81 results about "Seizure detection" 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.

A device comprises a head mounting arrangement sized and shaped to be worn on a user's head and a plurality of electrodes disposed on the arrangement so that, when the arrangement is worn on the user's head, the electrodes contact target portions of a scalp to detect electrical activity of a brain of the user in combination with an image capture device disposed on the arrangement so that, when the arrangement is worn on the user's head, a field of view of the image capture device includes a portion of an anatomy of the user and a processing unit generating EEG data from the electrical activity, wherein, when the EEG data is indicative of an epileptic event, the processing unit activates the image capture device to capture video data of the user and may store the EEG and/or the video data with transmission of warning signals to one or more remote displaying and/or computing arrangements.

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. The various embodiments of the system of the present invention were developed for the brain activity and preferably EEG monitoring of a single patient or multiple patients. Preferably, the system or monitor of 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. This analysis yields a seizure index whose value is directly related to the current presence of ictal activity in the signal. In addition, 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. The real-time seizure index, and the historical seizure probability index, can be used in the context of an emergency and/or clinical situation 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.

Systems, methods and media for detecting a seizure use one or more 3D cameras to monitor an individual. The 3D cameras may detect rigidity and/or rapid movements associated with the tonic or clonic phases of a seizure. Body position and/or movements consistent with a seizure may cause the system or media to alert the individual, a central monitoring system, caregivers, and/or others.

Systems and methods for the detection of pathological conditions in a brain analyze signal properties of the brain electrical activity recordings. The pathological conditions to be detected include the occurrence of a seizure event. The systems and methods calculate a seizure susceptibility index (SSI) for real time brain function monitoring and for a pre-screening process of seizure detection.

Methods, systems, and apparatus for detecting the seizure in a patient using a medical device. The determination is performed by collecting cardiac data; determining valid heart beats suitable for seizure detection from the cardiac data; calculating heart rate data of interest from the valid heart beats; and identifying a seizure event from the heart rate data. The medical device may then take a responsive action, such as warning, logging the time of the seizure, computing and storing one or more seizure severity indices, and/or treating the seizure.

A system and method for analyzing video images including images of an animal to detect and classify seizure behavior of the animal. Seizure-salient features are detected from identified postures and identified movements of body parts of the animal. The seizure-salient features are used to detect and classify the occurrences of seizures.

A neurostimulation device includes a plurality of electrodes adapted to be electrically connected to a subject to receive multichannel electrical signals from the subject's brain, a multichannel seizure detection unit electrically connected to the plurality of electrical leads to receive the multichannel electrical signals, and a neurostimulation unit in communication with the multichannel seizure detection unit. The plurality of electrodes are at least three electrodes such that the multichannel electrical signals are at least three channels of electrical signals, and the multichannel seizure detection unit detects a presence of a seizure based on multichannel statistics from the multichannel electrical signals including higher order combinations than two-channel combinations.

A neurostimulation device includes a plurality of electrodes adapted to be electrically connected to a subject to receive multichannel electrical signals from the subject's brain, a multichannel seizure detection unit electrically connected to the plurality of electrical leads to receive the multichannel electrical signals, and a neurostimulation unit in communication with the multichannel seizure detection unit. The plurality of electrodes are at least three electrodes such that the multichannel electrical signals are at least three channels of electrical signals, and the multichannel seizure detection unit detects a presence of a seizure based on multichannel statistics from the multichannel electrical signals including higher order combinations than two-channel combinations. Another embodiment of the invention includes determining a singular vector centrality (SVC) for each of the electrodes in order to detect the seizure onset.

Methods for detecting a seizure, by use of a short term average/long term average (STA/LTA) algorithm applied to body data. A non-transitive, computer-readable storage device for storing data that when executed by a processor, perform such a method.

Methods and devices for performing division approximation in implantable and wearable self-powered medical devices. The present invention provides rapid methods for performing an approximation of division on fixed point numbers, where the methods are easily implemented in small, low power consumption devices as may be found in implantable medical devices. One example of use is in rapidly determining the approximate ratio between foreground and background activity in seizure detection algorithms. Some methods approximate the ratio of Numerator (N) to Denominator (D) by raising 2 to the power of the difference in the number of zeros to the left of the Most Significant Set Bit (MSSB) of D vs. N. Some methods may also pad bits to the right of the approximate ratio MSSB using bits from the right of the N MSSB, and/or pre-process the smaller of D or N by rounding the value upward. Methods may be implemented in firmware and/or in discrete logic.

Method and apparatus for improved processing for multi-channel signals. In an exemplary embodiment, an anomaly metric is computed for a multi-channel signal over a time window. The magnitude of the anomaly metric may be used to determine whether an anomaly is present in the multi-channel signal over the time window. In an exemplary embodiment, the anomaly metric may be a condition number associated with the singular values of the multi-channel signal over the time window, as further adjusted by the number of channels to produce a data condition number. Applications of the anomaly metric computation include the scrubbing of signal archives for epileptic seizure detection/prediction/counter-prediction algorithm training, pre-processing of multi-channel signals for real-time monitoring of bio-systems, and boot-up and/or adaptive self-checking of such systems during normal operation.

A method of and system for monitoring a patient's EEG (electroencephalogram) during TMS (Transcranial Magnetic Stimulation) are disclosed. The system comprises a TMS device to generate, when in an active state, a plurality of magnetic pulses, which can be applied either as a burst, comprising a plurality of pulses grouped together, or as individual pulses, to the patient's head, in accordance with a TMS treatment protocol. An EEG system is provided to measure EEG data resulting from the TMS treatment protocol being applied to the patient. The system further comprises control means in communication with the TMD device and the EEG system, the control means being arranged to activate the EEG system during the time periods when the TMS device is not generating pulses, such that the EEG data measurement is continuously applied or interleaved with the magnetic pulses being generated in accordance with the TMS treatment protocol, so as to monitor treatment efficacy and detect potential seizures.

A motion monitoring system and a method of obtaining and analyzing motion data that uses an accelerometer or an image capture device to acquire subject motion data. The motion data may be obtained from an accelerometer applied to a chest of the subject, or from an image capture device configured to view the subject during the detected motion. The motion data is analyzed to distinguish between a seizure type of motion and a non-seizure type of motion, with the subject motion characterized by at least one of, and combinations of, motion amplitude or magnitude, motion period or frequency, motion bandwidth, subject position, and subject change in position over a time period of the detected motion. The system and method further includes the generation of an output in response to an identification of the seizure and non-seizure types of motion.

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.

A system and method for detecting and predicting neurological events with an implantable device uses a relatively low-power central processing unit in connection with signal processing circuitry to identify features (including half waves) and calculate window-based characteristics (including line lengths and areas under the curve of the waveform) in an electrographic signal received from a patient's brain. The features and window-based characteristics are combinable in various ways according to the invention to detect and predict neurological events in real time, enabling responsive action by the implantable device.

The invention, which belongs to the fields of biomedical engineering and machine learning, discloses an EEG epileptic attack detection method based on deep channel attention perception. According to the invention, an attention mechanism is introduced into multi-channel EEG epileptic attack detection to train an end-to-end deep channel attention perception model. With the model, the deep features of a brain wave signal can be extracted; and contribution scores of all channels to epileptic detection can be learned to select a most relevant EEG channel dynamically. Compared with the prior art, onthe basis of combination of deep feature extraction and the attention mechanism, the most relevant EEG channel is selected dynamically and the epileptic features are expressed synergistically, so that the fusion features have the channel perception capability; and the epileptic detection rate is increased and the interpretability is enhanced.