Researchers are developing wearable devices to predict epileptic seizures

Researchers at the University of Negev Ben Gurion have developed Epines, the first new device for detecting and predicting epileptic seizures based on a unique machine learning algorithm. Wearable devices can generate advance warnings about the next seizure that will be sent to your smartphone up to an hour before the onset of a seizure. The system is licensed for further development and commercialization by BGU Technology Transfer Company BGN Technologies, BGU’s Cognitive Brain Science Division Dr. Oren Shriki, and NeuroHelp, a startup recently established by the scientific founder of NeuroHelp. It was donated.

Epilepsy is a very prevalent and sometimes debilitating neurological disorder. Up to 30% of patients do not respond appropriately to antiepileptic drugs and are always afraid of imminent seizures.Viable seizure prediction for such patients Terminal It can significantly improve your quality of life and avoid seizure-related injuries. Current seizure alarms can detect seizures in real time, but cannot warn in advance that a seizure is imminent.

Epiness is a seizure prediction and detection device based on a new breakthrough combination of EEG-based monitoring. brain Working with a unique machine learning algorithm. This device combines a wearable EEG device with state-of-the-art software that minimizes the number of EEG electrodes required and optimizes electrode placement on the scalp. Advanced machine learning algorithms filter noise not related to brain activity, extract useful measurements of underlying brain dynamics, and expect no pre-epileptic brain activity and seizures. It is designed to distinguish brain activity.

“Epileptic seizures put epileptic patients at a variety of preventable risks, including falls, burns, and other injuries,” said Dr. Oren Schriki. “Unfortunately, there is currently no seizure predictor that can alert patients and prepare for the next seizure. Therefore, the machine learning algorithms we have developed can accurately predict an imminent seizure up to an hour ago. The device under development is accurate and user-friendly because the algorithm has shown that the number of EEG electrodes required can be significantly reduced. Prototypes currently being evaluated in subsequent clinical trials. Is developing. Year. ”

“An accurate and easy-to-use seizure predictor is a very necessary and unmet medical need, as epilepsy that is not properly controlled by drug therapy is widespread and reaches up to 30% of epilepsy cases,” he said. Hadar Ron said. NeuroHelp. “Current seizure alarms can detect seizures during the onset of seizures. Most seizures rely on changes in movement such as muscle spasms and falls. Epines predicts future seizures and patients And its caretaker are unique in that they can have seizures. Precautions and prevention of injuries. This is also the only device based on brain activity rather than muscle movement or heart rate. Epilepsy is drug resistant. I am confident that it will be a valuable tool in the management of seizures. ”

Josh Peleg, CEO of BGN Technologies, a technology transfer company at BGU, said: Innovative solutions for the benefit of people suffering from epilepsy Earlier this month, NeuroHelp won the highest award at the Silicon Negev Startup Competition. This is an important recognition of the outstanding potential of this technology, which is based on a unique combination of brain research and artificial intelligence. Know-how developed in Dr. Shriki’s laboratory. ”

new algorithm Was developed and tested using EEG data from a large dataset of epilepsy patients monitored for several days prior to surgery. Patient data was divided into short pre-seizure (pre-seizure) segments.Seizures) Or after a seizure. Several machine learning algorithms of varying complexity are trained with pre-assigned training data (including 80% of the initial EEG data) and their predictive and electrode-dependent performance is evaluated in the remaining 20% ​​of the data. it was done. The algorithm with the best predictive performance reached 97% accuracy and maintained near-optimal performance even with a relatively small number of electrodes (95%).