64 results about "Seizure detection" patented technology

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 invention relates to an epileptic seizure detection method and system based on self-attention mechanism and GRU-LSTM fusion, and the method comprises the following steps: (1) carrying out the preprocessing of original electroencephalogram signal data, and extracting time domain and nonlinear features; (2) randomly dividing the data set into a training set, a test set and a verification set; (3) constructing a deep learning model fusing a self-attention mechanism, a gating circulation unit and a long short-term memory network; (4) extracting fusion features from the trained deep learning model, and inputting the fusion features into a support vector machine classifier to perform epileptic seizure and non-seizure classification; and (5) outputting a classification result through multi-modal feature fusion, long and short term dependence modeling and adaptive feature selection. The method has the advantages that a self-attention mechanism, a gating circulation unit, a long-short-term memory network and a support vector machine classifier are combined, deep features of electroencephalogram signals are extracted through a multi-stage processing flow, and finally epileptic seizure and non-seizure classification is carried out.

The invention discloses a method for detecting and classifying electroencephalogram events of electroencephalogram signals by using a target detection model DINO, and belongs to the technical field of deep learning. The method mainly comprises three stages of data set acquisition, data preprocessing, feature extraction and detection classification evaluation. According to the method, a complete electroencephalogram data file can be input, a tedious post-processing process is not needed, many defects of post-processing are overcome, an end-to-end model is constructed, original data are directly input, and an event detection result is output; meanwhile, many limitations existing in manual detection are overcome, a universal automatic electroencephalogram event detection framework is established by using a deep learning method, a doctor can be assisted to play a role in tasks such as epileptic seizure detection, sleep staging and artifact detection in various clinical application scenes, the electroencephalogram event labeling efficiency is improved, meanwhile, the burden of the clinician is reduced, and the workload of the clinician is reduced. And the pressure of medical resources is relieved.

Disclosed are a wearable depressive disorder recognition and seizure detection method and device. The method includes the following steps of: acquiring multi-modal data of a target user by using a wearable device; inputting the multi-modal data into a trained depressive disorder recognition model to determine whether the target user is a depression disorder person or a normal person; and inputting the corresponding multi-modal data to a trained depressive disorder seizure detection model to determine whether the target user is in a seizure state in the case of a determined depressive disorder person.

The invention belongs to the technical field of biomedical signal processing, and discloses an SEEG attack detection method based on a time-frequency space-dynamic graph attention network, which comprises the following steps: constructing an epilepsy brain network sequence, defining SEEG lead points as nodes of a graph, and calculating directed connection edge weights among the nodes through a multiband directed transfer function; constructing a node feature matrix based on epilepsy induced epilepsy index features; arranging the multiband epilepsy brain network according to a time sequence to form a dynamic sequence; extracting space-spectrum correlation by using a GATv2 dynamic attention mechanism, and dynamically weighting node connection weight through a learnable attention matrix; and in combination with a time sequence convolutional network and a time attention mechanism, key time sequence features are extracted, identified and focused. According to the method, a good dynamic propagation characteristic learning capability is obtained, an SEEG pathological connection mode is disclosed, a model mechanism corresponds to an epileptic seizure propagation mechanism, and the method has a good application prospect in the aspects of epileptic seizure detection and epileptic seizure auxiliary positioning.

Disclosed electroencephalogram (EEG) monitoring and detection systems and methods can detect differentiated electrographic seizure characteristics of different seizure types across a diverse group of patients. Disclosed systems and methods can utilize EEG data collected by discrete wireless EEG sensors positioned on a scalp of a patient.

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.

The invention discloses an epileptic seizure detection and positioning system and method combining time convolution and Transform network, and the system comprises a data obtaining module which is configured to be used for obtaining a to-be-detected multi-channel electroencephalogram signal through an electrode, a multi-channel electroencephalogram amplifier and an A / D converter which are connected in sequence, dividing the multi-channel electroencephalogram signal into a plurality of multi-channel electroencephalogram signal segments according to time; the data processing module is configured to be used for extracting time-frequency characteristics of each multi-channel electroencephalogram signal segment through S transformation and performing dimension reduction processing; the epileptic seizure detection module is configured to be used for inputting the multi-channel electroencephalogram signal fragments subjected to dimension reduction processing into the trained TCN-Transformer model to obtain an epileptic seizure detection result; and the epilepsy positioning module is configured to calculate the weight of each channel based on the epilepsy seizure probability score when the epilepsy seizure is detected, generate the final activation vector of each channel based on the weight, and realize the positioning and visualization of the epilepsy seizure based on the activation vector. According to the method, the multi-scale time-frequency-space characteristics from the multi-channel EEG data can be more comprehensively captured, so that the accuracy and robustness of epileptic seizure detection and focus positioning are improved.

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.

A method of seizure detection, the method comprising: receiving an EEG signal generated based on electrical brain activity of a patient; determining a plurality of metrics based on the EEG signal, the plurality of metrics indicating non-linear features of the EEG signal; performing a preliminary analysis with one of the plurality of metrics, wherein the preliminary analysis indicates that the EEG signal is significant or that the EEG signal is insignificant, the one of the plurality of metrics being eigenvalues of the EEG signal, and the preliminary analysis comprising monitoring a moving window of eigenvalues of the EEG signal, and determining whether the eigenvalues are decreasing; responsive to a determination in the preliminary analysis that the eigenvalues are decreasing, performing a secondary analysis with one or more other metrics of the plurality of metrics to determine whether the EEG signal indicates a candidate seizure; and generating a seizure alert indicating that the EEG signal indicates the candidate seizure responsive to a determination in the secondary analysis that the EEG signal indicates the candidate 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.

The present invention discloses a low-power wearable epilepsy seizure detection system based on multi-level classification. First-level pre-classification is performed according to the characteristics of physiological signal data, and the obtained positive sample data is input into a detection model for fine second-level classification. Then, the final detection result is obtained by combining the first-level pre-classification and the second-level classification results, which can improve the detection accuracy; multiple modalities of physiological signals are synchronously collected to construct a stable epilepsy seizure detection model for the fusion of multiple modalities; data of healthy people's daily activities is added as negative samples to the data set used for constructing the classification model, making the constructed classification model more suitable for the actual life scenario and meeting the real needs of epilepsy patients; for the problem of data imbalance, a data imbalance processing solution based on the prior knowledge of human activities is adopted, and data imbalance processing is carried out by extracting the standard deviation of the resultant acceleration, the main frequency of the resultant acceleration in the frequency domain, the peak-to-peak value of the resultant acceleration, and setting empirical thresholds.

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.

Disclosed electroencephalogram (EEG) monitoring and detection systems and methods can detect differentiated electrographic seizure characteristics of different seizure types across a diverse group of patients. Disclosed systems and methods can utilize EEG data collected by discrete wireless EEG sensors positioned on a scalp of a patient.

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.

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.

A method, comprising receiving a time series of patient body signal, determining first and second sliding time windows for the time series; applying an autoregression algorithm, comprising: applying an autoregression analysis to each of the first and second windows, yielding autoregression coefficients and a residual variance for each window; estimating a parameter vector for each window based on the autoregression coefficients and residual variances; and determining a difference between the parameter vectors; and determining seizure onset and seizure termination based on the difference between the parameter vectors. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

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 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.