A method of detecting a seizure includes collecting volatile organic compounds with a collector material of a collector; separating a mixture of the volatile organic compounds into its constituent chemicals with a gas chromatography column; ionizing the constituent chemicals to create ionized chemicals and detecting the ionized chemicals; and analyzing the ionized chemicals to identify seizure-indicative volatile organic compounds.

The present specification describes a system for diagnosing or screening one or more pathologies in a patient. The system includes a headset with at least one microphone or accelerometer to passively receive vibrations generated by the cerebral vasculature of the patient's brain, computing devices coupled with the headset for processing the received vibrations to obtain a unique signal, and a signal analyzer to analyze the signal in order to determine if the data includes patterns uniquely indicative of at least one of tension headaches, migraines, depression, dementia, Alzheimer's disease, epilepsy, Parkinson's disease, autism, cerebral vasospasm and meningitis.

A neuroactivity monitoring system includes a camera configured to acquire image data of a patient positioned on the patient support and a monitoring device in communication with the camera. The monitoring device uses the acquired image data of the camera to identify and track patient landmarks, such as facial and/or posture landmarks, and, based on the tracked movement, characterize patient neuroactivity.

A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

A device has a content processing component operable in a content processing state, and a content transducer configured to provide augmented reality data to a user of the device based on an output of the content processing component. The device has a receiver operable to receive captured data indicative of a health condition of a user of the device. The device has a processor configured to process the captured data to identify a trigger indicative of a possible change in the health condition of a user, and in response to the trigger, modify the content processing state of the content processing component such that at least one characteristic of the augmented reality data is modified.

Provided are systems, methods, and devices for providing mediation or alleviation of sleep disorders and insomnia. Systems may include an interface, processing devices, and a controller. The interface is configured to obtain measurements from a brain of a user with sleeping disorders and insomnia. A first processing device is configured to generate multiple brain state parameters characterizing one or more features of a brain state of the user. A second processing device is configured to generate models of the brain of the user based on the plurality of brain state parameters and the plurality of measurements, and determine, using the models and training data comprising one or more mediation data points, a mediation procedure for reducing one or more symptoms of the sleeping disorder or insomnia. The mediation procedure is provided to one or more entities, and one or more control signals are generated by the controller based on the mediation procedure.

A system is provided which, in at least some embodiments, can read the vital signs of the body of a user utilizing a sensing device such as a smartwatch or smart phone (for example utilizing the iOS, Android or Pebble operating systems) and apply algorithms to interpret the vital signs and then send a notification with an escalation process to nominated carriers if the patient interpreted as having a fall or fit or seizure. In at least some embodiments doctors or other parties can log in to a secured dashboard and check a patient data in real time. Doctors or other parties can analyze the history of the patient. In at least some embodiments, users/patients can also use data to keep track of fall or fit or seizure episodes and monitor their progress.

Devices, systems and methods are disclosed for treating symptoms of a user, such as anxiety, depression and/or epileptic seizures, by electrical non-invasive stimulation of a nerve, such as the vagus nerve. A stimulator has a contact surface for contacting an outer skin surface of a patient. The stimulator is configured to wirelessly receive a signal from the software application that includes an electrical stimulation signal comprising at least one electrical impulse sufficient to modulate a nerve within a patient and to ameliorate one or more symptoms in the patient. The stimulator is configured to transmit the electrical impulse through the contact surface and the outer skin surface of the user.

Systems and methods of the present disclosure enable improved seizure detection and/or prediction using a seizure monitoring system. The system receives a data stream including wearable sensor data associated with a user, where the data stream includes electrodermal activity data and where the electrodermal activity data includes circadian rhythm-dependent amplitudes. The system receives a time associated with a seizure of the user. The system trains seizure machine learning model to identify a pre-ictal period associated with a time segment based on the circadian rhythm dependent amplitudes and the time associated with the seizure. The system deploys the seizure machine learning model to ingest a new data stream. Based on the new data stream, the seizure machine learning model predicts a seizure likelihood in a prediction period.