A thalamic medical device system enables automated detection of epileptic seizures and optionally providing contingent thalamic stimulation triggered by automated seizure detection. The thalamic medical device system includes an implantable medical device connected to an electrode assembly and a lead. The electrode assembly has at least one electrode surgically implanted into an anterior thalamic nuclei or a centro-median thalamic nuclei of a brain. The implanted medical device is configured to detect electrical signals and analyze detected electrical signals using a seizure detection algorithm, and further configured to deliver electrical stimulation to the anterior thalamic nuclei or centro-median thalamic nuclei via the electrode assembly when a seizure is detected.

A biological signal acquirer is attached to a subject and acquires a biological signal of the subject. A transmitter carried by the subject transmits the biological signal. A first communication port and a first camera are installed in a first location and connectable to a network. A second communication port and a second camera are installed in a second location and connectable to the network. A biological information acquiring device is connectable to the network and provided with a switcher. The switcher acquires, when communication establishment between the transmitter and the first communication port is detected, the biological signal through the first communication port as well as a first image taken by the first camera, and acquires, when communication establishment between the transmitter and the second communication port is detected, the biological signal through the second communication port as well as a second image taken by the second camera.

A system for predicting epileptic seizures includes sensors operable to record a wearer's brain activity. The sensors electronically communicate with a processor configured to receive and store output EEG oscillations and activities. A threshold electrical fluctuation level is identified as the level electrical activity experienced at the onset of a seizure event, and is then stored in the PDA memory as a predetermined threshold value. The processor analyzes the input EEG data logged for a recording period, and the logged data is broken into a number of data values across a series of individual set sampling periods. Convert collected data value readings for individual sampling periods as a non-linear measure value using fractal dimension, P&H and/or Lyapunov weighing. The calculated values for a predicted next time intervals extending the sampling period is projected forward and compared against the predetermined threshold value to indicate a likely seizure event.

Embodiments may provide multimodal diagnostic systems and methods for detecting neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD), depression, PTSD, schizophrenia, dementia and many others. For example, a system for monitoring brain activity may comprise a plurality of sensors, each adapted to monitor a physical or physiological parameter and output a signal representing the monitored physical or physiological parameter, wherein the plurality of sensors includes at least one sensor configured to monitor a brain activity parameter, a data collection device adapted to receive the plurality of signals from the plurality of sensors and to process the signals to form digital data representing the monitored physical or physiological parameters, and a data processing device adapted to process digital data representing the monitored physical or physiological parameters to determine presence of a neurological disorder or condition.

Systems, devices and methods are described for physiological monitoring, for example monitoring EEG signals to detect the onset or probability of adverse events. The systems, devices and methods discussed herein may monitor received EEG signals to identify trends or patterns in the signal that are either indicative of ongoing seizures or indicative of a future risk of seizure. The systems, devices and methods provide the user with increased control and flexibility in the monitoring processes that produce the alerts. In particular, in some implementations the physician is able to make adjustments during monitoring and customize the process by which EEG data is displayed and analyzed during the patient monitoring without pausing the monitoring to make the adjustments.

The invention relates to modulating epileptogenicity in a brain of an epileptic patient. The method according to the invention comprises the steps of: providing a virtual brain; providing a model of an epileptogenic and of a propagation zones and loading said models in the virtual brain to create a virtual epileptic brain; acquiring data of the brain of the epileptic patient; identifying, in said data, a location of at least one possible epileptogenic zone; fitting the virtual epileptic brain against the data acquired from the epileptic patient and parametrizing said at least one possible epileptogenic zone in the virtual epileptic brain as an epileptogenic zone; and simulating, within the virtual epileptic brain, the effect of a network modulation mimicking a clinical intervention of the brain of the patient.

An apparatus for minimally-invasive, including non-invasive, prevention and/or treatment of hydrocephalus and method for use of the same are disclosed. In one embodiment of the apparatus, a housing is sized for superjacent contact with a skull having a fontanel. Within the housing, a compartment includes a pressure applicator, such as a fluid-filled bladder, under the control of a pressure regulator. The pressure applicator is configured to selectively apply an external pressure to the fontanel. The compartment includes a pressure sensor configured to measure intracranial pulse pressure of the fontanel. Further, in one embodiment, the apparatus can cause pulse pressure modulation by adjusting the intracranial pulse pressure via the pressure applicator. This enables a non-invasive measurement of the pulse pressure and modulation thereof in infants, for example.

A method for improved seizure detection includes capturing neural data for a first set of brain regions; calculating inter-region correlations between pairs of the first set of brain regions from the neural data; detecting a period of hyposynchrony from the inter-region correlations; after detecting the period of hyposynchrony, detecting a period of hypersynchrony from the inter-region correlations; and based on a transition from the period of hyposynchrony to the period of hypersynchrony, detecting a first seizure.

Embodiments are related to a headset integrated into a healthcare platform. The headset comprises one or more sensors embedded into a frame of the headset, a controller coupled to the one or more sensors, and a transceiver coupled to the controller. The one or more sensors capture health information data for a user wearing the headset. The controller pre-processes at least a portion of the captured health information data to generate a pre-processed portion of the health information data. The transceiver communicates the health information data and the pre-processed portion of health information data to an intermediate device communicatively coupled to the headset. The intermediate device processes at least one of the health information data and the pre-processed portion of health information data to generate processed health information data for a health-related diagnostic of the user.

Systems, methods, and other techniques for monitoring, including non-invasive monitoring, of biological markers based on the interaction, temporal association, or coincidence of brain activity and periphery activity in a mammal are provided. Systems and methods for generating a behavioral state-independent representation of cardiac activity and for identifying cardiac events and/or brain-periphery, e.g., brain-cardiac, temporal associations useful as biomarkers of disease such as, e.g., neurologic disease, in a mammal are also provided.