improved.

The present invention discloses a wearable monitoring instrument for monitoring the partial psychomotor seizure of an epileptic. The wearable monitoring instrument includes a remote processing terminal and four detectors which can be worn on wrists and ankles; the detector comprises an acceleration sensor, a signal conditioning unit, a processing unit, a storage unit and a ZigBee transmitting-receiving unit, wherein the acceleration sensor is connected with the signal conditioning unit, the signal conditioning unit is connected with the processing unit, the processing unit is connected with the storage unit and the ZigBee transmitting-receiving unit; at least one detector is in wireless connection with the remote processing terminal; and the remote processing terminal comprises a data processing module. With the wearable monitoring instrument of the invention adopted, the movement conditions of various parts of the epileptic can be monitored in real time, and analysis and judgment can be carried out according to monitored information; and the limb movement information of the epileptic in seizure can be recorded, and the type of the epilepsy seizure of the epileptic can be obtained through analysis.

The invention provides an epileptic intracranial electroencephalography signal warning method based on a deep convolutional attention network. According to the method, through acquiring an intracranial electroencephalography (iEEG) signal at an ANT (Anterior Nucleus of the Thalamus) of an epilepsy patient for analysis, extracting and fusing a multi-scale time sequence characteristic and a multi-spectrum characteristic of the iEEG signal, simultaneously adopting an attention mechanism, and concerning the most significant features of the iEEG signal in an epileptic seizure period, the warning accuracy of the network is obviously improved. In one embodiment of the invention, verification is carried out on an iEEG signal data set containing five epilepsy patients, the average epilepsy warning accuracy (Sensitivity, Sn) of a single patient can reach 95.0%, the false predicting rate (FPR) per hour is less than 0.15, the warning effect and the model generalization ability are superior to those of an existing epilepsy warning method, accurate and rapid warning of epilepsy is realized, and thus, the method is of great significance in diagnosis and nerve regulation on clinical epilepsy diseases.

The invention discloses a long-term-electroencephalogram automatic epilepsy detection method based on a convolutional neural network. The method includes the following steps that long-term multichannel electroencephalogram signals of epilepsy patients are subjected to synchronous framing sampling, and are labeled; each frame of electroencephalogram signal is denoised with band-pass filter; the channel-exclusive convolutional neural network is constructed; the network is trained, and a detector is obtained. According to the long-term-electroencephalogram automatic epilepsy detection method based on the convolutional neural network, the channel-exclusive convolutional neural network is benefited from the deep learning technology, a deep abstraction of an electroencephalogram mode is obtainedthrough layer-by-layer convolution correlation computation, and automatic detection of epilepsy seizures is achieved.

The invention relates to a telescopic protective alarm bed rail restraint. The bed rail restraint at least comprises an epilepsy analysis module, which is used for receiving and analyzing related datasignals, collected by the epilepsy data collector, about a target object in a target area. The epilepsy analysis system is characterized in that the epilepsy analysis module controls the operation ofthe protection part on the bed body based on the coupling relationship between the response data of the epilepsy analysis module and at least one epilepsy data collector with different awakening conditions and awakening levels; the epilepsy data collector is used for effectively protecting a patient on a bed body during epileptic seizure, and at least comprises an electroencephalogram detection device, an infrared probe and a camera. According to the invention, while a convenient protection function is provided, selective detection can be carried out on the epilepsy patient in the morbidity process, a large number of invalid data signals are prevented from being generated, and the overall safety and detection efficiency of the epilepsy detection process are improved.

A method for reducing epileptic seizures in a subject includes the steps of providing a vibration motor coupled to a controller configured to control vibratory motion of the vibration motor, positioning at least one vibration motor on a limb of the subject, and generating a vibratory stimulation signal configured to stimulate proprioceptive nerves in the limb to trigger kinesthetic cues that stimulate nerves in cerebellar and pontine areas of the brain to suppress seizure activity.

Disclosed are methods and systems for treating epilepsy by stimulating a main trunk of a vagus nerve, or a left vagus nerve, when the patient has had no seizure or a seizure that is not characterized by cardiac changes such as an increase in heart rate, and stimulating a cardiac branch of a vagus nerve, or a right vagus nerve, when the patient has had a seizure characterized by cardiac changes such as a heart rate increase.

Provided herein are methods and compositions related to treating or preventing seizures. In some aspects, provided herein are methods of treating or preventing seizures in a subject by administering to the subject a composition comprising Parabacteroides and Akkermansia bacteria.

The invention provides a multi-dimensional enhanced epileptic seizure prediction system based on a graph convolutional network, the scheme takes a multi-channel spatial relationship as a breakthrough opening, the contribution degree of the multi-channel spatial relationship to epileptic seizure prediction is explored from three dimensions of frequency + space + time. The model comprises three parts, respectively an information reconstruction space, a graph encoder and a space-time predictor. The information reconstruction space and the graph encoder mentioned by the model allow feature enhancement and feature extraction of richer epilepsy electroencephalogram signals, and especially explores the correlation among electroencephalogram channels, so that feature representation is enhanced, and the epilepsy electroencephalogram seizure prediction accuracy is improved; meanwhile, the core structure of the space-time predictor in the scheme adopts a gating circulation unit which is used for exploring the law of epilepsy electroencephalogram signals on the time level, and the purpose of improving the model operation efficiency is achieved by reducing the network parameter scale.

The invention provides a compound of formula (I):

(where R₁, Q, A and R₂ are disclosed herein) or a pharmaceutically acceptable salt thereof (a “Pyridine-alkynyl Compound”); pharmaceutical compositions comprising an effective amount of a Pyridine-alkynyl Compound; and methods for treating or preventing a condition such as pain, urinary incontinence, an addictive disorder, Parkinson's disease, parkinsonism, anxiety, epilepsy, a seizure, stroke, a pruritic condition, psychosis, a cognitive disorder, a memory deficit, restricted brain function, Huntington's chorea, amyotrophic lateral sclerosis, dementia, retinopathy, a muscle spasm, a migraine, vomiting, dyskinesia or depression in an animal comprising administering to an animal in need thereof an effective amount of a Pyridine-alkynyl Compound.