This application relates to an epilepsy monitoring device. In one aspect, the device includes a first unit having a first body portion arranged on a user's head, a sensor unit that is connected to the first body portion and includes a first sensor for measuring a cranial nerve signal, and a stimulation unit that is connected to the first body portion and applies cranial nerve treatment stimulation to the brain according to a provided brain stimulation signal. The device may also include a second unit electrically connected to the first unit and having a second body portion arranged on a body other than the user's head, a battery unit that is arranged in the second body portion and supplies power to the first unit, and a communication unit for wireless communication with an external device.

A method for identifying changes in an epilepsy patient's disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.

An external data retrieval apparatus receives a low resolution version of a physiological signal from an active implantable medical device and determines if the physiological signal represents a clinically significant event. The apparatus provides an indication of such determination to the implantable medical device. If the physiological signal does represent a clinically significant event, the apparatus receives a full download of the physiological signal from the implantable device.

In one aspect, the invention comprises an implantable living electrode comprising a substantially cylindrical extracellular matrix core; one or more neurons implanted along or within the substantially cylindrical extracellular matrix core, the one or more neurons including one or more optogenetic or magnetogenetic neurons proximal to a first end of the implantable living electrode.

In some embodiments, a display system comprising a head-mountable, augmented reality display is configured to perform a neurological analysis and to provide a perception aid based on an environmental trigger associated with the neurological condition. Performing the neurological analysis may include determining a reaction to a stimulus by receiving data from the one or more inwardly-directed sensors; and identifying a neurological condition associated with the reaction. In some embodiments, the perception aid may include a reminder, an alert, or virtual content that changes a property, e.g. a color, of a real object. The augmented reality display may be configured to display virtual content by outputting light with variable wavefront divergence, and to provide an accommodation-vergence mismatch of less than 0.5 diopters, including less than 0.25 diopters.

This document discusses, among other things, systems and methods for monitoring a patient at risk of epilepsy. A system comprises a sensor circuit that senses from the patient at least first and second physiological or functional signals. A wellness detector circuit can detect an epileptic event using the sensed physiological or functional signals, or additionally classify the epileptic event into one of epileptic seizure types. The system can generate a wellness indicator based on a trend of the physiological or functional signal during the detected epileptic event. The wellness indicator indicates an impact of the detected epileptic event on the health status of the patient. The system includes an output unit configured to output the detection of the epileptic event or the wellness indicator to a user or a process.

We report method of detecting a pathological body state of a patient, comprising receiving a body signal of the patient; determining a body index from said body signal; determining an activity level of said patient; determining a value range for said body index for said patient, based at least in part on said activity level; comparing said body index to said value range; and detecting a pathological state when said body index is outside said value range. 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.

Presented are concepts for detecting an ictal of a subject. One such concept generates an ictal detection threshold based on a current interictal period of the subject. An ictal of the subject may then detected based on an identifier of a potential ictal of the subject and the ictal detection threshold. Adaptation of an ictal detection threshold based on an interictal period of the subject may help to improve the accuracy of ictal detection.

The invention relates to a method for the real-time monitoring and evaluation of the state of a patient with a neurological condition from the indicative parameters of the state of the patient obtained by means of an EEG, comprising a step of measuring the EEG by means of at least one sensor, a step of processing the measured values, a step of extracting at least one set of values of the indicative parameters of the state of the patient, each set of values being extracted from time segments of the EEG, a step of calculating for each time segment the risk level of the patient suffering a crisis due to the neurological condition, the risk level being calculated by applying at least one mathematical classification model to each corresponding set of values, and a step of classifying the state of the patient between at least an alert or preictal state and a non-alert or non-preictal state, depending on a threshold level.

A method and a system for processing an electroencephalogram (EEG) signal are provided. The method for processing the EEG signal includes: performing a spike detection on the EEG signal to obtain a spike distribution waveform, performing an instantaneous frequency oscillation energy analysis on the EEG signal to obtain multiple energy distribution waveforms; performing a complexity analysis on the EEG signal to obtain a complexity change waveform, obtaining a determination result of a specified neural waveform based on the spike distribution waveform, the energy distribution waveforms, and the complexity change waveform, and outputting the determination result.