Wearable AI Device That Predicts Epileptic Seizures Developed by Ben-Gurion University

NeuroHelp, a spinoff company at Ben-Gurion University of the Negev (BGU), has developed EpinessTM, the first wearable AI device that detects and predicts epileptic seizures up to one hour prior to seizure onset via smartphone alert, which could be a valuable tool in the management of drug-resistant epilepsy. Current alarm devices offer real-time seizure detection but are unable to provide advanced warnings.

Epiness was developed by Dr. Oren Shriki and his team at the BGU Department of Cognitive and Brain Sciences at the Inter-faculty Brain Science School. NeuroHelp was founded by BGN Technologies, the BGU technology transfer company, and Dr. Shriki, the company’s scientific founder.

Epilepsy is a central nervous system (neurological) disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, sensations and sometimes loss of awareness. Up to 30% of patients do not adequately respond to drugs and live with constant fear of impending seizures. For such patients, a viable seizure prediction device could offer a substantial improvement in quality of life, enabling them to avoid seizure-related injuries.

Epiness is a seizure prediction and detection device based on a ground-breaking combination of EEG-based monitoring of brain activity together with proprietary machine-learning algorithms. The device combines a wearable electroencephalogram (EEG) device with software that minimises the number of EEG electrodes and optimises placement on the scalp. The algorithms filter noise unrelated to brain activity, extract informative measures of the underlying brain dynamics, and distinguish between brain activity before an expected epileptic seizure and when a seizure is not expected.

The new algorithm was developed and tested using EEG data from a large dataset of people with epilepsy that were monitored for several days prior to surgery. The algorithm with the best prediction performance reached a 97% level of accuracy, with near optimal performance maintained (95%) even with relatively few electrodes.

“Epileptic seizures expose patients to various preventable hazards, including falls, burns and other injuries,” says Dr. Shriki. “Currently there are no seizure-predicting devices that can alert patients and allow them to prepare for upcoming seizures. The device will be both accurate and user-friendly, since the algorithms significantly reduce the number of EEG electrodes necessary. We are currently developing a prototype that will be assessed in clinical trials later this year.”

Dr. Hadar Ron, chair of NeuroHelp, says, “Current seizure alert devices depend on changes in movement, such as muscle spasms or falls. Epiness is also the only device that is based on brain activity rather than muscle movements or heart rate. We are confident that Epiness will be a valuable tool in the management of drug-resistant epilepsy.”

Josh Peleg, chief executive officer of BGN, says, “NeuroHelp was recently founded as part of BGU’s Oazis accelerator, a program of BGU’s Yazamut360 entrepreneurship centre.” Empowered by the IBM Alpha Zone, Oazis gives startups access to mentorship, technical training and support, and the IBM infrastructure.

“Last week, NeuroHelp won first prize in the SilicoNegev startup competition, an important recognition of the outstanding potential of this technology, which is based on a unique combination of brain research and artificial intelligence developed at Dr. Shriki’s laboratory,” says Peleg.