Article, originally posted on MedicalXPress.com
Ben-Gurion University of the Negev researchers have developed Epiness, a device for detecting and predicting epileptic seizures based on machine-learning algorithms. The wearable device can generate an advanced warning about an upcoming seizure that will be sent to a smartphone up to an hour prior to its onset. The system was out-licensed for further development and commercialization to NeuroHelp, a startup company that was recently founded by BGN Technologies, the technology transfer company of BGU and Dr. Oren Shriki, of BGU’s Department of Cognitive and Brain Sciences and NeuroHelp’s scientific founder.
Epiness is a seizure prediction and detection device that is based on a new, ground-breaking combination of EEG-based monitoring of brain activity together with proprietary machine-learning algorithms. The device combines a wearable EEG device with state-of-the-art software that minimizes the number of necessary EEG electrodes and optimizes electrode placement on the scalp. The sophisticated machine-learning algorithms are designed to filter noise that is not related to brain activity, extract informative measures of the underlying brain dynamics, and distinguish between brain activity before an expected epileptic seizure and brain activity when a seizure is not expected to occur.