Wearable technology is developing at a fast pace to benefit human life. Epilepsy is a highly pervasive disease interfering seriously people’s life. Around 30% of patients live under constant fear of impending seizures for inadequately responding to anti-epileptic drugs. Researchers at Ben-Gurion University of the Negev (BGU) developed Epiness™, a new, first-of-its-kind device for detecting and predicting epileptic seizures based on proprietary machine-learning algorithms. The wearable device can inform users of the risk of seizures happening up to an hour prior to its occurrence.
A new, ground-breaking combination of EEG-based monitoring of brain activity together with proprietary machine-learning algorithms, provides the foundation to Epiness, a seizure prediction and detection device. 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.
“Epileptic seizures expose epilepsy patients to various preventable hazards, including falls, burns, and other injuries,” said Dr. Oren Shriki, the Department of Cognitive and Brain Sciences at BGU and NeuroHelp’s scientific founder. “Unfortunately, currently there are no seizure-predicting devices that can alert patients and allow them to prepare for upcoming seizures. We are therefore very excited that the machine-learning algorithms that we developed enable accurate prediction of impending seizures up to one hour prior to their occurrence. Since we have also shown that our algorithms enable a significant reduction in the number of necessary EEG electrodes, the device we are developing is both accurate and user-friendly. We are currently developing a prototype that will be assessed in clinical trials later this year.”
A large dataset of people with epilepsy that were monitored for several days prior to surgery had provided EEG data for developing the new algorithm. The algorithm with the best prediction performance reached a 97% level of accuracy, with near-optimal performance maintained (95%) even with relatively few electrodes.