38 results about "Seizure detection" patented technology

The invention discloses an epileptic seizure detection system based on a multi-dimensional hypergraph fusion network, which comprises a multi-modal data collector, a signal preprocessor, a feature extractor, a multi-dimensional hypergraph builder, a hypergraph feature extractor, a feature fusion device, a classifier and a time sequence prediction corrector, the multi-dimensional hypergraph constructor constructs three hypergraph structures, namely an intra-modal hypergraph, an inter-modal hypergraph and a time sequence hypergraph; the feature fusion device integrates the features extracted by the three hypergraphs to generate a comprehensive feature vector; the classifier classifies the fusion features through a two-layer feedforward neural network, and outputs probability prediction of epileptic seizure; the time sequence prediction corrector applies a majority voting smoothing filter to carry out post-processing on a classification result, isolated misclassification is eliminated, and the time sequence consistency of prediction is enhanced. The invention provides an innovative multi-dimensional hypergraph fusion network, and the robustness and accuracy of epileptic seizure detection are remarkably improved by simultaneously modeling high-order relationships in modals, between modals and time sequence dimensions.

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.

Implementations described and claimed herein provide systems and methods for a cloud-based seizure management platform for personalized management of seizures while providing assured connectedness across multiple stakeholders. The systems and methods address the needs of a patient in the area of seizure management, through such functionalities as seizure detection, seizure histories and other health-related data management, stakeholder connectedness, tachyphylaxis detection, drug titration guidance, and / or treatment aggressiveness management. The system provides for access for multiple parties, including allowing neurologists and / or caregivers to monitor progression of neurological conditions on a continuous basis while at home or otherwise remote from the patient. The unique methodology of the seizure management system includes wearable technologies coupled with artificial intelligence, machine-learning algorithms, and data modeling techniques to enable personalization of care.

The invention discloses an unbalanced classification method for electroencephalogram data in epilepsy detection, and relates to the technical field of data optimization and big data processing. According to the method, firstly, covariance matrixes of majority class samples and minority class samples are calculated, linear transformation is achieved through matrix decomposition, and the minority class samples inherit global distribution characteristics of the majority class samples; and then, in the transformed feature space, sorting samples based on mahalanobis distance and performing partition pairing, selecting sample pairs with large difference to generate convex combination synthesis samples, and ensuring sample diversity and boundary consistency. Experimental results show that on a CHB-MIT electroencephalogram data set, the method effectively solves the problems that a traditional oversampling technology is prone to expanding minority class decision boundaries and generated samples are lack of diversity, and the reliability of epileptic seizure detection is remarkably improved.

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 application discloses a system and method for synchronously detecting epilepsy attack and type identification, and the system comprises an electroencephalogram signal processing module, a multi-scale time-space-frequency feature electroencephalogram fusion module, an epilepsy attack detection task branch, a cross-task attention interaction module, an epilepsy attack type classification branch and a multi-task learning optimization module; the cross-task attention interaction module realizes bidirectional feature sharing between the task branches by establishing an information interaction mechanism at a feature level, so that the epilepsy attack detection task can utilize fine-grained structural information related to attack types to improve detection precision, and meanwhile, the attack type classification task can utilize attack time positioning and context information provided by the detection task; the application realizes collaborative modeling of epilepsy attack detection and attack type classification, improves the accuracy, robustness and clinical application value of epilepsy attack identification by sharing feature representation and cross-task information interaction, while ensuring the calculation efficiency.

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 application belongs to the technical field of electroencephalogram signal detection, and specifically discloses an epilepsy electroencephalogram signal detection system and method based on a diffusion attention model. The system comprises: an acquisition unit configured to acquire an electroencephalogram signal to be detected and perform preprocessing; a reconstruction unit configured to input the preprocessed electroencephalogram signal into a diffusion attention model to perform denoising and simultaneously extract epilepsy seizure-related features; wherein the diffusion attention model uses a diffusion model to reconstruct the signal by gradually denoising and introduces an adaptive gradient correction term to iteratively optimize the signal; in the reconstruction process at each stage, the adaptive attention mechanism is used to enhance the extraction of epilepsy seizure-related features; and a detection unit configured to use a classifier to classify the epilepsy seizure-related features to obtain an epilepsy seizure detection result. The application improves the feature extraction capability of high-dimensional noisy electroencephalogram signals and significantly improves the accuracy of epilepsy seizure detection.

