In one embodiment, a method that determine parameters corresponding to shape and volume of the one or more brain structures from the one or more meshes (36) and provides the parameters to a classifier to estimate a probability of a brain abnormality based on the parameters.

A method for determining the severity of a mammalian dive response (MDR) triggered during a patient's seizure that includes the following steps: (1) collecting an ictal specimen, the ictal specimen being a saliva or any other secretion from the mouth of the patient when the patient is in a peri-ictal period; (2) analyzing the ictal specimen to measure a level of a specimen marker, the specimen marker being a cellular or non-cellular biological component; and (3) determining the severity of the MDR or other autonomic reflex based on the level of the specimen marker.

One aspect of the present disclosure includes a closed-loop therapy delivery system for regulating an endogenous level of a first neuromodulatory agent in a subject. The therapy delivery system includes a sensing component, a delivery component, and a controller. The sensing component is configured to detect an extracellular level of the first neuromodulatory agent. The delivery component is configured to deliver an amount of a second neuromodulatory agent to an intraparenchymal target site of the subject. The controller is configured to coordinate operation of the sensing component and the delivery component.

A seizure early-warning method and a system thereof, relating to a technical field of artificial intelligence. The method includes: acquiring health data of a user (S110); extracting first feature parameters from the acquired health data (S120); inputting the extracted first feature parameters into a preset seizure probability estimation model, so as to obtain a seizure probability (S130), wherein the seizure probability estimation model being generated by training a classification model by means of historical health data of a plurality of patients, and the historical health data of the patients including health data of the patients before the seizure; and determining, according to the seizure probability, whether to issue a seizure early-warning notification (S140). The system is a system configured to execute the method. The described technical solution can implement seizure early-warning.

One aspect of the present disclosure relates to a system that can identify a focal area of abnormal brain interactions in a subject. Time series data can be received that corresponds to recordings from a plurality of regions in a brain of the subject during a resting period. Based on the time series data, an information inflow associated with each of the plurality of regions can be determined. The focal area of the abnormal brain interactions can be identified as one of the plurality of regions having a maximum information inflow.

A stroke detection apparatus comprises a data processing device comprising a processor and at least one wearable sensor configured to generate movement data of at least a portion of the user's body. The data processing device is configured to process first movement data for a first movement and second movement data for a second movement received from the at least one wearable sensor. Wherein the data processing device is configured to determine asymmetry of user's movement based on the first and second movement data and generate a stroke detection signal in dependence on the determined asymmetry.

Disclosed systems include a self-contained electroencephalogram (EEG) recording patch comprising a first electrode, a second electrode and wherein the first and second electrodes cooperate to measure a skin-electrode impedance, a substrate containing circuitry for generating an EEG signal from the measured skin-electrode impedance, amplifying the EEG signal, digitizing the EEG signal, and retrievably storing the EGG signal. The patch also comprises a power source and an enclosure that houses the substrate, the power source, and the first and second electrodes in a unitary package.

The temporal correlation between a bioelectrical brain signal of a patient and patient motion data, such as a signal indicative of patient motion or a patient posture indicator, is displayed by a display device. In some examples, the patient posture indicator comprises a graphical representation of at least a portion of a body of the patient. In some examples, the temporal correlation between a bioelectrical brain signal, a signal indicative of patient motion, and a signal indicative of cardiac activity of the patient is displayed by the display device.

A method for assessment of brain signals of a patient includes determining, by one or more processors, a cluster of neural data occurring at a brain of the patient and outputting, by the one or more processors, a request for a user to provide patient state information for the cluster of the neural data in response to determining that the cluster of the neural data is occurring at the brain of the patient. The method further includes associating, by the one or more processors, the patient state information with the cluster of the neural data to generate patient assessment information and outputting, by the one or more processors, the patient assessment information.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, an identity of a vehicle operator may be determined and a vehicle operator profile and/or operating data regarding autonomous operation features of the vehicle may be received after the vehicle operator opts into a rewards program and agrees to share their data. Autonomous operation and vehicle operator risk levels associated with operation of the autonomous or semi-autonomous vehicle may be determined. Based upon the risk levels and/or comparison thereof, one or more autonomous operation features may be disengaged. A preparedness level of the vehicle operator to assume or reassume control of operating the vehicle is determined prior to disengagement. If satisfactory, an alert is presented to the vehicle operator prior to disengagement of the autonomous operation features.