A system and method for detecting seizures in a patient are disclosed. The method includes measuring at least one signal associated with the patient over a first time period, and calculating a first slope having a rate of change in a first signal of the at least one signal over the first time period. The first signal is a heart rate. The method also includes determining a difference between the first slope and a reference slope, as well as detecting an epileptic seizure based at least upon the difference between the first slope and the reference slope. Furthermore, the method includes quantifying the epileptic seizure by calculating a severity of the seizure. The severity is the product of a seizure duration and a seizure intensity.

The present disclosure relates generally to compositions and methods for determining whether a patient suffers from a traumatic brain injury (TBI) by detecting the presence of an amyloid beta protein in an eye of the patient. Also provided are compositions and methods for preparing a patient for diagnosis and treatment of traumatic brain injury (TB).

Proposed are concepts for predicting the likelihood of a brain injury of a subject resulting from a fall event experienced by the subject. Such concepts may therefore be useful for determining a need for further medical assessment or treatment of a fall by the subject. According to an exemplary concept, data from one or more sensors is used to determine information about a fall of a subject. Such information is then used in conjunction with a prediction algorithm to derive a probability of a brain injury.

A system for outputting a visual representation of a brain of a patient is configured to receive sensor data representing a behavior of a region of the brain of the patient. The system retrieves mapping data that maps a prediction value to the region. The prediction value is indicative of an effect on a behavior of the patient responsive to a treatment of the region, the mapping data being indexed to a patient identifier. The system receives, responsive to an application of a stimulation to the region, sensor data representing behavior of the region. The system executes a model that updates, based on the sensor data, the prediction value for the region. The system updates, responsive to executing the model, the mapping data by including the updated prediction value in the mapping data. The system outputs a visual representation of the updated mapping data comprising the updated prediction value.

An electrode device for recording electrophysiological neurosignals in nervous tissue of a living being includes a bundle of insulated electrical cables, where each cable has an electrical wire made of electrically conductive material and an insulation layer which covers and insulates the electrical wire. An electrical connector connects the electrical wires to a recording device. A free end of the bundle of insulated electrical cables distant from the electrical connector includes an implantation section for implantation in the nervous tissue of the living being. An electrophysiological recording system will have at least one such electrode device and a computer program arranged for execution on a computer.

A system and method of accurately measuring a pressure (Pp) within a human brain using totally-implanted or partially-external hardware. The system includes a first pressure transducer configured to measure a cerebrospinal fluid pressure (Pcsf) within a ventricle of a brain, a second pressure transducer configured to indirectly measure a second pressure (Pp) within a space of the brain, and an adjustable implanted valve controller configured to calculate the effective differential pressure (Pei=Pcsf−Pp) between the measured pressures Pcsf and the Pp and determine whether the effective differential pressure measurement represents a true secondary pressure.

The problem to be solved is to provide a system and the like for augmenting supervisory data while maintaining the relationship among a plurality supervisory data used for machine learning. The present disclosure provides a system for augmenting supervisory data used for machine learning, the system including an obtaining means that obtains a plurality of supervisory data, a first processing means that derives a covariance matrix from the plurality of supervisory data, a second processing means that decomposes the covariance matrix, and a third processing means that applies a random number to the decomposed matrix.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing brain emulation neural networks on user devices. One of the methods includes obtaining, by a first component of a user device, a network input; processing, by the first component of the user device, the network input using an artificial neural network to generate a network output, wherein the artificial neural network has a network architecture that has been determined according to a synaptic connectivity graph, wherein the synaptic connectivity graph represents synaptic connectivity between neurons in a brain of a biological organism; and providing the network output for use by one or more second components of the user device.

System and methods for identifying a brain condition of a patient subject to a plurality of disease states are provided, in some aspects, the method includes receiving imaging data associated with a patient's brain acquired using an imaging system, and constructing a classifier having signatures corresponding to a plurality of disease states. The method also includes applying the classifier to the imaging data to determine a degree to which the patient expresses at least one of the plurality of disease states, and determining a brain condition of the patient using the determined degree. The method further includes generating a report indicative of the brain condition of the patient.

A brain-function image data augmentation method includes: a step of providing a target database including a plurality of image-data information; a step of, based on a plurality of image-data expected information in an expectation-value database, calculating a ratio of the plurality of image-data expected information with respect to different ages; a step of, based on the plurality of image-data information and the ratio, obtaining image-data ratio information with respect to an estimated age; a step of establishing a relationship for each pair of the image-data information and the image-data expected information; a step of, based on the relationship, the ratio and the image-data information, calculating an estimated image-data information with respect to the estimated age; and, a step of combining linearly the estimated image-data information and the image-data ratio information so as to generate an augmented image-data information with respect to the estimated age.