Techniques for automatically detecting a seizure of a patient are presented. The techniques include: obtaining patient EEG data, where the patient EEG data represents an EEG of the patient for a plurality of channels, each channel representing a respective location in or on a brain of the patient; evaluating a tendency to act as a sink for each channel; determining, for each tendency to act as a sink, a respective energy distribution according to frequency; assessing an entropy of each energy distribution according to frequency; measuring, from at least one of the plurality of entropy quantifications, at least one sink tendency entropy drop value; identifying, based on the sink tendency entropy drop value, a presence of a patient seizure proximate to a time of a sink tendency entropy drop; and outputting an indication of the patient seizure.

This specification describes techniques for seizure detection in mammals. A first set of electrodes is used to stimulate a region of a brain of a mammal, and an electrical signal sensed by a second subset of electrodes is acquired during stimulation. The electrical signal is segmented into time-segmented portions according to a value of a time parameter. For each time-segmented portion of the electrical signal, a power is determined for the portion of the signal within a frequency band defined by a value of a frequency parameter. A classifier is used to classify the time-segmented portion of the signal as seizure positive or seizure negative based on the determined power within the frequency band. The values of the time and frequency parameters can result in the classifier achieving an effective level of performance defined by an area under a precision-recall curve (AU-PRC) for the classifier of at least 0.5.

The application provides a severe encephalitis patient seizure automatic detection device based on brain network optimization, characterized by comprising an acquisition module, a preprocessing module and a detection module, the acquisition module acquires original electroencephalogram signals of the encephalitis patient to be detected; the preprocessing module inputs the detected electroencephalogram signals after preprocessing into a recognition model in the detection module to obtain a seizure detection result; the recognition model in the detection module integrates multiple brain networks and multiple features, more comprehensively reflects the relationship between each node in the brain network from multiple related dimensions; and the network layer and feature layer optimization based on the improved genetic algorithm realizes the optimal determination of multiple network weighting coefficients and multiple network weighting coefficients, takes the optimal network layer and feature layer as machine learning input, thereby greatly improving the accuracy of epilepsy recognition.

A kind of epilepsy signal detection method based on channel selection time convolution network, the score of electroencephalogram channel is calculated by Max-Relevance and Min-Redundancy (MRMR), and electroencephalogram channel is sorted according to score, and the most suitable electroencephalogram channel is selected according to sorting, to complete the channel selection of electroencephalogram signal, the feature of electroencephalogram signal after channel selection is trained using neural convolution network, the trained model is first carried out epilepsy signal seizure detection based on fragment to select the optimal channel electroencephalogram data of patient, then the particle swarm optimization algorithm is used to optimize Kalman filter parameters to carry out epilepsy signal seizure detection based on event;The present application has the advantages of high sensitivity, high accuracy and low false alarm rate, is suitable for processing long-term, unbalanced electroencephalogram signal, and effectively improves the performance of epilepsy automatic detection.

We report a method of determining an occurrence of an epileptic convulsive seizure in a patient, comprising: receiving body data from a patient during a first time period, determining a work level relating to said first time period at least based partially upon said body data; determining whether said work level exceeds an extreme work level threshold; performing a responsive action, in response to a determination that said work level exceeds said extreme work level threshold. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

This specification describes techniques for seizure detection in mammals. A first set of electrodes is used to stimulate a region of a brain of a mammal, and an electrical signal sensed by a second subset of electrodes is acquired during stimulation. The electrical signal is segmented into time-segmented portions according to a value of a time parameter. For each time-segmented portion of the electrical signal, a power is determined for the portion of the signal within a frequency band defined by a value of a frequency parameter. A classifier is used to classify the time-segmented portion of the signal as seizure positive or seizure negative based on the determined power within the frequency band. The values of the time and frequency parameters can result in the classifier achieving an effective level of performance defined by an area under a precision-recall curve (AU-PRC) for the classifier of at least 0.5.

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.

This invention proposes a method for detecting epileptic seizure signals based on fuzzy clustering and long short-term memory networks. The method involves acquiring and preprocessing physiological signals, followed by feature extraction. Fuzzy C-means clustering (FCM) is used to divide samples into non-seizure phase samples and potential seizure phase samples. Potential seizure phase samples are reconstructed and input into an LSTM model for training. The trained LSTM model is then used to estimate the seizure probability of potential seizure phase samples. This invention employs a two-level classifier, which reduces algorithm complexity and improves real-time performance by filtering motion states. Furthermore, the combination of fuzzy C-means clustering and long short-term memory networks enhances the utilization of temporal information, effectively improving algorithm performance and enabling rapid and accurate determination of epileptic seizures, thereby improving the accuracy and practicality of epileptic seizure detection.

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 relates to a single-channel seizure detection device, comprising: a data acquisition unit configured to acquire single-channel electroencephalogram data to be detected and to pre-process the single-channel electroencephalogram data to be detected; an electroencephalogram feature extraction unit configured to extract time-frequency features of the pre-processed single-channel electroencephalogram data and to perform standardization processing on the extracted time-frequency features; and a seizure detection unit configured to sequentially pass the standardization-processed time-frequency features through an input layer, an Embedding layer, a feature generalization module, a global pooling layer, a 1x1 convolution layer and a Linear output layer to obtain a detection result; the application combines the light convolution calculation advantages of the partial convolution idea and the advantages of the Style-domain generalization algorithm, and further divides and processes frequency domain information to reduce the increase of calculation amount, so that the detection model enhances the cross-patient detection effect of single-channel seizure electroencephalogram and makes the network lighter, and the generalization and real-time performance of the detection model are greatly improved.

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.

The present disclosure provides systems and methods for seizure detection. The epileptic seizure detection method may include receiving a plurality of electroencephalogram (EEG) signals for a subject via a plurality of channels, preprocessing the plurality of EEG signals by segmenting the plurality of EEG signals for each channel into a plurality of time data segments, extracting a plurality of features from each time data segment for each channel, and applying a machine learning algorithm to the plurality of features to perform seizure binary classification for each time data segment of each channel. A control strategy can be employed to determine the epileptic seizure burden of the aggregated epileptic seizure binary classification. A notification may be generated when the seizure burden equals or exceeds a threshold. A healthcare practitioner may use the notification to assess whether the subject is likely to be at risk of having an epileptic seizure.

The present invention relates to a quantitative electroencephalogram (QEEG) monitor and system capable of monitoring and displaying simultaneously neuropathological characteristic and activity of both sides of a subject's brain. The methods include various indices and examination of differences in these indices by which neurophysiological conditions or problems can be identified and treated. These methods, and the systems and devices using these methods preferably can be used for identifying these neurophysiological conditions or brain dysfunction with monitors and methods for seizure detection, for sedation monitoring, for anesthesia monitoring, and the like. These bilateral brain monitoring methods and systems, and the devices using these methods can be used by individuals or clinicians with little or no training in signal analysis or processing. These bilateral monitoring methods can also be used in a range of applications.

The invention discloses an epileptic seizure detection system based on a double-branch space-time diagram neural network. The epileptic seizure detection system comprises an electroencephalogram signal collector, a signal preprocessor, a double-branch diagram constructor, a double-branch diagram neural network feature learning device, a feature fusion device and a classifier. Wherein the double-branch graph constructor comprises a time branch and a space branch; the time branch maps a down-sampled electroencephalogram sequence into a graph structure for each channel by adopting a horizontal visibility graph algorithm; the spatial branch constructs node features based on multi-scale visual graph network statistical features, and constructs edge weights based on dispersion indexes and cross-channel phase amplitude coupling. The double-branch graph neural network feature learner automatically learns time dynamic features and space correlation features from two types of graph structures, and aggregates multi-channel information through an attention mechanism. And the feature fusion device adopts an attention mechanism to carry out weighted fusion on the double-branch features. According to the invention, the accuracy of epilepsy detection is significantly improved by fusing spatial-temporal features and graph neural network automatic learning through a double-branch architecture.

The application discloses a PICU seizure detection system and a storage medium, and a program is stored on the storage medium. When the program is executed by a processor, the following steps are implemented: real-time acquisition of electroencephalogram data, electrocardiogram data and electromyogram data of a subject with epilepsy; segmentation of the electroencephalogram data into multiple electroencephalogram data segments, and detection of each electroencephalogram data segment to obtain suspicious electroencephalogram data segments; acquisition of electrocardiogram data segments and electromyogram data segments according to the suspicious electroencephalogram data segments, and feature extraction of the suspicious electroencephalogram data segments, the electrocardiogram data segments and the electromyogram data segments to obtain electroencephalogram suspicious segment features, electrocardiogram suspicious segment features and electromyogram suspicious segment features; and detection of whether the suspicious electroencephalogram data segments are real seizure segments.

The application discloses a kind of epilepsy detection and positioning system and method combined time convolution and Transformer network, comprising: data acquisition module is configured to be used to obtain the multichannel electroencephalogram signal to be measured by sequentially connected electrode, multichannel electroencephalograph amplifier and A / D converter, and multichannel electroencephalogram signal is divided into multiple multichannel electroencephalogram signal segments according to time;Data processing module is configured to be used for each multichannel electroencephalogram signal segment, the time-frequency characteristics of multichannel electroencephalogram signal segment are extracted by S transform, and dimension reduction processing is carried out;Epilepsy detection module is configured to be used to input the multichannel electroencephalogram signal segment of dimension reduction processing to trained TCN-Transformer model, and obtain epilepsy detection result;Epilepsy positioning module is configured to be used to calculate the weight of each channel based on epilepsy seizure probability score when detecting epilepsy, generate the final activation vector of each channel based on weight, and realize the positioning and visualization of epilepsy seizure based on the activation vector.The application can more comprehensively capture multiscale time-frequency space features from multichannel EEG data, so as to improve the accuracy and robustness of epilepsy detection and lesion positioning.

The application discloses a seizure detection system based on EEG feature distribution adaptation transfer learning, comprising: a data acquisition module, which acquires source domain electroencephalogram data and target domain electroencephalogram data; a data preprocessing module, which performs data segmentation, time-frequency decomposition, feature extraction and feature vector construction on the electroencephalogram data of the source domain and the target domain; a feature transfer learning and electroencephalogram pattern classification module, which iteratively calculates a feature space transformation matrix based on field distribution adaptation to minimize the distribution difference between the electroencephalogram feature samples of the source domain and the target domain; in the feature transfer learning process, the transformed source domain electroencephalogram feature samples are used to train a classifier to realize pattern classification of the target domain samples; the weight factor of the distribution adaptation and the feature space transformation matrix are iteratively updated to realize training of the classifier and pattern classification of the target domain electroencephalogram data and seizure detection. The problem of complex model and time-consuming training of the existing seizure detection technology is solved.

A wearable system for epileptic seizure detection, comprising an eyeglasses frame, with a left arm and a right arm configured to rest over the ears of an intended person wearing the eyeglasses, a first pair of electrodes located in the left arm, and a second pair of electrodes located in the right arm, the first pair of electrodes and the second pair of electrodes arranged such to be in contact with the skull of the intended person wearing the eyeglasses, and an EEG signal acquiring system integral to the left and right arms, connected to measuring outputs of the respective first pair and second pair of electrodes.

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.

We report a method of determining an occurrence of an epileptic convulsive seizure in a patient, comprising: receiving body data from a patient during a first time period, determining a work level relating to said first time period at least based partially upon said body data; determining whether said work level exceeds an extreme work level threshold; performing a responsive action, in response to a determination that said work level exceeds said extreme work level threshold. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

The embodiment of the application provides a feature fusion-based seizure detection method and device and network equipment, the method comprises the following steps: acquiring channel data of each channel of electroencephalogram signal, and performing feature extraction on the channel data according to a convolutional neural network to obtain first features of the channel; performing correlation analysis between channels according to the first features to determine second features between the channels; fusing the first features and the second features to obtain fused features; inputting the fused features into a classification model containing a multi-head attention mechanism to determine a seizure analysis result. The method fuses the features in the channel and the features between the channels, comprehensively considers the channel data itself and the correlation between the channel data, and can more accurately identify the seizure.

Medical device for recording EEG with automated seizure detection'